python/GettingStarted.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will learn about the whylogs Python library and its output. \n",
    "\n",
    "# Getting Started with whylogs Profile Summaries\n",
    "\n",
    "We will first read sample raw data into Pandas from a file and explore that data briefly. To run whylogs, we will then import the whylogs library, initialize a logging session with whylogs, and create a profile for our data, producing a whylogs profile summary. Finally, we will explore some of the profile summary features.\n",
    "\n",
    "To get started, we will import a few standard data science Python libraries."
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: whylabs-datasketches>=2.2.0b1 in /Users/andy/miniconda3/envs/demo/lib/python3.8/site-packages (from whylogs->-r requirements.txt (line 6)) (2.2.0b1)\n",
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      "Requirement already satisfied: jdcal in /Users/andy/miniconda3/envs/demo/lib/python3.8/site-packages (from openpyxl==3.0.6->whylogs->-r requirements.txt (line 6)) (1.4.1)\n",
      "Collecting argparse\n",
      "  Using cached argparse-1.4.0-py2.py3-none-any.whl (23 kB)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/andy/miniconda3/envs/demo/lib/python3.8/site-packages (from scikit-learn==0.24.1->whylogs->-r requirements.txt (line 6)) (2.1.0)\n",
      "Requirement already satisfied: scipy>=0.19.1 in /Users/andy/miniconda3/envs/demo/lib/python3.8/site-packages (from scikit-learn==0.24.1->whylogs->-r requirements.txt (line 6)) (1.6.1)\n",
      "Requirement already satisfied: joblib>=0.11 in /Users/andy/miniconda3/envs/demo/lib/python3.8/site-packages (from scikit-learn==0.24.1->whylogs->-r requirements.txt (line 6)) (1.0.1)\n",
      "Requirement already satisfied: pytz>=2017.3 in /Users/andy/miniconda3/envs/demo/lib/python3.8/site-packages (from pandas>1.0->whylogs->-r requirements.txt (line 6)) (2021.1)\n",
      "Installing collected packages: argparse\n",
      "Successfully installed argparse-1.4.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import os.path\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "whylogs allows you to generate and store key characteristics of a growing dataset efficiently. In machine learning, datasets often consist of both input features and outputs of the model. In deployed systems, you often have a relatively static training dataset as well as a growing dataset from model input and output at inference time.\n",
    "\n",
    "## Downloading and exploring the raw Lending Club data\n",
    "\n",
    "In our case, we will download and explore a sample from the Lending Club dataset before logging a whylogs profile summary. Lending Club is a peer-to-peer lending and alternative investing website on which members can apply for personal loans and invest in personal loans to other Lending Club members. The company published a dataset with information spanning several years. This particular dataset contains only the accepted loans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our sample input data is stored in `lending_club_demo.csv`. You may use the Jupyter command `!` in front of cell contents to execute a Bash command (e.g. `cd`) to navigate if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_file = \"lending_club_demo.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's read that data file into a Pandas dataframe and look at the entries for *January 2017*.\n",
    "\n",
    "Each row refers to a particular loan instance, while each column refers to a variable in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "                 id  member_id     loan_amnt   funded_amnt  funded_amnt_inv  \\\n",
       "count  3.090000e+02        0.0    309.000000    309.000000       309.000000   \n",
       "mean   9.637541e+07        NaN  14511.407767  14511.407767     14506.957929   \n",
       "std    1.648219e+06        NaN   9011.801950   9011.801950      9011.257397   \n",
       "min    6.895309e+07        NaN   1000.000000   1000.000000      1000.000000   \n",
       "25%    9.627937e+07        NaN   7500.000000   7500.000000      7500.000000   \n",
       "50%    9.653771e+07        NaN  12000.000000  12000.000000     12000.000000   \n",
       "75%    9.681416e+07        NaN  20000.000000  20000.000000     20000.000000   \n",
       "max    9.752976e+07        NaN  40000.000000  40000.000000     40000.000000   \n",
       "\n",
       "         int_rate  installment     annual_inc         dti  delinq_2yrs  ...  \\\n",
       "count  309.000000   309.000000     309.000000  309.000000   309.000000  ...   \n",
       "mean    13.479159   446.427476   80151.667184   18.561489     0.372168  ...   \n",
       "std      5.168002   280.454947   51337.356187    9.955114     0.929671  ...   \n",
       "min      5.320000    32.930000   10000.000000    0.290000     0.000000  ...   \n",
