9.2.1 Analysis of mentioned people (restricted by BirthDate).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "import dateparser\n",
    "%pylab inline\n",
    "#print dateparser.parse(\" 1200\").date()\n",
    "\n",
    "#load about_pages\n",
    "#df=pd.read_csv('de/all_data_bias_labour_gender.csv',sep='\\t', encoding='utf-8',index_col=0)#885\n",
    "#Remove data where there is no male person neither female  simultaneously\n",
    "#df=df[(df.male_person_amount>0)| (df.female_person_amount>0)]#257\n",
    "#print \"There are \",len(df),\"profesions with at least one person\"\n",
    "#There are  257 profesions with at least one person"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_m_w=pd.DataFrame.from_csv(\"de/people_with_birthDate.csv\",encoding=\"utf8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 2, 3, 4, 5, 6, 7]), <a list of 7 Text xticklabel objects>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Bg8EAHx8fVFZW4vTp00hLS8O4ceMwfPjwZo9VYxBt374dS5YswcMPP4wRI0Zg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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbaaf208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=df_m_w.boxplot(column=[\"m_ratio\",\"m_ratio_1960\",\"m_ratio_1940\",\"m_ratio_1900_1940\",\"m_ratio_18-19th\",\n",
    "                         \"m_ratio_17-18th\",\"m_ratio_-17th\"],\n",
    "                 return_type=\"axes\",showfliers=False)\n",
    "plt.xticks(rotation=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4+H/03J1qamphbi5/uJYSEREZGfMH+aU767RKpYJSqQQA7N69G15eXg8UVlys/ofNa0ip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\nAwAAAAhJREFUEcT/ASJgyKu/ZN5lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb499a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_m_w.boxplot(column=list(df_m_w.columns[:14].values),return_type='axes',showfliers=False)\n",
    "plt.xticks(rotation=70)\n",
    "plt.ylim(0,40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "std_2=df_m_w.m_ratio.std()*2\n",
    "mean_m=df_m_w.m_ratio.mean()\n",
    "std_2_=df_m_w.m_ratio_1960.std()*2\n",
    "mean_m_=df_m_w.m_ratio_1960.mean()\n",
    "df1=df_m_w[ abs(df_m_w.m_ratio - mean_m)<std_2][\"m_ratio\"]\n",
    "df2=df_m_w[ abs(df_m_w.m_ratio_1960 - mean_m_)<std_2_][\"m_ratio_1960\"]\n",
    "df_m_w2=pd.concat([df1,df2],axis=1)\n",
    "\n",
    "std_2=df_m_w.m_ratio_1940.std()*2\n",
    "mean_m=df_m_w.m_ratio_1940.mean()\n",
    "df2=df_m_w[ abs(df_m_w.m_ratio_1940 - mean_m)<std_2][\"m_ratio_1940\"]\n",
    "df_m_w2=pd.concat([df_m_w2,df2],axis=1)\n",
    "\n",
    "std_2=df_m_w.m_ratio_1900_1940.std()*2\n",
    "mean_m=df_m_w.m_ratio_1900_1940.mean()\n",
    "df2=df_m_w[ abs(df_m_w.m_ratio_1900_1940 - mean_m)<std_2][\"m_ratio_1900_1940\"]\n",
    "df_m_w2=pd.concat([df_m_w2,df2],axis=1)\n",
    "\n",
    "std_2=df_m_w[\"m_ratio_18-19th\"].std()*2\n",
    "mean_m=df_m_w[\"m_ratio_18-19th\"].mean()\n",
    "df2=df_m_w[ abs(df_m_w[\"m_ratio_18-19th\"] - mean_m)<std_2][\"m_ratio_18-19th\"]\n",
    "df_m_w2=pd.concat([df_m_w2,df2],axis=1)\n",
    "\n",
    "std_2=df_m_w[\"m_ratio_17-18th\"].std()*2\n",
    "mean_m=df_m_w[\"m_ratio_17-18th\"].mean()\n",
    "df2=df_m_w[ abs(df_m_w[\"m_ratio_17-18th\"] - mean_m)<std_2][\"m_ratio_17-18th\"]\n",
    "df_m_w2=pd.concat([df_m_w2,df2],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\matplotlib\\__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "image/png": 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e5kN47NjxAIC0tH4qt4QigffJCk9cApP2udGjx6rckt7NZDIhIcGGhAQbTCaT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x95f81d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "g=sns.violinplot(data=df_m_w2,cut=0,trim=True,inner=\"quartile\",\n",
    "                 palette=[sns.xkcd_rgb[\"light green\"]])\n",
    "plt.xticks(rotation=25)\n",
    "plt.ylim(0,1.02)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "144.0 83.0 2122.0 234.0\n"
     ]
    }
   ],
   "source": [
    "print df_m_w.men_1960.sum(),df_m_w.women_1960.sum(),df_m_w.all_men.sum()-df_m_w.men_1960.sum(),\\\n",
    "df_m_w.all_women.sum()-df_m_w.women_1960.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\matplotlib\\__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "image/png": 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vxfn6+YshXOTGjKkCYFwlnAioUONCr7CJKKmmJhmE4RzMyx+MtnKd9tqUfxjC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa85ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns \n",
