01ATG_notas.ipynb

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   "source": [
    "## ¡Te damos la bienvenida a la experiencia ATGenomics! \n",
    "\n",
    "### En el taller \"Exploración de Datos Biológicos con Pandas\" aprenderemos a usar diversas funciones de la biblioteca pandas. El objetivo principal es que aprendas a realizar un análisis exploratorio de tus datos bioinformáticos y te diviertas en el proceso. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Cuadernillo de notas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./imgs/line.png\" align=\"left\"/>"
   ]
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   "cell_type": "markdown",
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   "source": [
    "### Los temas de este cuadernillo estarán divididos en 03 módulos:\n",
    "\n",
    "### Módulo 01\n",
    "\n",
    "* ¿Por qué python?\n",
    "* Algunas características de python\n",
    "* ¿Cómo usar python?\n",
    "* Jupyter Notebook\n",
    "* El ecosistema de python\n",
    "* ¿Qué es una biblioteca / library?\n",
    "* Pandas\n",
    "\n",
    "### Módulo 02\n",
    "\n",
    "* Importar módulos para descarga de archivos\n",
    "* Pandas para datos biológicos\n",
    "* Formato gff3\n",
    "* DataFrame\n",
    "* ¿Gráficas en pandas?\n",
    "* BED genes\n",
    "* BED transcripts\n",
    "* Resumen: ¿Qué vimos?\n",
    "\n",
    "### Módulo 03\n",
    "\n",
    "* Reciclando código: función\n",
    "* Reciclando código: método\n",
    "* Reciclando código: script\n",
    "\n",
    "***\n",
    "\n",
    "#### Atributos de Pandas\n",
    "- `df.shape`\n",
    "\n",
    "#### Métodos de pandas\n",
    "\n",
    "- `df.head()`\n",
    "- `df.tail()`\n",
    "- `df.isna()`\n",
    "- `df.sum()`\n",
    "- `df.groupby()`\n",
    "- `df.count()`\n",
    "- `df[x].unique()`\n",
    "- `df.reset_index()`\n",
    "- `df.str.contains()`\n",
    "- `df.isin()`\n",
    "- `df.replace()`\n",
    "- `df.str.replace()`\n",
    "- `df.str.split()`\n",
    "- `df.to_numeric()`\n",
    "- `df.value_counts()`\n",
    "\n",
    "##### Funciones built-in\n",
    "- `len()`\n",
    "- `print()`\n",
    "\n",
    "##### Métodos built-in\n",
    "- `join()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Módulo 01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./imgs/line.png\" align=\"left\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ¿Por qué python?\n",
    "\n",
    "* Es un lenguaje multiplataforma\n",
    "* Es un lenguaje open-source\n",
    "* Tiene una comunidad muy grande\n",
    "* Tiene capacidades de desarrollo back-end y front-end\n",
    "* Posee infinidad de paquetes (pip y conda)\n",
    "* Pocas y simples líneas de código\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./imgs/img_python.jpg\" align=\"center\" width=\"950\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algunas características de python\n",
    "\n",
    "* multipropósito\n",
    "* interpretado\n",
    "* interactivo\n",
    "* de alto nivel\n",
    "* orientado a objetos\n",
    "\n",
    "## ¿Cómo usar python?\n",
    "\n",
    "* A través del intérprete\n",
    "\n",
    "Sesión interactiva de python, también llamada **python shell (>>>)**\n",
    "\n",
    "Utilizando el intérprete en la terminal (scripting mode): **python hello.py**\n",
    "\n",
    "\n",
    "* Utilizando *\"Interactive Python\"*\n",
    "\n",
    "Una sesión interactiva IPython en terminal **(In[1]:)**\n",
    "\n",
    "Una IPython notebook, conocida como **Jupyter Notebook**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter notebook\n",
    "\n",
    "En el menú de la interfaz gráfica (File, Edit, View, Insert, Cell, Kernel, Widgets y Help) puedes realizar varias acciones relacionadas con tu Jupyter notebook. Sin embargo, existen atajos que pueden ser de utilidad.\n",
    "\n",
    "Combinación | Descripción\n",
    "--- | ---\n",
    "*enter* en una celda | modo de edición \n",
    "*esc* | salir de modo edición\n",
    "*shift* o *control* + *enter* en una celda | ejecución de celda (cuando es código)\n",
    "A | crea un bloque arriba\n",
    "B | crea un bloque abajo\n",
    "*esc* + dd | borrar celda\n",
    "Z | undo\n",
    "*command* + A | seleccionar todo el texto de una celda\n",
    "*control* + A | ir al inicio de una línea\n",
    "*control* + E | ir al final de una línea\n",
    "*esc* + *command* + C | copiar\n",
    "*esc* + *command* + V | pegar / vaciar\n",
    "*esc* +  M | convierte la celda en markdown\n",
    "*esc* + Y | convierte la celda en código\n",
    "*esc* + *shift* + flechas arriba y abajo | para seleccionar celdas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## \"python es un lenguaje megadiverso...\" o la gran Pythontitlán\n",
    "\n",
    "<img src=\"./imgs/img_python2.png\" align=\"center\" width=\"950\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ¿Qué es una biblioteca / paquete / library?\n",
    "\n",
    "* En programación una biblioteca es una colección de rutinas precompiladas\n",
    "* Las rutinas, a veces llamadas módulos, se almacenan en formato de objeto\n",
    "* Las bibliotecas son particularmente útiles para almacenar rutinas de uso frecuente\n",
    "* Permiten el acceso de funcionalidades del sistema y soluciones estandarizadas a problemas de programación, etc."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas\n",
    "\n",
    "<img src=\"./imgs/pandas.png\" align=\"center\"/>\n",
    "\n",
    "*El nombre se deriva del término \"panel data\", un término econométrico para conjuntos de datos que incluyen observaciones durante múltiples períodos de tiempo.*\n",
    "\n",
    "[Pandas](https://pandas.pydata.org/pandas-docs/stable/index.html) es una biblioteca de software escrita como extensión de NumPy para manipulación y análisis de datos. NumPy añade un potente tipo matricial a Python. Los principales tipos de datos que pueden representarse con pandas son:\n",
    "\n",
    "* Datos tabulares con columnas de tipo heterogéneo con etiquetas en columnas y filas\n",
    "* Series temporales\n",
    "\n",
    "Pandas proporciona herramientas que permiten:\n",
    "\n",
    "* leer y escribir datos en diferentes formatos: CSV, Microsoft Excel, bases SQL y formato HDF5\n",
    "* seleccionar y filtrar de manera sencilla tablas de datos en función de posición, valor o etiquetas\n",
    "* fusionar y unir datos\n",
    "* transformar datos aplicando funciones tanto en global como por ventanas\n",
    "* manipulación de series temporales\n",
    "* hacer gráficas\n",
    "\n",
    "En pandas existen **tres tipos básicos de objetos** todos ellos basados a su vez en Numpy:\n",
    "\n",
    "* Series (listas, 1D),\n",
    "* DataFrame (tablas, 2D) y\n",
    "* Panels (tablas 3D).\n",
    "\n",
    "Para mayor detalle puedes consultar su [documentación](https://bioinf.comav.upv.es/courses/linux/python/pandas.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Módulo 02"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./imgs/line.png\" align=\"left\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importar módulos para descarga de archivos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importamos los módulos necesarios, si queremos utilizar unicamente Python para descargar los archivos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import gzip\n",
    "import wget\n",
    "import requests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos un archivo de requirements.txt el cual se puede utilizar con pip para instalar los paquetes necesarion, con las versiones necesarias. Ya sea para instalarlo en un entorno o en nuestro sistema base, en el caso de usar el gestor de paquetes `conda`"
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   "source": [
    "import session_info\n",
    "session_info.show(write_req_file=True, req_file_name=\"wbdsla-requirements.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los archivos GFF3 se descargaron de la siguiente ubicación: http://ftp.ensembl.org/pub/release-104/gff3/homo_sapiens/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizando los módulos previamente cargados, creo una carpeta, descargo el archivo y lo escribo a disco. Parece ser una tarea demasiado complicada pero las capacidades de los módulos va mas allá de esta simple tarea."
   ]
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       "43154053"
      ]
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     "execution_count": 3,
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     "output_type": "execute_result"
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   ],
   "source": [
    "os.makedirs('data', exist_ok=True)\n",
    "url = 'http://ftp.ensembl.org/pub/release-104/gff3/homo_sapiens/Homo_sapiens.GRCh38.104.gff3.gz'\n",
    "r = requests.get(url, allow_redirects=True)\n",
    "filename = \"./data/Homo_sapiens.GRCh38.104.gff3.gz\"\n",
    "open(filename, \"wb\").write(r.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una manera clásica de acceder al contenido de un archivo comprimido es utilizar `gzip.open()` para abrir el archivo y leer línea por línea con el método `readlines()`. En este ejemplo, seleccionamos los primeros 50 líneas haciendo slicing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
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     "text": [
      "[b'##gff-version 3\\n', b'##sequence-region   1 1 248956422\\n', b'##sequence-region   10 1 133797422\\n', b'##sequence-region   11 1 135086622\\n', b'##sequence-region   12 1 133275309\\n', b'##sequence-region   13 1 114364328\\n', b'##sequence-region   14 1 107043718\\n', b'##sequence-region   15 1 101991189\\n', b'##sequence-region   16 1 90338345\\n', b'##sequence-region   17 1 83257441\\n', b'##sequence-region   18 1 80373285\\n', b'##sequence-region   19 1 58617616\\n', b'##sequence-region   2 1 242193529\\n', b'##sequence-region   20 1 64444167\\n', b'##sequence-region   21 1 46709983\\n', b'##sequence-region   22 1 50818468\\n', b'##sequence-region   3 1 198295559\\n', b'##sequence-region   4 1 190214555\\n', b'##sequence-region   5 1 181538259\\n', b'##sequence-region   6 1 170805979\\n', b'##sequence-region   7 1 159345973\\n', b'##sequence-region   8 1 145138636\\n', b'##sequence-region   9 1 138394717\\n', b'##sequence-region   GL000008.2 1 209709\\n', b'##sequence-region   GL000009.2 1 201709\\n', b'##sequence-region   GL000194.1 1 191469\\n', b'##sequence-region   GL000195.1 1 182896\\n', b'##sequence-region   GL000205.2 1 185591\\n', b'##sequence-region   GL000208.1 1 92689\\n', b'##sequence-region   GL000213.1 1 164239\\n', b'##sequence-region   GL000214.1 1 137718\\n', b'##sequence-region   GL000216.2 1 176608\\n', b'##sequence-region   GL000218.1 1 161147\\n', b'##sequence-region   GL000219.1 1 179198\\n', b'##sequence-region   GL000220.1 1 161802\\n', b'##sequence-region   GL000221.1 1 155397\\n', b'##sequence-region   GL000224.1 1 179693\\n', b'##sequence-region   GL000225.1 1 211173\\n', b'##sequence-region   GL000226.1 1 15008\\n', b'##sequence-region   KI270302.1 1 2274\\n', b'##sequence-region   KI270303.1 1 1942\\n', b'##sequence-region   KI270304.1 1 2165\\n', b'##sequence-region   KI270305.1 1 1472\\n', b'##sequence-region   KI270310.1 1 1201\\n', b'##sequence-region   KI270311.1 1 12399\\n', b'##sequence-region   KI270312.1 1 998\\n', b'##sequence-region   KI270315.1 1 2276\\n', b'##sequence-region   KI270316.1 1 1444\\n', b'##sequence-region   KI270317.1 1 37690\\n', b'##sequence-region   KI270320.1 1 4416\\n']\n"
     ]
    }
   ],
   "source": [
    "with gzip.open(filename) as f:\n",
    "    print(f.readlines()[0:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\"><b>Tip: </b> También se pueden descargar los archivos desde la terminal o aún mejor... desde jupyter notebook. </div> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En sistemos operativos Linux y MacOS Jupyter Notebooks permite utilizar comandos podemos utilizar comandos de la línea de comandos: `pwd`, `ls`, `echo`. Podemos usarlos de dos maneras, mediante el *magic command* `%%bash` o mediante el prefijo `!` al inicio de cada celda. A continuación podemos descargar el archivo y visualizar las primeras diez líneas utilizando las dos formas mencionadas anteriormente."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%%bash\n",
    "\n",
    "mkdir -p data/\n",
    "\n",
    "wget -q -P data/ http://ftp.ensembl.org/pub/release-104/gtf/homo_sapiens/Homo_sapiens.GRCh38.104.chr.gtf.gz"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "! zcat < data/Homo_sapiens.GRCh38.104.chr.gtf.gz | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ¡Manos a la obra! Pandas para datos biológicos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./imgs/img_pandas1.jpeg\" align=\"center\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Formato gff3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El formato que estaremos usando es el GFF3: https://xkcd.com/927/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GTF (General Transfer Format) is an extension of GFF version 2 \n",
    "and used to represent transcription models. GFF (General Feature Format) \n",
    "consists of one line per feature, each containing 9 columns of data. \n",
    "\n",
    "The 'type' of gene features is:\n",
    " * \"gene\" for protein-coding genes\n",
    " * \"ncRNA_gene\" for RNA genes\n",
    " * \"pseudogene\" for pseudogenes\n",
    "The 'type' of transcript features is:\n",
    " * \"mRNA\" for protein-coding transcripts\n",
    " * a specific type or RNA transcript such as \"snoRNA\" or \"lnc_RNA\"\n",
    " * \"pseudogenic_transcript\" for pseudogenes\n",
    "All transcripts are linked to \"exon\" features.\n",
    "Protein-coding transcripts are linked to \"CDS\", \"five_prime_UTR\", and\n",
    "\"three_prime_UTR\" features.\n",
    "\n",
    "Attributes for feature types:\n",
    "(square brackets indicate data which is not available for all features)\n",
    " * region types:\n",
    "    * ID: Unique identifier, format \"<region_type>:<region_name>\"\n",
    "    * [Alias]: A comma-separated list of aliases, usually including the\n",
    "      INSDC accession\n",
    "    * [Is_circular]: Flag to indicate circular regions\n",
    " * gene types:\n",
    "    * ID: Unique identifier, format \"gene:<gene_stable_id>\"\n",
    "    * biotype: Ensembl biotype, e.g. \"protein_coding\", \"pseudogene\"\n",
    "    * gene_id: Ensembl gene stable ID\n",
    "    * version: Ensembl gene version\n",
    "    * [Name]: Gene name\n",
    "    * [description]: Gene description\n",
    " * transcript types:\n",
    "    * ID: Unique identifier, format \"transcript:<transcript_stable_id>\"\n",
    "    * Parent: Gene identifier, format \"gene:<gene_stable_id>\"\n",
    "    * biotype: Ensembl biotype, e.g. \"protein_coding\", \"pseudogene\"\n",
    "    * transcript_id: Ensembl transcript stable ID\n",
    "    * version: Ensembl transcript version\n",
    "    * [Note]: If the transcript sequence has been edited (i.e. differs\n",
    "      from the genomic sequence), the edits are described in a note.\n",
    " * exon\n",
    "    * Parent: Transcript identifier, format \"transcript:<transcript_stable_id>\"\n",
    "    * exon_id: Ensembl exon stable ID\n",
    "    * version: Ensembl exon version\n",
    "    * constitutive: Flag to indicate if exon is present in all\n",
    "      transcripts\n",
    "    * rank: Integer that show the 5'->3' ordering of exons\n",
    " * CDS\n",
    "    * ID: Unique identifier, format \"CDS:<protein_stable_id>\"\n",
    "    * Parent: Transcript identifier, format \"transcript:<transcript_stable_id>\"\n",
    "    * protein_id: Ensembl protein stable ID\n",
    "    * version: Ensembl protein version\n",
    "\n",
    "Metadata:\n",
    " * genome-build - Build identifier of the assembly e.g. GRCh37.p11\n",
    " * genome-version - Version of this assembly e.g. GRCh37\n",
    " * genome-date - The date of the release of this assembly e.g. 2009-02\n",
    " * genome-build-accession - Genome accession e.g. GCA_000001405.14\n",
    " * genebuild-last-updated - Date of the last genebuild update e.g. 2013-09\n",
    "\n",
    "\n",
    "<dl>\n",
    "    <dt>Column 1: \"seqid\"</dt>\n",
    "    <dd>The ID of the landmark used to establish the coordinate system for the current feature. IDs may contain any characters, but must escape any characters not in the set [a-zA-Z0-9.:^*$@!+_?-|]. In particular, IDs may not contain unescaped whitespace and must not begin with an unescaped \"&gt;\".</dd>\n",
    "    <dt>Column 2: \"source\"</dt>\n",
    "    <dd>The source is a free text qualifier intended to describe the algorithm or operating procedure that generated this feature. Typically this is the name of a piece of software, such as \"Genescan\" or a database name, such as \"Genbank.\" In effect, the source is used to extend the feature ontology by adding a qualifier to the type creating a new composite type that is a subclass of the type in the type column.</dd>\n",
    "    <dt>Column 3: \"type\"</dt>\n",
    "    <dd>The type of the feature (previously called the \"method\"). This is constrained to be either a term from the Sequence Ontology or an SO accession number. The latter alternative is distinguished using the syntax SO:000000. In either case, it must be sequence_feature (SO:0000110) or an is_a child of it.</dd>\n",
    "    <dt>Columns 4 & 5: \"start\" and \"end\"</dt>\n",
    "    <dd>\n",
    "        <p>The start and end coordinates of the feature are given in positive 1-based integer coordinates, relative to the landmark given in column one. Start is always less than or equal to end. For features that cross the origin of a circular feature (e.g. most bacterial genomes, plasmids, and some viral genomes), the requirement for start to be less than or equal to end is satisfied by making end = the position of the end + the length of the landmark feature.</p>\n",
    "        <p>For zero-length features, such as insertion sites, start equals end and the implied site is to the right of the indicated base in the direction of the landmark.</p>\n",
    "    </dd>\n",
    "    <dt>Column 6: \"score\"</dt>\n",
    "    <dd>The score of the feature, a floating point number. As in earlier versions of the format, the semantics of the score are ill-defined. It is strongly recommended that E-values be used for sequence similarity features, and that P-values be used for ab initio gene prediction features.</dd>\n",
    "    <dt>Column 7: \"strand\"</dt>\n",
    "    <dd>The strand of the feature. + for positive strand (relative to the landmark), - for minus strand, and . for features that are not stranded. In addition, ? can be used for features whose strandedness is relevant, but unknown.</dd>\n",
    "    <dt>Column 8: \"phase\"</dt>\n",
    "    <dd>\n",
    "        <p>For features of type \"CDS\", the phase indicates where the next codon begins relative to the 5' end (where the 5' end of the CDS is relative to the strand of the CDS feature) of the current CDS feature. For clarification the 5' end for CDS features on the plus strand is the feature's start and and the 5' end for CDS features on the minus strand is the feature's end.  The phase is one of the integers 0, 1, or 2, indicating the number of bases forward from the start of the current CDS feature the next codon begins.  A phase of \"0\" indicates that a codon begins on the first nucleotide of the CDS feature (i.e. 0 bases forward), a phase of \"1\" indicates that the codon begins at the second nucleotide of this CDS feature and a phase of \"2\" indicates that the codon begins at the third nucleotide of this region. Note that ‘Phase’ in the context of a GFF3 CDS feature should not be confused with the similar concept of frame that is also a common concept in bioinformatics.  Frame is generally calculated as a value for a given base relative to the start of the complete open reading frame (ORF) or the codon (e.g. <codon position> modulo 3) while CDS phase describes the start of the next codon relative to a given CDS feature.</p>            \n",
    "        <p>The phase is REQUIRED for all CDS features.</p>\n",
    "    </dd>\n",
    "    <dt>Column 9: \"attributes\"</dt>\n",
    "    <dd>\n",
    "        <p>A list of feature attributes in the format tag=value. Multiple tag=value pairs are separated by semicolons. URL escaping rules are used for tags or values containing the following characters: \",=;\". Spaces are allowed in this field, but tabs must be replaced with the %09 URL escape. Attribute values do not need to be and should not be quoted. The quotes should be included as part of the value by parsers and not stripped.</p>\n",
    "    </dd>\n",
    "</dl>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./imgs/img_gff.jpg\" align=\"center\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cargamos el archivo como DataFrame, en formato de TEXTO. Una ventaja que tiene Python es que no es **cohersivo**, sus métodos y funciones no tratan de ejercer coerción para forzar un determinado tipo de dato a fin de que el código funcione."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/Homo_sapiens.GRCh38.104.gff3.gz', \n",
    "            sep='\\t', \n",
    "            comment='#',\n",
    "            header=None,\n",
    "            dtype=str,\n",
    "            names=[\"seqid\", \"source\", \"type\", \"start\",\n",
    "                   \"end\", \"score\", \"strand\", \"phase\", \"attributes\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algo de información del DataFrame, memory_usage nos dará el uso de memoria que consume el DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3150455 entries, 0 to 3150454\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Dtype \n",
      "---  ------      ----- \n",
      " 0   seqid       object\n",
      " 1   source      object\n",
      " 2   type        object\n",
      " 3   start       object\n",
      " 4   end         object\n",
      " 5   score       object\n",
      " 6   strand      object\n",
      " 7   phase       object\n",
      " 8   attributes  object\n",
      "dtypes: object(9)\n",
      "memory usage: 1.9 GB\n"
     ]
    }
   ],
   "source": [
    "df.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las dimensiones del DataFrame, filas y columnas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3150455, 9)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las primeras 5 líneas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>248956422</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10469</td>\n",
       "      <td>11240</td>\n",
       "      <td>1.3e+03</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>external_name=oe %3D 0.79;logic_name=cpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10650</td>\n",
       "      <td>10657</td>\n",
       "      <td>0.999</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>logic_name=eponine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10655</td>\n",
       "      <td>10657</td>\n",
       "      <td>0.999</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>logic_name=eponine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10678</td>\n",
       "      <td>10687</td>\n",
       "      <td>0.999</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>logic_name=eponine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid  source               type  start        end    score strand phase  \\\n",
       "0     1  GRCh38         chromosome      1  248956422        .      .     .   \n",
       "1     1       .  biological_region  10469      11240  1.3e+03      .     .   \n",
       "2     1       .  biological_region  10650      10657    0.999      +     .   \n",
       "3     1       .  biological_region  10655      10657    0.999      -     .   \n",
       "4     1       .  biological_region  10678      10687    0.999      +     .   \n",
       "\n",
       "                                          attributes  \n",
       "0  ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...  \n",
       "1           external_name=oe %3D 0.79;logic_name=cpg  \n",
       "2                                 logic_name=eponine  \n",
       "3                                 logic_name=eponine  \n",
       "4                                 logic_name=eponine  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las últimas 5 líneas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3150450</th>\n",
       "      <td>Y</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>26626966</td>\n",
       "      <td>26627137</td>\n",
       "      <td>0.994</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>external_name=rank %3D 1;logic_name=firstef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3150451</th>\n",
       "      <td>Y</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>26627457</td>\n",
       "      <td>26628186</td>\n",
       "      <td>0.997</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>external_name=rank %3D 1;logic_name=firstef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3150452</th>\n",
