stocks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stock Trades by Members of the US House of Representatives\n",
    "\n",
    "This project uses public data about the stock trades made by members of the US House of Representatives. This data is collected and maintained by Timothy Carambat as part of the [House Stock Watcher](https://housestockwatcher.com/) project. The project describes itself as follows:\n",
    "\n",
    "> With recent and ongoing investigations of incumbent congressional members being investigated for potentially violating the STOCK act. This website compiles this publicly available information in a format that is easier to digest then the original PDF source.\n",
    ">\n",
    "> Members of Congress must report periodic reports of their asset transactions. This website is purely for an informative purpose and aid in transparency.\n",
    ">\n",
    "> This site does not manipluate or censor any of the information from the original source. All data is transcribed by our community of contributors, which you can join for free by going to our transcription tool. Our moderation team takes great care in ensuring the accuracy of the information.\n",
    ">\n",
    "> This site is built and maintained by Timothy Carambat and supported with our contributors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some interesting questions to consider for this data set include:\n",
    "\n",
    "- Is there a difference in stock trading behavior between political parties? For example:\n",
    "    - does one party trade more often?\n",
    "    - does one party make larger trades?\n",
    "    - do the two parties invest in different stocks or sectors? For instance, do Democrats invest in Tesla more than Republicans?\n",
    "- What congresspeople have made the most trades?\n",
    "- What companies are most traded by congresspeople?\n",
    "- Is there evidence of insider trading? For example, Boeing stock dropped sharply in February 2020. Were there a suspiciously-high number of sales of Boeing before the drop?\n",
    "- When are stocks bought and sold? Is there a day of the week that is most common? Or a month of the year?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting the Data\n",
    "\n",
    "The full data set of stock trade disclosures is available as a CSV or as JSON at https://housestockwatcher.com/api.\n",
    "\n",
    "This data set does not, however, contain the political affiliation of the congresspeople. If you wish to investigate a question that relies on having this information, you'll need to find another dataset that contains it and perform a merge. *Hint*: Kaggle is a useful source of data sets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Cleaning and EDA\n",
    "\n",
    "- Clean the data.\n",
    "    - Certain fields have \"missing\" data that isn't labeled as missing. For example, there are fields with the value \"--.\" Do some exploration to find those values and convert them to null values.\n",
    "    - You may also want to clean up the date columns to enable time-series exploration.\n",
    "- Understand the data in ways relevant to your question using univariate and bivariate analysis of the data as well as aggregations.\n",
    "\n",
    "\n",
    "### Assessment of Missingness\n",
    "\n",
    "- Assess the missingness per the requirements in `project03.ipynb`\n",
    "\n",
    "### Hypothesis Test / Permutation Test\n",
    "Find a hypothesis test or permutation test to perform. You can use the questions at the top of the notebook for inspiration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary of Findings\n",
    "\n",
    "### Introduction\n",
    "\n",
    "The following notebook analyzes publicly available information regarding stock trades made by US House of Representatives members to reach inferences. We first cleaned up the dataset and combined _The 116th U.S. House of Representatives_ at [https://www.kaggle.com/datasets/aavigan/house-of-representatives-congress-116](https://www.kaggle.com/datasets/aavigan/house-of-representatives-congress-116) which contains information regarding political affiliation of congresspeople. Next, we evaluate the dataset's `owner` column's missingness association.\n",
    "\n",
    "After that, we began processing the dataset for insights, and try to explore answers to the following questions:\n",
    "- Does one party trade more often?\n",
    "- Does one party make larger trades?\n",
    "- What congresspeople have made the most trades?\n",
    "- What companies are most traded by congresspeople?\n",
    "- When are stocks bought and sold? Is there a day of the week that is most common? Or a month of the year?\n",
    "\n",
    "### Cleaning and EDA\n",
    "\n",
    "After obtaining the complete dataset of stock trade disclosures from https://housestockwatcher.com/api, we discover that the data need to be cleaned up since they are fairly untidy. To clean it, we did what is shown below:\n",
    "1. Change `disclosure_date` and `transaction_date` column to `datetime` type.\n",
    "2. Replace '--' value in `ticker` column with `np.NaN`.\n",
    "3. Replace '--' value in `owner` column with `np.NaN`.\n",
    "4. Convert `amount` to a `pd.Categorical` series.\n",
    "\n",
    "The political affiliation of congressmen is missing from the dataset after it has been cleaned up, therefore we choose to utilize one from __Kaggle__ at [https://www.kaggle.com/datasets/aavigan/house-of-representatives-congress-116](https://www.kaggle.com/datasets/aavigan/house-of-representatives-congress-116). Due of the distinctions in the names between the two datasets, we combined them using the first and last names of each participant. Then we examine a few rare occurrences and manually resolve them. We were able to successfully integrate stock trade activity with representative political allegiance as an outcome. \n",
    "\n",
    "Moving on, we process to EDA and find out that:\n",
    "- `owner`, `ticker`, `transaction_date`, and `asset_description` are 4 columns that contain missing data, some of the missingness in `transaction_date` is because of the incorrect value. For example, there are a few cell with value `0009-06-09` which is clearly not a valid date. \n",
    "- `transaction_date` range between `2012-06-19` and `2022-10-21`.\n",
    "- Most of the congresspeople are either from `Democrat` or `Republican`, there is only one house member who is listed as `Independent` regrading their political affiliation.\n",
    "\n",
    "#### What congresspeople have made the most trades?\n",
    "- By plotting the value counts of `representative` column, we have discovered that the representative __Josh Gottheimer__ has made the most trades.\n",
    "\n",
    "#### What companies are most traded by congresspeople?\n",
    "- By plotting the value counts of `ticker` column, we have discovered that the ticker __MSFT__, which is __Microsoft Corp.__, has the most trade transactions.\n",
    "\n",
    "#### When are stocks bought and sold? Is there a day of the week that is most common? Or a month of the year?\n",
    "- By grouping the dateset by weekday of `transaction_date`, such that most of the transactions happened during weekdays, while only a tiny amount of transactions are done in weekend. Among weekdays, __Thurday__ seems to have a slightly higher transaction volume.\n",
    "- By grouping the dataset by month of `transation_date`, we discover that __February__ is the month has largest volume of transactions.\n",