    "df_m_w[\"m_ratio_before1960\"]=df_m_w.apply(lambda x: (float(x.all_men)-float(x.men_1960))/\\\n",
    "                                          float(x.all_men-x.men_1960+x.all_women-x.women_1960) \\\n",
    "                                    if float(x.all_men-x.men_1960+x.all_women-x.women_1960)>0 else None, axis = 1)\n",
    "g=sns.violinplot(data=df_m_w[[\"m_ratio_before1960\",\"m_ratio_1960\"]],cut=0,trim=True,inner=\"quartile\",\n",
    "                 palette=[sns.xkcd_rgb[\"light green\"]])\n",
    "plt.ylim(0,1.02)\n",
    "plt.show()\n",
    "\n",
    "std_2=df_m_w[\"m_ratio_before1960\"].std()*2\n",
    "mean_m=df_m_w[\"m_ratio_before1960\"].mean()\n",
    "df2=df_m_w[ abs(df_m_w[\"m_ratio_before1960\"] - mean_m)<std_2][\"m_ratio_before1960\"]\n",
    "df_m_w2=pd.concat([df_m_w2,df2],axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pb3SdIAOcmtIQ4C1fBy7LMkSfBFsnWzzKItKt5OTk0AqI7laMgXskGAwGJCfz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa3ce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.violinplot(data=df_m_w2[[\"m_ratio_before1960\",\"m_ratio_1960\"]],cut=0,trim=True,inner=\"quartile\",\n",
    "                 palette=[sns.xkcd_rgb[\"light green\"]])\n",
    "plt.ylim(0,1.02)\n",
    "plt.ylabel(\"Ratio of mentioned men in an article\")\n",
    "#plt.xlabel(\"\")\n",
    "plt.xticks([0,1],[\"before 1960\",\"after 1960\"])\n",
    "#plt.savefig(\"de/plots/violin_1960.png\",dpi=500, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m_ratio</th>\n",
       "      <th>m_ratio_1960</th>\n",
       "      <th>m_ratio_1940</th>\n",
       "      <th>m_ratio_1900_1940</th>\n",
       "      <th>m_ratio_18-19th</th>\n",
       "      <th>m_ratio_17-18th</th>\n",
       "      <th>m_ratio_before1960</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>237.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>232.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.926208</td>\n",
       "      <td>0.731044</td>\n",
       "      <td>0.866333</td>\n",
       "      <td>0.938492</td>\n",
       "      <td>0.969288</td>\n",
       "      <td>0.997114</td>\n",
       "      <td>0.937619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.136868</td>\n",
       "      <td>0.387094</td>\n",
       "      <td>0.202517</td>\n",
       "      <td>0.136935</td>\n",
       "      <td>0.103555</td>\n",
       "      <td>0.022907</td>\n",
       "      <td>0.127308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.169231</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.915663</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.943182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          m_ratio  m_ratio_1960  m_ratio_1940  m_ratio_1900_1940  \\\n",
       "count  237.000000     73.000000    128.000000         129.000000   \n",
       "mean     0.926208      0.731044      0.866333           0.938492   \n",
       "std      0.136868      0.387094      0.202517           0.136935   \n",
       "min      0.500000      0.000000      0.169231           0.500000   \n",
       "25%      0.915663      0.500000      0.750000           1.000000   \n",
       "50%      1.000000      1.000000      1.000000           1.000000   \n",
       "75%      1.000000      1.000000      1.000000           1.000000   \n",
       "max      1.000000      1.000000      1.000000           1.000000   \n",
       "\n",
       "       m_ratio_18-19th  m_ratio_17-18th  m_ratio_before1960  \n",
       "count       129.000000        63.000000          232.000000  \n",
       "mean          0.969288         0.997114            0.937619  \n",
       "std           0.103555         0.022907            0.127308  \n",
       "min           0.500000         0.818182            0.500000  \n",
       "25%           1.000000         1.000000            0.943182  \n",
       "50%           1.000000         1.000000            1.000000  \n",
       "75%           1.000000         1.000000            1.000000  \n",
       "max           1.000000         1.000000            1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_m_w2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_m_w2.m_ratio_1960.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71.2328767123\n",