       "      <td>Y</td>\n",
       "      <td>havana</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>56855244</td>\n",
       "      <td>56855488</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000235857;Name=CTBP2P1;biotype=p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3150453</th>\n",
       "      <td>Y</td>\n",
       "      <td>havana</td>\n",
       "      <td>pseudogenic_transcript</td>\n",
       "      <td>56855244</td>\n",
       "      <td>56855488</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000431853;Parent=gene:ENSG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3150454</th>\n",
       "      <td>Y</td>\n",
       "      <td>havana</td>\n",
       "      <td>exon</td>\n",
       "      <td>56855244</td>\n",
       "      <td>56855488</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=transcript:ENST00000431853;Name=ENSE000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        seqid  source                    type     start       end  score  \\\n",
       "3150450     Y       .       biological_region  26626966  26627137  0.994   \n",
       "3150451     Y       .       biological_region  26627457  26628186  0.997   \n",
       "3150452     Y  havana              pseudogene  56855244  56855488      .   \n",
       "3150453     Y  havana  pseudogenic_transcript  56855244  56855488      .   \n",
       "3150454     Y  havana                    exon  56855244  56855488      .   \n",
       "\n",
       "        strand phase                                         attributes  \n",
       "3150450      -     .        external_name=rank %3D 1;logic_name=firstef  \n",
       "3150451      +     .        external_name=rank %3D 1;logic_name=firstef  \n",
       "3150452      +     .  ID=gene:ENSG00000235857;Name=CTBP2P1;biotype=p...  \n",
       "3150453      +     .  ID=transcript:ENST00000431853;Parent=gene:ENSG...  \n",
       "3150454      +     .  Parent=transcript:ENST00000431853;Name=ENSE000...  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unas 5 filas al azar. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>88338</th>\n",
       "      <td>1</td>\n",
       "      <td>havana</td>\n",
       "      <td>exon</td>\n",
       "      <td>45561789</td>\n",
       "      <td>45561878</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=transcript:ENST00000434299;Name=ENSE000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1418108</th>\n",
       "      <td>18</td>\n",
       "      <td>havana</td>\n",
       "      <td>CDS</td>\n",
       "      <td>50271501</td>\n",
       "      <td>50271540</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>ID=CDS:ENSP00000465923;Parent=transcript:ENST0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1504334</th>\n",
       "      <td>19</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>CDS</td>\n",
       "      <td>16496039</td>\n",
       "      <td>16496129</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ID=CDS:ENSP00000269881;Parent=transcript:ENST0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288569</th>\n",
       "      <td>17</td>\n",
       "      <td>havana</td>\n",
       "      <td>CDS</td>\n",
       "      <td>42910915</td>\n",
       "      <td>42910960</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>1</td>\n",
       "      <td>ID=CDS:ENSP00000466202;Parent=transcript:ENST0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191290</th>\n",
       "      <td>3</td>\n",
       "      <td>havana</td>\n",
       "      <td>CDS</td>\n",
       "      <td>160485359</td>\n",
       "      <td>160485376</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ID=CDS:ENSP00000420451;Parent=transcript:ENST0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        seqid          source  type      start        end score strand phase  \\\n",
       "88338       1          havana  exon   45561789   45561878     .      +     .   \n",
       "1418108    18          havana   CDS   50271501   50271540     .      -     1   \n",
       "1504334    19  ensembl_havana   CDS   16496039   16496129     .      -     0   \n",
       "1288569    17          havana   CDS   42910915   42910960     .      +     1   \n",
       "2191290     3          havana   CDS  160485359  160485376     .      -     0   \n",
       "\n",
       "                                                attributes  \n",
       "88338    Parent=transcript:ENST00000434299;Name=ENSE000...  \n",
       "1418108  ID=CDS:ENSP00000465923;Parent=transcript:ENST0...  \n",
       "1504334  ID=CDS:ENSP00000269881;Parent=transcript:ENST0...  \n",
       "1288569  ID=CDS:ENSP00000466202;Parent=transcript:ENST0...  \n",
       "2191290  ID=CDS:ENSP00000420451;Parent=transcript:ENST0...  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los tipos de datos por columna, a pesar de tener datos numericos, son considerados como object. Un objeto puede ser un string, list o diccionario, uno o todos a la vez en una sola columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seqid         object\n",
       "source        object\n",
       "type          object\n",
       "start         object\n",
       "end           object\n",
       "score         object\n",
       "strand        object\n",
       "phase         object\n",
       "attributes    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Las columnas contienen NAs? ¿Cuántos? No hay diferencia entre df.null() y df.na(), a menos que se utilice el NaN de Numpy, `np.nan`, el cual es un tipo de dato `float64`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seqid         0\n",
       "source        0\n",
       "type          0\n",
       "start         0\n",
       "end           0\n",
       "score         0\n",
       "strand        0\n",
       "phase         0\n",
       "attributes    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La suma total de NAs de todo el DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Cómo se llaman mis columnas?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['seqid', 'source', 'type', 'start', 'end', 'score', 'strand', 'phase',\n",
       "       'attributes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El índice por default es numérico, si se define una columna como el index, con df.set_index('mi_columna'), entonces el índice tendrá un valor. Debemos de recordar que los índices tienen que tener un valor único. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=3150455, step=1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([      0,       1,       2, ..., 3150452, 3150453, 3150454])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "#for x in df.index.values:\n",
    "#    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora empezamos ver lo que nos depara cada columna del DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>248956422</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10469</td>\n",
       "      <td>11240</td>\n",
       "      <td>1.3e+03</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>external_name=oe %3D 0.79;logic_name=cpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10650</td>\n",
       "      <td>10657</td>\n",
       "      <td>0.999</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>logic_name=eponine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10655</td>\n",
       "      <td>10657</td>\n",
       "      <td>0.999</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>logic_name=eponine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10678</td>\n",
       "      <td>10687</td>\n",
       "      <td>0.999</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>logic_name=eponine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid  source               type  start        end    score strand phase  \\\n",
       "0     1  GRCh38         chromosome      1  248956422        .      .     .   \n",
       "1     1       .  biological_region  10469      11240  1.3e+03      .     .   \n",
       "2     1       .  biological_region  10650      10657    0.999      +     .   \n",
       "3     1       .  biological_region  10655      10657    0.999      -     .   \n",
       "4     1       .  biological_region  10678      10687    0.999      +     .   \n",
       "\n",
       "                                          attributes  \n",
       "0  ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...  \n",
       "1           external_name=oe %3D 0.79;logic_name=cpg  \n",
       "2                                 logic_name=eponine  \n",
       "3                                 logic_name=eponine  \n",
       "4                                 logic_name=eponine  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Cuáles son los valores únicos de la primera columna? Seleccionamos con la sintaxis `df['unacolumna']` en lugar de `df.unacolumna`. ¿Por qué?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19',\n",
       "       '2', '20', '21', '22', '3', '4', '5', '6', '7', '8', '9',\n",
       "       'GL000008.2', 'GL000009.2', 'GL000194.1', 'GL000195.1',\n",
       "       'GL000205.2', 'GL000208.1', 'GL000213.1', 'GL000214.1',\n",
       "       'GL000216.2', 'GL000218.1', 'GL000219.1', 'GL000220.1',\n",
       "       'GL000221.1', 'GL000224.1', 'GL000225.1', 'GL000226.1',\n",
       "       'KI270302.1', 'KI270303.1', 'KI270304.1', 'KI270305.1',\n",
       "       'KI270310.1', 'KI270311.1', 'KI270312.1', 'KI270315.1',\n",
       "       'KI270316.1', 'KI270317.1', 'KI270320.1', 'KI270322.1',\n",
       "       'KI270329.1', 'KI270330.1', 'KI270333.1', 'KI270334.1',\n",
       "       'KI270335.1', 'KI270336.1', 'KI270337.1', 'KI270338.1',\n",
       "       'KI270340.1', 'KI270362.1', 'KI270363.1', 'KI270364.1',\n",
       "       'KI270366.1', 'KI270371.1', 'KI270372.1', 'KI270373.1',\n",
       "       'KI270374.1', 'KI270375.1', 'KI270376.1', 'KI270378.1',\n",
       "       'KI270379.1', 'KI270381.1', 'KI270382.1', 'KI270383.1',\n",
       "       'KI270384.1', 'KI270385.1', 'KI270386.1', 'KI270387.1',\n",
       "       'KI270388.1', 'KI270389.1', 'KI270390.1', 'KI270391.1',\n",
       "       'KI270392.1', 'KI270393.1', 'KI270394.1', 'KI270395.1',\n",
       "       'KI270396.1', 'KI270411.1', 'KI270412.1', 'KI270414.1',\n",
       "       'KI270417.1', 'KI270418.1', 'KI270419.1', 'KI270420.1',\n",
       "       'KI270422.1', 'KI270423.1', 'KI270424.1', 'KI270425.1',\n",
       "       'KI270429.1', 'KI270435.1', 'KI270438.1', 'KI270442.1',\n",
       "       'KI270448.1', 'KI270465.1', 'KI270466.1', 'KI270467.1',\n",
       "       'KI270468.1', 'KI270507.1', 'KI270508.1', 'KI270509.1',\n",
       "       'KI270510.1', 'KI270511.1', 'KI270512.1', 'KI270515.1',\n",
       "       'KI270516.1', 'KI270517.1', 'KI270518.1', 'KI270519.1',\n",
       "       'KI270521.1', 'KI270522.1', 'KI270528.1', 'KI270529.1',\n",
       "       'KI270530.1', 'KI270538.1', 'KI270539.1', 'KI270544.1',\n",
       "       'KI270548.1', 'KI270579.1', 'KI270580.1', 'KI270581.1',\n",
       "       'KI270582.1', 'KI270583.1', 'KI270584.1', 'KI270587.1',\n",
       "       'KI270588.1', 'KI270589.1', 'KI270590.1', 'KI270591.1',\n",
       "       'KI270593.1', 'KI270706.1', 'KI270707.1', 'KI270708.1',\n",
       "       'KI270709.1', 'KI270710.1', 'KI270711.1', 'KI270712.1',\n",
       "       'KI270713.1', 'KI270714.1', 'KI270715.1', 'KI270716.1',\n",
       "       'KI270717.1', 'KI270718.1', 'KI270719.1', 'KI270720.1',\n",
       "       'KI270721.1', 'KI270722.1', 'KI270723.1', 'KI270724.1',\n",
       "       'KI270725.1', 'KI270726.1', 'KI270727.1', 'KI270728.1',\n",
       "       'KI270729.1', 'KI270730.1', 'KI270731.1', 'KI270732.1',\n",
       "       'KI270733.1', 'KI270734.1', 'KI270735.1', 'KI270736.1',\n",
       "       'KI270737.1', 'KI270738.1', 'KI270739.1', 'KI270740.1',\n",
       "       'KI270741.1', 'KI270742.1', 'KI270743.1', 'KI270744.1',\n",
       "       'KI270745.1', 'KI270746.1', 'KI270747.1', 'KI270748.1',\n",
       "       'KI270749.1', 'KI270750.1', 'KI270751.1', 'KI270752.1',\n",
       "       'KI270753.1', 'KI270754.1', 'KI270755.1', 'KI270756.1',\n",
       "       'KI270757.1', 'MT', 'X', 'Y'], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['seqid'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The KI* and GL* scaffolds are unplaced scaffolds, which often don't have a know chromosome association."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Cuántos valores únicos hay?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "194"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['seqid'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seleccionamos los valores que contengan los strings KI y GL, lo cual nos generará valores booleanos que podemos usar en la selección de todos aquellos `True` usando el tipo de selección anterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          False\n",
       "1          False\n",
       "2          False\n",
       "3          False\n",
       "4          False\n",
       "           ...  \n",
       "3150450    False\n",
       "3150451    False\n",
       "3150452    False\n",
       "3150453    False\n",
       "3150454    False\n",
       "Name: seqid, Length: 3150455, dtype: bool"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['seqid'].str.contains('[KI, GL]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Así:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3037110</th>\n",
       "      <td>GL000008.2</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>scaffold</td>\n",
       "      <td>1</td>\n",
       "      <td>209709</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=scaffold:GL000008.2;Alias=chr4_GL000008v2_r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3037111</th>\n",
       "      <td>GL000008.2</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>83284</td>\n",
       "      <td>83394</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>external_name=rank %3D 1;logic_name=firstef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3037112</th>\n",
       "      <td>GL000008.2</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>83318</td>\n",
       "      <td>83804</td>\n",
       "      <td>342</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>external_name=oe %3D 0.85;logic_name=cpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3037113</th>\n",
       "      <td>GL000008.2</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>83763</td>\n",
       "      <td>84145</td>\n",
       "      <td>1</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>external_name=rank %3D 1;logic_name=firstef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3037114</th>\n",
       "      <td>GL000009.2</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>scaffold</td>\n",
       "      <td>1</td>\n",
       "      <td>201709</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=scaffold:GL000009.2;Alias=chr14_GL000009v2_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3040357</th>\n",
       "      <td>KI270754.1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>38828</td>\n",
       "      <td>39304</td>\n",
       "      <td>298</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>external_name=oe %3D 0.83;logic_name=cpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3040358</th>\n",
       "      <td>KI270754.1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>39221</td>\n",
       "      <td>39224</td>\n",
       "      <td>0.999</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>logic_name=eponine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3040359</th>\n",
       "      <td>KI270755.1</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>scaffold</td>\n",
       "      <td>1</td>\n",
       "      <td>36723</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=scaffold:KI270755.1;Alias=chrUn_KI270755v1,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3040360</th>\n",
       "      <td>KI270756.1</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>scaffold</td>\n",
       "      <td>1</td>\n",
       "      <td>79590</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=scaffold:KI270756.1;Alias=chrUn_KI270756v1,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3040361</th>\n",
       "      <td>KI270757.1</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>scaffold</td>\n",
       "      <td>1</td>\n",
       "      <td>71251</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=scaffold:KI270757.1;Alias=chrUn_KI270757v1,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3252 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              seqid  source               type  start     end  score strand  \\\n",
       "3037110  GL000008.2  GRCh38           scaffold      1  209709      .      .   \n",
       "3037111  GL000008.2       .  biological_region  83284   83394      1      -   \n",
       "3037112  GL000008.2       .  biological_region  83318   83804    342      .   \n",
       "3037113  GL000008.2       .  biological_region  83763   84145      1      +   \n",
       "3037114  GL000009.2  GRCh38           scaffold      1  201709      .      .   \n",
       "...             ...     ...                ...    ...     ...    ...    ...   \n",
       "3040357  KI270754.1       .  biological_region  38828   39304    298      .   \n",
       "3040358  KI270754.1       .  biological_region  39221   39224  0.999      -   \n",
       "3040359  KI270755.1  GRCh38           scaffold      1   36723      .      .   \n",
       "3040360  KI270756.1  GRCh38           scaffold      1   79590      .      .   \n",
       "3040361  KI270757.1  GRCh38           scaffold      1   71251      .      .   \n",
       "\n",
       "        phase                                         attributes  \n",
       "3037110     .  ID=scaffold:GL000008.2;Alias=chr4_GL000008v2_r...  \n",
       "3037111     .        external_name=rank %3D 1;logic_name=firstef  \n",
       "3037112     .           external_name=oe %3D 0.85;logic_name=cpg  \n",
       "3037113     .        external_name=rank %3D 1;logic_name=firstef  \n",
       "3037114     .  ID=scaffold:GL000009.2;Alias=chr14_GL000009v2_...  \n",
       "...       ...                                                ...  \n",
       "3040357     .           external_name=oe %3D 0.83;logic_name=cpg  \n",
       "3040358     .                                 logic_name=eponine  \n",
       "3040359     .  ID=scaffold:KI270755.1;Alias=chrUn_KI270755v1,...  \n",
       "3040360     .  ID=scaffold:KI270756.1;Alias=chrUn_KI270756v1,...  \n",
       "3040361     .  ID=scaffold:KI270757.1;Alias=chrUn_KI270757v1,...  \n",
       "\n",
       "[3252 rows x 9 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['seqid'].str.contains('[KI, GL]')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Cuántos elementos hay? Podemos usar shape o `len(df[df['seqid'].str.contains('[KI, GL]')].shape)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3252, 9)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['seqid'].str.contains('[KI, GL]')].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La virgulilla nos permite hacer una inversión booleana, cambia los True por False y viceversa. Selecciona aquellos que NO contienen KI y GL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
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       "      <td>1</td>\n",
       "      <td>GRCh38</td>\n",
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       "      <td>1</td>\n",
       "      <td>248956422</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>10469</td>\n",
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       "      <th>2</th>\n",
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       "      <td>biological_region</td>\n",
       "      <td>10650</td>\n",
       "      <td>10657</td>\n",
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       "      <td>biological_region</td>\n",
       "      <td>10655</td>\n",
       "      <td>10657</td>\n",
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       "      <td>-</td>\n",
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       "      <td>logic_name=eponine</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>biological_region</td>\n",
       "      <td>10678</td>\n",
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       "      <th>3150450</th>\n",
       "      <td>Y</td>\n",
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       "      <td>biological_region</td>\n",
       "      <td>26626966</td>\n",
       "      <td>26627137</td>\n",
       "      <td>0.994</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>external_name=rank %3D 1;logic_name=firstef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3150451</th>\n",
       "      <td>Y</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>26627457</td>\n",
       "      <td>26628186</td>\n",
       "      <td>0.997</td>\n",
       "      <td>+</td>\n",
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       "      <td>external_name=rank %3D 1;logic_name=firstef</td>\n",
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       "    <tr>\n",
       "      <th>3150452</th>\n",
       "      <td>Y</td>\n",
       "      <td>havana</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>56855244</td>\n",
       "      <td>56855488</td>\n",
       "      <td>.</td>\n",
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       "      <td>ID=gene:ENSG00000235857;Name=CTBP2P1;biotype=p...</td>\n",
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       "      <th>3150453</th>\n",
       "      <td>Y</td>\n",
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       "      <td>56855244</td>\n",
       "      <td>56855488</td>\n",
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       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000431853;Parent=gene:ENSG...</td>\n",
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       "      <th>3150454</th>\n",
       "      <td>Y</td>\n",
       "      <td>havana</td>\n",
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       "      <td>56855244</td>\n",
       "      <td>56855488</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=transcript:ENST00000431853;Name=ENSE000...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>3147203 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        seqid  source                    type     start        end    score  \\\n",
       "0           1  GRCh38              chromosome         1  248956422        .   \n",
       "1           1       .       biological_region     10469      11240  1.3e+03   \n",
       "2           1       .       biological_region     10650      10657    0.999   \n",
       "3           1       .       biological_region     10655      10657    0.999   \n",
       "4           1       .       biological_region     10678      10687    0.999   \n",
       "...       ...     ...                     ...       ...        ...      ...   \n",
       "3150450     Y       .       biological_region  26626966   26627137    0.994   \n",
       "3150451     Y       .       biological_region  26627457   26628186    0.997   \n",
       "3150452     Y  havana              pseudogene  56855244   56855488        .   \n",
       "3150453     Y  havana  pseudogenic_transcript  56855244   56855488        .   \n",
       "3150454     Y  havana                    exon  56855244   56855488        .   \n",
       "\n",
       "        strand phase                                         attributes  \n",
       "0            .     .  ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...  \n",
       "1            .     .           external_name=oe %3D 0.79;logic_name=cpg  \n",
       "2            +     .                                 logic_name=eponine  \n",
       "3            -     .                                 logic_name=eponine  \n",
       "4            +     .                                 logic_name=eponine  \n",
       "...        ...   ...                                                ...  \n",
       "3150450      -     .        external_name=rank %3D 1;logic_name=firstef  \n",
       "3150451      +     .        external_name=rank %3D 1;logic_name=firstef  \n",
       "3150452      +     .  ID=gene:ENSG00000235857;Name=CTBP2P1;biotype=p...  \n",
       "3150453      +     .  ID=transcript:ENST00000431853;Parent=gene:ENSG...  \n",
       "3150454      +     .  Parent=transcript:ENST00000431853;Name=ENSE000...  \n",
       "\n",
       "[3147203 rows x 9 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[~df['seqid'].str.contains('[KI, GL]')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al seleccionar elementos de un DataFrame estaremos alterando el índice. Lo podemos resetear para que vuelva a empezar desde 0. Cuidado cuando estamos utilizando un índice diferente al numérico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>index</th>\n",
       "      <th>seqid</th>\n",
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       "      <th>1</th>\n",
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       "      <th>3147198</th>\n",