    "\n",
    "### Assessment of Missingness\n",
    "\n",
    "In this section we decided to evaluate the missingness of `owner` column as it has the most missing values across all columns. It has values like `self`, `joint`, `dependent`, and `np.NaN`. We think the missingness of `owner` column could be associated with `type` column. This concept arises from the fact that `type` describes the sort of transaction that is performed; if the type of transaction is not a stock exchange, it is less likely to fall into the `self`, `joint`, or `dependant` categories and end up as an empty value.\n",
    "\n",
    "In order to validate this assumption, we have to perform a permutation test. To begin with, we determine the test statistic to be __Total Variation Distance (TVD)__, as `type` is a categorical data. Then, we calculate the observed statistic for the original dataset, which is `0.07390`. Afterwards, we shuffle the `owner` column and calculate the simulate statistics. By repeating this prcoess for 5,000 times, we then calculate the p-value for this permutation. As a result, we get a p-value of `0.0` which indicates that none of the simulate statistics has a more extreme result than the observed statistics. In conclusion, we conclude that the missingness of `owner` is __Missing at Random (MAR)__, and it's dependent on `type` column the most.\n",
    "\n",
    "### Hypothesis Test\n",
    "\n",
    "#### Which party trade more often?\n",
    "\n",
    "* **Null hypothesis**: the distribution of trading frequency among congresspeople from different party is the same. The difference between the two observed sample is due to chance.\n",
    "\n",
    "* **Alternative hypothesis**: the distribution of trading frequency among congresspeople from different party are different.\n",
    "\n",
    "For the test statistics, we calculate the average trading transactions per month of each party and take the absolute difference between them. The observed statistics is `58.5469`, and we shuffle the `party` column and run the permutation test for 5,000 times. At the end, we get a p-value of `0.8756`, which indicates that majority of the permutation test cases have more extreme result than the observed statistics. Therefore, we __fail to reject__ the null hypothesis, the distribution of trading frequency among congresspeople from various parties is probrably the same.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-31T23:36:28.652554Z",
     "start_time": "2019-10-31T23:36:27.180520Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'  # Higher resolution figures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load transaction dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>disclosure_year</th>\n",
       "      <th>disclosure_date</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>owner</th>\n",
       "      <th>ticker</th>\n",
       "      <th>asset_description</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>representative</th>\n",
       "      <th>district</th>\n",
       "      <th>ptr_link</th>\n",
       "      <th>cap_gains_over_200_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021</td>\n",
       "      <td>10/04/2021</td>\n",
       "      <td>2021-09-27</td>\n",
       "      <td>joint</td>\n",
       "      <td>BP</td>\n",
       "      <td>BP plc</td>\n",
       "      <td>purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>Hon. Virginia Foxx</td>\n",
       "      <td>NC05</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>10/04/2021</td>\n",
       "      <td>2021-09-13</td>\n",
       "      <td>joint</td>\n",
       "      <td>XOM</td>\n",
       "      <td>Exxon Mobil Corporation</td>\n",
       "      <td>purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>Hon. Virginia Foxx</td>\n",
       "      <td>NC05</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021</td>\n",
       "      <td>10/04/2021</td>\n",
       "      <td>2021-09-10</td>\n",
       "      <td>joint</td>\n",
       "      <td>ILPT</td>\n",
       "      <td>Industrial Logistics Properties Trust - Common...</td>\n",
       "      <td>purchase</td>\n",
       "      <td>$15,001 - $50,000</td>\n",
       "      <td>Hon. Virginia Foxx</td>\n",
       "      <td>NC05</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021</td>\n",
       "      <td>10/04/2021</td>\n",
       "      <td>2021-09-28</td>\n",
       "      <td>joint</td>\n",
       "      <td>PM</td>\n",
       "      <td>Phillip Morris International Inc</td>\n",
       "      <td>purchase</td>\n",
       "      <td>$15,001 - $50,000</td>\n",
       "      <td>Hon. Virginia Foxx</td>\n",
       "      <td>NC05</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021</td>\n",
       "      <td>10/04/2021</td>\n",
       "      <td>2021-09-17</td>\n",
       "      <td>self</td>\n",
       "      <td>BLK</td>\n",
       "      <td>BlackRock Inc</td>\n",
       "      <td>sale_partial</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>Hon. Alan S. Lowenthal</td>\n",
       "      <td>CA47</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   disclosure_year disclosure_date transaction_date  owner ticker  \\\n",
       "0             2021      10/04/2021       2021-09-27  joint     BP   \n",
       "1             2021      10/04/2021       2021-09-13  joint    XOM   \n",
       "2             2021      10/04/2021       2021-09-10  joint   ILPT   \n",
       "3             2021      10/04/2021       2021-09-28  joint     PM   \n",
       "4             2021      10/04/2021       2021-09-17   self    BLK   \n",
       "\n",
       "                                   asset_description          type  \\\n",
       "0                                             BP plc      purchase   \n",
       "1                            Exxon Mobil Corporation      purchase   \n",
       "2  Industrial Logistics Properties Trust - Common...      purchase   \n",
       "3                   Phillip Morris International Inc      purchase   \n",
       "4                                      BlackRock Inc  sale_partial   \n",
       "\n",
       "              amount          representative district  \\\n",
       "0   $1,001 - $15,000      Hon. Virginia Foxx     NC05   \n",
       "1   $1,001 - $15,000      Hon. Virginia Foxx     NC05   \n",
       "2  $15,001 - $50,000      Hon. Virginia Foxx     NC05   \n",
       "3  $15,001 - $50,000      Hon. Virginia Foxx     NC05   \n",
       "4   $1,001 - $15,000  Hon. Alan S. Lowenthal     CA47   \n",
       "\n",
       "                                            ptr_link  cap_gains_over_200_usd  \n",
       "0  https://disclosures-clerk.house.gov/public_dis...                   False  \n",
       "1  https://disclosures-clerk.house.gov/public_dis...                   False  \n",
       "2  https://disclosures-clerk.house.gov/public_dis...                   False  \n",
       "3  https://disclosures-clerk.house.gov/public_dis...                   False  \n",
       "4  https://disclosures-clerk.house.gov/public_dis...                   False  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions = pd.read_csv('data/all_transactions.csv')\n",
    "transactions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning and EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-31T23:36:28.657068Z",
     "start_time": "2019-10-31T23:36:28.654650Z"
    }
   },