      "6.84931506849\n",
      "21.9178082192\n"
     ]
    }
   ],
   "source": [
    "df_w3=df_m_w2[~df_m_w2.m_ratio_1960.isnull()]#73\n",
    "print 100*float(len(df_w3[df_w3.m_ratio_1960>0.5]))/len(df_w3)#52\n",
    "print 100*float(len(df_w3[df_w3.m_ratio_1960==0.5]))/len(df_w3)#5\n",
    "print 100*float(len(df_w3[df_w3.m_ratio_1960<0.5]))/len(df_w3)#16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71.2328767123\n",
      "6.84931506849\n",
      "21.9178082192\n"
     ]
    }
   ],
   "source": [
    "df_w3=df_m_w2[~df_m_w2.m_ratio_1960.isnull()]#73\n",
    "print 100*float(len(df_w3[df_w3.m_ratio_1960>0.55]))/len(df_w3)#\n",
    "print 100*float(len(df_w3[(df_w3.m_ratio_1960>=0.45)&(df_w3.m_ratio_1960<=0.55)]))/len(df_w3)#\n",
    "print 100*float(len(df_w3[df_w3.m_ratio_1960<0.45]))/len(df_w3)#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>men_1960</th>\n",
       "      <th>women_1960</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Buchhändler</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Butler</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dompteur</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kameramann</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Videojournalist</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 men_1960  women_1960\n",
       "Buchhändler             2           2\n",
       "Butler                  1           1\n",
       "Dompteur                1           1\n",
       "Kameramann              2           2\n",
       "Videojournalist         1           1"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_m_w.loc[list(df_w3[df_w3.m_ratio_1960==0.5].index),[\"men_1960\",\"women_1960\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>men_1960</th>\n",
       "      <th>women_1960</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Botin</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chefredakteur</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datenschutzbeauftragter</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diakon</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diener</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dirigent</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hilfspolizei</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Journalist</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kürschner</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mediaberater</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <td>9</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moderator (Beruf)</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prior</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rabbiner</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stuntman</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tätowierer</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         men_1960  women_1960\n",
       "Botin                           0           1\n",
       "Chefredakteur                   0           1\n",
       "Datenschutzbeauftragter         0           1\n",
       "Diakon                          0           1\n",
       "Diener                          0           1\n",
       "Dirigent                        0           2\n",
       "Hilfspolizei                    0           1\n",
       "Journalist                      2           3\n",
       "Kürschner                       0           1\n",
       "Mediaberater                    0           1\n",
       "Model                           9          36\n",
       "Moderator (Beruf)               0           1\n",
       "Prior                           0           1\n",
       "Rabbiner                        1           5\n",
       "Stuntman                        1           2\n",
       "Tätowierer                      0           3"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_m_w.loc[list(df_w3[df_w3.m_ratio_1960<0.5].index),[\"men_1960\",\"women_1960\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xEWTV19ezfv1l8PV/IBXNHHnBjwqTjmeI7klCIMBll11FMFg6bc+VLhKppa2tg77dPViW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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdd42cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66.6666666667\n",