       "25%     10.490000   235.260000   49680.000000   12.480000     0.000000  ...   \n",
       "50%     12.740000   370.480000   66000.000000   18.100000     0.000000  ...   \n",
       "75%     15.990000   582.260000   98000.000000   23.350000     0.000000  ...   \n",
       "max     30.940000  1400.690000  400000.000000  109.220000     8.000000  ...   \n",
       "\n",
       "       deferral_term  hardship_amount  hardship_length  hardship_dpd  \\\n",
       "count            0.0              0.0              0.0           0.0   \n",
       "mean             NaN              NaN              NaN           NaN   \n",
       "std              NaN              NaN              NaN           NaN   \n",
       "min              NaN              NaN              NaN           NaN   \n",
       "25%              NaN              NaN              NaN           NaN   \n",
       "50%              NaN              NaN              NaN           NaN   \n",
       "75%              NaN              NaN              NaN           NaN   \n",
       "max              NaN              NaN              NaN           NaN   \n",
       "\n",
       "       orig_projected_additional_accrued_interest  \\\n",
       "count                                         0.0   \n",
       "mean                                          NaN   \n",
       "std                                           NaN   \n",
       "min                                           NaN   \n",
       "25%                                           NaN   \n",
       "50%                                           NaN   \n",
       "75%                                           NaN   \n",
       "max                                           NaN   \n",
       "\n",
       "       hardship_payoff_balance_amount  hardship_last_payment_amount  \\\n",
       "count                             0.0                           0.0   \n",
       "mean                              NaN                           NaN   \n",
       "std                               NaN                           NaN   \n",
       "min                               NaN                           NaN   \n",
       "25%                               NaN                           NaN   \n",
       "50%                               NaN                           NaN   \n",
       "75%                               NaN                           NaN   \n",
       "max                               NaN                           NaN   \n",
       "\n",
       "       settlement_amount  settlement_percentage  settlement_term  \n",
       "count                0.0                    0.0              0.0  \n",
       "mean                 NaN                    NaN              NaN  \n",
       "std                  NaN                    NaN              NaN  \n",
       "min                  NaN                    NaN              NaN  \n",
       "25%                  NaN                    NaN              NaN  \n",
       "50%                  NaN                    NaN              NaN  \n",
       "75%                  NaN                    NaN              NaN  \n",
       "max                  NaN                    NaN              NaN  \n",
       "\n",
       "[8 rows x 114 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data = pd.read_csv(os.path.join(data_file))\n",
    "data = full_data[full_data['issue_d'] == 'Jan-2017']\n",
    "\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interesting Lending Club dataset variables\n",
    "\n",
    "**`emp_length` (categorical, string)**:\n",
    "> length of employment in years as text entries\n",
    "\n",
    "**`annual_inc` (numeric)**:\n",
    "> the self-reported annual income provided by the borrower during registration\n",
    "\n",
    "**`dti` (numeric)**:\n",
    "> ratio calculated using the borrower’s total monthly debt payments over their total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income\n",
    "\n",
    "**`issue_d` (timestamp, string)**:\n",
    "> the month (and year) which the loan was funded -- useful for backfilling data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running whylogs for logging a single dataset\n",
    "\n",
    "Let's import a function from whylogs that will allow us to create a logging session.\n",
    "\n",
    "This session can be connected with multiple writers that output the results of our profiling in JSON, a flat CSV, or binary protobuf format. These profiles can be stored locally or in an AWS S3 bucket in the cloud. Additional writing functionality will be added over time.\n",
    "\n",
    "Let's create a default session below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from whylogs import get_or_create_session\n",
    "\n",
    "session = get_or_create_session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quickly log a dataframe\n",
    "\n",