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15674 entries, 0 to 15673\n",
      "Data columns (total 12 columns):\n",
      " #   Column                  Non-Null Count  Dtype         \n",
      "---  ------                  --------------  -----         \n",
      " 0   disclosure_year         15674 non-null  int64         \n",
      " 1   disclosure_date         15674 non-null  datetime64[ns]\n",
      " 2   transaction_date        15667 non-null  datetime64[ns]\n",
      " 3   owner                   8346 non-null   object        \n",
      " 4   ticker                  14378 non-null  object        \n",
      " 5   asset_description       15670 non-null  object        \n",
      " 6   type                    15674 non-null  object        \n",
      " 7   amount                  15674 non-null  category      \n",
      " 8   representative          15674 non-null  object        \n",
      " 9   district                15674 non-null  object        \n",
      " 10  ptr_link                15674 non-null  object        \n",
      " 11  cap_gains_over_200_usd  15674 non-null  bool          \n",
      "dtypes: bool(1), category(1), datetime64[ns](2), int64(1), object(7)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "cleaned = transactions.copy()\n",
    "\n",
    "# convert `disclosure_date`, `transaction_date` to datetime type\n",
    "cleaned['disclosure_date'] = pd.to_datetime(cleaned['disclosure_date'])\n",
    "cleaned['transaction_date'] = pd.to_datetime(cleaned['transaction_date'], errors='coerce')\n",
    "\n",
    "# change `ticker` null values\n",
    "cleaned['ticker'] = cleaned['ticker'].replace('--', np.NaN)\n",
    "\n",
    "# cahnge `owner` null values\n",
    "cleaned['owner'] = cleaned['owner'].replace('--', np.NaN)\n",
    "\n",
    "# convert `amount` to categorical type\n",
    "cleaned['amount'] = pd.Categorical(cleaned['amount'])\n",
    "\n",
    "cleaned.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "disclosure_year              0\n",
       "disclosure_date              0\n",
       "transaction_date             7\n",
       "owner                     7328\n",
       "ticker                    1296\n",
       "asset_description            4\n",
       "type                         0\n",
       "amount                       0\n",
       "representative               0\n",
       "district                     0\n",
       "ptr_link                     0\n",
       "cap_gains_over_200_usd       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned.isna().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine with political affliation dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Virginia Foxx\n",
       "1      Virginia Foxx\n",
       "2      Virginia Foxx\n",
       "3      Virginia Foxx\n",
       "4    Alan  Lowenthal\n",
       "Name: representative, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove unwanted name suffixs\n",
    "suffixs = ['Hon\\\\.', 'Mr\\\\.', 'Mrs\\\\.', 'None', 'Aston', 'S\\\\.', 'W\\\\.']\n",
    "cleaned['representative'] = (cleaned['representative']\n",
    "                             .str.replace('|'.join(suffixs), '', regex=True)\n",
    "                             .str.strip())\n",
    "\n",
    "cleaned['representative'].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    virginia\n",
       "1    virginia\n",
       "2    virginia\n",
       "3    virginia\n",
       "4        alan\n",
       "Name: first_name, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split representative name into `first_name` and `last_name` for later merge \n",
    "cleaned['first_name'] = cleaned['representative'].apply(lambda x: x.split()[0].lower())\n",
    "cleaned['last_name'] = cleaned['representative'].apply(lambda x: x.split()[-1].lower())\n",
    "\n",
    "# fix special cases\n",
    "cleaned.loc[cleaned['representative'] == 'Neal Patrick Dunn MD, FACS', 'last_name'] = 'dunn'\n",
    "\n",
    "cleaned['first_name'].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>party</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Republican</td>\n",
       "      <td>don</td>\n",
       "      <td>young</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Republican</td>\n",
       "      <td>jerry</td>\n",
       "      <td>carl</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Republican</td>\n",
       "      <td>felix</td>\n",
       "      <td>moore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Republican</td>\n",
       "      <td>mike</td>\n",
       "      <td>rogers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Republican</td>\n",
       "      <td>robert</td>\n",
       "      <td>aderholt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Republican</td>\n",
       "      <td>mo</td>\n",
       "      <td>brooks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Republican</td>\n",
       "      <td>gary</td>\n",
       "      <td>palmer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Democrat</td>\n",
       "      <td>terri</td>\n",
       "      <td>sewell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Republican</td>\n",
       "      <td>rick</td>\n",
       "      <td>crawford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Republican</td>\n",
       "      <td>french</td>\n",
       "      <td>hill</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        party first_name last_name\n",
       "0  Republican        don     young\n",
       "1  Republican      jerry      carl\n",
       "2  Republican      felix     moore\n",
       "3  Republican       mike    rogers\n",
       "4  Republican     robert  aderholt\n",
       "5  Republican         mo    brooks\n",
       "6  Republican       gary    palmer\n",
       "7    Democrat      terri    sewell\n",
       "8  Republican       rick  crawford\n",
       "9  Republican     french      hill"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import member table 1\n",
    "members1 = pd.read_csv('data/us-house.csv')\n",
    "members1 = members1[['party', 'first_name', 'last_name']]\n",
    "members1['first_name'] = members1['first_name'].str.lower()\n",
    "members1['last_name'] = members1['last_name'].str.lower()\n",
    "members1['party'] = members1['party'].str.capitalize()\n",
    "\n",
    "members1.head(10)\n"
   ]
  },
  {
   "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",
       "    }\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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ralph</td>\n",
       "      <td>abraham</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alma</td>\n",
       "      <td>adams</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>robert</td>\n",
       "      <td>aderholt</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pete</td>\n",
       "      <td>aguilar</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rick</td>\n",
       "      <td>allen</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>colin</td>\n",
       "      <td>allred</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>justin</td>\n",
       "      <td>amash</td>\n",
       "      <td>Independent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mark</td>\n",
       "      <td>amodei</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>kelly</td>\n",
       "      <td>armstrong</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>jodey</td>\n",
