Prac_Pandas_Solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This exercise is concerned with baby names in the US between the years 2000 and 2018. The exercise tasks include exploratory data analysis and some data visualization. With these exercises, you will get to practice with some of the most commonly used Pandas features for hand-on data analytics.\n",
    "\n",
    "Data source: catalog.data.gov\n",
    "\n",
    "Inspiration for this exercise: PhantomInsights@github"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": false
   },
   "source": [
    "The data set is available as a CSV file named \"baby_names_2000.csv\" on GitHub [here](https://github.com/akmand/datasets)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": false
   },
   "source": [
    "**Exercise 1:** Place this CSV file under the same directory where your Jupyter Notebook file is. \n",
    "\n",
    "Import Pandas as \"pd\" and NumPy as \"np\". \n",
    "\n",
    "Read the data into a Pandas data frame called `df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import io\n",
    "import requests\n",
    "\n",
    "# so that we can see all the columns\n",
    "pd.set_option('display.max_columns', None) \n",
    "\n",
    "df_url = 'https://raw.githubusercontent.com/akmand/datasets/master/baby_names_2000.csv'\n",
    "url_content = requests.get(df_url, verify=False).content\n",
    "df = pd.read_csv(io.StringIO(url_content.decode('utf-8')))\n",
    "\n",
    "# df = pd.read_csv('baby_names_2000.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 2:** How many rows are there in this dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "624236"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 3:** How many columns does this dataset have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 4:** What are the names of columns in this dataset? Format the output as a Python list, please.\n",
    "\n",
    "**Hint:** Use the `columns` attribute of `df` and cast the result to a Python list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['year', 'name', 'gender', 'count']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 5:** Display the first 10 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>Emily</td>\n",
       "      <td>F</td>\n",
       "      <td>25956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>Hannah</td>\n",
       "      <td>F</td>\n",
       "      <td>23082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>Madison</td>\n",
       "      <td>F</td>\n",
       "      <td>19968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ashley</td>\n",
       "      <td>F</td>\n",
       "      <td>17997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>17702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2000</td>\n",
       "      <td>Alexis</td>\n",
       "      <td>F</td>\n",
       "      <td>17629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2000</td>\n",
       "      <td>Samantha</td>\n",
       "      <td>F</td>\n",
       "      <td>17265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2000</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>F</td>\n",
       "      <td>15709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2000</td>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>15099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2000</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>F</td>\n",
       "      <td>15078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year       name gender  count\n",
       "0  2000      Emily      F  25956\n",
       "1  2000     Hannah      F  23082\n",
       "2  2000    Madison      F  19968\n",
       "3  2000     Ashley      F  17997\n",
       "4  2000      Sarah      F  17702\n",
       "5  2000     Alexis      F  17629\n",
       "6  2000   Samantha      F  17265\n",
       "7  2000    Jessica      F  15709\n",
       "8  2000  Elizabeth      F  15099\n",
       "9  2000     Taylor      F  15078"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 6:** Display the last 10 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>624226</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zuber</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624227</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zubeyr</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624228</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zyell</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624229</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zyheem</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624230</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zykeem</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624231</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zylas</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624232</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624233</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zyrie</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624234</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zyron</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624235</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zzyzx</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        year    name gender  count\n",
       "624226  2018   Zuber      M      5\n",
       "624227  2018  Zubeyr      M      5\n",
       "624228  2018   Zyell      M      5\n",
       "624229  2018  Zyheem      M      5\n",
       "624230  2018  Zykeem      M      5\n",
       "624231  2018   Zylas      M      5\n",
       "624232  2018   Zyran      M      5\n",
       "624233  2018   Zyrie      M      5\n",
       "624234  2018   Zyron      M      5\n",