    "You can call `log_dataframe` to quickly log a Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<whylogs.core.datasetprofile.DatasetProfile at 0x7fbe78b3c7f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.log_dataframe(data.head(100), 'demo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# whylogs output\n",
    "\n",
    "Now that we've logged our dataset, we can see the output of the whylogs profiling process in the newly created directory. WhyLogs logger creates an `output` directory within our original directory. This directory in turn contains folders with various summaries for our sample dataset called `demo`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current working directory: /Volumes/Workspace/whylogs-examples/python\n"
     ]
    }
   ],
   "source": [
    "print(\"Current working directory:\", os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "whylogs-output/demo/dataset_summary/freq_numbers/dataset_summary-batch.json\n",
      "whylogs-output/demo/dataset_summary/json/dataset_summary-batch.json\n",
      "whylogs-output/demo/dataset_summary/flat_table/dataset_summary-batch.csv\n",
      "whylogs-output/demo/dataset_summary/histogram/dataset_summary-batch.json\n",
      "whylogs-output/demo/dataset_summary/frequent_strings/dataset_summary-batch.json\n",
      "whylogs-output/demo/dataset_profile/protobuf/datase_profile-batch.bin\n",
      "whylogs-output/another-dataset/dataset_summary/freq_numbers/dataset_summary-1498867200000.json\n",
      "whylogs-output/another-dataset/dataset_summary/freq_numbers/dataset_summary-1600732800000.json\n",
      "whylogs-output/another-dataset/dataset_summary/json/dataset_summary-1498867200000.json\n",
      "whylogs-output/another-dataset/dataset_summary/json/dataset_summary-1600732800000.json\n",
      "whylogs-output/another-dataset/dataset_summary/flat_table/dataset_summary-1498867200000.csv\n",
      "whylogs-output/another-dataset/dataset_summary/flat_table/dataset_summary-1600732800000.csv\n",
      "whylogs-output/another-dataset/dataset_summary/histogram/dataset_summary-1498867200000.json\n",
      "whylogs-output/another-dataset/dataset_summary/histogram/dataset_summary-1600732800000.json\n",
      "whylogs-output/another-dataset/dataset_summary/frequent_strings/dataset_summary-1498867200000.json\n",
      "whylogs-output/another-dataset/dataset_summary/frequent_strings/dataset_summary-1600732800000.json\n",
      "whylogs-output/another-dataset/dataset_profile/protobuf/datase_profile-1498867200000.bin\n",
      "whylogs-output/another-dataset/dataset_profile/protobuf/datase_profile-1600732800000.bin\n"
     ]
    }
   ],
   "source": [
    "!find whylogs-output -type f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the Logger API\n",
    "The Logger API can be used to log data profiles to memory as well. This data stays in memory until you call `.close()`, either explicitly or using the `with` statement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "with session.logger(dataset_name=\"another-dataset\", dataset_timestamp=datetime.datetime(2017, 1, 1, 0, 0)) as logger:\n",
    "    logger.log_dataframe(data.head(100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, you can see that the dataset has the timestamp added as the suffix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "whylogs-output/demo/dataset_summary/freq_numbers/dataset_summary-batch.json\n",
      "whylogs-output/demo/dataset_summary/json/dataset_summary-batch.json\n",
      "whylogs-output/demo/dataset_summary/flat_table/dataset_summary-batch.csv\n",
      "whylogs-output/demo/dataset_summary/histogram/dataset_summary-batch.json\n",
      "whylogs-output/demo/dataset_summary/frequent_strings/dataset_summary-batch.json\n",
      "whylogs-output/demo/dataset_profile/protobuf/datase_profile-batch.bin\n",
      "whylogs-output/another-dataset/dataset_summary/freq_numbers/dataset_summary-1483228800000.json\n",
      "whylogs-output/another-dataset/dataset_summary/freq_numbers/dataset_summary-1498867200000.json\n",
      "whylogs-output/another-dataset/dataset_summary/freq_numbers/dataset_summary-1600732800000.json\n",
      "whylogs-output/another-dataset/dataset_summary/json/dataset_summary-1483228800000.json\n",
      "whylogs-output/another-dataset/dataset_summary/json/dataset_summary-1498867200000.json\n",
      "whylogs-output/another-dataset/dataset_summary/json/dataset_summary-1600732800000.json\n",
      "whylogs-output/another-dataset/dataset_summary/flat_table/dataset_summary-1483228800000.csv\n",
      "whylogs-output/another-dataset/dataset_summary/flat_table/dataset_summary-1498867200000.csv\n",
      "whylogs-output/another-dataset/dataset_summary/flat_table/dataset_summary-1600732800000.csv\n",
      "whylogs-output/another-dataset/dataset_summary/histogram/dataset_summary-1483228800000.json\n",