      "15.1515151515\n",
      "18.1818181818\n"
     ]
    }
   ],
   "source": [
    "#use only professions with at least 2 mentioned persons\n",
    "df_new=df_m_w[df_m_w.men_1960+df_m_w.women_1960>1]\n",
    "g=sns.violinplot(data=df_new[[\"m_ratio_before1960\",\"m_ratio_1960\"]],cut=0,trim=True,inner=\"quartile\",\n",
    "                 palette=[sns.xkcd_rgb[\"light green\"]])\n",
    "plt.ylabel(\"Ratio of mentioned men in an article\")\n",
    "#plt.xlabel(\"\")\n",
    "plt.xticks([0,1],[\"before 1960\",\"after 1960\"])\n",
    "plt.show()\n",
    "print 100*float(len(df_new[df_new.m_ratio_1960>0.5]))/len(df_new)#n=22\n",
    "print 100*float(len(df_new[df_new.m_ratio_1960==0.5]))/len(df_new)#n=5\n",
    "print 100*float(len(df_new[df_new.m_ratio_1960<0.5]))/len(df_new)#n=6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>all_men</th>\n",
       "      <th>men_-17th</th>\n",
       "      <th>men_17-18th</th>\n",
       "      <th>men_18-19th</th>\n",
       "      <th>men_1900_1940</th>\n",
       "      <th>men_1940</th>\n",
       "      <th>men_1960</th>\n",
       "      <th>all_women</th>\n",
       "      <th>women_-17th</th>\n",
       "      <th>women_17-18th</th>\n",
       "      <th>...</th>\n",
       "      <th>women_1960</th>\n",
       "      <th>m_ratio</th>\n",
       "      <th>m_ratio_1960</th>\n",
       "      <th>m_ratio_1940</th>\n",
       "      <th>m_ratio_1900_1940</th>\n",
       "      <th>m_ratio_18-19th</th>\n",
       "      <th>m_ratio_17-18th</th>\n",
       "      <th>m_ratio_-17th</th>\n",
       "      <th>m_ratio_before1960</th>\n",
       "      <th>m_ratio_before1940</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dirigent</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Journalist</th>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>36</td>\n",
       "      <td>0.227848</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.169231</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rabbiner</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stuntman</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tätowierer</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            all_men  men_-17th  men_17-18th  men_18-19th  men_1900_1940  \\\n",
       "Dirigent          5          4            0            1              0   \n",
       "Journalist       20          2            1            2              5   \n",
       "Model            18          2            0            2              3   \n",
       "Rabbiner         10          3            1            2              2   \n",
       "Stuntman         11          0            0            4              4   \n",
       "Tätowierer       14          1            0            1              8   \n",
       "\n",
       "            men_1940  men_1960  all_women  women_-17th  women_17-18th  \\\n",
       "Dirigent           0         0          3            0              0   \n",
       "Journalist        10         2         12            0              0   \n",
       "Model             11         9         61            0              0   \n",
       "Rabbiner           2         1          9            1              0   \n",
       "Stuntman           3         1          2            0              0   \n",
       "Tätowierer         4         0          6            0              0   \n",
       "\n",
       "                   ...          women_1960   m_ratio  m_ratio_1960  \\\n",
       "Dirigent           ...                   2  0.625000      0.000000   \n",
       "Journalist         ...                   3  0.625000      0.400000   \n",
       "Model              ...                  36  0.227848      0.200000   \n",
       "Rabbiner           ...                   5  0.526316      0.166667   \n",
       "Stuntman           ...                   2  0.846154      0.333333   \n",
       "Tätowierer         ...                   3  0.700000      0.000000   \n",
       "\n",
       "            m_ratio_1940  m_ratio_1900_1940  m_ratio_18-19th  m_ratio_17-18th  \\\n",
       "Dirigent        0.000000                NaN              0.5              NaN   \n",
       "Journalist      0.526316           0.625000              1.0                1   \n",