       "      <td>3150450</td>\n",
       "      <td>Y</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>26626966</td>\n",
       "      <td>26627137</td>\n",
       "      <td>0.994</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>external_name=rank %3D 1;logic_name=firstef</td>\n",
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       "    <tr>\n",
       "      <th>3147199</th>\n",
       "      <td>3150451</td>\n",
       "      <td>Y</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>26627457</td>\n",
       "      <td>26628186</td>\n",
       "      <td>0.997</td>\n",
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       "      <td>external_name=rank %3D 1;logic_name=firstef</td>\n",
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       "    <tr>\n",
       "      <th>3147200</th>\n",
       "      <td>3150452</td>\n",
       "      <td>Y</td>\n",
       "      <td>havana</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>56855244</td>\n",
       "      <td>56855488</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
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       "      <td>ID=gene:ENSG00000235857;Name=CTBP2P1;biotype=p...</td>\n",
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       "      <td>3150453</td>\n",
       "      <td>Y</td>\n",
       "      <td>havana</td>\n",
       "      <td>pseudogenic_transcript</td>\n",
       "      <td>56855244</td>\n",
       "      <td>56855488</td>\n",
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       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000431853;Parent=gene:ENSG...</td>\n",
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       "      <th>3147202</th>\n",
       "      <td>3150454</td>\n",
       "      <td>Y</td>\n",
       "      <td>havana</td>\n",
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       "      <td>56855488</td>\n",
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      ],
      "text/plain": [
       "           index seqid  source                    type     start        end  \\\n",
       "0              0     1  GRCh38              chromosome         1  248956422   \n",
       "1              1     1       .       biological_region     10469      11240   \n",
       "2              2     1       .       biological_region     10650      10657   \n",
       "3              3     1       .       biological_region     10655      10657   \n",
       "4              4     1       .       biological_region     10678      10687   \n",
       "...          ...   ...     ...                     ...       ...        ...   \n",
       "3147198  3150450     Y       .       biological_region  26626966   26627137   \n",
       "3147199  3150451     Y       .       biological_region  26627457   26628186   \n",
       "3147200  3150452     Y  havana              pseudogene  56855244   56855488   \n",
       "3147201  3150453     Y  havana  pseudogenic_transcript  56855244   56855488   \n",
       "3147202  3150454     Y  havana                    exon  56855244   56855488   \n",
       "\n",
       "           score strand phase  \\\n",
       "0              .      .     .   \n",
       "1        1.3e+03      .     .   \n",
       "2          0.999      +     .   \n",
       "3          0.999      -     .   \n",
       "4          0.999      +     .   \n",
       "...          ...    ...   ...   \n",
       "3147198    0.994      -     .   \n",
       "3147199    0.997      +     .   \n",
       "3147200        .      +     .   \n",
       "3147201        .      +     .   \n",
       "3147202        .      +     .   \n",
       "\n",
       "                                                attributes  \n",
       "0        ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...  \n",
       "1                 external_name=oe %3D 0.79;logic_name=cpg  \n",
       "2                                       logic_name=eponine  \n",
       "3                                       logic_name=eponine  \n",
       "4                                       logic_name=eponine  \n",
       "...                                                    ...  \n",
       "3147198        external_name=rank %3D 1;logic_name=firstef  \n",
       "3147199        external_name=rank %3D 1;logic_name=firstef  \n",
       "3147200  ID=gene:ENSG00000235857;Name=CTBP2P1;biotype=p...  \n",
       "3147201  ID=transcript:ENST00000431853;Parent=gene:ENSG...  \n",
       "3147202  Parent=transcript:ENST00000431853;Name=ENSE000...  \n",
       "\n",
       "[3147203 rows x 10 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[~df['seqid'].str.contains('[KI, GL]')].reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por default reset_index mantiene el índice anterior en una nueva columna al principio del DataFrame, para eliminarlo hay que usar la opción drop=True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>248956422</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10469</td>\n",
       "      <td>11240</td>\n",
       "      <td>1.3e+03</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>external_name=oe %3D 0.79;logic_name=cpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10650</td>\n",
       "      <td>10657</td>\n",
       "      <td>0.999</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>logic_name=eponine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10655</td>\n",
       "      <td>10657</td>\n",
       "      <td>0.999</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>logic_name=eponine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10678</td>\n",
       "      <td>10687</td>\n",
       "      <td>0.999</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>logic_name=eponine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3147198</th>\n",
       "      <td>Y</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>26626966</td>\n",
       "      <td>26627137</td>\n",
       "      <td>0.994</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>external_name=rank %3D 1;logic_name=firstef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3147199</th>\n",
       "      <td>Y</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>26627457</td>\n",
       "      <td>26628186</td>\n",
       "      <td>0.997</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>external_name=rank %3D 1;logic_name=firstef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3147200</th>\n",
       "      <td>Y</td>\n",
       "      <td>havana</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>56855244</td>\n",
       "      <td>56855488</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000235857;Name=CTBP2P1;biotype=p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3147201</th>\n",
       "      <td>Y</td>\n",
       "      <td>havana</td>\n",
       "      <td>pseudogenic_transcript</td>\n",
       "      <td>56855244</td>\n",
       "      <td>56855488</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000431853;Parent=gene:ENSG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3147202</th>\n",
       "      <td>Y</td>\n",
       "      <td>havana</td>\n",
       "      <td>exon</td>\n",
       "      <td>56855244</td>\n",
       "      <td>56855488</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=transcript:ENST00000431853;Name=ENSE000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3147203 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        seqid  source                    type     start        end    score  \\\n",
       "0           1  GRCh38              chromosome         1  248956422        .   \n",
       "1           1       .       biological_region     10469      11240  1.3e+03   \n",
       "2           1       .       biological_region     10650      10657    0.999   \n",
       "3           1       .       biological_region     10655      10657    0.999   \n",
       "4           1       .       biological_region     10678      10687    0.999   \n",
       "...       ...     ...                     ...       ...        ...      ...   \n",
       "3147198     Y       .       biological_region  26626966   26627137    0.994   \n",
       "3147199     Y       .       biological_region  26627457   26628186    0.997   \n",
       "3147200     Y  havana              pseudogene  56855244   56855488        .   \n",
       "3147201     Y  havana  pseudogenic_transcript  56855244   56855488        .   \n",
       "3147202     Y  havana                    exon  56855244   56855488        .   \n",
       "\n",
       "        strand phase                                         attributes  \n",
       "0            .     .  ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...  \n",
       "1            .     .           external_name=oe %3D 0.79;logic_name=cpg  \n",
       "2            +     .                                 logic_name=eponine  \n",
       "3            -     .                                 logic_name=eponine  \n",
       "4            +     .                                 logic_name=eponine  \n",
       "...        ...   ...                                                ...  \n",
       "3147198      -     .        external_name=rank %3D 1;logic_name=firstef  \n",
       "3147199      +     .        external_name=rank %3D 1;logic_name=firstef  \n",
       "3147200      +     .  ID=gene:ENSG00000235857;Name=CTBP2P1;biotype=p...  \n",
       "3147201      +     .  ID=transcript:ENST00000431853;Parent=gene:ENSG...  \n",
       "3147202      +     .  Parent=transcript:ENST00000431853;Name=ENSE000...  \n",
       "\n",
       "[3147203 rows x 9 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[~df['seqid'].str.contains('[KI, GL]')].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[~df['seqid'].str.contains('[KI, GL]')].reset_index(drop=True)\n",
    "\n",
    "# Para hacerlo sin sobreescribir. \n",
    "# df[~df['seqid'].str.contains('[KI, GL]')].reset_index(drop=True, inplace=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Revisamos nuevamente las dimensiones del DataFrame y los valores únicos de la primera columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3147203, 9)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>248956422</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10469</td>\n",
       "      <td>11240</td>\n",
       "      <td>1.3e+03</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>external_name=oe %3D 0.79;logic_name=cpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10650</td>\n",
       "      <td>10657</td>\n",
       "      <td>0.999</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>logic_name=eponine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10655</td>\n",
       "      <td>10657</td>\n",
       "      <td>0.999</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>logic_name=eponine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10678</td>\n",
       "      <td>10687</td>\n",
       "      <td>0.999</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>logic_name=eponine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid  source               type  start        end    score strand phase  \\\n",
       "0     1  GRCh38         chromosome      1  248956422        .      .     .   \n",
       "1     1       .  biological_region  10469      11240  1.3e+03      .     .   \n",
       "2     1       .  biological_region  10650      10657    0.999      +     .   \n",
       "3     1       .  biological_region  10655      10657    0.999      -     .   \n",
       "4     1       .  biological_region  10678      10687    0.999      +     .   \n",
       "\n",
       "                                          attributes  \n",
       "0  ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...  \n",
       "1           external_name=oe %3D 0.79;logic_name=cpg  \n",
       "2                                 logic_name=eponine  \n",
       "3                                 logic_name=eponine  \n",
       "4                                 logic_name=eponine  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19',\n",
       "       '2', '20', '21', '22', '3', '4', '5', '6', '7', '8', '9', 'MT',\n",
       "       'X', 'Y'], dtype=object)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['seqid'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La columna seqid es un object, un string, por lo que podemos contar cuántas veces aparece cada \"palabra\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     287267\n",
       "2     239555\n",
       "3     197436\n",
       "17    187275\n",
       "11    186380\n",
       "19    178496\n",
       "12    176285\n",
       "7     150745\n",
       "6     149339\n",
       "16    142856\n",
       "5     140966\n",
       "4     130425\n",
       "10    123063\n",
       "9     116880\n",
       "8     116850\n",
       "15    114298\n",
       "14    113519\n",
       "X     101837\n",
       "20     72376\n",
       "22     67740\n",
       "18     55949\n",
       "13     52208\n",
       "21     37202\n",
       "Y       8130\n",
       "MT       126\n",
       "Name: seqid, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['seqid'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ¿Gráficas en pandas?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Qué tal una gráfica para tener un mejor panorama?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['seqid'].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por default la función plot grafica líneas, podemos utilizar plot como método y usar la función bar(). También podemos usar el tipo de gráfica como opción de la función plot: `df['seqid'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['seqid'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Y si mantenemos el orden original?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['seqid'].value_counts(sort=False).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>248956422</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10469</td>\n",
       "      <td>11240</td>\n",
       "      <td>1.3e+03</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>external_name=oe %3D 0.79;logic_name=cpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10650</td>\n",
       "      <td>10657</td>\n",
       "      <td>0.999</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>logic_name=eponine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10655</td>\n",
       "      <td>10657</td>\n",
       "      <td>0.999</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>logic_name=eponine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>.</td>\n",
       "      <td>biological_region</td>\n",
       "      <td>10678</td>\n",
       "      <td>10687</td>\n",
       "      <td>0.999</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>logic_name=eponine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid  source               type  start        end    score strand phase  \\\n",
       "0     1  GRCh38         chromosome      1  248956422        .      .     .   \n",
       "1     1       .  biological_region  10469      11240  1.3e+03      .     .   \n",
       "2     1       .  biological_region  10650      10657    0.999      +     .   \n",
       "3     1       .  biological_region  10655      10657    0.999      -     .   \n",
       "4     1       .  biological_region  10678      10687    0.999      +     .   \n",
       "\n",
       "                                          attributes  \n",
       "0  ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...  \n",
       "1           external_name=oe %3D 0.79;logic_name=cpg  \n",
       "2                                 logic_name=eponine  \n",
       "3                                 logic_name=eponine  \n",
       "4                                 logic_name=eponine  "
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Y la segunda columna? Podemos utilizar una aproximación similar a la columna 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['GRCh38', '.', 'havana', 'mirbase', 'ensembl_havana', 'ensembl',\n",
       "       'havana_tagene', 'ensembl_havana_tagene', 'insdc'], dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['source'].unique()\n",
    "\n",
    "# TAGENE is from long reads RNASeq data\n",
    "# Enseml es anotación automátia, Havana es información curada manualmente\n",
    "# ensembl_havana es una aproximación híbrida"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['source'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El valor GRCh38 es el que menos veces aparece, ¿de qué se trata?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           True\n",
       "1          False\n",
       "2          False\n",
       "3          False\n",
       "4          False\n",
       "           ...  \n",
       "3147198    False\n",
       "3147199    False\n",
       "3147200    False\n",
       "3147201    False\n",
       "3147202    False\n",
       "Name: source, Length: 3147203, dtype: bool"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['source'] == 'GRCh38'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al principio de cada registro `seqid` de cada cromosoma aparece con el valor `GRCh38` en la columna `type` la longitud total de cada cromosoma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>248956422</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287267</th>\n",
       "      <td>10</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>133797422</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:10;Alias=CM000672.2,chr10,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410330</th>\n",
       "      <td>11</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>135086622</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:11;Alias=CM000673.2,chr11,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596710</th>\n",
       "      <td>12</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>133275309</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:12;Alias=CM000674.2,chr12,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772995</th>\n",
       "      <td>13</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>114364328</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:13;Alias=CM000675.2,chr13,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825203</th>\n",
       "      <td>14</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>107043718</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:14;Alias=CM000676.2,chr14,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938722</th>\n",
       "      <td>15</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>101991189</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:15;Alias=CM000677.2,chr15,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053020</th>\n",
       "      <td>16</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>90338345</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:16;Alias=CM000678.2,chr16,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195876</th>\n",
       "      <td>17</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>83257441</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:17;Alias=CM000679.2,chr17,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383151</th>\n",
       "      <td>18</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>80373285</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:18;Alias=CM000680.2,chr18,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439100</th>\n",
       "      <td>19</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>58617616</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:19;Alias=CM000681.2,chr19,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1617596</th>\n",
       "      <td>2</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>242193529</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:2;Alias=CM000664.2,chr2,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1857151</th>\n",
       "      <td>20</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>64444167</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:20;Alias=CM000682.2,chr20,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929527</th>\n",
       "      <td>21</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>46709983</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:21;Alias=CM000683.2,chr21,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966729</th>\n",
       "      <td>22</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>50818468</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:22;Alias=CM000684.2,chr22,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034469</th>\n",
       "      <td>3</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>198295559</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:3;Alias=CM000665.2,chr3,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2231905</th>\n",
       "      <td>4</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>190214555</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:4;Alias=CM000666.2,chr4,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2362330</th>\n",
       "      <td>5</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>181538259</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:5;Alias=CM000667.2,chr5,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2503296</th>\n",
       "      <td>6</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>170805979</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:6;Alias=CM000668.2,chr6,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2652635</th>\n",
       "      <td>7</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>159345973</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:7;Alias=CM000669.2,chr7,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2803380</th>\n",
       "      <td>8</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>145138636</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:8;Alias=CM000670.2,chr8,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2920230</th>\n",
       "      <td>9</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>138394717</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:9;Alias=CM000671.2,chr9,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3037110</th>\n",
       "      <td>MT</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>16569</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:MT;Alias=chrM,J01415.2,NC_012920.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3037236</th>\n",
       "      <td>X</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>156040895</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:X;Alias=CM000685.2,chrX,NC_00002...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3139113</th>\n",
       "      <td>Y</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>2781480</td>\n",
       "      <td>56887902</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:Y;Alias=CM000686.2,chrY,NC_00002...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        seqid  source        type    start        end score strand phase  \\\n",
       "0           1  GRCh38  chromosome        1  248956422     .      .     .   \n",
       "287267     10  GRCh38  chromosome        1  133797422     .      .     .   \n",
       "410330     11  GRCh38  chromosome        1  135086622     .      .     .   \n",
       "596710     12  GRCh38  chromosome        1  133275309     .      .     .   \n",
       "772995     13  GRCh38  chromosome        1  114364328     .      .     .   \n",
       "825203     14  GRCh38  chromosome        1  107043718     .      .     .   \n",
       "938722     15  GRCh38  chromosome        1  101991189     .      .     .   \n",
       "1053020    16  GRCh38  chromosome        1   90338345     .      .     .   \n",
       "1195876    17  GRCh38  chromosome        1   83257441     .      .     .   \n",
       "1383151    18  GRCh38  chromosome        1   80373285     .      .     .   \n",
       "1439100    19  GRCh38  chromosome        1   58617616     .      .     .   \n",
       "1617596     2  GRCh38  chromosome        1  242193529     .      .     .   \n",
       "1857151    20  GRCh38  chromosome        1   64444167     .      .     .   \n",
       "1929527    21  GRCh38  chromosome        1   46709983     .      .     .   \n",
       "1966729    22  GRCh38  chromosome        1   50818468     .      .     .   \n",
       "2034469     3  GRCh38  chromosome        1  198295559     .      .     .   \n",
       "2231905     4  GRCh38  chromosome        1  190214555     .      .     .   \n",
       "2362330     5  GRCh38  chromosome        1  181538259     .      .     .   \n",
       "2503296     6  GRCh38  chromosome        1  170805979     .      .     .   \n",
       "2652635     7  GRCh38  chromosome        1  159345973     .      .     .   \n",
       "2803380     8  GRCh38  chromosome        1  145138636     .      .     .   \n",
       "2920230     9  GRCh38  chromosome        1  138394717     .      .     .   \n",
       "3037110    MT  GRCh38  chromosome        1      16569     .      .     .   \n",