      "whylogs-output/another-dataset/dataset_summary/histogram/dataset_summary-1498867200000.json\n",
      "whylogs-output/another-dataset/dataset_summary/histogram/dataset_summary-1600732800000.json\n",
      "whylogs-output/another-dataset/dataset_summary/frequent_strings/dataset_summary-1483228800000.json\n",
      "whylogs-output/another-dataset/dataset_summary/frequent_strings/dataset_summary-1498867200000.json\n",
      "whylogs-output/another-dataset/dataset_summary/frequent_strings/dataset_summary-1600732800000.json\n",
      "whylogs-output/another-dataset/dataset_profile/protobuf/datase_profile-1498867200000.bin\n",
      "whylogs-output/another-dataset/dataset_profile/protobuf/datase_profile-1600732800000.bin\n",
      "whylogs-output/another-dataset/dataset_profile/protobuf/datase_profile-1483228800000.bin\n"
     ]
    }
   ],
   "source": [
    "!find whylogs-output -type f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interacting with Dataset Profiles\n",
    "\n",
    "Instead of interacting with the Logger, which writes to disk, sometimes you may want to use a `DatasetProfile` object directly.\n",
    "\n",
    "You can use `session.new_profile` to create an empty profile:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile = session.new_profile(dataset_name=\"in-memory\", \n",
    "                              dataset_timestamp=datetime.datetime(2017, 1, 1, 0, 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Profiling a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile.track_dataframe(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This DatasetProfile object, stored in the `profile` variable, can now be referenced from Python.\n",
    "\n",
    "This object contains helpful information about the profile, such as the session ID, the dates associated with both the data and the session, as well as user-specified metadata and tags."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's transform the dataset profile into the flat summary form. Unlike the binary `protobuf.bin` file and the hierarchical `whylogs.json` file that was written using the logger, the summary format makes it much easier to analyze and run data science processes on the data. This structure is much more flat, a table format or a single depth dictionary format organized by variable.\n",
    "\n",
    "These less hierarchical formats were also created with the `log_dataframe` functionality and can be found in the `summary_summary.csv`, `summary_histogram.json` and `summary_strings.json` files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "summaries = profile.flat_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first take a look at the overall summary for the profiled dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>null_count</th>\n",
       "      <th>bool_count</th>\n",
       "      <th>numeric_count</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>stddev</th>\n",
       "      <th>nunique_numbers</th>\n",
       "      <th>...</th>\n",
       "      <th>nunique_str_upper</th>\n",
       "      <th>quantile_0.0000</th>\n",
       "      <th>quantile_0.0100</th>\n",
       "      <th>quantile_0.0500</th>\n",
       "      <th>quantile_0.2500</th>\n",
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       "      <th>quantile_0.9500</th>\n",
       "      <th>quantile_0.9900</th>\n",
       "      <th>quantile_1.0000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bc_open_to_buy</td>\n",
       "      <td>309.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>96285.0</td>\n",
       "      <td>11781.862295</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15110.810631</td>\n",
       "      <td>302.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>15545.000000</td>\n",
       "      <td>43811.0</td>\n",
       "      <td>74544.0</td>\n",
       "      <td>96285.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mths_since_rcnt_il</td>\n",
       "      <td>309.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>23.013158</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.996225</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>86.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sec_app_collections_12_mths_ex_med</td>\n",
       "      <td>309.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chargeoff_within_12_mths</td>\n",
       "      <td>309.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003236</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.056888</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>settlement_percentage</td>\n",
       "      <td>309.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>pymnt_plan</td>\n",
       "      <td>309.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>total_rec_prncp</td>\n",
       "      <td>309.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>5266.577896</td>\n",
       "      <td>262.7</td>\n",