       "      <td>arrington</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name  last_name        party\n",
       "0      ralph    abraham   Republican\n",
       "1       alma      adams     Democrat\n",
       "2     robert   aderholt   Republican\n",
       "3       pete    aguilar     Democrat\n",
       "4       rick      allen   Republican\n",
       "5      colin     allred     Democrat\n",
       "6     justin      amash  Independent\n",
       "7       mark     amodei   Republican\n",
       "8      kelly  armstrong   Republican\n",
       "9      jodey  arrington   Republican"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import member table 2\n",
    "members2 = pd.read_csv('data/house_members_116.csv')\n",
    "members2['first_name'] = members2['name'].apply(\n",
    "    lambda x: x.split('-')[0].lower())\n",
    "members2['last_name'] = members2['name'].apply(\n",
    "    lambda x: x.split('-')[-1].lower())\n",
    "members2 = members2.rename(columns={'current_party': 'party'})[\n",
    "    ['first_name', 'last_name', 'party']]\n",
    "\n",
    "# unify party values\n",
    "members2.loc[members2['party'] == 'Democratic', 'party'] = 'Democrat'\n",
    "\n",
    "members2.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(547, 3)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine 2 member tables\n",
    "members = (pd.concat([members1, members2])\n",
    "           .sort_values(['first_name', 'last_name'])\n",
    "           .drop_duplicates(subset=['first_name', 'last_name'])\n",
    "           .reset_index(drop=True))\n",
    "\n",
    "# fix mismatch names\n",
    "members.loc[members['first_name'] == 'k', 'first_name'] = 'k.'\n",
    "members.loc[members['first_name'] == 'raul', 'first_name'] = 'raúl'\n",
    "members.loc[members['first_name'] == 'wm', 'first_name'] = 'wm.'\n",
    "members.loc[members['first_name'] == 'ro', 'first_name'] = 'rohit'\n",
    "members.loc[members['first_name'] == 'cynthia', 'first_name'] = 'cindy'\n",
    "members.loc[members['last_name'] == 'allen', 'first_name'] = 'richard'\n",
    "members.loc[members['last_name'] == 'steube', 'first_name'] = 'greg'\n",
    "members.loc[members['last_name'] == 'banks', 'first_name'] = 'james'\n",
    "members.loc[(members['first_name'] == 'j') & (\n",
    "    members['last_name'] == 'hill'), 'first_name'] = 'james'\n",
    "members.loc[(members['first_name'] == 'mike') & (\n",
    "    members['last_name'] == 'garcia'), 'first_name'] = 'michael'\n",
    "members.loc[members['last_name'] == 'crenshaw', 'first_name'] = 'daniel'\n",
    "members.loc[members['last_name'] == 'taylor', 'first_name'] = 'nicholas'\n",
    "members.loc[members['last_name'] == 'gallagher', 'first_name'] = 'michael'\n",
    "members.loc[(members['first_name'] == 'gregory') & (\n",
    "    members['last_name'] == 'murphy'), 'first_name'] = 'greg'\n",
    "members.loc[members['first_name'] == 'ashley', 'last_name'] = 'arenholz'\n",
    "members.loc[members['last_name'] == 'buck', 'first_name'] = 'kenneth'\n",
    "members.loc[members['last_name'] == 'costa', 'first_name'] = 'james'\n",
    "members.loc[members['last_name'] == 'hagedorn', 'first_name'] = 'james'\n",
    "\n",
    "# drop duplicate rows\n",
    "members = members.drop_duplicates(subset=['first_name', 'last_name'])\n",
    "\n",
    "# output cleaned representative table\n",
    "members.to_csv('data/cleaned_members.csv', index=False)\n",
    "\n",
    "members.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transaction table with member info table\n",
    "combined = cleaned.merge(members, how='left', on=['first_name', 'last_name'])\n",
    "\n",
    "combined.loc[combined['party'].isna(), 'representative'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15674, 15)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15674, 12)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.to_csv('data/combined_transactions.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 567
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined['disclosure_year'].plot(kind='hist', density=True, bins=range(2019,2025,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 561
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined['type'].value_counts().plot(kind='bar')\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 698
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined['amount'].value_counts().plot(kind='barh');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 679
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined['representative'].value_counts().head(10).plot(kind='barh');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 565
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined['district'].value_counts().head(10).plot(kind='barh');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 574
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined['ticker'].value_counts().head(10).plot(kind='barh');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assessment of Missingness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-31T23:36:28.662099Z",
     "start_time": "2019-10-31T23:36:28.660016Z"
    }
   },
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "disclosure_year              0\n",
       "disclosure_date              0\n",
       "transaction_date             7\n",
       "owner                     7328\n",
       "ticker                    1296\n",
       "asset_description            4\n",
       "type                         0\n",
       "amount                       0\n",
       "representative               0\n",
       "district                     0\n",
       "ptr_link                     0\n",
       "cap_gains_over_200_usd       0\n",
       "first_name                   0\n",
       "last_name                    0\n",
       "party                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['joint', 'self', nan, 'dependent'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined['owner'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 434,
       "width": 625
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 434,
       "width": 562
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.07390401960199536, 0.0)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_dist(df, missing_col, col):\n",
    "    dist = (\n",
    "        df\n",
    "        .assign(**{f'{missing_col}_null': df[missing_col].isna()})\n",
    "        .pivot_table(index=col, columns=f'{missing_col}_null', aggfunc='size', fill_value=0)\n",
    "    )\n",
    "    dist = dist / dist.sum()\n",
    "    \n",
    "    dist.plot(kind='barh', legend=True, title=f\"{col.capitalize()} by Missingness of {missing_col.capitalize()}\")\n",
    "    plt.show()\n",
    "    \n",
    "    return dist\n",
    "\n",
    "\n",
    "def calc_tvd(df, missing_col, col):\n",
    "    dist = (\n",
    "        df\n",
    "        .assign(**{f'{missing_col}_null': df[missing_col].isna()})\n",
    "        .pivot_table(index=col, columns=f'{missing_col}_null', aggfunc='size', fill_value=0)\n",
    "    )\n",
    "    dist = dist / dist.sum()\n",
    "    return dist.diff(axis=1).iloc[:, -1].abs().sum() / 2\n",
    "\n",
    "\n",
    "def missingness_perm_test(df, missing_col, col):\n",
    "    shuffled = df.copy()\n",
    "    shuffled[f'{missing_col}_null'] = shuffled[missing_col].isna()\n",
    "    \n",
    "    make_dist(df, missing_col, col)\n",
    "    obs_tvd = calc_tvd(df, missing_col, col)\n",
    "\n",
    "    n_repetitions = 1000\n",