       "3037236     X  GRCh38  chromosome        1  156040895     .      .     .   \n",
       "3139113     Y  GRCh38  chromosome  2781480   56887902     .      .     .   \n",
       "\n",
       "                                                attributes  \n",
       "0        ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...  \n",
       "287267   ID=chromosome:10;Alias=CM000672.2,chr10,NC_000...  \n",
       "410330   ID=chromosome:11;Alias=CM000673.2,chr11,NC_000...  \n",
       "596710   ID=chromosome:12;Alias=CM000674.2,chr12,NC_000...  \n",
       "772995   ID=chromosome:13;Alias=CM000675.2,chr13,NC_000...  \n",
       "825203   ID=chromosome:14;Alias=CM000676.2,chr14,NC_000...  \n",
       "938722   ID=chromosome:15;Alias=CM000677.2,chr15,NC_000...  \n",
       "1053020  ID=chromosome:16;Alias=CM000678.2,chr16,NC_000...  \n",
       "1195876  ID=chromosome:17;Alias=CM000679.2,chr17,NC_000...  \n",
       "1383151  ID=chromosome:18;Alias=CM000680.2,chr18,NC_000...  \n",
       "1439100  ID=chromosome:19;Alias=CM000681.2,chr19,NC_000...  \n",
       "1617596  ID=chromosome:2;Alias=CM000664.2,chr2,NC_00000...  \n",
       "1857151  ID=chromosome:20;Alias=CM000682.2,chr20,NC_000...  \n",
       "1929527  ID=chromosome:21;Alias=CM000683.2,chr21,NC_000...  \n",
       "1966729  ID=chromosome:22;Alias=CM000684.2,chr22,NC_000...  \n",
       "2034469  ID=chromosome:3;Alias=CM000665.2,chr3,NC_00000...  \n",
       "2231905  ID=chromosome:4;Alias=CM000666.2,chr4,NC_00000...  \n",
       "2362330  ID=chromosome:5;Alias=CM000667.2,chr5,NC_00000...  \n",
       "2503296  ID=chromosome:6;Alias=CM000668.2,chr6,NC_00000...  \n",
       "2652635  ID=chromosome:7;Alias=CM000669.2,chr7,NC_00000...  \n",
       "2803380  ID=chromosome:8;Alias=CM000670.2,chr8,NC_00000...  \n",
       "2920230  ID=chromosome:9;Alias=CM000671.2,chr9,NC_00000...  \n",
       "3037110   ID=chromosome:MT;Alias=chrM,J01415.2,NC_012920.1  \n",
       "3037236  ID=chromosome:X;Alias=CM000685.2,chrX,NC_00002...  \n",
       "3139113  ID=chromosome:Y;Alias=CM000686.2,chrY,NC_00002...  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['source'] == 'GRCh38']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si intentamos graficar para entenderlo mejor generará error porque el dtypes del DataFrame es object, strings. Podemos definir una columna, o varias, con un tipo en particular de tipo de dato usando `astype()`, `integer` en este caso. `df[df['source'] == 'GRCh38'].plot.bar(x='seqid', y='end')`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='seqid'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['source'] == 'GRCh38'].astype({'end': int}).plot.bar(x='seqid', y='end')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El cromosoma con mayor número de nucleótidos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>248956422</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1617596</th>\n",
       "      <td>2</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>242193529</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:2;Alias=CM000664.2,chr2,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034469</th>\n",
       "      <td>3</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>198295559</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:3;Alias=CM000665.2,chr3,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2231905</th>\n",
       "      <td>4</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>190214555</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:4;Alias=CM000666.2,chr4,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2362330</th>\n",
       "      <td>5</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>181538259</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:5;Alias=CM000667.2,chr5,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2503296</th>\n",
       "      <td>6</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>170805979</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:6;Alias=CM000668.2,chr6,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2652635</th>\n",
       "      <td>7</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>159345973</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:7;Alias=CM000669.2,chr7,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3037236</th>\n",
       "      <td>X</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>156040895</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:X;Alias=CM000685.2,chrX,NC_00002...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2803380</th>\n",
       "      <td>8</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>145138636</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:8;Alias=CM000670.2,chr8,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2920230</th>\n",
       "      <td>9</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>138394717</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:9;Alias=CM000671.2,chr9,NC_00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410330</th>\n",
       "      <td>11</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>135086622</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:11;Alias=CM000673.2,chr11,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287267</th>\n",
       "      <td>10</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>133797422</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:10;Alias=CM000672.2,chr10,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596710</th>\n",
       "      <td>12</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>133275309</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:12;Alias=CM000674.2,chr12,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772995</th>\n",
       "      <td>13</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>114364328</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:13;Alias=CM000675.2,chr13,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825203</th>\n",
       "      <td>14</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>107043718</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:14;Alias=CM000676.2,chr14,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938722</th>\n",
       "      <td>15</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>101991189</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:15;Alias=CM000677.2,chr15,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053020</th>\n",
       "      <td>16</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>90338345</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:16;Alias=CM000678.2,chr16,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195876</th>\n",
       "      <td>17</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>83257441</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:17;Alias=CM000679.2,chr17,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383151</th>\n",
       "      <td>18</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>80373285</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:18;Alias=CM000680.2,chr18,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1857151</th>\n",
       "      <td>20</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>64444167</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:20;Alias=CM000682.2,chr20,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439100</th>\n",
       "      <td>19</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>58617616</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:19;Alias=CM000681.2,chr19,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3139113</th>\n",
       "      <td>Y</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>2781480</td>\n",
       "      <td>56887902</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:Y;Alias=CM000686.2,chrY,NC_00002...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966729</th>\n",
       "      <td>22</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>50818468</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:22;Alias=CM000684.2,chr22,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929527</th>\n",
       "      <td>21</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>46709983</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:21;Alias=CM000683.2,chr21,NC_000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3037110</th>\n",
       "      <td>MT</td>\n",
       "      <td>GRCh38</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>16569</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=chromosome:MT;Alias=chrM,J01415.2,NC_012920.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        seqid  source        type    start        end score strand phase  \\\n",
       "0           1  GRCh38  chromosome        1  248956422     .      .     .   \n",
       "1617596     2  GRCh38  chromosome        1  242193529     .      .     .   \n",
       "2034469     3  GRCh38  chromosome        1  198295559     .      .     .   \n",
       "2231905     4  GRCh38  chromosome        1  190214555     .      .     .   \n",
       "2362330     5  GRCh38  chromosome        1  181538259     .      .     .   \n",
       "2503296     6  GRCh38  chromosome        1  170805979     .      .     .   \n",
       "2652635     7  GRCh38  chromosome        1  159345973     .      .     .   \n",
       "3037236     X  GRCh38  chromosome        1  156040895     .      .     .   \n",
       "2803380     8  GRCh38  chromosome        1  145138636     .      .     .   \n",
       "2920230     9  GRCh38  chromosome        1  138394717     .      .     .   \n",
       "410330     11  GRCh38  chromosome        1  135086622     .      .     .   \n",
       "287267     10  GRCh38  chromosome        1  133797422     .      .     .   \n",
       "596710     12  GRCh38  chromosome        1  133275309     .      .     .   \n",
       "772995     13  GRCh38  chromosome        1  114364328     .      .     .   \n",
       "825203     14  GRCh38  chromosome        1  107043718     .      .     .   \n",
       "938722     15  GRCh38  chromosome        1  101991189     .      .     .   \n",
       "1053020    16  GRCh38  chromosome        1   90338345     .      .     .   \n",
       "1195876    17  GRCh38  chromosome        1   83257441     .      .     .   \n",
       "1383151    18  GRCh38  chromosome        1   80373285     .      .     .   \n",
       "1857151    20  GRCh38  chromosome        1   64444167     .      .     .   \n",
       "1439100    19  GRCh38  chromosome        1   58617616     .      .     .   \n",
       "3139113     Y  GRCh38  chromosome  2781480   56887902     .      .     .   \n",
       "1966729    22  GRCh38  chromosome        1   50818468     .      .     .   \n",
       "1929527    21  GRCh38  chromosome        1   46709983     .      .     .   \n",
       "3037110    MT  GRCh38  chromosome        1      16569     .      .     .   \n",
       "\n",
       "                                                attributes  \n",
       "0        ID=chromosome:1;Alias=CM000663.2,chr1,NC_00000...  \n",
       "1617596  ID=chromosome:2;Alias=CM000664.2,chr2,NC_00000...  \n",
       "2034469  ID=chromosome:3;Alias=CM000665.2,chr3,NC_00000...  \n",
       "2231905  ID=chromosome:4;Alias=CM000666.2,chr4,NC_00000...  \n",
       "2362330  ID=chromosome:5;Alias=CM000667.2,chr5,NC_00000...  \n",
       "2503296  ID=chromosome:6;Alias=CM000668.2,chr6,NC_00000...  \n",
       "2652635  ID=chromosome:7;Alias=CM000669.2,chr7,NC_00000...  \n",
       "3037236  ID=chromosome:X;Alias=CM000685.2,chrX,NC_00002...  \n",
       "2803380  ID=chromosome:8;Alias=CM000670.2,chr8,NC_00000...  \n",
       "2920230  ID=chromosome:9;Alias=CM000671.2,chr9,NC_00000...  \n",
       "410330   ID=chromosome:11;Alias=CM000673.2,chr11,NC_000...  \n",
       "287267   ID=chromosome:10;Alias=CM000672.2,chr10,NC_000...  \n",
       "596710   ID=chromosome:12;Alias=CM000674.2,chr12,NC_000...  \n",
       "772995   ID=chromosome:13;Alias=CM000675.2,chr13,NC_000...  \n",
       "825203   ID=chromosome:14;Alias=CM000676.2,chr14,NC_000...  \n",
       "938722   ID=chromosome:15;Alias=CM000677.2,chr15,NC_000...  \n",
       "1053020  ID=chromosome:16;Alias=CM000678.2,chr16,NC_000...  \n",
       "1195876  ID=chromosome:17;Alias=CM000679.2,chr17,NC_000...  \n",
       "1383151  ID=chromosome:18;Alias=CM000680.2,chr18,NC_000...  \n",
       "1857151  ID=chromosome:20;Alias=CM000682.2,chr20,NC_000...  \n",
       "1439100  ID=chromosome:19;Alias=CM000681.2,chr19,NC_000...  \n",
       "3139113  ID=chromosome:Y;Alias=CM000686.2,chrY,NC_00002...  \n",
       "1966729  ID=chromosome:22;Alias=CM000684.2,chr22,NC_000...  \n",
       "1929527  ID=chromosome:21;Alias=CM000683.2,chr21,NC_000...  \n",
       "3037110   ID=chromosome:MT;Alias=chrM,J01415.2,NC_012920.1  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['source'] == 'GRCh38'].astype({'end': int}).sort_values(by='end', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['source'].value_counts().plot.bar()      # ; at the end supress the <AxesSubplot:> matplotlib output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Vamos a usar sólo las anotaciones de ensembl_havana? vamos a usar sólo las anotaciones de ensembl_havana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>65419</td>\n",
       "      <td>71585</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000186092;Name=OR4F5;biotype=pro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>ncRNA_gene</td>\n",
       "      <td>89295</td>\n",
       "      <td>133723</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000238009;biotype=lncRNA;descrip...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000284733;Name=OR4F29;biotype=pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000426406;Parent=gene:ENSG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>exon</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=transcript:ENST00000426406;Name=ENSE000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3147060</th>\n",
       "      <td>Y</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>25975549</td>\n",
       "      <td>25988069</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000172283;Name=PRYP4;biotype=unp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3147061</th>\n",
       "      <td>Y</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>pseudogenic_transcript</td>\n",
       "      <td>25975549</td>\n",
       "      <td>25988069</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000306589;Parent=gene:ENSG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3147062</th>\n",
       "      <td>Y</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>exon</td>\n",
       "      <td>25975549</td>\n",
       "      <td>25975617</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=transcript:ENST00000306589;Name=ENSE000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3147063</th>\n",
       "      <td>Y</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>exon</td>\n",
       "      <td>25978204</td>\n",
       "      <td>25978319</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=transcript:ENST00000306589;Name=ENSE000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3147064</th>\n",
       "      <td>Y</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>exon</td>\n",
       "      <td>25987811</td>\n",
       "      <td>25988069</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=transcript:ENST00000306589;Name=ENSE000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>656222 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        seqid          source                    type     start       end  \\\n",
       "82          1  ensembl_havana                    gene     65419     71585   \n",
       "92          1  ensembl_havana              ncRNA_gene     89295    133723   \n",
       "370         1  ensembl_havana                    gene    450740    451678   \n",
       "371         1  ensembl_havana                    mRNA    450740    451678   \n",
       "372         1  ensembl_havana                    exon    450740    451678   \n",
       "...       ...             ...                     ...       ...       ...   \n",
       "3147060     Y  ensembl_havana              pseudogene  25975549  25988069   \n",
       "3147061     Y  ensembl_havana  pseudogenic_transcript  25975549  25988069   \n",
       "3147062     Y  ensembl_havana                    exon  25975549  25975617   \n",
       "3147063     Y  ensembl_havana                    exon  25978204  25978319   \n",
       "3147064     Y  ensembl_havana                    exon  25987811  25988069   \n",
       "\n",
       "        score strand phase                                         attributes  \n",
       "82          .      +     .  ID=gene:ENSG00000186092;Name=OR4F5;biotype=pro...  \n",
       "92          .      -     .  ID=gene:ENSG00000238009;biotype=lncRNA;descrip...  \n",
       "370         .      -     .  ID=gene:ENSG00000284733;Name=OR4F29;biotype=pr...  \n",
       "371         .      -     .  ID=transcript:ENST00000426406;Parent=gene:ENSG...  \n",
       "372         .      -     .  Parent=transcript:ENST00000426406;Name=ENSE000...  \n",
       "...       ...    ...   ...                                                ...  \n",
       "3147060     .      +     .  ID=gene:ENSG00000172283;Name=PRYP4;biotype=unp...  \n",
       "3147061     .      +     .  ID=transcript:ENST00000306589;Parent=gene:ENSG...  \n",
       "3147062     .      +     .  Parent=transcript:ENST00000306589;Name=ENSE000...  \n",
       "3147063     .      +     .  Parent=transcript:ENST00000306589;Name=ENSE000...  \n",
       "3147064     .      +     .  Parent=transcript:ENST00000306589;Name=ENSE000...  \n",
       "\n",
       "[656222 rows x 9 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['source'] == \"ensembl_havana\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reseteamos el index, los expertos recomendaría usar df.copy(), pero YOLO."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['source'] == \"ensembl_havana\"].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>65419</td>\n",
       "      <td>71585</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000186092;Name=OR4F5;biotype=pro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>ncRNA_gene</td>\n",
       "      <td>89295</td>\n",
       "      <td>133723</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000238009;biotype=lncRNA;descrip...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000284733;Name=OR4F29;biotype=pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000426406;Parent=gene:ENSG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>exon</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=transcript:ENST00000426406;Name=ENSE000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid          source        type   start     end score strand phase  \\\n",
       "0     1  ensembl_havana        gene   65419   71585     .      +     .   \n",
       "1     1  ensembl_havana  ncRNA_gene   89295  133723     .      -     .   \n",
       "2     1  ensembl_havana        gene  450740  451678     .      -     .   \n",
       "3     1  ensembl_havana        mRNA  450740  451678     .      -     .   \n",
       "4     1  ensembl_havana        exon  450740  451678     .      -     .   \n",
       "\n",
       "                                          attributes  \n",
       "0  ID=gene:ENSG00000186092;Name=OR4F5;biotype=pro...  \n",
       "1  ID=gene:ENSG00000238009;biotype=lncRNA;descrip...  \n",
       "2  ID=gene:ENSG00000284733;Name=OR4F29;biotype=pr...  \n",
       "3  ID=transcript:ENST00000426406;Parent=gene:ENSG...  \n",
       "4  Parent=transcript:ENST00000426406;Name=ENSE000...  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(656222, 9)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recuerden que estamos en Python, podemos utilizar un bucle para iterar a través de cada columna para obtener los valos únicos de cada una de ellas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seqid 24\n",
      "source 1\n",
      "type 14\n",
      "start 239601\n",
      "end 239396\n",
      "score 1\n",
      "strand 2\n",
      "phase 4\n",
      "attributes 379657\n"
     ]
    }
   ],
   "source": [
    "for x in df.columns:\n",
    "    print(x, len(df[x].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\"><b>Avanzado: </b>No es necesario correr lo siguiente. </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También se puede usar una comprehensión de listas si lo que queremos es una lista de listas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['seqid', 24],\n",
       " ['source', 1],\n",
       " ['type', 14],\n",
       " ['start', 239601],\n",
       " ['end', 239396],\n",
       " ['score', 1],\n",
       " ['strand', 2],\n",
       " ['phase', 4],\n",
       " ['attributes', 379657]]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[[x, len(df[x].unique())] for x in df.columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Me pregunto ¿cuáles son las categorías en aquellas columnas que tienen menos de 30 diferentes categorías?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seqid ['1' '10' '11' '12' '13' '14' '15' '16' '17' '18' '19' '2' '20' '21' '22'\n",
      " '3' '4' '5' '6' '7' '8' '9' 'X' 'Y']\n",
      "source ['ensembl_havana']\n",
      "type ['gene' 'ncRNA_gene' 'mRNA' 'exon' 'CDS' 'three_prime_UTR'\n",
      " 'five_prime_UTR' 'lnc_RNA' 'pseudogene' 'pseudogenic_transcript'\n",
      " 'unconfirmed_transcript' 'V_gene_segment' 'ncRNA' 'C_gene_segment']\n",
      "score ['.']\n",
      "strand ['+' '-']\n",
      "phase ['.' '0' '1' '2']\n"
     ]
    }
   ],
   "source": [
    "for x in df.columns:\n",
    "    if len(df[x].unique()) < 30:\n",
    "        print(x, df[x].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\"><b>Avanzado: </b>No es necesario correr lo siguiente. </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "O bien, utilizar una comprensión de listas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['seqid',\n",
       "  array(['1', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19',\n",
       "         '2', '20', '21', '22', '3', '4', '5', '6', '7', '8', '9', 'X', 'Y'],\n",
       "        dtype=object)],\n",
       " ['source', array(['ensembl_havana'], dtype=object)],\n",
       " ['type',\n",
       "  array(['gene', 'ncRNA_gene', 'mRNA', 'exon', 'CDS', 'three_prime_UTR',\n",
       "         'five_prime_UTR', 'lnc_RNA', 'pseudogene',\n",
       "         'pseudogenic_transcript', 'unconfirmed_transcript',\n",
       "         'V_gene_segment', 'ncRNA', 'C_gene_segment'], dtype=object)],\n",
       " ['score', array(['.'], dtype=object)],\n",
       " ['strand', array(['+', '-'], dtype=object)],\n",
       " ['phase', array(['.', '0', '1', '2'], dtype=object)]]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[[x, df[x].unique()] for x in df.columns if len(df[x].unique()) < 30 ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos configurar algunos parámetros como el tamaño, título, paleta de colores. Para gráficas mas elaboras podemos usar Seaborn o Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['type'].value_counts().plot.bar(figsize=(12, 8), colormap='aaaa');  \n",
    "# Tip: se puede poner cualquier cosa en colormap y el error nos da las paletas disponibles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['type'].value_counts().plot.bar(figsize=(12, 8), colormap='Accent', fontsize=16);  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hay varios types que tienen muchos elementos. Quisiera aquellos que tienen categorías mas abudantes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "exon                      282669\n",
       "CDS                       260288\n",
       "five_prime_UTR             37708\n",
       "three_prime_UTR            29700\n",
       "mRNA                       25757\n",
       "gene                       18775\n",
       "lnc_RNA                      655\n",
       "ncRNA_gene                   335\n",
       "pseudogene                   192\n",
       "pseudogenic_transcript       101\n",
       "V_gene_segment                26\n",
       "unconfirmed_transcript        12\n",
       "C_gene_segment                 3\n",
       "ncRNA                          1\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "exon                       True\n",
       "CDS                        True\n",
       "five_prime_UTR             True\n",
       "three_prime_UTR            True\n",
       "mRNA                       True\n",
       "gene                       True\n",
       "lnc_RNA                   False\n",
       "ncRNA_gene                False\n",
       "pseudogene                False\n",
       "pseudogenic_transcript    False\n",
       "V_gene_segment            False\n",
       "unconfirmed_transcript    False\n",
       "C_gene_segment            False\n",
       "ncRNA                     False\n",