       "      <td>6502.059928</td>\n",
       "      <td>276.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>262.700012</td>\n",
       "      <td>349.440002</td>\n",
       "      <td>848.909973</td>\n",
       "      <td>1697.630005</td>\n",
       "      <td>2965.600098</td>\n",
       "      <td>5597.330078</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>35000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>all_util</td>\n",
       "      <td>309.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>56.757282</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.046084</td>\n",
       "      <td>87.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>89.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>sec_app_mort_acc</td>\n",
       "      <td>309.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 column  count  null_count  bool_count  \\\n",
       "0                   sec_app_open_act_il  309.0         0.0         0.0   \n",
       "1                        bc_open_to_buy  309.0         0.0         0.0   \n",
       "2                    mths_since_rcnt_il  309.0         0.0         0.0   \n",
       "3    sec_app_collections_12_mths_ex_med  309.0         0.0         0.0   \n",
       "4              chargeoff_within_12_mths  309.0         0.0         0.0   \n",
       "..                                  ...    ...         ...         ...   \n",
       "145               settlement_percentage  309.0         0.0         0.0   \n",
       "146                          pymnt_plan  309.0         0.0         0.0   \n",
       "147                     total_rec_prncp  309.0         0.0         0.0   \n",
       "148                            all_util  309.0         0.0         0.0   \n",
       "149                    sec_app_mort_acc  309.0         0.0         0.0   \n",
       "\n",
       "     numeric_count      max          mean    min        stddev  \\\n",
       "0              0.0      0.0      0.000000    0.0      0.000000   \n",
       "1            305.0  96285.0  11781.862295    0.0  15110.810631   \n",
       "2            304.0    228.0     23.013158    1.0     27.996225   \n",
       "3              0.0      0.0      0.000000    0.0      0.000000   \n",
       "4            309.0      1.0      0.003236    0.0      0.056888   \n",
       "..             ...      ...           ...    ...           ...   \n",
       "145            0.0      0.0      0.000000    0.0      0.000000   \n",
       "146            0.0      0.0      0.000000    0.0      0.000000   \n",
       "147          309.0  35000.0   5266.577896  262.7   6502.059928   \n",
       "148          309.0    117.0     56.757282    2.0     21.046084   \n",
       "149            0.0      0.0      0.000000    0.0      0.000000   \n",
       "\n",
       "     nunique_numbers  ...  nunique_str_upper  quantile_0.0000  \\\n",
       "0                0.0  ...                0.0              NaN   \n",
       "1              302.0  ...                0.0         0.000000   \n",
       "2               70.0  ...                0.0         1.000000   \n",
       "3                0.0  ...                0.0              NaN   \n",
       "4                2.0  ...                0.0         0.000000   \n",
       "..               ...  ...                ...              ...   \n",
       "145              0.0  ...                0.0              NaN   \n",
       "146              0.0  ...                1.0              NaN   \n",
       "147            276.0  ...                0.0       262.700012   \n",
       "148             87.0  ...                0.0         2.000000   \n",
       "149              0.0  ...                0.0              NaN   \n",
       "\n",
       "     quantile_0.0100  quantile_0.0500  quantile_0.2500  quantile_0.5000  \\\n",
       "0                NaN              NaN              NaN              NaN   \n",
       "1          10.000000       155.000000      2004.000000      6784.000000   \n",
       "2           1.000000         3.000000         7.000000        14.000000   \n",
       "3                NaN              NaN              NaN              NaN   \n",
       "4           0.000000         0.000000         0.000000         0.000000   \n",
       "..               ...              ...              ...              ...   \n",
       "145              NaN              NaN              NaN              NaN   \n",
       "146              NaN              NaN              NaN              NaN   \n",
       "147       349.440002       848.909973      1697.630005      2965.600098   \n",
       "148        10.000000        18.000000        43.000000        58.000000   \n",
       "149              NaN              NaN              NaN              NaN   \n",
       "\n",
       "     quantile_0.7500  quantile_0.9500  quantile_0.9900  quantile_1.0000  \n",
       "0                NaN              NaN              NaN              NaN  \n",