       "Model           0.169231           0.300000              1.0              NaN   \n",
       "Rabbiner        0.222222           0.666667              1.0                1   \n",
       "Stuntman        0.600000           1.000000              1.0              NaN   \n",
       "Tätowierer      0.444444           0.888889              1.0              NaN   \n",
       "\n",
       "            m_ratio_-17th  m_ratio_before1960  m_ratio_before1940  \n",
       "Dirigent             1.00            0.833333            0.833333  \n",
       "Journalist           1.00            0.666667            0.769231  \n",
       "Model                1.00            0.264706            0.500000  \n",
       "Rabbiner             0.75            0.692308            0.800000  \n",
       "Stuntman              NaN            1.000000            1.000000  \n",
       "Tätowierer           1.00            0.823529            0.909091  \n",
       "\n",
       "[6 rows x 23 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new[df_new.m_ratio_1960<0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MWW RankSum P for before and after 1960 distributions = 6.90110150336e-37 z_stat= -12.6879004586\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=11269.0, pvalue=1.2560467958190435e-39)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#chech whether distributions are significantly different\n",
    "from scipy import stats  \n",
    "\n",
    "m_before1960=df_m_w[\"m_ratio_before1960\"].values\n",
    "m_after1960=df_m_w[\"m_ratio_1960\"].values\n",
    "\n",
    "z_stat, p_val = stats.ranksums(m_before1960, m_after1960)   \n",
    "print \"MWW RankSum P for before and after 1960 distributions =\", p_val , \"z_stat=\",z_stat\n",
    "stats.mannwhitneyu(m_before1960, m_after1960)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MWW RankSum P for before and after 1960 distributions = 3.45413615653e-40 z_stat= -13.2700362508\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=10306.0, pvalue=5.4289963993534552e-43)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats  \n",
    "z_stat, p_val = stats.ranksums(df_m_w.m_ratio.values, m_after1960)   \n",
    "print \"MWW RankSum P for before and after 1960 distributions =\", p_val , \"z_stat=\",z_stat\n",
    "stats.mannwhitneyu(df_m_w.m_ratio.values, m_after1960) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "512.0 177.0 1754.0 140.0\n"
     ]
    }
   ],
   "source": [
    "print df_m_w.men_1940.sum(),df_m_w.women_1940.sum(),df_m_w.all_men.sum()-df_m_w.men_1940.sum(),\\\n",
    "df_m_w.all_women.sum()-df_m_w.women_1940.sum()\n",
    "#print df_m_w.m_ratio_1940"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9NdhFnd8YxjSILBtQXl6OUE9qlbF+PMOYKD49GMOezHdV669RWsowzmcMYxpS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\ncuz/BwAWF9P7p3wcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb49ec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UH+q0srHNTR34P5dQ1Ce+ahQRdUpMpd95YyXYUlYfI6KRkSQJRqMx5m5qdlHr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\nCaFdFErOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbc29b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_m_w[\"m_ratio_before1940\"]=df_m_w.apply(lambda x: (float(x.all_men)-float(x.men_1940))/\\\n",
    "                                          float(x.all_men-x.men_1940+x.all_women-x.women_1940) \\\n",
    "                                    if float(x.all_men-x.men_1940+x.all_women-x.women_1940)>0 else None, axis = 1)\n",
    "g=sns.violinplot(data=df_m_w[[\"m_ratio_before1940\",\"m_ratio_1940\"]],cut=0,trim=True,inner=\"quartile\",\n",
    "                 palette=[sns.xkcd_rgb[\"light green\"]])\n",
    "plt.ylim(0,1.02)\n",
    "plt.show()\n",
    "\n",
    "std_2=df_m_w[\"m_ratio_before1940\"].std()*2\n",
    "mean_m=df_m_w[\"m_ratio_before1940\"].mean()\n",
    "df2=df_m_w[ abs(df_m_w[\"m_ratio_before1940\"] - mean_m)<std_2][\"m_ratio_before1940\"]\n",
    "df_m_w2=pd.concat([df_m_w2,df2],axis=1)\n",
    "\n",
    "g=sns.violinplot(data=df_m_w2[[\"m_ratio_before1940\",\"m_ratio_1940\"]],cut=0,trim=True,inner=\"quartile\",\n",
    "                 palette=[sns.xkcd_rgb[\"light green\"]])\n",
    "plt.ylim(0,1.02)\n",