       "Name: type, dtype: bool"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['type'].value_counts() > 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "exon               282669\n",
       "CDS                260288\n",
       "five_prime_UTR      37708\n",
       "three_prime_UTR     29700\n",
       "mRNA                25757\n",
       "gene                18775\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['type'].value_counts()[ df['type'].value_counts() > 1000 ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecciono aquellas categorías de `type` que cuenten con mas de mil registros y lo mando a una lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['exon', 'CDS', 'five_prime_UTR', 'three_prime_UTR', 'mRNA', 'gene']"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['type'].value_counts()[df['type'].value_counts() > 1000].index.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "types_col = df['type'].value_counts()[df['type'].value_counts() > 1000].index.to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si buscamos un match parcial, usamos `df.str.contains()` para Series, si hacemos un match completo, usamos `df.isin()` para todo el DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          True\n",
       "1         False\n",
       "2          True\n",
       "3          True\n",
       "4          True\n",
       "          ...  \n",
       "656217    False\n",
       "656218    False\n",
       "656219     True\n",
       "656220     True\n",
       "656221     True\n",
       "Name: type, Length: 656222, dtype: bool"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['type'].isin(types_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['type'].isin(types_col)].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>65419</td>\n",
       "      <td>71585</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000186092;Name=OR4F5;biotype=pro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000284733;Name=OR4F29;biotype=pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000426406;Parent=gene:ENSG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>exon</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=transcript:ENST00000426406;Name=ENSE000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>CDS</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ID=CDS:ENSP00000409316;Parent=transcript:ENST0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid          source  type   start     end score strand phase  \\\n",
       "0     1  ensembl_havana  gene   65419   71585     .      +     .   \n",
       "1     1  ensembl_havana  gene  450740  451678     .      -     .   \n",
       "2     1  ensembl_havana  mRNA  450740  451678     .      -     .   \n",
       "3     1  ensembl_havana  exon  450740  451678     .      -     .   \n",
       "4     1  ensembl_havana   CDS  450740  451678     .      -     0   \n",
       "\n",
       "                                          attributes  \n",
       "0  ID=gene:ENSG00000186092;Name=OR4F5;biotype=pro...  \n",
       "1  ID=gene:ENSG00000284733;Name=OR4F29;biotype=pr...  \n",
       "2  ID=transcript:ENST00000426406;Parent=gene:ENSG...  \n",
       "3  Parent=transcript:ENST00000426406;Name=ENSE000...  \n",
       "4  ID=CDS:ENSP00000409316;Parent=transcript:ENST0...  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(655453, 9)"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las columnas de start, end, score, strans, score, phase, contienen muy pocos valores únicos, no requieren de nuestro atención por ahora. No centraremos en la columna que contiene los atributos. Seleccionamos los primeros 10 elementos de la Serie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    ID=gene:ENSG00000186092;Name=OR4F5;biotype=pro...\n",
       "1    ID=gene:ENSG00000284733;Name=OR4F29;biotype=pr...\n",
       "2    ID=transcript:ENST00000426406;Parent=gene:ENSG...\n",
       "3    Parent=transcript:ENST00000426406;Name=ENSE000...\n",
       "4    ID=CDS:ENSP00000409316;Parent=transcript:ENST0...\n",
       "5    ID=gene:ENSG00000284662;Name=OR4F16;biotype=pr...\n",
       "6    ID=transcript:ENST00000332831;Parent=gene:ENSG...\n",
       "7                    Parent=transcript:ENST00000332831\n",
       "8    Parent=transcript:ENST00000332831;Name=ENSE000...\n",
       "9    ID=CDS:ENSP00000329982;Parent=transcript:ENST0...\n",
       "Name: attributes, dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['attributes'][0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para columnas que contienen strings muy largos se puede modificar las dimensiones del dataframe, pero eso afectaría a la demás celdas también. Una solución sería iterar a través de cada elemento de la columna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ID=gene:ENSG00000186092;Name=OR4F5;biotype=protein_coding;description=olfactory receptor family 4 subfamily F member 5 [Source:HGNC Symbol%3BAcc:HGNC:14825];gene_id=ENSG00000186092;logic_name=ensembl_havana_gene_homo_sapiens;version=7',\n",
       " 'ID=gene:ENSG00000284733;Name=OR4F29;biotype=protein_coding;description=olfactory receptor family 4 subfamily F member 29 [Source:HGNC Symbol%3BAcc:HGNC:31275];gene_id=ENSG00000284733;logic_name=ensembl_havana_gene_homo_sapiens;version=2',\n",
       " 'ID=transcript:ENST00000426406;Parent=gene:ENSG00000284733;Name=OR4F29-201;biotype=protein_coding;ccdsid=CCDS72675.1;tag=basic;transcript_id=ENST00000426406;transcript_support_level=NA (assigned to previous version 2);version=4',\n",
       " 'Parent=transcript:ENST00000426406;Name=ENSE00002316283;constitutive=1;ensembl_end_phase=-1;ensembl_phase=-1;exon_id=ENSE00002316283;rank=1;version=4',\n",
       " 'ID=CDS:ENSP00000409316;Parent=transcript:ENST00000426406;protein_id=ENSP00000409316',\n",
       " 'ID=gene:ENSG00000284662;Name=OR4F16;biotype=protein_coding;description=olfactory receptor family 4 subfamily F member 16 [Source:HGNC Symbol%3BAcc:HGNC:15079];gene_id=ENSG00000284662;logic_name=ensembl_havana_gene_homo_sapiens;version=1',\n",
       " 'ID=transcript:ENST00000332831;Parent=gene:ENSG00000284662;Name=OR4F16-201;biotype=protein_coding;ccdsid=CCDS41221.1;tag=basic;transcript_id=ENST00000332831;transcript_support_level=NA (assigned to previous version 3);version=4',\n",
       " 'Parent=transcript:ENST00000332831',\n",
       " 'Parent=transcript:ENST00000332831;Name=ENSE00002324228;constitutive=1;ensembl_end_phase=-1;ensembl_phase=-1;exon_id=ENSE00002324228;rank=1;version=3',\n",
       " 'ID=CDS:ENSP00000329982;Parent=transcript:ENST00000332831;protein_id=ENSP00000329982']"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for x in df['attributes'][0:10]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a crear un archivo bed para genes y otro para transcritos. Dividiremos el DataFrame principal en dos, uno para genes y otro para transcritos, seleccionando el `type` por gene y mRNA, y seleccionando algunos de los atributos de la columna `attributes`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "genes = df[df['type'] == 'gene'].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcripts = df[df['type'] == 'mRNA'].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Por qué decidimos seleccionar mRNA si sabemos que hay otras categorías?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['gene', 'mRNA', 'exon', 'CDS', 'three_prime_UTR', 'five_prime_UTR'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['type'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Cuál es el `type` que me permitirá tener `attributes` mas descriptivos? ¿`exon`, `CDS`, `mRNA`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Parent=transcript:ENST00000426406;Name=ENSE00002316283;constitutive=1;ensembl_end_phase=-1;ensembl_phase=-1;exon_id=ENSE00002316283;rank=1;version=4',\n",
       " 'Parent=transcript:ENST00000332831;Name=ENSE00002324228;constitutive=1;ensembl_end_phase=-1;ensembl_phase=-1;exon_id=ENSE00002324228;rank=1;version=3',\n",
       " 'Parent=transcript:ENST00000616125;Name=ENSE00003903077;constitutive=0;ensembl_end_phase=0;ensembl_phase=0;exon_id=ENSE00003903077;rank=1;version=2',\n",
       " 'Parent=transcript:ENST00000616125;Name=ENSE00003915575;constitutive=0;ensembl_end_phase=2;ensembl_phase=0;exon_id=ENSE00003915575;rank=2;version=1',\n",
       " 'Parent=transcript:ENST00000616125;Name=ENSE00003911839;constitutive=0;ensembl_end_phase=2;ensembl_phase=2;exon_id=ENSE00003911839;rank=3;version=1',\n",
       " 'Parent=transcript:ENST00000616125;Name=ENSE00003911340;constitutive=0;ensembl_end_phase=1;ensembl_phase=2;exon_id=ENSE00003911340;rank=4;version=1',\n",
       " 'Parent=transcript:ENST00000616125;Name=ENSE00003912201;constitutive=0;ensembl_end_phase=1;ensembl_phase=1;exon_id=ENSE00003912201;rank=5;version=1',\n",
       " 'Parent=transcript:ENST00000616125;Name=ENSE00003751272;constitutive=0;ensembl_end_phase=1;ensembl_phase=1;exon_id=ENSE00003751272;rank=6;version=1',\n",
       " 'Parent=transcript:ENST00000616125;Name=ENSE00003675531;constitutive=0;ensembl_end_phase=2;ensembl_phase=1;exon_id=ENSE00003675531;rank=7;version=1',\n",
       " 'Parent=transcript:ENST00000616125;Name=ENSE00002728091;constitutive=0;ensembl_end_phase=1;ensembl_phase=2;exon_id=ENSE00002728091;rank=8;version=1']"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for x in df[df['type'] == 'exon']['attributes'][0:10]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ID=CDS:ENSP00000409316;Parent=transcript:ENST00000426406;protein_id=ENSP00000409316',\n",
       " 'ID=CDS:ENSP00000329982;Parent=transcript:ENST00000332831;protein_id=ENSP00000329982',\n",
       " 'ID=CDS:ENSP00000484643;Parent=transcript:ENST00000616125;protein_id=ENSP00000484643',\n",
       " 'ID=CDS:ENSP00000484643;Parent=transcript:ENST00000616125;protein_id=ENSP00000484643',\n",
       " 'ID=CDS:ENSP00000484643;Parent=transcript:ENST00000616125;protein_id=ENSP00000484643',\n",
       " 'ID=CDS:ENSP00000484643;Parent=transcript:ENST00000616125;protein_id=ENSP00000484643',\n",
       " 'ID=CDS:ENSP00000484643;Parent=transcript:ENST00000616125;protein_id=ENSP00000484643',\n",
       " 'ID=CDS:ENSP00000484643;Parent=transcript:ENST00000616125;protein_id=ENSP00000484643',\n",
       " 'ID=CDS:ENSP00000484643;Parent=transcript:ENST00000616125;protein_id=ENSP00000484643',\n",
       " 'ID=CDS:ENSP00000484643;Parent=transcript:ENST00000616125;protein_id=ENSP00000484643']"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for x in df[df['type'] == 'CDS']['attributes'][0:10]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ID=transcript:ENST00000426406;Parent=gene:ENSG00000284733;Name=OR4F29-201;biotype=protein_coding;ccdsid=CCDS72675.1;tag=basic;transcript_id=ENST00000426406;transcript_support_level=NA (assigned to previous version 2);version=4',\n",
       " 'ID=transcript:ENST00000332831;Parent=gene:ENSG00000284662;Name=OR4F16-201;biotype=protein_coding;ccdsid=CCDS41221.1;tag=basic;transcript_id=ENST00000332831;transcript_support_level=NA (assigned to previous version 3);version=4',\n",
       " 'ID=transcript:ENST00000616125;Parent=gene:ENSG00000187634;Name=SAMD11-210;biotype=protein_coding;transcript_id=ENST00000616125;transcript_support_level=5 (assigned to previous version 4);version=5',\n",
       " 'ID=transcript:ENST00000618779;Parent=gene:ENSG00000187634;Name=SAMD11-214;biotype=protein_coding;transcript_id=ENST00000618779;transcript_support_level=5 (assigned to previous version 4);version=5',\n",
       " 'ID=transcript:ENST00000622503;Parent=gene:ENSG00000187634;Name=SAMD11-215;biotype=protein_coding;transcript_id=ENST00000622503;transcript_support_level=5 (assigned to previous version 4);version=5',\n",
       " 'ID=transcript:ENST00000327044;Parent=gene:ENSG00000188976;Name=NOC2L-201;biotype=protein_coding;ccdsid=CCDS3.1;tag=basic;transcript_id=ENST00000327044;transcript_support_level=1 (assigned to previous version 6);version=7',\n",
       " 'ID=transcript:ENST00000338591;Parent=gene:ENSG00000187961;Name=KLHL17-201;biotype=protein_coding;ccdsid=CCDS30550.1;tag=basic;transcript_id=ENST00000338591;transcript_support_level=1 (assigned to previous version 7);version=8',\n",
       " 'ID=transcript:ENST00000379410;Parent=gene:ENSG00000187583;Name=PLEKHN1-203;biotype=protein_coding;ccdsid=CCDS4.1;tag=basic;transcript_id=ENST00000379410;transcript_support_level=1 (assigned to previous version 7);version=8',\n",
       " 'ID=transcript:ENST00000379407;Parent=gene:ENSG00000187583;Name=PLEKHN1-201;biotype=protein_coding;ccdsid=CCDS53256.1;tag=basic;transcript_id=ENST00000379407;transcript_support_level=1;version=7',\n",
       " 'ID=transcript:ENST00000379409;Parent=gene:ENSG00000187583;Name=PLEKHN1-202;biotype=protein_coding;tag=basic;transcript_id=ENST00000379409;transcript_support_level=2;version=6']"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for x in df[df['type'] == 'mRNA']['attributes'][0:10]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿`ID=Transcript`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mRNA'], dtype=object)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['attributes'].str.contains('ID=transcript')].reset_index(drop=True)['type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>65419</td>\n",
       "      <td>71585</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000186092;Name=OR4F5;biotype=pro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000284733;Name=OR4F29;biotype=pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>685679</td>\n",
       "      <td>686673</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000284662;Name=OR4F16;biotype=pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>923923</td>\n",
       "      <td>944575</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000187634;Name=SAMD11;biotype=pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>944203</td>\n",
       "      <td>959309</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000188976;Name=NOC2L;biotype=pro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid          source  type   start     end score strand phase  \\\n",
       "0     1  ensembl_havana  gene   65419   71585     .      +     .   \n",
       "1     1  ensembl_havana  gene  450740  451678     .      -     .   \n",
       "2     1  ensembl_havana  gene  685679  686673     .      -     .   \n",
       "3     1  ensembl_havana  gene  923923  944575     .      +     .   \n",
       "4     1  ensembl_havana  gene  944203  959309     .      -     .   \n",
       "\n",
       "                                          attributes  \n",
       "0  ID=gene:ENSG00000186092;Name=OR4F5;biotype=pro...  \n",
       "1  ID=gene:ENSG00000284733;Name=OR4F29;biotype=pr...  \n",
       "2  ID=gene:ENSG00000284662;Name=OR4F16;biotype=pr...  \n",
       "3  ID=gene:ENSG00000187634;Name=SAMD11;biotype=pr...  \n",
       "4  ID=gene:ENSG00000188976;Name=NOC2L;biotype=pro...  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000426406;Parent=gene:ENSG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>685679</td>\n",
       "      <td>686673</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000332831;Parent=gene:ENSG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>925942</td>\n",
       "      <td>944153</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000616125;Parent=gene:ENSG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>925942</td>\n",
       "      <td>944153</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000618779;Parent=gene:ENSG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>925942</td>\n",
       "      <td>944153</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000622503;Parent=gene:ENSG...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid          source  type   start     end score strand phase  \\\n",
       "0     1  ensembl_havana  mRNA  450740  451678     .      -     .   \n",
       "1     1  ensembl_havana  mRNA  685679  686673     .      -     .   \n",
       "2     1  ensembl_havana  mRNA  925942  944153     .      +     .   \n",
       "3     1  ensembl_havana  mRNA  925942  944153     .      +     .   \n",
       "4     1  ensembl_havana  mRNA  925942  944153     .      +     .   \n",
       "\n",
       "                                          attributes  \n",
       "0  ID=transcript:ENST00000426406;Parent=gene:ENSG...  \n",
       "1  ID=transcript:ENST00000332831;Parent=gene:ENSG...  \n",
       "2  ID=transcript:ENST00000616125;Parent=gene:ENSG...  \n",
       "3  ID=transcript:ENST00000618779;Parent=gene:ENSG...  \n",
       "4  ID=transcript:ENST00000622503;Parent=gene:ENSG...  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcripts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora sí, podemos borrar el DataFrame inicial, y de paso liberamos memoria. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BED genes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se puede dividir los strings utilizando un caracter como delimitador y expandiendo cada segmento a un nueva Serie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID=gene:ENSG00000186092</td>\n",
       "      <td>Name=OR4F5</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=olfactory receptor family 4 subfam...</td>\n",
       "      <td>gene_id=ENSG00000186092</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID=gene:ENSG00000284733</td>\n",
       "      <td>Name=OR4F29</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=olfactory receptor family 4 subfam...</td>\n",
       "      <td>gene_id=ENSG00000284733</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID=gene:ENSG00000284662</td>\n",
       "      <td>Name=OR4F16</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=olfactory receptor family 4 subfam...</td>\n",
       "      <td>gene_id=ENSG00000284662</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID=gene:ENSG00000187634</td>\n",
       "      <td>Name=SAMD11</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=sterile alpha motif domain contain...</td>\n",
       "      <td>gene_id=ENSG00000187634</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID=gene:ENSG00000188976</td>\n",
       "      <td>Name=NOC2L</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=NOC2 like nucleolar associated tra...</td>\n",
       "      <td>gene_id=ENSG00000188976</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18770</th>\n",
       "      <td>ID=gene:ENSG00000183795</td>\n",
       "      <td>Name=BPY2B</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=basic charge Y-linked 2B [Source:H...</td>\n",
       "      <td>gene_id=ENSG00000183795</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18771</th>\n",
       "      <td>ID=gene:ENSG00000187191</td>\n",
       "      <td>Name=DAZ3</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=deleted in azoospermia 3 [Source:H...</td>\n",
       "      <td>gene_id=ENSG00000187191</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18772</th>\n",
       "      <td>ID=gene:ENSG00000205916</td>\n",
       "      <td>Name=DAZ4</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=deleted in azoospermia 4 [Source:H...</td>\n",
       "      <td>gene_id=ENSG00000205916</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18773</th>\n",
       "      <td>ID=gene:ENSG00000185894</td>\n",
       "      <td>Name=BPY2C</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=basic charge Y-linked 2C [Source:H...</td>\n",
       "      <td>gene_id=ENSG00000185894</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18774</th>\n",
       "      <td>ID=gene:ENSG00000172288</td>\n",
       "      <td>Name=CDY1</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=chromodomain Y-linked 1 [Source:HG...</td>\n",
       "      <td>gene_id=ENSG00000172288</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18775 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             0            1                       2  \\\n",
       "0      ID=gene:ENSG00000186092   Name=OR4F5  biotype=protein_coding   \n",
       "1      ID=gene:ENSG00000284733  Name=OR4F29  biotype=protein_coding   \n",
       "2      ID=gene:ENSG00000284662  Name=OR4F16  biotype=protein_coding   \n",
       "3      ID=gene:ENSG00000187634  Name=SAMD11  biotype=protein_coding   \n",
       "4      ID=gene:ENSG00000188976   Name=NOC2L  biotype=protein_coding   \n",
       "...                        ...          ...                     ...   \n",
       "18770  ID=gene:ENSG00000183795   Name=BPY2B  biotype=protein_coding   \n",
       "18771  ID=gene:ENSG00000187191    Name=DAZ3  biotype=protein_coding   \n",
       "18772  ID=gene:ENSG00000205916    Name=DAZ4  biotype=protein_coding   \n",
       "18773  ID=gene:ENSG00000185894   Name=BPY2C  biotype=protein_coding   \n",
       "18774  ID=gene:ENSG00000172288    Name=CDY1  biotype=protein_coding   \n",
       "\n",
       "                                                       3  \\\n",
       "0      description=olfactory receptor family 4 subfam...   \n",
       "1      description=olfactory receptor family 4 subfam...   \n",
       "2      description=olfactory receptor family 4 subfam...   \n",
       "3      description=sterile alpha motif domain contain...   \n",
       "4      description=NOC2 like nucleolar associated tra...   \n",
       "...                                                  ...   \n",
       "18770  description=basic charge Y-linked 2B [Source:H...   \n",
       "18771  description=deleted in azoospermia 3 [Source:H...   \n",
       "18772  description=deleted in azoospermia 4 [Source:H...   \n",
       "18773  description=basic charge Y-linked 2C [Source:H...   \n",
       "18774  description=chromodomain Y-linked 1 [Source:HG...   \n",
       "\n",
       "                             4                                            5  \\\n",
       "0      gene_id=ENSG00000186092  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "1      gene_id=ENSG00000284733  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "2      gene_id=ENSG00000284662  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "3      gene_id=ENSG00000187634  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "4      gene_id=ENSG00000188976  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "...                        ...                                          ...   \n",
       "18770  gene_id=ENSG00000183795  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "18771  gene_id=ENSG00000187191  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "18772  gene_id=ENSG00000205916  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "18773  gene_id=ENSG00000185894  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "18774  gene_id=ENSG00000172288  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "\n",
       "                6  \n",
       "0       version=7  \n",
       "1       version=2  \n",
       "2       version=1  \n",
       "3      version=13  \n",
       "4      version=11  \n",
       "...           ...  \n",
       "18770   version=8  \n",
       "18771  version=15  \n",
       "18772  version=12  \n",
       "18773   version=8  \n",
       "18774   version=7  \n",
       "\n",
       "[18775 rows x 7 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes['attributes'].str.split(';', expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>65419</td>\n",
       "      <td>71585</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000186092;Name=OR4F5;biotype=pro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000284733;Name=OR4F29;biotype=pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>685679</td>\n",
       "      <td>686673</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000284662;Name=OR4F16;biotype=pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>923923</td>\n",
       "      <td>944575</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000187634;Name=SAMD11;biotype=pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>944203</td>\n",
       "      <td>959309</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000188976;Name=NOC2L;biotype=pro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid          source  type   start     end score strand phase  \\\n",
       "0     1  ensembl_havana  gene   65419   71585     .      +     .   \n",
       "1     1  ensembl_havana  gene  450740  451678     .      -     .   \n",
       "2     1  ensembl_havana  gene  685679  686673     .      -     .   \n",
       "3     1  ensembl_havana  gene  923923  944575     .      +     .   \n",
       "4     1  ensembl_havana  gene  944203  959309     .      -     .   \n",
       "\n",
       "                                          attributes  \n",
       "0  ID=gene:ENSG00000186092;Name=OR4F5;biotype=pro...  \n",
       "1  ID=gene:ENSG00000284733;Name=OR4F29;biotype=pr...  \n",
       "2  ID=gene:ENSG00000284662;Name=OR4F16;biotype=pr...  \n",
       "3  ID=gene:ENSG00000187634;Name=SAMD11;biotype=pr...  \n",
       "4  ID=gene:ENSG00000188976;Name=NOC2L;biotype=pro...  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.columns = ['id', 'name', 'biotype', 'description', 'gene_id', 'logic_name', 'version']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Renombramos las columnas de este nuevo DataFrame utilizando el método `rename()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>biotype</th>\n",