       "1       15545.000000          43811.0          74544.0          96285.0  \n",
       "2          27.000000             86.0            130.0            228.0  \n",
       "3                NaN              NaN              NaN              NaN  \n",
       "4           0.000000              0.0              0.0              1.0  \n",
       "..               ...              ...              ...              ...  \n",
       "145              NaN              NaN              NaN              NaN  \n",
       "146              NaN              NaN              NaN              NaN  \n",
       "147      5597.330078          20000.0          35000.0          35000.0  \n",
       "148        72.000000             89.0            106.0            117.0  \n",
       "149              NaN              NaN              NaN              NaN  \n",
       "\n",
       "[150 rows x 32 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary = summaries['summary']\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the streaming mode\n",
    "\n",
    "It's convenient to call whylogs on a batch of data with a Pandas dataframe. However, in practice you might have only individual data points. In that case, `whylogs` can be called on each individual datum (Python dictionary object in this case).\n",
    "\n",
    "The following example shows how we can stream through individual data points by iterating with a dataframe and extracting rows as an object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile2 = session.new_profile(dataset_name=\"in-memory\", \n",
    "                              dataset_timestamp=datetime.datetime(2017, 1, 1, 0, 0))\n",
    "for i, row in data.iterrows():\n",
    "    profile2.track(row.to_dict())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The counter should now be updated incrementally, and the two profiles can be merged:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_profile = profile.merge(profile2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Streaming mode isn't limited to just the API. We can also merge the profiles across different sessions to get a holistic view:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "309\n",
      "309\n",
      "618\n"
     ]
    }
   ],
   "source": [
    "print(profile.columns['dti'].counters.count)\n",
    "print(profile2.columns['dti'].counters.count)\n",
    "print(merged_profile.columns['dti'].counters.count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## whylogs output\n",
    "\n",
    "We can see that this summary object is much smaller at **roughly 150 rows x 32 columns** than the original dataset at **1000 rows x 151 columns**. Smaller storage sizes are important in reducing costs and making it easier for your data scientists to complete monitoring and post-analysis on large amounts of data.\n",
    "\n",
    "Each row of our flat profile summary contains the name of the variable found in the original dataset, in the column called `column`.\n",
    "\n",
    "We can also see a number of useful metrics as columns in our summary: descriptive statistics, type information, unique estimates and bounds, as well as specially formulated metrics like inferred_dtype and dtype_fraction.\n",
    "\n",
    "Let's explore the output of the whylogs profiler to check on a few of the interesting variables we mentioned earlier. For example, let's look at the  `funded_amnt` variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
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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>65</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>column</th>\n",
       "      <td>funded_amnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>309.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_count</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bool_count</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numeric_count</th>\n",
       "      <td>309.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14511.407767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stddev</th>\n",
       "      <td>9011.80195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nunique_numbers</th>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nunique_numbers_lower</th>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nunique_numbers_upper</th>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inferred_dtype</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dtype_fraction</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_unknown_count</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_null_count</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_fractional_count</th>\n",