    "plt.xticks([0,1],[\"before 1940\",\"after 1940\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "result_df = pd.concat([df_m_w, df], axis=1, join_axes=[df_m_w.index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jxgz5+vrKz89PCxYsUFZWlieyAQAAC5mWAC+v05s4HA5JUnl5uesxAABQf5meGNi3b1+N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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe2110b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=result_df.boxplot(by='Bias',column=\"m_ratio_before1940\", return_type=\"axes\")\n",
    "plt.ylabel(\"ratio of male persons in article\")\n",
    "plt.title(\"\")\n",
    "plt.show()\n",
    "#plt.xticks(range(1,4),[\"female\\nn=2\",\"male\\nn=234\",\"neutral\\nn=21\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bias\n",
      "female       2\n",
      "male       231\n",
      "neutral     21\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m_ratio</th>\n",
       "      <th>all_men</th>\n",
       "      <th>all_women</th>\n",
       "      <th>men_1940</th>\n",
       "      <th>women_1940</th>\n",
       "      <th>m_ratio_1940</th>\n",
       "      <th>m_ratio_1960</th>\n",
       "      <th>m_ratio_before1940</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gleichstellungsbeauftragte</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hebamme</th>\n",
       "      <td>0.785714</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             m_ratio  all_men  all_women  men_1940  \\\n",
       "Gleichstellungsbeauftragte  0.000000        0          1         0   \n",
       "Hebamme                     0.785714       11          3         2   \n",
       "\n",
       "                            women_1940  m_ratio_1940  m_ratio_1960  \\\n",
       "Gleichstellungsbeauftragte           1             0           NaN   \n",
       "Hebamme                              0             1             1   \n",
       "\n",
       "                            m_ratio_before1940  \n",
       "Gleichstellungsbeauftragte                 NaN  \n",
       "Hebamme                                   0.75  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print result_df.groupby(\"Bias\").size()\n",
    "result_df[result_df.Bias==\"female\"][[\"m_ratio\",\"all_men\",\"all_women\",\"men_1940\",\"women_1940\",\n",
    "                                     \"m_ratio_1940\",\"m_ratio_1960\",\"m_ratio_before1940\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "13\n",
      "127\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m_ratio</th>\n",
       "      <th>all_men</th>\n",
       "      <th>all_women</th>\n",
       "      <th>men_1940</th>\n",
       "      <th>women_1940</th>\n",
       "      <th>m_ratio_1940</th>\n",
       "      <th>m_ratio_1960</th>\n",
       "      <th>m_ratio_before1940</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apotheker</th>\n",
       "      <td>0.952381</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bote</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botin</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bundeskanzlerin</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diakon</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diakonin</th>\n",
       "      <td>0.818182</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fachkraft für Automatenservice</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Filmgeschäftsführung</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hilfspolizei</th>\n",
       "      <td>0.888889</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hostess</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <td>0.227848</td>\n",
       "      <td>18</td>\n",
       "      <td>61</td>\n",
       "      <td>11</td>\n",
       "      <td>54</td>\n",
       "      <td>0.169231</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mönchtum</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taxi</th>\n",
       "      <td>0.823529</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 m_ratio  all_men  all_women  men_1940  \\\n",
       "Apotheker                       0.952381       20          1         1   \n",
       "Bote                            1.000000        1          0         1   \n",
       "Botin                           0.500000        1          1         0   \n",
       "Bundeskanzlerin                 0.000000        0          3         0   \n",