       "      <th>description</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>logic_name</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID=gene:ENSG00000186092</td>\n",
       "      <td>Name=OR4F5</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=olfactory receptor family 4 subfam...</td>\n",
       "      <td>gene_id=ENSG00000186092</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID=gene:ENSG00000284733</td>\n",
       "      <td>Name=OR4F29</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=olfactory receptor family 4 subfam...</td>\n",
       "      <td>gene_id=ENSG00000284733</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID=gene:ENSG00000284662</td>\n",
       "      <td>Name=OR4F16</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=olfactory receptor family 4 subfam...</td>\n",
       "      <td>gene_id=ENSG00000284662</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID=gene:ENSG00000187634</td>\n",
       "      <td>Name=SAMD11</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=sterile alpha motif domain contain...</td>\n",
       "      <td>gene_id=ENSG00000187634</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID=gene:ENSG00000188976</td>\n",
       "      <td>Name=NOC2L</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=NOC2 like nucleolar associated tra...</td>\n",
       "      <td>gene_id=ENSG00000188976</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18770</th>\n",
       "      <td>ID=gene:ENSG00000183795</td>\n",
       "      <td>Name=BPY2B</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=basic charge Y-linked 2B [Source:H...</td>\n",
       "      <td>gene_id=ENSG00000183795</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18771</th>\n",
       "      <td>ID=gene:ENSG00000187191</td>\n",
       "      <td>Name=DAZ3</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=deleted in azoospermia 3 [Source:H...</td>\n",
       "      <td>gene_id=ENSG00000187191</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18772</th>\n",
       "      <td>ID=gene:ENSG00000205916</td>\n",
       "      <td>Name=DAZ4</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=deleted in azoospermia 4 [Source:H...</td>\n",
       "      <td>gene_id=ENSG00000205916</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18773</th>\n",
       "      <td>ID=gene:ENSG00000185894</td>\n",
       "      <td>Name=BPY2C</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=basic charge Y-linked 2C [Source:H...</td>\n",
       "      <td>gene_id=ENSG00000185894</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18774</th>\n",
       "      <td>ID=gene:ENSG00000172288</td>\n",
       "      <td>Name=CDY1</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=chromodomain Y-linked 1 [Source:HG...</td>\n",
       "      <td>gene_id=ENSG00000172288</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18775 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            id         name                 biotype  \\\n",
       "0      ID=gene:ENSG00000186092   Name=OR4F5  biotype=protein_coding   \n",
       "1      ID=gene:ENSG00000284733  Name=OR4F29  biotype=protein_coding   \n",
       "2      ID=gene:ENSG00000284662  Name=OR4F16  biotype=protein_coding   \n",
       "3      ID=gene:ENSG00000187634  Name=SAMD11  biotype=protein_coding   \n",
       "4      ID=gene:ENSG00000188976   Name=NOC2L  biotype=protein_coding   \n",
       "...                        ...          ...                     ...   \n",
       "18770  ID=gene:ENSG00000183795   Name=BPY2B  biotype=protein_coding   \n",
       "18771  ID=gene:ENSG00000187191    Name=DAZ3  biotype=protein_coding   \n",
       "18772  ID=gene:ENSG00000205916    Name=DAZ4  biotype=protein_coding   \n",
       "18773  ID=gene:ENSG00000185894   Name=BPY2C  biotype=protein_coding   \n",
       "18774  ID=gene:ENSG00000172288    Name=CDY1  biotype=protein_coding   \n",
       "\n",
       "                                             description  \\\n",
       "0      description=olfactory receptor family 4 subfam...   \n",
       "1      description=olfactory receptor family 4 subfam...   \n",
       "2      description=olfactory receptor family 4 subfam...   \n",
       "3      description=sterile alpha motif domain contain...   \n",
       "4      description=NOC2 like nucleolar associated tra...   \n",
       "...                                                  ...   \n",
       "18770  description=basic charge Y-linked 2B [Source:H...   \n",
       "18771  description=deleted in azoospermia 3 [Source:H...   \n",
       "18772  description=deleted in azoospermia 4 [Source:H...   \n",
       "18773  description=basic charge Y-linked 2C [Source:H...   \n",
       "18774  description=chromodomain Y-linked 1 [Source:HG...   \n",
       "\n",
       "                       gene_id                                   logic_name  \\\n",
       "0      gene_id=ENSG00000186092  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "1      gene_id=ENSG00000284733  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "2      gene_id=ENSG00000284662  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "3      gene_id=ENSG00000187634  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "4      gene_id=ENSG00000188976  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "...                        ...                                          ...   \n",
       "18770  gene_id=ENSG00000183795  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "18771  gene_id=ENSG00000187191  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "18772  gene_id=ENSG00000205916  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "18773  gene_id=ENSG00000185894  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "18774  gene_id=ENSG00000172288  logic_name=ensembl_havana_gene_homo_sapiens   \n",
       "\n",
       "          version  \n",
       "0       version=7  \n",
       "1       version=2  \n",
       "2       version=1  \n",
       "3      version=13  \n",
       "4      version=11  \n",
       "...           ...  \n",
       "18770   version=8  \n",
       "18771  version=15  \n",
       "18772  version=12  \n",
       "18773   version=8  \n",
       "18774   version=7  \n",
       "\n",
       "[18775 rows x 7 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_names = {0: 'id', 1: 'name', 2: 'biotype', 3: 'description', 4: 'gene_id', 5: 'logic_name', 6: 'version'}\n",
    "genes['attributes'].str.split(';', expand=True).rename(new_names, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si todo funciona, lo enviamos a una nueva variable. Es una buena práctica utilizar `df.copy()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes = genes['attributes'].str.split(';', expand=True).rename(new_names, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concatenamos los dos DataFrames con una unión tipo `join='outer'` usando concat. Para más información pueden revisar: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "genes_attributes = pd.concat([genes, attributes], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos eliminar la columna attributes `df.drop()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "genes_attributes.drop('attributes', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>biotype</th>\n",
       "      <th>description</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>logic_name</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>65419</td>\n",
       "      <td>71585</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000186092</td>\n",
       "      <td>Name=OR4F5</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=olfactory receptor family 4 subfam...</td>\n",
       "      <td>gene_id=ENSG00000186092</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000284733</td>\n",
       "      <td>Name=OR4F29</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=olfactory receptor family 4 subfam...</td>\n",
       "      <td>gene_id=ENSG00000284733</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>685679</td>\n",
       "      <td>686673</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000284662</td>\n",
       "      <td>Name=OR4F16</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=olfactory receptor family 4 subfam...</td>\n",
       "      <td>gene_id=ENSG00000284662</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>923923</td>\n",
       "      <td>944575</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000187634</td>\n",
       "      <td>Name=SAMD11</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=sterile alpha motif domain contain...</td>\n",
       "      <td>gene_id=ENSG00000187634</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>944203</td>\n",
       "      <td>959309</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=gene:ENSG00000188976</td>\n",
       "      <td>Name=NOC2L</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>description=NOC2 like nucleolar associated tra...</td>\n",
       "      <td>gene_id=ENSG00000188976</td>\n",
       "      <td>logic_name=ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>version=11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid          source  type   start     end score strand phase  \\\n",
       "0     1  ensembl_havana  gene   65419   71585     .      +     .   \n",
       "1     1  ensembl_havana  gene  450740  451678     .      -     .   \n",
       "2     1  ensembl_havana  gene  685679  686673     .      -     .   \n",
       "3     1  ensembl_havana  gene  923923  944575     .      +     .   \n",
       "4     1  ensembl_havana  gene  944203  959309     .      -     .   \n",
       "\n",
       "                        id         name                 biotype  \\\n",
       "0  ID=gene:ENSG00000186092   Name=OR4F5  biotype=protein_coding   \n",
       "1  ID=gene:ENSG00000284733  Name=OR4F29  biotype=protein_coding   \n",
       "2  ID=gene:ENSG00000284662  Name=OR4F16  biotype=protein_coding   \n",
       "3  ID=gene:ENSG00000187634  Name=SAMD11  biotype=protein_coding   \n",
       "4  ID=gene:ENSG00000188976   Name=NOC2L  biotype=protein_coding   \n",
       "\n",
       "                                         description                  gene_id  \\\n",
       "0  description=olfactory receptor family 4 subfam...  gene_id=ENSG00000186092   \n",
       "1  description=olfactory receptor family 4 subfam...  gene_id=ENSG00000284733   \n",
       "2  description=olfactory receptor family 4 subfam...  gene_id=ENSG00000284662   \n",
       "3  description=sterile alpha motif domain contain...  gene_id=ENSG00000187634   \n",
       "4  description=NOC2 like nucleolar associated tra...  gene_id=ENSG00000188976   \n",
       "\n",
       "                                    logic_name     version  \n",
       "0  logic_name=ensembl_havana_gene_homo_sapiens   version=7  \n",
       "1  logic_name=ensembl_havana_gene_homo_sapiens   version=2  \n",
       "2  logic_name=ensembl_havana_gene_homo_sapiens   version=1  \n",
       "3  logic_name=ensembl_havana_gene_homo_sapiens  version=13  \n",
       "4  logic_name=ensembl_havana_gene_homo_sapiens  version=11  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes_attributes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En algunos casos es conveniente realizar *data cleaning* para limpiar el dataframe de aquellos valores que no son relevates: acentos, mayúsculas, caracteres extraños, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>biotype</th>\n",
       "      <th>description</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>logic_name</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>65419</td>\n",
       "      <td>71585</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000186092</td>\n",
       "      <td>OR4F5</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>olfactory receptor family 4 subfamily F member 5</td>\n",
       "      <td>ENSG00000186092</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000284733</td>\n",
       "      <td>OR4F29</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>olfactory receptor family 4 subfamily F member...</td>\n",
       "      <td>ENSG00000284733</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>685679</td>\n",
       "      <td>686673</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000284662</td>\n",
       "      <td>OR4F16</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>olfactory receptor family 4 subfamily F member...</td>\n",
       "      <td>ENSG00000284662</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>923923</td>\n",
       "      <td>944575</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000187634</td>\n",
       "      <td>SAMD11</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>sterile alpha motif domain containing 11</td>\n",
       "      <td>ENSG00000187634</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>944203</td>\n",
       "      <td>959309</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000188976</td>\n",
       "      <td>NOC2L</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>NOC2 like nucleolar associated transcriptional...</td>\n",
       "      <td>ENSG00000188976</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18770</th>\n",
       "      <td>Y</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>24607560</td>\n",
       "      <td>24639207</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000183795</td>\n",
       "      <td>BPY2B</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>basic charge Y-linked 2B</td>\n",
       "      <td>ENSG00000183795</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18771</th>\n",
       "      <td>Y</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>24763069</td>\n",
       "      <td>24813492</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000187191</td>\n",
       "      <td>DAZ3</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>deleted in azoospermia 3</td>\n",
       "      <td>ENSG00000187191</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18772</th>\n",
       "      <td>Y</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>24833843</td>\n",
       "      <td>24907040</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000205916</td>\n",
       "      <td>DAZ4</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>deleted in azoospermia 4</td>\n",
       "      <td>ENSG00000205916</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18773</th>\n",
       "      <td>Y</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>25030901</td>\n",
       "      <td>25062548</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000185894</td>\n",
       "      <td>BPY2C</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>basic charge Y-linked 2C</td>\n",
       "      <td>ENSG00000185894</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18774</th>\n",
       "      <td>Y</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>25622162</td>\n",
       "      <td>25624902</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000172288</td>\n",
       "      <td>CDY1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>chromodomain Y-linked 1</td>\n",
       "      <td>ENSG00000172288</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18775 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seqid          source  type     start       end score strand phase  \\\n",
       "0         1  ensembl_havana  gene     65419     71585     .      +     .   \n",
       "1         1  ensembl_havana  gene    450740    451678     .      -     .   \n",
       "2         1  ensembl_havana  gene    685679    686673     .      -     .   \n",
       "3         1  ensembl_havana  gene    923923    944575     .      +     .   \n",
       "4         1  ensembl_havana  gene    944203    959309     .      -     .   \n",
       "...     ...             ...   ...       ...       ...   ...    ...   ...   \n",
       "18770     Y  ensembl_havana  gene  24607560  24639207     .      +     .   \n",
       "18771     Y  ensembl_havana  gene  24763069  24813492     .      -     .   \n",
       "18772     Y  ensembl_havana  gene  24833843  24907040     .      +     .   \n",
       "18773     Y  ensembl_havana  gene  25030901  25062548     .      -     .   \n",
       "18774     Y  ensembl_havana  gene  25622162  25624902     .      +     .   \n",
       "\n",
       "                    id    name         biotype  \\\n",
       "0      ENSG00000186092   OR4F5  protein_coding   \n",
       "1      ENSG00000284733  OR4F29  protein_coding   \n",
       "2      ENSG00000284662  OR4F16  protein_coding   \n",
       "3      ENSG00000187634  SAMD11  protein_coding   \n",
       "4      ENSG00000188976   NOC2L  protein_coding   \n",
       "...                ...     ...             ...   \n",
       "18770  ENSG00000183795   BPY2B  protein_coding   \n",
       "18771  ENSG00000187191    DAZ3  protein_coding   \n",
       "18772  ENSG00000205916    DAZ4  protein_coding   \n",
       "18773  ENSG00000185894   BPY2C  protein_coding   \n",
       "18774  ENSG00000172288    CDY1  protein_coding   \n",
       "\n",
       "                                             description          gene_id  \\\n",
       "0      olfactory receptor family 4 subfamily F member 5   ENSG00000186092   \n",
       "1      olfactory receptor family 4 subfamily F member...  ENSG00000284733   \n",
       "2      olfactory receptor family 4 subfamily F member...  ENSG00000284662   \n",
       "3              sterile alpha motif domain containing 11   ENSG00000187634   \n",
       "4      NOC2 like nucleolar associated transcriptional...  ENSG00000188976   \n",
       "...                                                  ...              ...   \n",
       "18770                          basic charge Y-linked 2B   ENSG00000183795   \n",
       "18771                          deleted in azoospermia 3   ENSG00000187191   \n",
       "18772                          deleted in azoospermia 4   ENSG00000205916   \n",
       "18773                          basic charge Y-linked 2C   ENSG00000185894   \n",
       "18774                           chromodomain Y-linked 1   ENSG00000172288   \n",
       "\n",
       "                             logic_name version  \n",
       "0      ensembl_havana_gene_homo_sapiens       7  \n",
       "1      ensembl_havana_gene_homo_sapiens       2  \n",
       "2      ensembl_havana_gene_homo_sapiens       1  \n",
       "3      ensembl_havana_gene_homo_sapiens      13  \n",
       "4      ensembl_havana_gene_homo_sapiens      11  \n",
       "...                                 ...     ...  \n",
       "18770  ensembl_havana_gene_homo_sapiens       8  \n",
       "18771  ensembl_havana_gene_homo_sapiens      15  \n",
       "18772  ensembl_havana_gene_homo_sapiens      12  \n",
       "18773  ensembl_havana_gene_homo_sapiens       8  \n",
       "18774  ensembl_havana_gene_homo_sapiens       7  \n",
       "\n",
       "[18775 rows x 15 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(genes_attributes.replace('.*=', '', regex=True)\n",
    "                 .replace('gene:', '', regex=True)\n",
    "                 .replace('\\[Source.*', '',regex=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "genes_attributes = (genes_attributes.replace('.*=', '', regex=True)\n",
    "                                    .replace('gene:', '', regex=True)\n",
    "                                    .replace(' \\[Source.*', '',regex=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>biotype</th>\n",
       "      <th>description</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>logic_name</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>65419</td>\n",
       "      <td>71585</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000186092</td>\n",
       "      <td>OR4F5</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>olfactory receptor family 4 subfamily F member 5</td>\n",
       "      <td>ENSG00000186092</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000284733</td>\n",
       "      <td>OR4F29</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>olfactory receptor family 4 subfamily F member 29</td>\n",
       "      <td>ENSG00000284733</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>685679</td>\n",
       "      <td>686673</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000284662</td>\n",
       "      <td>OR4F16</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>olfactory receptor family 4 subfamily F member 16</td>\n",
       "      <td>ENSG00000284662</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>923923</td>\n",
       "      <td>944575</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000187634</td>\n",
       "      <td>SAMD11</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>sterile alpha motif domain containing 11</td>\n",
       "      <td>ENSG00000187634</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>944203</td>\n",
       "      <td>959309</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000188976</td>\n",
       "      <td>NOC2L</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>NOC2 like nucleolar associated transcriptional...</td>\n",
       "      <td>ENSG00000188976</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid          source  type   start     end score strand phase  \\\n",
       "0     1  ensembl_havana  gene   65419   71585     .      +     .   \n",
       "1     1  ensembl_havana  gene  450740  451678     .      -     .   \n",
       "2     1  ensembl_havana  gene  685679  686673     .      -     .   \n",
       "3     1  ensembl_havana  gene  923923  944575     .      +     .   \n",
       "4     1  ensembl_havana  gene  944203  959309     .      -     .   \n",
       "\n",
       "                id    name         biotype  \\\n",
       "0  ENSG00000186092   OR4F5  protein_coding   \n",
       "1  ENSG00000284733  OR4F29  protein_coding   \n",
       "2  ENSG00000284662  OR4F16  protein_coding   \n",
       "3  ENSG00000187634  SAMD11  protein_coding   \n",
       "4  ENSG00000188976   NOC2L  protein_coding   \n",
       "\n",
       "                                         description          gene_id  \\\n",
       "0   olfactory receptor family 4 subfamily F member 5  ENSG00000186092   \n",
       "1  olfactory receptor family 4 subfamily F member 29  ENSG00000284733   \n",
       "2  olfactory receptor family 4 subfamily F member 16  ENSG00000284662   \n",
       "3           sterile alpha motif domain containing 11  ENSG00000187634   \n",
       "4  NOC2 like nucleolar associated transcriptional...  ENSG00000188976   \n",
       "\n",
       "                         logic_name version  \n",
       "0  ensembl_havana_gene_homo_sapiens       7  \n",
       "1  ensembl_havana_gene_homo_sapiens       2  \n",
       "2  ensembl_havana_gene_homo_sapiens       1  \n",
       "3  ensembl_havana_gene_homo_sapiens      13  \n",
       "4  ensembl_havana_gene_homo_sapiens      11  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes_attributes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['olfactory receptor family 4 subfamily F member 5',\n",
       " 'olfactory receptor family 4 subfamily F member 29',\n",
       " 'olfactory receptor family 4 subfamily F member 16',\n",
       " 'sterile alpha motif domain containing 11',\n",
       " 'NOC2 like nucleolar associated transcriptional repressor',\n",
       " 'kelch like family member 17',\n",
       " 'pleckstrin homology domain containing N1',\n",
       " 'PPARGC1 and ESRR induced regulator%2C muscle 1',\n",
       " 'hes family bHLH transcription factor 4',\n",
       " 'ISG15 ubiquitin like modifier',\n",
       " 'agrin',\n",
       " 'ring finger protein 223',\n",
       " 'chromosome 1 open reading frame 159',\n",
       " 'tubulin tyrosine ligase like 10',\n",
       " 'TNF receptor superfamily member 18',\n",
       " 'TNF receptor superfamily member 4',\n",
       " 'stromal cell derived factor 4',\n",
       " 'beta-1%2C3-galactosyltransferase 6',\n",
       " 'C1q and TNF related 12',\n",
       " 'ubiquitin conjugating enzyme E2 J2']"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for x in genes_attributes['description'][0:20]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following types of BED files are supported by bedtools:\n",
    "\n",
    "+ BED3: A BED file where each feature is described by chrom, start, and end.\n",
    "For example: `chr1          11873   14409`\n",
    "+ BED4: A BED file where each feature is described by chrom, start, end, and name.\n",
    "For example: `chr1  11873  14409  uc001aaa.3`\n",
    "+ BED5: A BED file where each feature is described by chrom, start, end, name, and score.\n",
    "For example: `chr1 11873 14409 uc001aaa.3 0`\n",
    "+ BED6: A BED file where each feature is described by chrom, start, end, name, score, and strand.\n",
    "For example: `chr1 11873 14409 uc001aaa.3 0 +`\n",