       "624235  2018   Zzyzx      M      5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 7:** Replace the gender \"M\" with \"B\" for boy, and \"F\" with \"G\" for girl."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['gender'] = df['gender'].replace({'M': 'B', 'F': 'G'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 8:** What is the earliest year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 9:** What is the most recent year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 10:** How many unique names are there regardless of gender?\n",
    "\n",
    "**Hint:** Use the `nunique()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68265"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 11:** How many unique names for boys?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30724"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['gender'] == 'B']['name'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 12:** How many unique names for girls?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43936"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['gender'] == 'G']['name'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optional Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 13:** The *gender* variable is categorical with two levels: *B, G*. You will now define a new data frame called `pivot_df` by \"spreading\" these two values into two columns where the cell values will be the sum of all the counts over all the years. You need to have a unique name in each row. To be clear, your new data frame needs to have exactly three columns: *name, B, G*. This is called a **pivot table**. Once you define your new data frame, display the top 5 rows.\n",
    "\n",
    "**Hint:** For this, you will need to use the `pivot_table()` function with the `np.sum` aggregation. In particular, you will need to run the line below.\n",
    "```Python\n",
    "pivot_df = df.pivot_table(index = 'name', columns = 'gender', values = 'count', aggfunc = np.sum)\n",
    "```\n",
    "After that, you will need to run the `dropna()` and `reset_index()` functions.\n",
    "\n",
    "\n",
    "**Bonus:** Get rid of the columns' name on the top left corner. That is, set the data frame's columns' `name` attribute to \"None\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>B</th>\n",
       "      <th>G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aaden</td>\n",
       "      <td>4828.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aadi</td>\n",
       "      <td>851.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aadyn</td>\n",
       "      <td>516.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aalijah</td>\n",
       "      <td>212.0</td>\n",
       "      <td>134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aaliyah</td>\n",
       "      <td>85.0</td>\n",
       "      <td>79254.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name       B        G\n",
       "0    Aaden  4828.0      5.0\n",
       "1     Aadi   851.0     16.0\n",
       "2    Aadyn   516.0     16.0\n",
       "3  Aalijah   212.0    134.0\n",
       "4  Aaliyah    85.0  79254.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_df = df.pivot_table(index = 'name', columns = 'gender', values = 'count', aggfunc = np.sum)\n",
    "pivot_df.dropna(inplace = True)\n",
    "pivot_df.reset_index(inplace = True)\n",
    "pivot_df.columns.name = None # this is to get rid of columns' name, \"gender\"\n",
    "pivot_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 14:** How many unique names are there that are gender-neutral (that is, names used for both boys and girls)? For more meaningful results, use names where both boy and girl counts are at least 1000.\n",
    "\n",
    "**Hint:** Use your new pivot table from above and assign the query result to back to `pivot_df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "279"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique names for gender neutral\n",
    "pivot_df = pivot_df[(pivot_df['B'] >= 1000) & (pivot_df['G'] >= 1000)]\n",
    "pivot_df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 15:** For the pivot table, define a new column *Total* which is the sum of boy and girl name counts for each name. Set the new column's data type to an integer using the `astype()` function with the `int` option. Then display 10 randomly selected rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>name</th>\n",
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       "      <th>4903</th>\n",
       "      <td>Quinn</td>\n",
       "      <td>19920.0</td>\n",
       "      <td>28408.0</td>\n",
       "      <td>48328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5112</th>\n",
       "      <td>Rian</td>\n",
       "      <td>2311.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>4211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4839</th>\n",
       "      <td>Peyton</td>\n",
       "      <td>40055.0</td>\n",
       "      <td>64141.0</td>\n",
       "      <td>104196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5263</th>\n",
       "      <td>Rylan</td>\n",
       "      <td>26265.0</td>\n",
       "      <td>4689.0</td>\n",
       "      <td>30954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4225</th>\n",
       "      <td>Mason</td>\n",
       "      <td>236543.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>238563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1740</th>\n",
       "      <td>Dominique</td>\n",
       "      <td>5365.0</td>\n",
       "      <td>8040.0</td>\n",
       "      <td>13405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2820</th>\n",
       "      <td>Jensen</td>\n",