    "    tvds = []\n",
    "    for _ in range(n_repetitions):\n",
    "\n",
    "        # Shuffling genders and assigning back to the DataFrame\n",
    "        shuffled[col] = np.random.permutation(shuffled[col])\n",
    "\n",
    "        # Computing and storing TVD\n",
    "        tvd = calc_tvd(shuffled, missing_col, col)\n",
    "        tvds.append(tvd)\n",
    "\n",
    "    tvds = np.array(tvds)\n",
    "    pval = np.mean(tvds >= obs_tvd)\n",
    "    \n",
    "    # Draw the p-value graph\n",
    "    pd.Series(tvds).plot(kind='hist', density=True, ec='w', bins=10, title=f'p-value: {pval}', label='Simulated TVDs')\n",
    "    plt.axvline(x=obs_tvd, color='red', linewidth=4, label='Observed TVD')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "    return obs_tvd, pval\n",
    "\n",
    "missingness_perm_test(combined, 'owner', 'type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'type_dist' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [19], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtype_dist\u001b[49m\u001b[38;5;241m.\u001b[39mplot(kind\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbarh\u001b[39m\u001b[38;5;124m'\u001b[39m, legend\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mType by Missingness of Owner\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'type_dist' is not defined"
     ]
    }
   ],
   "source": [
    "type_dist.plot(kind='barh', legend=True, title=\"Type by Missingness of Owner\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'type_dist' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m obs_tvd \u001b[38;5;241m=\u001b[39m \u001b[43mtype_dist\u001b[49m\u001b[38;5;241m.\u001b[39mdiff(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39miloc[:, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mabs()\u001b[38;5;241m.\u001b[39msum() \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m      2\u001b[0m obs_tvd\n",
      "\u001b[0;31mNameError\u001b[0m: name 'type_dist' is not defined"
     ]
    }
   ],
   "source": [
    "obs_tvd = type_dist.diff(axis=1).iloc[:, -1].abs().sum() / 2\n",
    "obs_tvd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "shuffled = combined.copy()\n",
    "shuffled['owner_null'] = shuffled['owner'].isna()\n",
    "\n",
    "n_repetitions = 1000\n",
    "tvds = []\n",
    "for _ in range(n_repetitions):\n",
    "    \n",
    "    # Shuffling genders and assigning back to the DataFrame\n",
    "    shuffled['type'] = np.random.permutation(shuffled['type'])\n",
    "    \n",
    "    # Computing and storing TVD\n",
    "    pivoted = (\n",
    "        shuffled\n",
    "        .pivot_table(index='type', columns='owner_null', aggfunc='size')\n",
    "        .apply(lambda x: x / x.sum(), axis=0)\n",
    "    )\n",
    "    \n",
    "    tvd = pivoted.diff(axis=1).iloc[:, -1].abs().sum() / 2\n",
    "    tvds.append(tvd)\n",
    "    \n",
    "tvds = np.array(tvds)\n",
    "tvds[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pval = np.mean(tvds >= obs_tvd)\n",
    "\n",
    "pd.Series(tvds).plot(kind='hist', density=True, ec='w', bins=10, title=f'p-value: {pval}', label='Simulated TVDs')\n",
    "plt.axvline(x=obs_tvd, color='red', linewidth=4, label='Observed TVD')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we conclude that the missingness of `owner` is __MAR__, and it's dependent on `type` column the most. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis Test / Permutation Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Which party trade more often?\n",
    "\n",
    "* **Null hypothesis**: the distribution of trading frequency among congresspeople from different party is the same. The difference between the two observed sample is due to chance.\n",
    "\n",
    "* **Alternative hypothesis**: the distribution of trading frequency among congresspeople from different party are different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-31T23:36:28.666489Z",
     "start_time": "2019-10-31T23:36:28.664381Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "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>disclosure_year</th>\n",
       "      <th>disclosure_date</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>owner</th>\n",
       "      <th>ticker</th>\n",
       "      <th>asset_description</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>representative</th>\n",
       "      <th>district</th>\n",
       "      <th>ptr_link</th>\n",
       "      <th>cap_gains_over_200_usd</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021</td>\n",
       "      <td>2021-10-04</td>\n",
       "      <td>2021-09-27</td>\n",
       "      <td>joint</td>\n",
       "      <td>BP</td>\n",
       "      <td>BP plc</td>\n",
       "      <td>purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>Virginia Foxx</td>\n",
       "      <td>NC05</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "      <td>virginia</td>\n",
       "      <td>foxx</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>2021-10-04</td>\n",
       "      <td>2021-09-13</td>\n",
       "      <td>joint</td>\n",
       "      <td>XOM</td>\n",
       "      <td>Exxon Mobil Corporation</td>\n",
       "      <td>purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>Virginia Foxx</td>\n",
       "      <td>NC05</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "      <td>virginia</td>\n",
       "      <td>foxx</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021</td>\n",
       "      <td>2021-10-04</td>\n",
       "      <td>2021-09-10</td>\n",
       "      <td>joint</td>\n",
       "      <td>ILPT</td>\n",
       "      <td>Industrial Logistics Properties Trust - Common...</td>\n",
       "      <td>purchase</td>\n",
       "      <td>$15,001 - $50,000</td>\n",
       "      <td>Virginia Foxx</td>\n",
       "      <td>NC05</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "      <td>virginia</td>\n",
       "      <td>foxx</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021</td>\n",
       "      <td>2021-10-04</td>\n",
       "      <td>2021-09-28</td>\n",
       "      <td>joint</td>\n",
       "      <td>PM</td>\n",
       "      <td>Phillip Morris International Inc</td>\n",
       "      <td>purchase</td>\n",
       "      <td>$15,001 - $50,000</td>\n",
       "      <td>Virginia Foxx</td>\n",
       "      <td>NC05</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "      <td>virginia</td>\n",
       "      <td>foxx</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021</td>\n",
       "      <td>2021-10-04</td>\n",
       "      <td>2021-09-17</td>\n",
       "      <td>self</td>\n",
       "      <td>BLK</td>\n",
       "      <td>BlackRock Inc</td>\n",
       "      <td>sale_partial</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>Alan  Lowenthal</td>\n",
       "      <td>CA47</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "      <td>alan</td>\n",
       "      <td>lowenthal</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   disclosure_year disclosure_date transaction_date  owner ticker  \\\n",
       "0             2021      2021-10-04       2021-09-27  joint     BP   \n",
       "1             2021      2021-10-04       2021-09-13  joint    XOM   \n",
       "2             2021      2021-10-04       2021-09-10  joint   ILPT   \n",
       "3             2021      2021-10-04       2021-09-28  joint     PM   \n",