       "      <td>309.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_integral_count</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_boolean_count</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_string_count</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nunique_str</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nunique_str_lower</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nunique_str_upper</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quantile_0.0000</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quantile_0.0100</th>\n",
       "      <td>1200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quantile_0.0500</th>\n",
       "      <td>3200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quantile_0.2500</th>\n",
       "      <td>7350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quantile_0.5000</th>\n",
       "      <td>12000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quantile_0.7500</th>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quantile_0.9500</th>\n",
       "      <td>35000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quantile_0.9900</th>\n",
       "      <td>36000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quantile_1.0000</th>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 65\n",
       "column                  funded_amnt\n",
       "count                         309.0\n",
       "null_count                      0.0\n",
       "bool_count                      0.0\n",
       "numeric_count                 309.0\n",
       "max                         40000.0\n",
       "mean                   14511.407767\n",
       "min                          1000.0\n",
       "stddev                   9011.80195\n",
       "nunique_numbers               117.0\n",
       "nunique_numbers_lower         117.0\n",
       "nunique_numbers_upper         117.0\n",
       "inferred_dtype                  2.0\n",
       "dtype_fraction                  1.0\n",
       "type_unknown_count              0.0\n",
       "type_null_count                 0.0\n",
       "type_fractional_count         309.0\n",
       "type_integral_count             0.0\n",
       "type_boolean_count              0.0\n",
       "type_string_count               0.0\n",
       "nunique_str                     0.0\n",
       "nunique_str_lower               0.0\n",
       "nunique_str_upper               0.0\n",
       "quantile_0.0000              1000.0\n",
       "quantile_0.0100              1200.0\n",
       "quantile_0.0500              3200.0\n",
       "quantile_0.2500              7350.0\n",
       "quantile_0.5000             12000.0\n",
       "quantile_0.7500             20000.0\n",
       "quantile_0.9500             35000.0\n",
       "quantile_0.9900             36000.0\n",
       "quantile_1.0000             40000.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary[summary['column']=='funded_amnt'].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may notice that the count for this variable was recorded as **309** hits, with a minimum loan amount of **$1,000.00 USD** and a maximum loan amount of **\\$40,000.00 USD**.\n",
    "\n",
    "For numerical variables like `funded_amnt`, we can view additional information in the histograms dictionary from the profile summaries object. The variable's histogram object contains bin edges along with counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bin_edges': [1000.0, 2300.0001333333335, 3600.000266666667, 4900.000400000001, 6200.000533333334, 7500.000666666667, 8800.000800000002, 10100.000933333335, 11400.001066666668, 12700.0012, 14000.001333333334, 15300.001466666668, 16600.001600000003, 17900.001733333334, 19200.00186666667, 20500.002, 21800.002133333335, 23100.00226666667, 24400.0024, 25700.002533333336, 27000.002666666667, 28300.002800000002, 29600.002933333337, 30900.003066666668, 32200.003200000003, 33500.00333333334, 34800.00346666667, 36100.003600000004, 37400.00373333334, 38700.00386666667, 40000.004], 'counts': [7, 12, 11, 34, 14, 19, 32, 8, 24, 9, 22, 14, 9, 9, 24, 7, 3, 5, 8, 2, 5, 3, 5, 3, 2, 0, 15, 0, 0, 3]}\n"
     ]
    }
   ],
   "source": [
    "histograms = summaries['hist']\n",
    "print(histograms['funded_amnt'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For another variable, `loan_status`, we can discover intriguing information within other metrics. This is because loan status is a categorical field that takes strings as inputs.\n",
    "\n",
    "Let's look at a few relevant metrics for this and other string variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>type_string_count</th>\n",
       "      <th>type_null_count</th>\n",
       "      <th>nunique_str</th>\n",
       "      <th>nunique_str_lower</th>\n",
       "      <th>nunique_str_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>309.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     type_string_count  type_null_count  nunique_str  nunique_str_lower  \\\n",
       "138              309.0              0.0          6.0                6.0   \n",