       "      <td>8065.0</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>9268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4977</th>\n",
       "      <td>Raven</td>\n",
       "      <td>1368.0</td>\n",
       "      <td>15491.0</td>\n",
       "      <td>16859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3888</th>\n",
       "      <td>Lennon</td>\n",
       "      <td>4307.0</td>\n",
       "      <td>4029.0</td>\n",
       "      <td>8336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3272</th>\n",
       "      <td>Karsen</td>\n",
       "      <td>2495.0</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>3669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name         B        G   Total\n",
       "4903      Quinn   19920.0  28408.0   48328\n",
       "5112       Rian    2311.0   1900.0    4211\n",
       "4839     Peyton   40055.0  64141.0  104196\n",
       "5263      Rylan   26265.0   4689.0   30954\n",
       "4225      Mason  236543.0   2020.0  238563\n",
       "1740  Dominique    5365.0   8040.0   13405\n",
       "2820     Jensen    8065.0   1203.0    9268\n",
       "4977      Raven    1368.0  15491.0   16859\n",
       "3888     Lennon    4307.0   4029.0    8336\n",
       "3272     Karsen    2495.0   1174.0    3669"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_df['Total'] = pivot_df['B'] + pivot_df['G']\n",
    "pivot_df['Total'] = pivot_df['Total'].astype(int)\n",
    "pivot_df.sample(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 16:** What are the top 10 boy names?\n",
    "\n",
    "**Hint:** First filter for boys in the `df` data frame (not `pivot_df`). Then use the `groupby()` function on the *names* column followed by the `sum()` function. \n",
    "\n",
    "Keep in mind that when an aggregation function such as `sum()` is called an a `groupby` object, it is applied to each group separately. \n",
    "\n",
    "Next, sort the resulting data frame by the *count* column in a descending order and finally get the top 10 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jacob</td>\n",
       "      <td>425864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>384597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>William</td>\n",
       "      <td>340194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>339287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>336999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Joshua</td>\n",
       "      <td>331180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>326682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alexander</td>\n",
       "      <td>309239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Noah</td>\n",
       "      <td>307073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Joseph</td>\n",
       "      <td>300849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name   count\n",
       "0      Jacob  425864\n",
       "1    Michael  384597\n",
       "2    William  340194\n",
       "3      Ethan  339287\n",
       "4    Matthew  336999\n",
       "5     Joshua  331180\n",
       "6     Daniel  326682\n",
       "7  Alexander  309239\n",
       "8       Noah  307073\n",
       "9     Joseph  300849"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_m = df[df['gender'] == 'B']\n",
    "df_m = df_m[['name', 'count']]\n",
    "df_m = df_m.groupby('name')\n",
    "df_m = df_m.sum()\n",
    "df_m.sort_values('count', ascending = False, inplace = True)\n",
    "df_m.reset_index(inplace = True)\n",
    "df_m.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 17:** How about the top 10 girl names?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Emma</td>\n",
       "      <td>358667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Emily</td>\n",
       "      <td>332839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Olivia</td>\n",
       "      <td>321581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Isabella</td>\n",
       "      <td>306218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sophia</td>\n",
       "      <td>286197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Madison</td>\n",
       "      <td>285066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Abigail</td>\n",
       "      <td>260121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ava</td>\n",
       "      <td>245496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>219243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hannah</td>\n",
       "      <td>214378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name   count\n",
       "0       Emma  358667\n",
       "1      Emily  332839\n",
       "2     Olivia  321581\n",
       "3   Isabella  306218\n",
       "4     Sophia  286197\n",
       "5    Madison  285066\n",
       "6    Abigail  260121\n",
       "7        Ava  245496\n",
       "8  Elizabeth  219243\n",
       "9     Hannah  214378"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f = df[df['gender'] == 'G']\n",
    "df_f = df_f[['name', 'count']]\n",
    "df_f = df_f.groupby('name')\n",
    "df_f = df_f.sum()\n",
    "df_f.sort_values('count', ascending = False, inplace = True)\n",
    "df_f.reset_index(inplace = True)\n",
    "df_f.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 18:** How about the top 10 gender-neutral names (after filtering for low-count names)?\n",
    "\n",
    "**Hint**: Use the `pivot_df` data frame and sort it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>name</th>\n",
       "      <th>B</th>\n",
       "      <th>G</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4354</th>\n",
       "      <td>Michael</td>\n",
       "      <td>384597.0</td>\n",
       "      <td>1121.0</td>\n",