       "Diakon                          0.833333        5          1         1   \n",
       "Diakonin                        0.818182        9          2         2   \n",
       "Fachkraft für Automatenservice  0.500000        1          1         1   \n",
       "Filmgeschäftsführung            1.000000        1          0         1   \n",
       "Hilfspolizei                    0.888889        8          1         4   \n",
       "Hostess                         0.500000        1          1         1   \n",
       "Model                           0.227848       18         61        11   \n",
       "Mönchtum                        1.000000       21          0         1   \n",
       "Taxi                            0.823529       14          3         8   \n",
       "\n",
       "                                women_1940  m_ratio_1940  m_ratio_1960  \\\n",
       "Apotheker                                0      1.000000           NaN   \n",
       "Bote                                     0      1.000000           NaN   \n",
       "Botin                                    1      0.000000          0.00   \n",
       "Bundeskanzlerin                          3      0.000000           NaN   \n",
       "Diakon                                   1      0.500000          0.00   \n",
       "Diakonin                                 0      1.000000           NaN   \n",
       "Fachkraft für Automatenservice           1      0.500000          1.00   \n",
       "Filmgeschäftsführung                     0      1.000000           NaN   \n",
       "Hilfspolizei                             1      0.800000          0.00   \n",
       "Hostess                                  1      0.500000           NaN   \n",
       "Model                                   54      0.169231          0.20   \n",
       "Mönchtum                                 0      1.000000          1.00   \n",
       "Taxi                                     3      0.727273          0.75   \n",
       "\n",
       "                                m_ratio_before1940  \n",
       "Apotheker                                 0.950000  \n",
       "Bote                                           NaN  \n",
       "Botin                                     1.000000  \n",
       "Bundeskanzlerin                                NaN  \n",
       "Diakon                                    1.000000  \n",
       "Diakonin                                  0.777778  \n",
       "Fachkraft für Automatenservice                 NaN  \n",
       "Filmgeschäftsführung                           NaN  \n",
       "Hilfspolizei                              1.000000  \n",
       "Hostess                                        NaN  \n",
       "Model                                     0.500000  \n",
       "Mönchtum                                  1.000000  \n",
       "Taxi                                      1.000000  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print len(result_df[(result_df.Bias==\"female\")&(~ result_df.m_ratio_1940.isnull())])\n",
    "print len(result_df[(result_df.Bias==\"neutral\")&(~ result_df.m_ratio_1940.isnull())])\n",
    "print len(result_df[(result_df.Bias==\"male\")&(~ result_df.m_ratio_1940.isnull())])\n",
    "result_df[(result_df.Bias==\"neutral\")&(~ result_df.m_ratio_1940.isnull())][[\"m_ratio\",\"all_men\",\"all_women\",\"men_1940\",\"women_1940\",\n",
    "                                     \"m_ratio_1940\",\"m_ratio_1960\",\"m_ratio_before1940\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lgJUrV+qFF15QSUmJli9fbnZNAADAA1yGgNmzZ2vDhg1au3atKioq9MEHH+jZZ5/1RG0A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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b83390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=result_df.boxplot(by='Bias',column=\"m_ratio_1940\", return_type=\"axes\")\n",
    "plt.ylabel(\"ratio of male persons in article\")\n",
    "plt.title(\"\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P8ZK0Xva9/Ty6W/ZhxoyzcNxxk8xujiN5PF4A9u9yTHete01uCQ1FarKb9AVh7P2FNdcY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\niIhsjEFORERkY/8fiQFgqIyy3rIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e09358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.violinplot(data=result_df,y=\"m_ratio_1940\",x=\"Bias\",cut=0,trim=True,inner=\"quartile\",order=[\"male\",\"neutral\",\"female\"],\n",