       "4             2021      2021-10-04       2021-09-17   self    BLK   \n",
       "\n",
       "                                   asset_description          type  \\\n",
       "0                                             BP plc      purchase   \n",
       "1                            Exxon Mobil Corporation      purchase   \n",
       "2  Industrial Logistics Properties Trust - Common...      purchase   \n",
       "3                   Phillip Morris International Inc      purchase   \n",
       "4                                      BlackRock Inc  sale_partial   \n",
       "\n",
       "              amount   representative district  \\\n",
       "0   $1,001 - $15,000    Virginia Foxx     NC05   \n",
       "1   $1,001 - $15,000    Virginia Foxx     NC05   \n",
       "2  $15,001 - $50,000    Virginia Foxx     NC05   \n",
       "3  $15,001 - $50,000    Virginia Foxx     NC05   \n",
       "4   $1,001 - $15,000  Alan  Lowenthal     CA47   \n",
       "\n",
       "                                            ptr_link  cap_gains_over_200_usd  \\\n",
       "0  https://disclosures-clerk.house.gov/public_dis...                   False   \n",
       "1  https://disclosures-clerk.house.gov/public_dis...                   False   \n",
       "2  https://disclosures-clerk.house.gov/public_dis...                   False   \n",
       "3  https://disclosures-clerk.house.gov/public_dis...                   False   \n",
       "4  https://disclosures-clerk.house.gov/public_dis...                   False   \n",
       "\n",
       "  first_name  last_name       party  \n",
       "0   virginia       foxx  Republican  \n",
       "1   virginia       foxx  Republican  \n",
       "2   virginia       foxx  Republican  \n",
       "3   virginia       foxx  Republican  \n",
       "4       alan  lowenthal    Democrat  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 434,
       "width": 576
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = combined.assign(transaction_year=combined['transaction_date'].dt.year,\n",
    "                     transaction_month=combined['transaction_date'].dt.month)\n",
    "df = (\n",
    "    df\n",
    "    .groupby(['transaction_year', 'transaction_month', 'party'])[['representative']]\n",
    "    .count()\n",
    "    .reset_index()\n",
    ")\n",
    "democrat_stats = df.loc[df['party'] == 'Democrat', 'representative'].sum() / (df['party'] == 'Democrat').sum()\n",
    "republican_stats =  df.loc[df['party'] == 'Republican', 'representative'].sum() / (df['party'] == 'Republican').sum()\n",
    "\n",
    "obs_stats = abs(democrat_stats - republican_stats)\n",
    "\n",
    "shuffled = combined.assign(transaction_year=combined['transaction_date'].dt.year,\n",
    "                           transaction_month=combined['transaction_date'].dt.month)\n",
    "\n",
    "n_repetitions = 5000\n",
    "stats = []\n",
    "for _ in range(n_repetitions):\n",
    "    \n",
    "    # Shuffling genders and assigning back to the DataFrame\n",
    "    shuffled['party'] = np.random.permutation(shuffled['party'])\n",
    "    \n",
    "    # Computing and storing TVD\n",
    "    pivoted = (\n",
    "        shuffled\n",
    "        .groupby(['transaction_year', 'transaction_month', 'party'])[['representative']]\n",
    "        .count()\n",
    "        .reset_index()\n",
    "    )\n",
    "    \n",
    "    democrat_stats = pivoted.loc[pivoted['party'] == 'Democrat', 'representative'].sum() / (pivoted['party'] == 'Democrat').sum()\n",
    "    republican_stats =  pivoted.loc[pivoted['party'] == 'Republican', 'representative'].sum() / (pivoted['party'] == 'Republican').sum()\n",
    "    stats.append(abs(democrat_stats - republican_stats))\n",
    "    \n",
    "stats = np.array(stats)\n",
    "pval = np.mean(stats >= obs_stats)\n",
    "\n",
    "pd.Series(stats).plot(kind='hist', density=True, ec='w', bins=10, title=f'p-value: {pval}', label='Simulated Stats')\n",
    "plt.axvline(x=obs_stats, color='red', linewidth=4, label='Observed Stats')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion\n",
    "\n",
    "The p-value of the permutation test is `0.8722`, which is way larger the the `0.05`. Thus, we __fail to reject__ the null hypothesis, which means that distribution of trading frequency among congresspeople from different party may be the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178.2, 119.65306122448979)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = combined.assign(transaction_year=combined['transaction_date'].dt.year,\n",
    "                     transaction_month=combined['transaction_date'].dt.month)\n",
    "df = (\n",
    "    df\n",
    "    .groupby(['transaction_year', 'transaction_month', 'party'])[['representative']]\n",
    "    .count()\n",
    "    .reset_index()\n",
    ")\n",
    "democrat_stats = df.loc[df['party'] == 'Democrat', 'representative'].sum() / (df['party'] == 'Democrat').sum()\n",
    "republican_stats =  df.loc[df['party'] == 'Republican', 'representative'].sum() / (df['party'] == 'Republican').sum()\n",
    "\n",
    "democrat_stats, republican_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58.5469387755102"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_stats = abs(democrat_stats - republican_stats)\n",
    "obs_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([66.50108932, 49.80153509, 60.01960784, 59.98214286, 65.33776224,\n",
       "       72.13642961, 68.75      , 66.50108932, 67.50325901, 85.53868093])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shuffled = combined.assign(transaction_year=combined['transaction_date'].dt.year,\n",
    "                           transaction_month=combined['transaction_date'].dt.month)\n",
    "\n",
    "n_repetitions = 5000\n",
    "stats = []\n",
    "for _ in range(n_repetitions):\n",
    "    \n",
    "    # Shuffling genders and assigning back to the DataFrame\n",
    "    shuffled['party'] = np.random.permutation(shuffled['party'])\n",
    "    \n",
    "    # Computing and storing TVD\n",
    "    pivoted = (\n",
    "        shuffled\n",
    "        .groupby(['transaction_year', 'transaction_month', 'party'])[['representative']]\n",
    "        .count()\n",
    "        .reset_index()\n",
    "    )\n",
    "    \n",
    "    democrat_stats = pivoted.loc[pivoted['party'] == 'Democrat', 'representative'].sum() / (pivoted['party'] == 'Democrat').sum()\n",
    "    republican_stats =  pivoted.loc[pivoted['party'] == 'Republican', 'representative'].sum() / (pivoted['party'] == 'Republican').sum()\n",
    "    stats.append(abs(democrat_stats - republican_stats))\n",
    "    \n",
    "stats = np.array(stats)\n",
    "stats[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 434,
       "width": 576
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pval = np.mean(stats >= obs_stats)\n",
    "\n",
    "pd.Series(stats).plot(kind='hist', density=True, ec='w', bins=10, title=f'p-value: {pval}', label='Simulated Stats')\n",
    "plt.axvline(x=obs_stats, color='red', linewidth=4, label='Observed Stats')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "party\n",
       "Democrat       2.596398\n",
       "Independent         inf\n",
       "Republican     3.725079\n",
       "dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1 = (\n",
    "    combined\n",
    "    .groupby('party')['transaction_date'].agg(['min', 'max'])\n",
    "    .diff(axis=1)\n",
    "    .iloc[:, -1]\n",
    "    .apply(lambda x: x.days)\n",