    "+ BED12: A BED file where each feature is described by all twelve columns listed above.\n",
    "For example: `chr1 11873 14409 uc001aaa.3 0 + 11873 11873 0 3 354,109,1189, 0,739,1347,`\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a generar el BED6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**chrom, start, end, name, score, and strand**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>biotype</th>\n",
       "      <th>description</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>logic_name</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>65419</td>\n",
       "      <td>71585</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000186092</td>\n",
       "      <td>OR4F5</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>olfactory receptor family 4 subfamily F member 5</td>\n",
       "      <td>ENSG00000186092</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000284733</td>\n",
       "      <td>OR4F29</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>olfactory receptor family 4 subfamily F member 29</td>\n",
       "      <td>ENSG00000284733</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>685679</td>\n",
       "      <td>686673</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000284662</td>\n",
       "      <td>OR4F16</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>olfactory receptor family 4 subfamily F member 16</td>\n",
       "      <td>ENSG00000284662</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>923923</td>\n",
       "      <td>944575</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000187634</td>\n",
       "      <td>SAMD11</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>sterile alpha motif domain containing 11</td>\n",
       "      <td>ENSG00000187634</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>gene</td>\n",
       "      <td>944203</td>\n",
       "      <td>959309</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ENSG00000188976</td>\n",
       "      <td>NOC2L</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>NOC2 like nucleolar associated transcriptional...</td>\n",
       "      <td>ENSG00000188976</td>\n",
       "      <td>ensembl_havana_gene_homo_sapiens</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid          source  type   start     end score strand phase  \\\n",
       "0     1  ensembl_havana  gene   65419   71585     .      +     .   \n",
       "1     1  ensembl_havana  gene  450740  451678     .      -     .   \n",
       "2     1  ensembl_havana  gene  685679  686673     .      -     .   \n",
       "3     1  ensembl_havana  gene  923923  944575     .      +     .   \n",
       "4     1  ensembl_havana  gene  944203  959309     .      -     .   \n",
       "\n",
       "                id    name         biotype  \\\n",
       "0  ENSG00000186092   OR4F5  protein_coding   \n",
       "1  ENSG00000284733  OR4F29  protein_coding   \n",
       "2  ENSG00000284662  OR4F16  protein_coding   \n",
       "3  ENSG00000187634  SAMD11  protein_coding   \n",
       "4  ENSG00000188976   NOC2L  protein_coding   \n",
       "\n",
       "                                         description          gene_id  \\\n",
       "0   olfactory receptor family 4 subfamily F member 5  ENSG00000186092   \n",
       "1  olfactory receptor family 4 subfamily F member 29  ENSG00000284733   \n",
       "2  olfactory receptor family 4 subfamily F member 16  ENSG00000284662   \n",
       "3           sterile alpha motif domain containing 11  ENSG00000187634   \n",
       "4  NOC2 like nucleolar associated transcriptional...  ENSG00000188976   \n",
       "\n",
       "                         logic_name version  \n",
       "0  ensembl_havana_gene_homo_sapiens       7  \n",
       "1  ensembl_havana_gene_homo_sapiens       2  \n",
       "2  ensembl_havana_gene_homo_sapiens       1  \n",
       "3  ensembl_havana_gene_homo_sapiens      13  \n",
       "4  ensembl_havana_gene_homo_sapiens      11  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes_attributes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seleccionamos las columnas necesarias:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "genes_bed = genes_attributes[['seqid', 'start', 'end', 'name', 'strand']].copy()  # df[], df[[lista]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recordemos `df.dtypes`, todas las columnas son objects. Podemos usar dos métodos diferentes para definir una Serie como numérico entero: pd.numeric() y astype('Int64')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           65419\n",
       "1          450740\n",
       "2          685679\n",
       "3          923923\n",
       "4          944203\n",
       "           ...   \n",
       "18770    24607560\n",
       "18771    24763069\n",
       "18772    24833843\n",
       "18773    25030901\n",
       "18774    25622162\n",
       "Name: start, Length: 18775, dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(genes_bed['start'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     65419\n",
       "1    450740\n",
       "2    685679\n",
       "3    923923\n",
       "4    944203\n",
       "Name: start, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes_bed['start'].astype('int64').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y todo por el pequeño detalle que el sistema de coordenadas de GFF empieza en 1 y el de BED empieza en 0, por lo que debemos de restar un número a todas las coordenadas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           65418\n",
       "1          450739\n",
       "2          685678\n",
       "3          923922\n",
       "4          944202\n",
       "           ...   \n",
       "18770    24607559\n",
       "18771    24763068\n",
       "18772    24833842\n",
       "18773    25030900\n",
       "18774    25622161\n",
       "Name: start, Length: 18775, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(genes_bed['start'])-1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "genes_bed['start'] = genes_bed['start'].astype('int64') - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>name</th>\n",
       "      <th>strand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>65418</td>\n",
       "      <td>71585</td>\n",
       "      <td>OR4F5</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>450739</td>\n",
       "      <td>451678</td>\n",
       "      <td>OR4F29</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>685678</td>\n",
       "      <td>686673</td>\n",
       "      <td>OR4F16</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>923922</td>\n",
       "      <td>944575</td>\n",
       "      <td>SAMD11</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>944202</td>\n",
       "      <td>959309</td>\n",
       "      <td>NOC2L</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid   start     end    name strand\n",
       "0     1   65418   71585   OR4F5      +\n",
       "1     1  450739  451678  OR4F29      -\n",
       "2     1  685678  686673  OR4F16      -\n",
       "3     1  923922  944575  SAMD11      +\n",
       "4     1  944202  959309   NOC2L      -"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes_bed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18775, 5)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes_bed.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LISTO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BED transcripts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000426406;Parent=gene:ENSG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>685679</td>\n",
       "      <td>686673</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000332831;Parent=gene:ENSG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>925942</td>\n",
       "      <td>944153</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000616125;Parent=gene:ENSG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>925942</td>\n",
       "      <td>944153</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000618779;Parent=gene:ENSG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>925942</td>\n",
       "      <td>944153</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000622503;Parent=gene:ENSG...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid          source  type   start     end score strand phase  \\\n",
       "0     1  ensembl_havana  mRNA  450740  451678     .      -     .   \n",
       "1     1  ensembl_havana  mRNA  685679  686673     .      -     .   \n",
       "2     1  ensembl_havana  mRNA  925942  944153     .      +     .   \n",
       "3     1  ensembl_havana  mRNA  925942  944153     .      +     .   \n",
       "4     1  ensembl_havana  mRNA  925942  944153     .      +     .   \n",
       "\n",
       "                                          attributes  \n",
       "0  ID=transcript:ENST00000426406;Parent=gene:ENSG...  \n",
       "1  ID=transcript:ENST00000332831;Parent=gene:ENSG...  \n",
       "2  ID=transcript:ENST00000616125;Parent=gene:ENSG...  \n",
       "3  ID=transcript:ENST00000618779;Parent=gene:ENSG...  \n",
       "4  ID=transcript:ENST00000622503;Parent=gene:ENSG...  "
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcripts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID=transcript:ENST00000426406</td>\n",
       "      <td>Parent=gene:ENSG00000284733</td>\n",
       "      <td>Name=OR4F29-201</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>ccdsid=CCDS72675.1</td>\n",
       "      <td>tag=basic</td>\n",
       "      <td>transcript_id=ENST00000426406</td>\n",
       "      <td>transcript_support_level=NA (assigned to previ...</td>\n",
       "      <td>version=4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID=transcript:ENST00000332831</td>\n",
       "      <td>Parent=gene:ENSG00000284662</td>\n",
       "      <td>Name=OR4F16-201</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>ccdsid=CCDS41221.1</td>\n",
       "      <td>tag=basic</td>\n",
       "      <td>transcript_id=ENST00000332831</td>\n",
       "      <td>transcript_support_level=NA (assigned to previ...</td>\n",
       "      <td>version=4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID=transcript:ENST00000616125</td>\n",
       "      <td>Parent=gene:ENSG00000187634</td>\n",
       "      <td>Name=SAMD11-210</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>transcript_id=ENST00000616125</td>\n",
       "      <td>transcript_support_level=5 (assigned to previo...</td>\n",
       "      <td>version=5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID=transcript:ENST00000618779</td>\n",
       "      <td>Parent=gene:ENSG00000187634</td>\n",
       "      <td>Name=SAMD11-214</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>transcript_id=ENST00000618779</td>\n",
       "      <td>transcript_support_level=5 (assigned to previo...</td>\n",
       "      <td>version=5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID=transcript:ENST00000622503</td>\n",
       "      <td>Parent=gene:ENSG00000187634</td>\n",
       "      <td>Name=SAMD11-215</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>transcript_id=ENST00000622503</td>\n",
       "      <td>transcript_support_level=5 (assigned to previo...</td>\n",
       "      <td>version=5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25752</th>\n",
       "      <td>ID=transcript:ENST00000382365</td>\n",
       "      <td>Parent=gene:ENSG00000187191</td>\n",
       "      <td>Name=DAZ3-202</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>ccdsid=CCDS35489.1</td>\n",
       "      <td>tag=basic</td>\n",
       "      <td>transcript_id=ENST00000382365</td>\n",
       "      <td>transcript_support_level=1 (assigned to previo...</td>\n",
       "      <td>version=7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25753</th>\n",
       "      <td>ID=transcript:ENST00000415508</td>\n",
       "      <td>Parent=gene:ENSG00000205916</td>\n",
       "      <td>Name=DAZ4-204</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>tag=basic</td>\n",
       "      <td>transcript_id=ENST00000415508</td>\n",
       "      <td>transcript_support_level=5</td>\n",
       "      <td>version=6</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25754</th>\n",
       "      <td>ID=transcript:ENST00000449750</td>\n",
       "      <td>Parent=gene:ENSG00000205916</td>\n",
       "      <td>Name=DAZ4-206</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>ccdsid=CCDS55557.1</td>\n",
       "      <td>tag=basic</td>\n",
       "      <td>transcript_id=ENST00000449750</td>\n",
       "      <td>transcript_support_level=5</td>\n",
       "      <td>version=7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25755</th>\n",
       "      <td>ID=transcript:ENST00000382432</td>\n",
       "      <td>Parent=gene:ENSG00000205916</td>\n",
       "      <td>Name=DAZ4-203</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>tag=basic</td>\n",
       "      <td>transcript_id=ENST00000382432</td>\n",
       "      <td>transcript_support_level=5</td>\n",
       "      <td>version=9</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25756</th>\n",
       "      <td>ID=transcript:ENST00000306609</td>\n",
       "      <td>Parent=gene:ENSG00000172288</td>\n",
       "      <td>Name=CDY1-201</td>\n",
       "      <td>biotype=protein_coding</td>\n",
       "      <td>ccdsid=CCDS14801.1</td>\n",
       "      <td>tag=basic</td>\n",
       "      <td>transcript_id=ENST00000306609</td>\n",
       "      <td>transcript_support_level=1</td>\n",
       "      <td>version=4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25757 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0                            1  \\\n",
       "0      ID=transcript:ENST00000426406  Parent=gene:ENSG00000284733   \n",
       "1      ID=transcript:ENST00000332831  Parent=gene:ENSG00000284662   \n",
       "2      ID=transcript:ENST00000616125  Parent=gene:ENSG00000187634   \n",
       "3      ID=transcript:ENST00000618779  Parent=gene:ENSG00000187634   \n",
       "4      ID=transcript:ENST00000622503  Parent=gene:ENSG00000187634   \n",
       "...                              ...                          ...   \n",
       "25752  ID=transcript:ENST00000382365  Parent=gene:ENSG00000187191   \n",
       "25753  ID=transcript:ENST00000415508  Parent=gene:ENSG00000205916   \n",
       "25754  ID=transcript:ENST00000449750  Parent=gene:ENSG00000205916   \n",
       "25755  ID=transcript:ENST00000382432  Parent=gene:ENSG00000205916   \n",
       "25756  ID=transcript:ENST00000306609  Parent=gene:ENSG00000172288   \n",
       "\n",
       "                     2                       3                              4  \\\n",
       "0      Name=OR4F29-201  biotype=protein_coding             ccdsid=CCDS72675.1   \n",
       "1      Name=OR4F16-201  biotype=protein_coding             ccdsid=CCDS41221.1   \n",
       "2      Name=SAMD11-210  biotype=protein_coding  transcript_id=ENST00000616125   \n",
       "3      Name=SAMD11-214  biotype=protein_coding  transcript_id=ENST00000618779   \n",
       "4      Name=SAMD11-215  biotype=protein_coding  transcript_id=ENST00000622503   \n",
       "...                ...                     ...                            ...   \n",
       "25752    Name=DAZ3-202  biotype=protein_coding             ccdsid=CCDS35489.1   \n",
       "25753    Name=DAZ4-204  biotype=protein_coding                      tag=basic   \n",
       "25754    Name=DAZ4-206  biotype=protein_coding             ccdsid=CCDS55557.1   \n",
       "25755    Name=DAZ4-203  biotype=protein_coding                      tag=basic   \n",
       "25756    Name=CDY1-201  biotype=protein_coding             ccdsid=CCDS14801.1   \n",
       "\n",
       "                                                       5  \\\n",
       "0                                              tag=basic   \n",
       "1                                              tag=basic   \n",
       "2      transcript_support_level=5 (assigned to previo...   \n",
       "3      transcript_support_level=5 (assigned to previo...   \n",
       "4      transcript_support_level=5 (assigned to previo...   \n",
       "...                                                  ...   \n",
       "25752                                          tag=basic   \n",
       "25753                      transcript_id=ENST00000415508   \n",
       "25754                                          tag=basic   \n",
       "25755                      transcript_id=ENST00000382432   \n",
       "25756                                          tag=basic   \n",
       "\n",
       "                                   6  \\\n",
       "0      transcript_id=ENST00000426406   \n",
       "1      transcript_id=ENST00000332831   \n",
       "2                          version=5   \n",
       "3                          version=5   \n",
       "4                          version=5   \n",
       "...                              ...   \n",
       "25752  transcript_id=ENST00000382365   \n",
       "25753     transcript_support_level=5   \n",
       "25754  transcript_id=ENST00000449750   \n",
       "25755     transcript_support_level=5   \n",
       "25756  transcript_id=ENST00000306609   \n",
       "\n",
       "                                                       7          8  \n",
       "0      transcript_support_level=NA (assigned to previ...  version=4  \n",
       "1      transcript_support_level=NA (assigned to previ...  version=4  \n",
       "2                                                   None       None  \n",
       "3                                                   None       None  \n",
       "4                                                   None       None  \n",
       "...                                                  ...        ...  \n",
       "25752  transcript_support_level=1 (assigned to previo...  version=7  \n",
       "25753                                          version=6       None  \n",
       "25754                         transcript_support_level=5  version=7  \n",
       "25755                                          version=9       None  \n",
       "25756                         transcript_support_level=1  version=4  \n",
       "\n",
       "[25757 rows x 9 columns]"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcripts['attributes'].str.split(';', expand=True) \n",
    "# El número de columnas es diferente para algunos rows. Se puede con pandas, pero ya no es \"básico\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para sortear este asunto seleccionamos unicamente las primeras tres columnas en las cuales todas las filas tienen la misma categoría."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <td>ID=transcript:ENST00000426406</td>\n",
       "      <td>Parent=gene:ENSG00000284733</td>\n",
       "      <td>Name=OR4F29-201</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID=transcript:ENST00000332831</td>\n",
       "      <td>Parent=gene:ENSG00000284662</td>\n",
       "      <td>Name=OR4F16-201</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID=transcript:ENST00000616125</td>\n",
       "      <td>Parent=gene:ENSG00000187634</td>\n",
       "      <td>Name=SAMD11-210</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID=transcript:ENST00000618779</td>\n",
       "      <td>Parent=gene:ENSG00000187634</td>\n",
       "      <td>Name=SAMD11-214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID=transcript:ENST00000622503</td>\n",
       "      <td>Parent=gene:ENSG00000187634</td>\n",
       "      <td>Name=SAMD11-215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>25752</th>\n",
       "      <td>ID=transcript:ENST00000382365</td>\n",
       "      <td>Parent=gene:ENSG00000187191</td>\n",
       "      <td>Name=DAZ3-202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25753</th>\n",
       "      <td>ID=transcript:ENST00000415508</td>\n",
       "      <td>Parent=gene:ENSG00000205916</td>\n",
       "      <td>Name=DAZ4-204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25754</th>\n",
       "      <td>ID=transcript:ENST00000449750</td>\n",
       "      <td>Parent=gene:ENSG00000205916</td>\n",
       "      <td>Name=DAZ4-206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25755</th>\n",
       "      <td>ID=transcript:ENST00000382432</td>\n",
       "      <td>Parent=gene:ENSG00000205916</td>\n",
       "      <td>Name=DAZ4-203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25756</th>\n",
       "      <td>ID=transcript:ENST00000306609</td>\n",
       "      <td>Parent=gene:ENSG00000172288</td>\n",
       "      <td>Name=CDY1-201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25757 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0                            1  \\\n",
       "0      ID=transcript:ENST00000426406  Parent=gene:ENSG00000284733   \n",
       "1      ID=transcript:ENST00000332831  Parent=gene:ENSG00000284662   \n",
       "2      ID=transcript:ENST00000616125  Parent=gene:ENSG00000187634   \n",
       "3      ID=transcript:ENST00000618779  Parent=gene:ENSG00000187634   \n",
       "4      ID=transcript:ENST00000622503  Parent=gene:ENSG00000187634   \n",
       "...                              ...                          ...   \n",
       "25752  ID=transcript:ENST00000382365  Parent=gene:ENSG00000187191   \n",
       "25753  ID=transcript:ENST00000415508  Parent=gene:ENSG00000205916   \n",
       "25754  ID=transcript:ENST00000449750  Parent=gene:ENSG00000205916   \n",
       "25755  ID=transcript:ENST00000382432  Parent=gene:ENSG00000205916   \n",
       "25756  ID=transcript:ENST00000306609  Parent=gene:ENSG00000172288   \n",
       "\n",
       "                     2  \n",
       "0      Name=OR4F29-201  \n",
       "1      Name=OR4F16-201  \n",
       "2      Name=SAMD11-210  \n",
       "3      Name=SAMD11-214  \n",
       "4      Name=SAMD11-215  \n",
       "...                ...  \n",
       "25752    Name=DAZ3-202  \n",
       "25753    Name=DAZ4-204  \n",
       "25754    Name=DAZ4-206  \n",
       "25755    Name=DAZ4-203  \n",
       "25756    Name=CDY1-201  \n",
       "\n",
       "[25757 rows x 3 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcripts['attributes'].str.split(';', expand=True).iloc[:, 0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las renombrar de la misma manera que con el DataFrame de genes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>parent_gene</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3</th>\n",
       "      <td>ID=transcript:ENST00000618779</td>\n",
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       "      <td>Name=SAMD11-215</td>\n",
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       "      <th>...</th>\n",
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       "      <td>ID=transcript:ENST00000382365</td>\n",
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       "      <th>25753</th>\n",
       "      <td>ID=transcript:ENST00000415508</td>\n",
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       "      <td>Name=DAZ4-204</td>\n",
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       "    <tr>\n",
       "      <th>25754</th>\n",
       "      <td>ID=transcript:ENST00000449750</td>\n",
       "      <td>Parent=gene:ENSG00000205916</td>\n",
       "      <td>Name=DAZ4-206</td>\n",
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       "    <tr>\n",
       "      <th>25755</th>\n",
       "      <td>ID=transcript:ENST00000382432</td>\n",
       "      <td>Parent=gene:ENSG00000205916</td>\n",
       "      <td>Name=DAZ4-203</td>\n",
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       "    <tr>\n",
       "      <th>25756</th>\n",
       "      <td>ID=transcript:ENST00000306609</td>\n",
       "      <td>Parent=gene:ENSG00000172288</td>\n",
       "      <td>Name=CDY1-201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25757 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  id                  parent_gene  \\\n",
       "0      ID=transcript:ENST00000426406  Parent=gene:ENSG00000284733   \n",
       "1      ID=transcript:ENST00000332831  Parent=gene:ENSG00000284662   \n",
       "2      ID=transcript:ENST00000616125  Parent=gene:ENSG00000187634   \n",
       "3      ID=transcript:ENST00000618779  Parent=gene:ENSG00000187634   \n",
       "4      ID=transcript:ENST00000622503  Parent=gene:ENSG00000187634   \n",
       "...                              ...                          ...   \n",
       "25752  ID=transcript:ENST00000382365  Parent=gene:ENSG00000187191   \n",
       "25753  ID=transcript:ENST00000415508  Parent=gene:ENSG00000205916   \n",
       "25754  ID=transcript:ENST00000449750  Parent=gene:ENSG00000205916   \n",
       "25755  ID=transcript:ENST00000382432  Parent=gene:ENSG00000205916   \n",
       "25756  ID=transcript:ENST00000306609  Parent=gene:ENSG00000172288   \n",
       "\n",
       "                  name  \n",
       "0      Name=OR4F29-201  \n",
       "1      Name=OR4F16-201  \n",
       "2      Name=SAMD11-210  \n",
       "3      Name=SAMD11-214  \n",
       "4      Name=SAMD11-215  \n",
       "...                ...  \n",