       "\n",
       "     nunique_str_upper  \n",
       "138                6.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary[summary['column']=='loan_status'][['type_string_count', \n",
    "                                           'type_null_count', \n",
    "                                           'nunique_str', \n",
    "                                           'nunique_str_lower', \n",
    "                                           'nunique_str_upper']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that there are **309** elements of string type. Also, the unique string fields show **6** unique strings. The lower and upper bounds for the estimate are also **6**, meaning that this is an exact number. You will see many instances of this -- DataSketches in whylogs finds exact estimates for numbers as high as 400 unique values.\n",
    "\n",
    "Let's now explore the frequent strings object from our profile summaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Current': 239, 'Fully Paid': 54, 'Charged Off': 7, 'Late (31-120 days)': 5, 'In Grace Period': 3, 'Late (16-30 days)': 1}\n"
     ]
    }
   ],
   "source": [
    "frequent_strings = summaries['frequent_strings']\n",
    "print(frequent_strings['loan_status'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Writing data to disk\n",
    "\n",
    "Sometimes you want to write your data out manually rather than relying on the Logger framework (it's more opinionated!), you can perform your own serialization and deserialization.\n",
    "\n",
    "whylogs uses protobuf for efficient storage. Here's how it works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile.write_protobuf(\"profile.bin\")\n",
    "roundtrip = profile.read_protobuf(\"profile.bin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(roundtrip.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing multiple datasets across time with whylogs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use the whylogs visualization tools, we'll need to import the `ProfileVisualizer` object and use the Altair visualization framework."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from whylogs.viz import ProfileVisualizer\n",
    "\n",
    "viz = ProfileVisualizer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've explored data for a single month, let's calculate profile summaries for a series of months. Normally, we'd expect whylogs to be operating on future data, so these new datasets would originate from data seen at inference time.\n",
    "\n",
    "But in special cases like this demo or diagnosing data collected prior to whylogs integration, it may be helpful to backfill with past data. Here we'll loop through subsets of data to create a list of profile summaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<whylogs.core.datasetprofile.DatasetProfile at 0x7fbe807ccb20>,\n",
       " <whylogs.core.datasetprofile.DatasetProfile at 0x7fbe8069d0a0>,\n",
       " <whylogs.core.datasetprofile.DatasetProfile at 0x7fbeb1e42d30>,\n",
       " <whylogs.core.datasetprofile.DatasetProfile at 0x7fbeb2091eb0>,\n",
       " <whylogs.core.datasetprofile.DatasetProfile at 0x7fbe78ce7b80>,\n",
       " <whylogs.core.datasetprofile.DatasetProfile at 0x7fbe8089ad30>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list of data profiles\n",
    "remaining_dates = ['Feb-2017', 'Mar-2017', 'Apr-2017', 'May-2017', 'Jun-2017']\n",
    "\n",
    "profiles = [profile]  # list with original profile\n",
    "for date in remaining_dates:\n",
    "    timestamp = datetime.datetime.strptime(date, '%b-%Y')\n",
    "    subset_data = full_data[full_data['issue_d']==date]\n",
    "    subset_prof = session.profile_dataframe(subset_data, \"demo\", dataset_timestamp=timestamp)\n",
    "    profiles.append(subset_prof)\n",
    "\n",
    "profiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's pass this list of profiles into the visualizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "viz.set_profiles(profiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now explore temporal visualizations of our profiles at a quick glance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz.plot_distribution(\"loan_amnt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz.plot_data_types(\"emp_length\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrapping up\n",
    "\n",
    "Once you're done with your session, you can close it. Closing a session will close all downstream loggers and force them to write to disk.\n",
    "\n",
    "Note that dataset profiles are kept in memory, so they won't be discarded with the session. You can use them as long as you like!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "session.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "demo",
   "language": "python",
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