    ")\n",
    "ser2 = combined.groupby('party')['representative'].count()\n",
    "\n",
    "ser2 / ser1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = combined.groupby('party')['representative'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1286810599946193"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calc_test_statistics(df):\n",
    "    counts = df.groupby('party')['representative'].count()\n",
    "    ranges = (\n",
    "        df\n",
    "        .groupby('party')['transaction_date'].agg(['min', 'max'])\n",
    "        .diff(axis=1)\n",
    "        .iloc[:, -1]\n",
    "        .apply(lambda x: x.days)\n",
    "    )\n",
    "    result = counts / ranges\n",
    "    return abs(result['Democrat'] - result['Republican'])\n",
    "\n",
    "\n",
    "calc_test_statistics(combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 434,
       "width": 567
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.53768289, 0.58560186, 0.59231296, 0.58560186, 1.17187577,\n",
       "       0.57323973, 0.59905285, 3.71209501, 0.74758808, 1.36546329])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def permutation_test(df, n_repetitions=1000):\n",
    "    \n",
    "    shuffled = df.copy()\n",
    "    obs_stats = calc_test_statistics(shuffled)\n",
    "\n",
    "    sim_stats = []\n",
    "    for _ in range(n_repetitions):\n",
    "\n",
    "        # Shuffling genders and assigning back to the DataFrame\n",
    "        shuffled['party'] = np.random.permutation(shuffled['party'])\n",
    "\n",
    "        # Computing and storing TVD\n",
    "        stats = calc_test_statistics(shuffled)\n",
    "        sim_stats.append(stats)\n",
    "        \n",
    "    sim_stats = np.array(sim_stats)\n",
    "    pval = np.mean(sim_stats >= obs_stats)\n",
    "    \n",
    "    pd.Series(sim_stats).plot(kind='hist', density=True, ec='w', bins=20, title=f'p-value: {pval}', label='Simulated Stats')\n",
    "    plt.axvline(x=obs_stats, color='red', linewidth=4, label='Observed Stats')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "    return pval, sim_stats\n",
    "\n",
    "pval, sim_stats = permutation_test(combined)\n",
    "sim_stats[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4204057417960114, 4.691782988465429)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_stats.min(), sim_stats.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>disclosure_year</th>\n",
       "      <th>disclosure_date</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>owner</th>\n",
       "      <th>ticker</th>\n",
       "      <th>asset_description</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>representative</th>\n",
       "      <th>district</th>\n",
       "      <th>ptr_link</th>\n",
       "      <th>cap_gains_over_200_usd</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10586</th>\n",
       "      <td>2021</td>\n",
       "      <td>2021-08-26</td>\n",
       "      <td>2012-06-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BLFSD</td>\n",
       "      <td>BioLife Solutions Inc</td>\n",
       "      <td>purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>Tom Malinowski</td>\n",
       "      <td>NJ07</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "      <td>tom</td>\n",
       "      <td>malinowski</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11456</th>\n",
       "      <td>2022</td>\n",
       "      <td>2022-03-03</td>\n",
       "      <td>2017-09-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SUP</td>\n",
       "      <td>Superior Industries International Inc Common S...</td>\n",
       "      <td>purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>Thomas Suozzi</td>\n",
       "      <td>NY03</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "      <td>thomas</td>\n",
       "      <td>suozzi</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11437</th>\n",
       "      <td>2022</td>\n",
       "      <td>2022-03-03</td>\n",
       "      <td>2017-12-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CAT</td>\n",
       "      <td>Caterpillar Inc</td>\n",
       "      <td>purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>Thomas Suozzi</td>\n",
       "      <td>NY03</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "      <td>thomas</td>\n",
       "      <td>suozzi</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11436</th>\n",
       "      <td>2022</td>\n",
       "      <td>2022-03-03</td>\n",
       "      <td>2018-04-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BA</td>\n",
       "      <td>Boeing Company</td>\n",
       "      <td>purchase</td>\n",
       "      <td>$15,001 - $50,000</td>\n",
       "      <td>Thomas Suozzi</td>\n",
       "      <td>NY03</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "      <td>thomas</td>\n",
       "      <td>suozzi</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11442</th>\n",
       "      <td>2022</td>\n",
       "      <td>2022-03-03</td>\n",
       "      <td>2018-04-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>Control4 Corporation</td>\n",
       "      <td>purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>Thomas Suozzi</td>\n",
       "      <td>NY03</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "      <td>thomas</td>\n",
       "      <td>suozzi</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11453</th>\n",
       "      <td>2022</td>\n",
       "      <td>2022-03-03</td>\n",
       "      <td>2018-05-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GE</td>\n",
       "      <td>General Electric Company</td>\n",
       "      <td>sale_full</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>Thomas Suozzi</td>\n",
       "      <td>NY03</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "      <td>thomas</td>\n",
       "      <td>suozzi</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11443</th>\n",
       "      <td>2022</td>\n",
       "      <td>2022-03-03</td>\n",
       "      <td>2018-06-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CRTL</td>\n",
       "      <td>Control4 Corporation</td>\n",
       "      <td>purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>Thomas Suozzi</td>\n",
       "      <td>NY03</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "      <td>thomas</td>\n",
       "      <td>suozzi</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11455</th>\n",
       "      <td>2022</td>\n",
       "      <td>2022-03-03</td>\n",
       "      <td>2018-06-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IBM</td>\n",
       "      <td>International Business Machines Corporation</td>\n",
       "      <td>sale_full</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>Thomas Suozzi</td>\n",
       "      <td>NY03</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "      <td>thomas</td>\n",
       "      <td>suozzi</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11457</th>\n",
       "      <td>2022</td>\n",
       "      <td>2022-03-03</td>\n",
       "      <td>2018-06-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VZ</td>\n",
       "      <td>Verizon Communications Inc</td>\n",
       "      <td>purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>Thomas Suozzi</td>\n",
       "      <td>NY03</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "      <td>thomas</td>\n",
       "      <td>suozzi</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4285</th>\n",
