linear_regression_by_year_all_depts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>movie_id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>percent_female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>862</td>\n",
       "      <td>1995-10-30</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8844</td>\n",
       "      <td>1995-12-15</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15602</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31357</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11862</td>\n",
       "      <td>1995-02-10</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30672</th>\n",
       "      <td>390959</td>\n",
       "      <td>2000-10-22</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30673</th>\n",
       "      <td>289923</td>\n",
       "      <td>2000-10-03</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30674</th>\n",
       "      <td>222848</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30675</th>\n",
       "      <td>30840</td>\n",
       "      <td>1991-05-13</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30676</th>\n",
       "      <td>67758</td>\n",
       "      <td>2003-08-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30677 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movie_id release_date  percent_female\n",
       "0           862   1995-10-30        0.181818\n",
       "1          8844   1995-12-15        0.000000\n",
       "2         15602   1995-12-22        0.000000\n",
       "3         31357   1995-12-22        0.333333\n",
       "4         11862   1995-02-10        0.500000\n",
       "...         ...          ...             ...\n",
       "30672    390959   2000-10-22        0.250000\n",
       "30673    289923   2000-10-03        0.000000\n",
       "30674    222848   1995-01-01        0.000000\n",
       "30675     30840   1991-05-13        0.500000\n",
       "30676     67758   2003-08-01        0.000000\n",
       "\n",
       "[30677 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Resources/percent_all.csv\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>movie_id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>percent_female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17904</th>\n",
       "      <td>43387</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18314</th>\n",
       "      <td>109364</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14064</th>\n",
       "      <td>83015</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20273</th>\n",
       "      <td>43395</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>43391</td>\n",
       "      <td>1950-01-04</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27614</th>\n",
       "      <td>374471</td>\n",
       "      <td>2017-09-14</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877</th>\n",
       "      <td>300665</td>\n",
       "      <td>2017-09-14</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30312</th>\n",
       "      <td>439502</td>\n",
       "      <td>2017-09-22</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28941</th>\n",
       "      <td>398818</td>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30090</th>\n",
       "      <td>341689</td>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30677 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movie_id release_date  percent_female\n",
       "17904     43387   1950-01-01        0.000000\n",
       "18314    109364   1950-01-01        0.000000\n",
       "14064     83015   1950-01-01        0.000000\n",
       "20273     43395   1950-01-01        0.000000\n",
       "2195      43391   1950-01-04        0.000000\n",
       "...         ...          ...             ...\n",
       "27614    374471   2017-09-14        0.333333\n",
       "26877    300665   2017-09-14        0.111111\n",
       "30312    439502   2017-09-22        0.000000\n",
       "28941    398818   2017-10-27        0.125000\n",
       "30090    341689   2017-12-27        0.200000\n",
       "\n",
       "[30677 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort data by release_date\n",
    "df=df.sort_values(\"release_date\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change release_date column from string to datetime\n",
    "df['release_date'] = pd.to_datetime(df['release_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                   int64\n",
       "release_date      datetime64[ns]\n",
       "percent_female           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# strip year from datetime column and make into new column\n",
    "df['year'] = df['release_date'].dt.year "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>movie_id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>percent_female</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17904</th>\n",
       "      <td>43387</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18314</th>\n",
       "      <td>109364</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14064</th>\n",
       "      <td>83015</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20273</th>\n",
       "      <td>43395</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>43391</td>\n",
       "      <td>1950-01-04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27614</th>\n",
       "      <td>374471</td>\n",
       "      <td>2017-09-14</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877</th>\n",
       "      <td>300665</td>\n",
       "      <td>2017-09-14</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30312</th>\n",
       "      <td>439502</td>\n",
       "      <td>2017-09-22</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28941</th>\n",
       "      <td>398818</td>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30090</th>\n",
       "      <td>341689</td>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30677 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movie_id release_date  percent_female  year\n",
       "17904     43387   1950-01-01        0.000000  1950\n",
       "18314    109364   1950-01-01        0.000000  1950\n",
       "14064     83015   1950-01-01        0.000000  1950\n",
       "20273     43395   1950-01-01        0.000000  1950\n",
       "2195      43391   1950-01-04        0.000000  1950\n",
       "...         ...          ...             ...   ...\n",
       "27614    374471   2017-09-14        0.333333  2017\n",
       "26877    300665   2017-09-14        0.111111  2017\n",
       "30312    439502   2017-09-22        0.000000  2017\n",
       "28941    398818   2017-10-27        0.125000  2017\n",
       "30090    341689   2017-12-27        0.200000  2017\n",
       "\n",
       "[30677 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                   int64\n",
       "release_date      datetime64[ns]\n",
       "percent_female           float64\n",
       "year                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1958     198\n",
       "1959     202\n",
       "1960     195\n",
       "1961     178\n",
       "1962     203\n",
       "1963     201\n",
       "1964     221\n",
       "1965     221\n",
       "1966     261\n",
       "1967     242\n",
       "1968     269\n",
       "1969     252\n",
       "1970     285\n",
       "1971     303\n",
       "1972     314\n",
       "1973     280\n",
       "1974     276\n",
       "1975     263\n",
       "1976     265\n",