       "      <td>385718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4634</th>\n",
       "      <td>Noah</td>\n",
       "      <td>307073.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>308733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4083</th>\n",
       "      <td>Madison</td>\n",
       "      <td>1374.0</td>\n",
       "      <td>285066.0</td>\n",
       "      <td>286440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5247</th>\n",
       "      <td>Ryan</td>\n",
       "      <td>257898.0</td>\n",
       "      <td>11380.0</td>\n",
       "      <td>269278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3956</th>\n",
       "      <td>Logan</td>\n",
       "      <td>250011.0</td>\n",
       "      <td>15626.0</td>\n",
       "      <td>265637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1778</th>\n",
       "      <td>Dylan</td>\n",
       "      <td>228700.0</td>\n",
       "      <td>10625.0</td>\n",
       "      <td>239325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4225</th>\n",
       "      <td>Mason</td>\n",
       "      <td>236543.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>238563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6034</th>\n",
       "      <td>Tyler</td>\n",
       "      <td>221702.0</td>\n",
       "      <td>5906.0</td>\n",
       "      <td>227608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2752</th>\n",
       "      <td>Jayden</td>\n",
       "      <td>206323.0</td>\n",
       "      <td>20046.0</td>\n",
       "      <td>226369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>Christian</td>\n",
       "      <td>220516.0</td>\n",
       "      <td>3962.0</td>\n",
       "      <td>224478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name         B         G   Total\n",
       "4354    Michael  384597.0    1121.0  385718\n",
       "4634       Noah  307073.0    1660.0  308733\n",
       "4083    Madison    1374.0  285066.0  286440\n",
       "5247       Ryan  257898.0   11380.0  269278\n",
       "3956      Logan  250011.0   15626.0  265637\n",
       "1778      Dylan  228700.0   10625.0  239325\n",
       "4225      Mason  236543.0    2020.0  238563\n",
       "6034      Tyler  221702.0    5906.0  227608\n",
       "2752     Jayden  206323.0   20046.0  226369\n",
       "1282  Christian  220516.0    3962.0  224478"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_df.sort_values('Total', ascending = False, inplace = True)\n",
    "pivot_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 19 (Matplotlib):** Plot the number of baby boys and baby girls on the same plot as a function of years. The y-axis should be the number of babies and the x-axis should be the year. Make sure you have a meaningful title, correct x and y axis labels, and also a legend.\n",
    "\n",
    "**Hint:** Define two new data frames using `df`, one for boys and one for girls. For each data frame, first group by the *year* variable and then use the `sum()` function. This will give you data frames with two columns each: `year` and `count`. You can directly pass these data frames into the `plot()` function. Make sure you define 'Boy' and 'Girl' plot labels for the `plot()` function calls. Use blue color for boys and red color for girls. Use a line width of 2 for both using the `lw` option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>year</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>1963202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>1942034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>1940525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>1974274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>1983722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year    count\n",
       "0  2000  1963202\n",
       "1  2001  1942034\n",
       "2  2002  1940525\n",
       "3  2003  1974274\n",
       "4  2004  1983722"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy_df = df[df['gender'] == 'B'].groupby('year').sum()\n",
    "girl_df = df[df['gender'] == 'G'].groupby('year').sum()\n",
    "boy_df.reset_index().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 358,
       "width": 501
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(boy_df, label = 'Boy', color = 'blue', lw = 2)\n",
    "plt.plot(girl_df, label = 'Girl', color = 'red', lw = 2)\n",
    "plt.title('Number of Babies per Year by Gender')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Babies')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 20 (Matplotlib):** We are now interested in two cute baby names: Emma and Emily. Plot the number of baby girls (that is, boys excluded) with these names on the same plot as a function of time as two separate lines. The y-axis should be the number of babies and the x-axis should be the year. Make sure you have a meaningful title, correct x and y labels, and also a legend.\n",
    "\n",
    "Which name seems to maintain its popularity over the years and which name seems to lose it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_emma =  df[(df['name'] == 'Emma') & (df['gender'] == 'G')]\n",
    "df_emily = df[(df['name'] == 'Emily') & (df['gender'] == 'G')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 358,
       "width": 509
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_emma['year'], df_emma['count'], label = 'Emma', color = 'pink', lw = 3)\n",
    "plt.plot(df_emily['year'], df_emily['count'], label = 'Emily', color = 'yellow', lw = 3)\n",
    "\n",
    "plt.title('Number of Babies by Name')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Babies')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "www.featureranking.com"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}