       "      <td>2021</td>\n",
       "      <td>2021-09-28</td>\n",
       "      <td>2018-09-08</td>\n",
       "      <td>dependent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iSelect Fund B St. Louis Inc</td>\n",
       "      <td>purchase</td>\n",
       "      <td>$1,001 - $15,000</td>\n",
       "      <td>Roger  Marshall</td>\n",
       "      <td>KS01</td>\n",
       "      <td>https://disclosures-clerk.house.gov/public_dis...</td>\n",
       "      <td>False</td>\n",
       "      <td>roger</td>\n",
       "      <td>marshall</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       disclosure_year disclosure_date transaction_date      owner ticker  \\\n",
       "10586             2021      2021-08-26       2012-06-19        NaN  BLFSD   \n",
       "11456             2022      2022-03-03       2017-09-05        NaN    SUP   \n",
       "11437             2022      2022-03-03       2017-12-06        NaN    CAT   \n",
       "11436             2022      2022-03-03       2018-04-17        NaN     BA   \n",
       "11442             2022      2022-03-03       2018-04-30        NaN   CTRL   \n",
       "11453             2022      2022-03-03       2018-05-08        NaN     GE   \n",
       "11443             2022      2022-03-03       2018-06-27        NaN   CRTL   \n",
       "11455             2022      2022-03-03       2018-06-27        NaN    IBM   \n",
       "11457             2022      2022-03-03       2018-06-27        NaN     VZ   \n",
       "4285              2021      2021-09-28       2018-09-08  dependent    NaN   \n",
       "\n",
       "                                       asset_description       type  \\\n",
       "10586                              BioLife Solutions Inc   purchase   \n",
       "11456  Superior Industries International Inc Common S...   purchase   \n",
       "11437                                    Caterpillar Inc   purchase   \n",
       "11436                                     Boeing Company   purchase   \n",
       "11442                               Control4 Corporation   purchase   \n",
       "11453                           General Electric Company  sale_full   \n",
       "11443                               Control4 Corporation   purchase   \n",
       "11455        International Business Machines Corporation  sale_full   \n",
       "11457                         Verizon Communications Inc   purchase   \n",
       "4285                        iSelect Fund B St. Louis Inc   purchase   \n",
       "\n",
       "                  amount   representative district  \\\n",
       "10586   $1,001 - $15,000   Tom Malinowski     NJ07   \n",
       "11456   $1,001 - $15,000    Thomas Suozzi     NY03   \n",
       "11437   $1,001 - $15,000    Thomas Suozzi     NY03   \n",
       "11436  $15,001 - $50,000    Thomas Suozzi     NY03   \n",
       "11442   $1,001 - $15,000    Thomas Suozzi     NY03   \n",
       "11453   $1,001 - $15,000    Thomas Suozzi     NY03   \n",
       "11443   $1,001 - $15,000    Thomas Suozzi     NY03   \n",
       "11455   $1,001 - $15,000    Thomas Suozzi     NY03   \n",
       "11457   $1,001 - $15,000    Thomas Suozzi     NY03   \n",
       "4285    $1,001 - $15,000  Roger  Marshall     KS01   \n",
       "\n",
       "                                                ptr_link  \\\n",
       "10586  https://disclosures-clerk.house.gov/public_dis...   \n",
       "11456  https://disclosures-clerk.house.gov/public_dis...   \n",
       "11437  https://disclosures-clerk.house.gov/public_dis...   \n",
       "11436  https://disclosures-clerk.house.gov/public_dis...   \n",
       "11442  https://disclosures-clerk.house.gov/public_dis...   \n",
       "11453  https://disclosures-clerk.house.gov/public_dis...   \n",
       "11443  https://disclosures-clerk.house.gov/public_dis...   \n",
       "11455  https://disclosures-clerk.house.gov/public_dis...   \n",
       "11457  https://disclosures-clerk.house.gov/public_dis...   \n",
       "4285   https://disclosures-clerk.house.gov/public_dis...   \n",
       "\n",
       "       cap_gains_over_200_usd first_name   last_name       party  \n",
       "10586                   False        tom  malinowski    Democrat  \n",
       "11456                   False     thomas      suozzi    Democrat  \n",
       "11437                   False     thomas      suozzi    Democrat  \n",
       "11436                   False     thomas      suozzi    Democrat  \n",
       "11442                   False     thomas      suozzi    Democrat  \n",
       "11453                   False     thomas      suozzi    Democrat  \n",
       "11443                   False     thomas      suozzi    Democrat  \n",
       "11455                   False     thomas      suozzi    Democrat  \n",
       "11457                   False     thomas      suozzi    Democrat  \n",
       "4285                    False      roger    marshall  Republican  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.sort_values('transaction_date').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>3079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>3059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>3358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>3021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>3075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count\n",
       "weekday         \n",
       "Friday      3079\n",
       "Monday      3059\n",
       "Saturday      48\n",
       "Sunday        27\n",
       "Thursday    3358\n",
       "Tuesday     3021\n",
       "Wednesday   3075"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def to_weekday(x):\n",
    "    day = x.weekday()\n",
    "    if day == 0:\n",
    "        return 'Monday'\n",
    "    elif day == 1:\n",
    "        return 'Tuesday'\n",
    "    elif day == 2:\n",
    "        return 'Wednesday'\n",
    "    elif day == 3:\n",
    "        return 'Thursday'\n",
    "    elif day == 4:\n",
    "        return 'Friday'\n",
    "    elif day == 5:\n",
    "        return 'Saturday'\n",
    "    else:\n",
    "        return 'Sunday'\n",
    "\n",
    "df = combined.assign(weekday=combined['transaction_date'].apply(to_weekday))\n",
    "df.groupby('weekday').count().rename(columns={'transaction_date': 'count'})[['count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>1561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>2110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>2081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>1438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>1006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>1485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>1056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>1131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count\n",
       "month       \n",
       "1.0     1561\n",
       "2.0     2110\n",
       "3.0     2081\n",
       "4.0     1438\n",
       "5.0     1006\n",
       "6.0     1485\n",
       "7.0      971\n",
       "8.0      972\n",
       "9.0     1056\n",
       "10.0     856\n",
       "11.0    1131\n",
       "12.0    1000"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = combined.assign(month=combined['transaction_date'].dt.month)\n",
    "df.groupby('month').count().rename(columns={'transaction_date': 'count'})[['count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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