       "1977     264\n",
       "1978     253\n",
       "1979     268\n",
       "1980     299\n",
       "1981     294\n",
       "1982     304\n",
       "1983     292\n",
       "1984     303\n",
       "1985     302\n",
       "1986     321\n",
       "1987     369\n",
       "1988     370\n",
       "1989     364\n",
       "1990     349\n",
       "1991     351\n",
       "1992     371\n",
       "1993     421\n",
       "1994     439\n",
       "1995     491\n",
       "1996     526\n",
       "1997     522\n",
       "1998     556\n",
       "1999     572\n",
       "2000     605\n",
       "2001     657\n",
       "2002     682\n",
       "2003     647\n",
       "2004     703\n",
       "2005     818\n",
       "2006     907\n",
       "2007     911\n",
       "2008    1032\n",
       "2009    1085\n",
       "2010    1008\n",
       "2011    1046\n",
       "2012    1058\n",
       "2013    1116\n",
       "2014    1183\n",
       "2015    1135\n",
       "2016    1018\n",
       "2017     367\n",
       "Name: movie_id, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check how many movies by year\n",
    "count_by_year=df.groupby([\"year\"])[\"movie_id\"].count()\n",
    "count_by_year.tail(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      68.000000\n",
       "mean      451.132353\n",
       "std       301.528044\n",
       "min       172.000000\n",
       "25%       249.500000\n",
       "50%       303.500000\n",
       "75%       580.250000\n",
       "max      1183.000000\n",
       "Name: movie_id, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check mean and standard deviation \n",
    "count_by_year.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group by year and average female_percent for all movies that year\n",
    "count_df = df.groupby(['year'])['percent_female'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1950    0.027519\n",
       "1951    0.032495\n",
       "1952    0.034082\n",
       "1953    0.033427\n",
       "1954    0.025205\n",
       "          ...   \n",
       "2013    0.210793\n",
       "2014    0.216478\n",
       "2015    0.212915\n",
       "2016    0.205907\n",
       "2017    0.252581\n",
       "Name: percent_female, Length: 68, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# turn series to dataframe \n",
    "dataframe = count_df.to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DONT RERUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change year from axis to column \n",
    "dataframe = dataframe.rename_axis('year').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>percent_female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1950</td>\n",
       "      <td>0.027519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1951</td>\n",
       "      <td>0.032495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1952</td>\n",
       "      <td>0.034082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1953</td>\n",
       "      <td>0.033427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954</td>\n",
       "      <td>0.025205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2013</td>\n",
       "      <td>0.210793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2014</td>\n",
       "      <td>0.216478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2015</td>\n",
       "      <td>0.212915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2016</td>\n",
       "      <td>0.205907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2017</td>\n",
       "      <td>0.252581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  percent_female\n",
       "0   1950        0.027519\n",
       "1   1951        0.032495\n",
       "2   1952        0.034082\n",
       "3   1953        0.033427\n",
       "4   1954        0.025205\n",
       "..   ...             ...\n",
       "63  2013        0.210793\n",
       "64  2014        0.216478\n",
       "65  2015        0.212915\n",
       "66  2016        0.205907\n",
       "67  2017        0.252581\n",
       "\n",
       "[68 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dataframe.year, dataframe.percent_female)\n",
    "plt.xlabel('Release Date')\n",
    "plt.ylabel('Percentage of Female Staff (All Departments)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataframe.year.values.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1950],\n",
       "       [1951],\n",
       "       [1952],\n",
       "       [1953],\n",
       "       [1954],\n",
       "       [1955],\n",
       "       [1956],\n",
       "       [1957],\n",
       "       [1958],\n",
       "       [1959],\n",
       "       [1960],\n",
       "       [1961],\n",
       "       [1962],\n",
       "       [1963],\n",
       "       [1964],\n",
       "       [1965],\n",
       "       [1966],\n",
       "       [1967],\n",
       "       [1968],\n",
       "       [1969],\n",
       "       [1970],\n",
       "       [1971],\n",
       "       [1972],\n",
       "       [1973],\n",
       "       [1974],\n",
       "       [1975],\n",
       "       [1976],\n",
       "       [1977],\n",
       "       [1978],\n",
       "       [1979],\n",
       "       [1980],\n",
       "       [1981],\n",
       "       [1982],\n",
       "       [1983],\n",
       "       [1984],\n",
       "       [1985],\n",
       "       [1986],\n",
       "       [1987],\n",
       "       [1988],\n",
       "       [1989],\n",
       "       [1990],\n",
       "       [1991],\n",
       "       [1992],\n",
       "       [1993],\n",
       "       [1994],\n",
       "       [1995],\n",
       "       [1996],\n",
       "       [1997],\n",
       "       [1998],\n",
       "       [1999],\n",
       "       [2000],\n",
       "       [2001],\n",
       "       [2002],\n",
       "       [2003],\n",
       "       [2004],\n",
       "       [2005],\n",
       "       [2006],\n",
       "       [2007],\n",
       "       [2008],\n",
       "       [2009],\n",
       "       [2010],\n",
       "       [2011],\n",
       "       [2012],\n",
       "       [2013],\n",
       "       [2014],\n",
       "       [2015],\n",
       "       [2016],\n",
       "       [2017]], dtype=int64)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(68, 1)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.027519\n",
       "1     0.032495\n",
       "2     0.034082\n",
       "3     0.033427\n",
       "4     0.025205\n",
       "        ...   \n",
       "63    0.210793\n",
       "64    0.216478\n",
       "65    0.212915\n",
       "66    0.205907\n",
       "67    0.252581\n",
       "Name: percent_female, Length: 68, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = dataframe.percent_female\n",
    "\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(68,)\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X)\n",
    "print(y_pred.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,3))\n",
    "\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X, y_pred, color='red')\n",
    "plt.xlabel('Release Date')\n",
    "plt.ylabel('Percentage of Female Staff (All Depts)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00326333]\n",
      "-6.370500225963147\n"
     ]
    }
   ],
   "source": [
    "print(model.coef_)\n",
    "print(model.intercept_)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "8460d4379f2db7ca1790c309fa08bdb08e0415783bba4b95e23aac2b72fbbcd2"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}