       "25752    Name=DAZ3-202  \n",
       "25753    Name=DAZ4-204  \n",
       "25754    Name=DAZ4-206  \n",
       "25755    Name=DAZ4-203  \n",
       "25756    Name=CDY1-201  \n",
       "\n",
       "[25757 rows x 3 columns]"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_names = {0: 'id', 1: 'parent_gene', 2: 'name'}\n",
    "transcripts['attributes'].str.split(';', expand=True).iloc[:, 0:3].rename(new_names, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes = transcripts['attributes'].str.split(';', expand=True).iloc[:, 0:3].rename(new_names, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcripts_attributes = pd.concat([transcripts, attributes], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcripts_attributes.drop('attributes', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>450740</td>\n",
       "      <td>451678</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>925942</td>\n",
       "      <td>944153</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=transcript:ENST00000616125</td>\n",
       "      <td>Parent=gene:ENSG00000187634</td>\n",
       "      <td>Name=SAMD11-210</td>\n",
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       "      <th>3</th>\n",
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       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
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       "      <td>ID=transcript:ENST00000618779</td>\n",
       "      <td>Parent=gene:ENSG00000187634</td>\n",
       "      <td>Name=SAMD11-214</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
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       "      <td>Parent=gene:ENSG00000187634</td>\n",
       "      <td>Name=SAMD11-215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid          source  type   start     end score strand phase  \\\n",
       "0     1  ensembl_havana  mRNA  450740  451678     .      -     .   \n",
       "1     1  ensembl_havana  mRNA  685679  686673     .      -     .   \n",
       "2     1  ensembl_havana  mRNA  925942  944153     .      +     .   \n",
       "3     1  ensembl_havana  mRNA  925942  944153     .      +     .   \n",
       "4     1  ensembl_havana  mRNA  925942  944153     .      +     .   \n",
       "\n",
       "                              id                         name          biotype  \n",
       "0  ID=transcript:ENST00000426406  Parent=gene:ENSG00000284733  Name=OR4F29-201  \n",
       "1  ID=transcript:ENST00000332831  Parent=gene:ENSG00000284662  Name=OR4F16-201  \n",
       "2  ID=transcript:ENST00000616125  Parent=gene:ENSG00000187634  Name=SAMD11-210  \n",
       "3  ID=transcript:ENST00000618779  Parent=gene:ENSG00000187634  Name=SAMD11-214  \n",
       "4  ID=transcript:ENST00000622503  Parent=gene:ENSG00000187634  Name=SAMD11-215  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcripts_attributes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>ensembl_havana</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>24833917</td>\n",
       "      <td>24907036</td>\n",
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       "      <th>25755</th>\n",
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       "      <td>24905683</td>\n",
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      ],
      "text/plain": [
       "      seqid          source  type     start       end score strand phase  \\\n",
       "0         1  ensembl_havana  mRNA    450740    451678     .      -     .   \n",
       "1         1  ensembl_havana  mRNA    685679    686673     .      -     .   \n",
       "2         1  ensembl_havana  mRNA    925942    944153     .      +     .   \n",
       "3         1  ensembl_havana  mRNA    925942    944153     .      +     .   \n",
       "4         1  ensembl_havana  mRNA    925942    944153     .      +     .   \n",
       "...     ...             ...   ...       ...       ...   ...    ...   ...   \n",
       "25752     Y  ensembl_havana  mRNA  24763069  24813393     .      -     .   \n",
       "25753     Y  ensembl_havana  mRNA  24833843  24907040     .      +     .   \n",
       "25754     Y  ensembl_havana  mRNA  24833917  24907036     .      +     .   \n",
       "25755     Y  ensembl_havana  mRNA  24833934  24905683     .      +     .   \n",
       "25756     Y  ensembl_havana  mRNA  25622162  25624902     .      +     .   \n",
       "\n",
       "                               id      parent_gene        name  \n",
       "0      transcript:ENST00000426406  ENSG00000284733  OR4F29-201  \n",
       "1      transcript:ENST00000332831  ENSG00000284662  OR4F16-201  \n",
       "2      transcript:ENST00000616125  ENSG00000187634  SAMD11-210  \n",
       "3      transcript:ENST00000618779  ENSG00000187634  SAMD11-214  \n",
       "4      transcript:ENST00000622503  ENSG00000187634  SAMD11-215  \n",
       "...                           ...              ...         ...  \n",
       "25752  transcript:ENST00000382365  ENSG00000187191    DAZ3-202  \n",
       "25753  transcript:ENST00000415508  ENSG00000205916    DAZ4-204  \n",
       "25754  transcript:ENST00000449750  ENSG00000205916    DAZ4-206  \n",
       "25755  transcript:ENST00000382432  ENSG00000205916    DAZ4-203  \n",
       "25756  transcript:ENST00000306609  ENSG00000172288    CDY1-201  \n",
       "\n",
       "[25757 rows x 11 columns]"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcripts_attributes.replace('.*=', '', regex=True).replace('transcript:', '', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcripts_attributes = (transcripts_attributes.replace('.*=', '', regex=True)\n",
    "                                                .replace('transcript:', '', regex=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "      seqid          source  type     start       end score strand phase  \\\n",
       "0         1  ensembl_havana  mRNA    450740    451678     .      -     .   \n",
       "1         1  ensembl_havana  mRNA    685679    686673     .      -     .   \n",
       "2         1  ensembl_havana  mRNA    925942    944153     .      +     .   \n",
       "3         1  ensembl_havana  mRNA    925942    944153     .      +     .   \n",
       "4         1  ensembl_havana  mRNA    925942    944153     .      +     .   \n",
       "...     ...             ...   ...       ...       ...   ...    ...   ...   \n",
       "25752     Y  ensembl_havana  mRNA  24763069  24813393     .      -     .   \n",
       "25753     Y  ensembl_havana  mRNA  24833843  24907040     .      +     .   \n",
       "25754     Y  ensembl_havana  mRNA  24833917  24907036     .      +     .   \n",
       "25755     Y  ensembl_havana  mRNA  24833934  24905683     .      +     .   \n",
       "25756     Y  ensembl_havana  mRNA  25622162  25624902     .      +     .   \n",
       "\n",
       "                               id      parent_gene        name  \n",
       "0      transcript:ENST00000426406  ENSG00000284733  OR4F29-201  \n",
       "1      transcript:ENST00000332831  ENSG00000284662  OR4F16-201  \n",
       "2      transcript:ENST00000616125  ENSG00000187634  SAMD11-210  \n",
       "3      transcript:ENST00000618779  ENSG00000187634  SAMD11-214  \n",
       "4      transcript:ENST00000622503  ENSG00000187634  SAMD11-215  \n",
       "...                           ...              ...         ...  \n",
       "25752  transcript:ENST00000382365  ENSG00000187191    DAZ3-202  \n",
       "25753  transcript:ENST00000415508  ENSG00000205916    DAZ4-204  \n",
       "25754  transcript:ENST00000449750  ENSG00000205916    DAZ4-206  \n",
       "25755  transcript:ENST00000382432  ENSG00000205916    DAZ4-203  \n",
       "25756  transcript:ENST00000306609  ENSG00000172288    CDY1-201  \n",
       "\n",
       "[25757 rows x 11 columns]"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcripts_attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcripts_bed = transcripts_attributes[['seqid', 'start', 'end', 'name', 'strand']]  # df[], df[[lista]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcripts_bed.loc[:, 'start'] = transcripts_bed['start'].astype('int64') - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>name</th>\n",
       "      <th>strand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>450739</td>\n",
       "      <td>451678</td>\n",
       "      <td>OR4F29-201</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>685678</td>\n",
       "      <td>686673</td>\n",
       "      <td>OR4F16-201</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>925941</td>\n",
       "      <td>944153</td>\n",
       "      <td>SAMD11-210</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>925941</td>\n",
       "      <td>944153</td>\n",
       "      <td>SAMD11-214</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>925941</td>\n",
       "      <td>944153</td>\n",
       "      <td>SAMD11-215</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid   start     end        name strand\n",
       "0     1  450739  451678  OR4F29-201      -\n",
       "1     1  685678  686673  OR4F16-201      -\n",
       "2     1  925941  944153  SAMD11-210      +\n",
       "3     1  925941  944153  SAMD11-214      +\n",
       "4     1  925941  944153  SAMD11-215      +"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcripts_bed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25757, 5)"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcripts_bed.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ¿Qué vimos?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Atributos de Pandas\n",
    "- `df.shape`\n",
    "\n",
    "#### Métodos de pandas\n",
    "\n",
    "- `df.head()`\n",
    "- `df.tail()`\n",
    "- `df.isna()`\n",
    "- `df.sum()`\n",
    "- `df.groupby()`\n",
    "- `df.count()`\n",
    "- `df[x].unique()`\n",
    "- `df.reset_index()`\n",
    "- `df.str.contains()`\n",
    "- `df.isin()`\n",
    "- `df.replace()`\n",
    "- `df.str.replace()`\n",
    "- `df.str.split()`\n",
    "- `df.to_numeric()`\n",
    "- `df.value_counts()`\n",
    "\n",
    "\n",
    "\n",
    "##### Funciones built-in\n",
    "- `len()`\n",
    "- `print()`\n",
    "\n",
    "##### Métodos built-in\n",
    "- `join()`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Módulo 03"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./imgs/line.png\" align=\"left\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reciclando código: función"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se puede crear una función para reutilizar en un notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/Homo_sapiens.GRCh38.104.gff3.gz', \n",
    "            sep='\\t', \n",
    "            comment='#',\n",
    "            header=None,\n",
    "            dtype=str,\n",
    "            names=[\"seqid\", \"source\", \"type\", \"start\",\n",
    "                   \"end\", \"score\", \"strand\", \"phase\", \"attributes\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_bed(df, bedtype):\n",
    "    \"\"\"\n",
    "    \n",
    "    Este es un comentario dentro de una función, también conocido como *docstrings*\n",
    "    \n",
    "    La Función se utiliza de la siguientes dos maneras: \n",
    "        - make_bed(df, 'genes')\n",
    "        - make_bed(df, 'transcripts')\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    # Ensembl_havana annotation exclude Mitocondrial \n",
    "    # and the KI* and GL* unplaced scaffolds\n",
    "\n",
    "    import pandas as pd\n",
    "\n",
    "    if not isinstance(df, pd.DataFrame):\n",
    "        msg = f'This function works with a GFF3 in a Pandas DataFrame only'\n",
    "        raise ValueError(msg)\n",
    "\n",
    "    tmp = df.copy()\n",
    "    tmp = (tmp[(tmp['source'] == \"ensembl_havana\") & \n",
    "               (tmp['type'].isin(['gene', 'mRNA']))]\n",
    "               .reset_index(drop=True))    \n",
    "  \n",
    "    if bedtype not in ['genes', 'transcripts']:\n",
    "        msg = f'Type argument {bedtype} is not accepted:\\n' \\\n",
    "               'Types: \"genes\", \"trancripts\"'\n",
    "        raise ValueError(msg)\n",
    "    \n",
    "    if bedtype == 'genes':\n",
    "        g = tmp[tmp['type'] == 'gene'].reset_index(drop=True).copy()\n",
    "        names = {0: 'id', 1: 'name', 2: 'biotype', \n",
    "                     3: 'description', 4: 'gene_id', \n",
    "                     5: 'logic_name', 6: 'version'}\n",
    "        attr = (g['attributes']\n",
    "                     .str.split(';', expand=True)\n",
    "                     .rename(names, axis=1)).copy()\n",
    "        g_attr = (pd.concat([g, attr], axis=1)\n",
    "                   .replace('.*=', '', regex=True)\n",
    "                   .replace('gene:', '', regex=True)\n",
    "                   .replace('\\[Source.*', '',regex=True))\n",
    "        g_bed = g_attr[['seqid', 'start', 'end',\n",
    "                        'name', 'strand']].copy()\n",
    "        g_bed['start'] = g_bed['start'].astype('int64') - 1\n",
    "        return g_bed\n",
    "    else:\n",
    "        t = tmp[tmp['type'] == 'mRNA'].reset_index(drop=True).copy()\n",
    "        names = {0: 'id', 1: 'parent_gene', 2: 'name'}\n",
    "        attr = (t['attributes']\n",
    "                    .str.split(';', expand=True)\n",
    "                    .iloc[:, 0:3]\n",
    "                    .rename(names, axis=1))\n",
    "        t_attr = (pd.concat([t, attr], axis=1)\n",
    "                    .replace('.*=', '', regex=True)\n",
    "                    .replace('transcript:', '', regex=True))\n",
    "        t_bed = t_attr[['seqid', 'start', 'end',\n",
    "                        'name', 'strand']].copy()\n",
    "        t_bed['start'] = t_bed['start'].astype('int64') - 1\n",
    "        return t_bed      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function make_bed in module __main__:\n",
      "\n",
      "make_bed(df, bedtype)\n",
      "    Este es un comentario dentro de una función, también conocido como *docstrings*\n",
      "    \n",
      "    La Función se utiliza de la siguientes dos maneras: \n",
      "        - make_bed(df, 'genes')\n",
      "        - make_bed(df, 'transcripts')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(make_bed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>start</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>65418</td>\n",
       "      <td>71585</td>\n",
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       "      <td>+</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>450739</td>\n",
       "      <td>451678</td>\n",
       "      <td>OR4F29</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>685678</td>\n",
       "      <td>686673</td>\n",
       "      <td>OR4F16</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>923922</td>\n",
       "      <td>944575</td>\n",
       "      <td>SAMD11</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>944202</td>\n",
       "      <td>959309</td>\n",
       "      <td>NOC2L</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>18770</th>\n",
       "      <td>Y</td>\n",
       "      <td>24607559</td>\n",
       "      <td>24639207</td>\n",
       "      <td>BPY2B</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18771</th>\n",
       "      <td>Y</td>\n",
       "      <td>24763068</td>\n",
       "      <td>24813492</td>\n",
       "      <td>DAZ3</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18772</th>\n",
       "      <td>Y</td>\n",
       "      <td>24833842</td>\n",
       "      <td>24907040</td>\n",
       "      <td>DAZ4</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18773</th>\n",
       "      <td>Y</td>\n",
       "      <td>25030900</td>\n",
       "      <td>25062548</td>\n",
       "      <td>BPY2C</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18774</th>\n",
       "      <td>Y</td>\n",
       "      <td>25622161</td>\n",
       "      <td>25624902</td>\n",
       "      <td>CDY1</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18775 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seqid     start       end    name strand\n",
       "0         1     65418     71585   OR4F5      +\n",
       "1         1    450739    451678  OR4F29      -\n",
       "2         1    685678    686673  OR4F16      -\n",
       "3         1    923922    944575  SAMD11      +\n",
       "4         1    944202    959309   NOC2L      -\n",
       "...     ...       ...       ...     ...    ...\n",
       "18770     Y  24607559  24639207   BPY2B      +\n",
       "18771     Y  24763068  24813492    DAZ3      -\n",
       "18772     Y  24833842  24907040    DAZ4      +\n",
       "18773     Y  25030900  25062548   BPY2C      -\n",
       "18774     Y  25622161  25624902    CDY1      +\n",
       "\n",
       "[18775 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_bed(df, 'genes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>seqid</th>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>944153</td>\n",
       "      <td>SAMD11-214</td>\n",
       "      <td>+</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>925941</td>\n",
       "      <td>944153</td>\n",
       "      <td>SAMD11-215</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>25752</th>\n",
       "      <td>Y</td>\n",
       "      <td>24763068</td>\n",
       "      <td>24813393</td>\n",
       "      <td>DAZ3-202</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25753</th>\n",
       "      <td>Y</td>\n",
       "      <td>24833842</td>\n",
       "      <td>24907040</td>\n",
       "      <td>DAZ4-204</td>\n",
       "      <td>+</td>\n",
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       "    <tr>\n",
       "      <th>25754</th>\n",
       "      <td>Y</td>\n",
       "      <td>24833916</td>\n",
       "      <td>24907036</td>\n",
       "      <td>DAZ4-206</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25755</th>\n",
       "      <td>Y</td>\n",
       "      <td>24833933</td>\n",
       "      <td>24905683</td>\n",
       "      <td>DAZ4-203</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25756</th>\n",
       "      <td>Y</td>\n",
       "      <td>25622161</td>\n",
       "      <td>25624902</td>\n",
       "      <td>CDY1-201</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25757 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seqid     start       end        name strand\n",
       "0         1    450739    451678  OR4F29-201      -\n",
       "1         1    685678    686673  OR4F16-201      -\n",
       "2         1    925941    944153  SAMD11-210      +\n",
       "3         1    925941    944153  SAMD11-214      +\n",
       "4         1    925941    944153  SAMD11-215      +\n",
       "...     ...       ...       ...         ...    ...\n",
       "25752     Y  24763068  24813393    DAZ3-202      -\n",
       "25753     Y  24833842  24907040    DAZ4-204      +\n",
       "25754     Y  24833916  24907036    DAZ4-206      +\n",
       "25755     Y  24833933  24905683    DAZ4-203      +\n",
       "25756     Y  25622161  25624902    CDY1-201      +\n",
       "\n",
       "[25757 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_bed(df, 'transcripts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reciclando código: método"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se guarda la función en un archivo: `gfftools.py` en una `carpeta` donde tengas tus scripts de Python. Dicho archivo se puede utilizar como un paquete, el cual puede contener una o mas funciones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agregar la `carpeta` al PATH, nos permitirá importar el módulo independientemente de la ubicación del notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('/home/atgenomics/carpeta')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from gfftools import make_bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/Homo_sapiens.GRCh38.104.gff3.gz', \n",
    "            sep='\\t', \n",
    "            comment='#',\n",
    "            header=None,\n",
    "            dtype=str,\n",
    "            names=[\"seqid\", \"source\", \"type\", \"start\",\n",
    "                   \"end\", \"score\", \"strand\", \"phase\", \"attributes\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>name</th>\n",
       "      <th>strand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>65418</td>\n",
       "      <td>71585</td>\n",
       "      <td>OR4F5</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>450739</td>\n",
       "      <td>451678</td>\n",
       "      <td>OR4F29</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>685678</td>\n",
       "      <td>686673</td>\n",
       "      <td>OR4F16</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>923922</td>\n",
       "      <td>944575</td>\n",
       "      <td>SAMD11</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>944202</td>\n",
       "      <td>959309</td>\n",
       "      <td>NOC2L</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>18770</th>\n",
       "      <td>Y</td>\n",
       "      <td>24607559</td>\n",
       "      <td>24639207</td>\n",
       "      <td>BPY2B</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18771</th>\n",
       "      <td>Y</td>\n",
       "      <td>24763068</td>\n",
       "      <td>24813492</td>\n",
       "      <td>DAZ3</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>18772</th>\n",
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       "      <td>24907040</td>\n",
       "      <td>DAZ4</td>\n",
       "      <td>+</td>\n",
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       "      <td>Y</td>\n",
       "      <td>25030900</td>\n",
       "      <td>25062548</td>\n",
       "      <td>BPY2C</td>\n",
       "      <td>-</td>\n",
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       "      <th>18774</th>\n",
       "      <td>Y</td>\n",
       "      <td>25622161</td>\n",
       "      <td>25624902</td>\n",
       "      <td>CDY1</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18775 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seqid     start       end    name strand\n",
       "0         1     65418     71585   OR4F5      +\n",
       "1         1    450739    451678  OR4F29      -\n",
       "2         1    685678    686673  OR4F16      -\n",
       "3         1    923922    944575  SAMD11      +\n",
       "4         1    944202    959309   NOC2L      -\n",
       "...     ...       ...       ...     ...    ...\n",
       "18770     Y  24607559  24639207   BPY2B      +\n",
       "18771     Y  24763068  24813492    DAZ3      -\n",
       "18772     Y  24833842  24907040    DAZ4      +\n",
       "18773     Y  25030900  25062548   BPY2C      -\n",
       "18774     Y  25622161  25624902    CDY1      +\n",
       "\n",
       "[18775 rows x 5 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_bed(df, 'genes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si agregamos mas funcions a `gfftools.py` podemos usar el módulo completo para usar las otras funciones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gfftools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gfftools.make_bed()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Por ejemplo \n",
    "# gfftools.make_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reciclando código: script"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se lo dejamos de tarea, como pista inicial pueden utilizar `sys.argv[0]` para que el script pueda utilizar directamente con el archivo GFF3 desde terminal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bloques de información"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./imgs/line.png\" align=\"left\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\"><b>Tip: </b> Use blue boxes for Tips and notes. If it's a note, you don't have to include the word \"Note\". </div> \n",
    "<div class=\"alert alert-block alert-warning\"><b>Example: </b> Use yellow boxes for examples that are not inside code cells, or use for mathematical formulas if needed. </div>\n",
    "<div class=\"alert alert-block alert-success\"><b>Up to you: </b>Use green boxes sparingly, and only for some specific purpose that the other boxes can't cover. For example, if you have a lot of related content to link to, maybe you decide to use green boxes for related links from each section of a notebook. </div>\n",
    "<div class=\"alert alert-block alert-danger\"><b>Just don't: </b>In general, just avoid the red boxes. </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Contacto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./imgs/line.png\" align=\"left\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Este curso es llevado a ti por [ATGenomics](https://atgenomics.com/), una iniciativa mexicana dedicada a la divulgación, promoción y capacitación en bioinformática.\n",
    "\n",
    "### [@solnavss](https://twitter.com/solnavss) / [@dianolasa](https://twitter.com/dianolasa) / [@zorbax](https://twitter.com/zorbax) / [@vflorelo](https://twitter.com/vflorelo)"
   ]
  }
 ],
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