    "                 palette=[sns.xkcd_rgb[\"sky blue\"], sns.xkcd_rgb[\"grey\"],sns.xkcd_rgb[\"light pink\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dg9xA4uLiIXtbmbUeGFpnkFPb1EAOtiN3u9yQJAnp6T0iWRYRtYJBbiBxcXEQ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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf176940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.violinplot(data=result_df,y=\"m_ratio_before1940\",x=\"Bias\",cut=0,trim=True,inner=\"quartile\",order=[\"male\",\"neutral\",\"female\"],\n",
    "                 palette=[sns.xkcd_rgb[\"sky blue\"], sns.xkcd_rgb[\"grey\"],sns.xkcd_rgb[\"light pink\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1.02)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TlZAkCV1dPUiO6FUtaxrrVxEORyh5y0ZkWcHsWXOQOF39uPHEtODPmTOvqu/j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\nc+MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf254470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.violinplot(data=df_m_w[[\"m_ratio\",\"m_ratio_1960\",\"m_ratio_1940\",\"m_ratio_1900_1940\"]],cut=0,trim=True,inner=\"quartile\",\n",
    "                 palette=[sns.xkcd_rgb[\"light green\"]])\n",
    "plt.ylim(0,1.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P4ciRIzh69CjOnj0b9prHHnsMM2bMwLPPPotHH30U//Zv/wa/X59J/1QnSTLm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\nrq4O/f7+++/H/fffr2/LqN/5oAxl0s7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11db0dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.violinplot(data=df_m_w[[\"m_ratio_18-19th\",\"m_ratio_17-18th\",\"m_ratio_-17th\"]],cut=0,trim=True,inner=\"quartile\",\n",
    "                 palette=[sns.xkcd_rgb[\"light green\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation between numbers mentioned people and labor market statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\seaborn\\linearmodels.py:1283: UserWarning: The `corrplot` function has been deprecated in favor of `heatmap` and will be removed in a forthcoming release. Please update your code.\n",
      "  warnings.warn((\"The `corrplot` function has been deprecated in favor \"\n",
      "C:\\Anaconda\\lib\\site-packages\\seaborn\\linearmodels.py:1349: UserWarning: The `symmatplot` function has been deprecated in favor of `heatmap` and will be removed in a forthcoming release. Please update your code.\n",
      "  warnings.warn((\"The `symmatplot` function has been deprecated in favor \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf2932b0>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FzMxMMjIyuHz5InXr1iMjI4O0tDQg+/KNkyejqV+/YYF57u6D8PcPws8vkI4d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\nc2yD4/YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ccc710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df_copy=pd.read_csv('de/all_persons_and_labor_market.csv', sep='\\t',index_col=0,encoding='utf-8')\n",
    "\n",
    "df_copy=pd.concat([df_copy,df_m_w[[\"all_men\",\"men_1940\",\"men_1960\",\"m_ratio_before1960\",\"m_ratio_1960\"]]],axis=1)\n",
    "df_copy[\"men_before1960\"]=df_copy.apply(lambda x: float(x[\"all_men\"])-float(x[\"men_1960\"]) if float(x[\"all_men\"])>0 else None, axis = 1)\n",
    "df_copy[\"men_before1940\"]=df_copy.apply(lambda x: float(x[\"all_men\"])-float(x[\"men_1940\"]) if float(x[\"all_men\"])>0 else None, axis = 1)\n",
    "\n",
    "#df_copy.dropna(inplace=True)\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "sns.corrplot(df_copy,method=\"spearman\",diag_names=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\ipykernel\\__main__.py:12: FutureWarning: icol(i) is deprecated. Please use .iloc[:,i]\n",
      "C:\\Anaconda\\lib\\site-packages\\ipykernel\\__main__.py:14: FutureWarning: icol(i) is deprecated. Please use .iloc[:,i]\n",
      "C:\\Anaconda\\lib\\site-packages\\ipykernel\\__main__.py:15: FutureWarning: icol(i) is deprecated. Please use .iloc[:,i]\n",
      "C:\\Anaconda\\lib\\site-packages\\ipykernel\\__main__.py:16: FutureWarning: icol(i) is deprecated. Please use .iloc[:,i]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>men</th>\n",
       "      <th>women</th>\n",
       "      <th>all_persons</th>\n",
       "      <th>men_ratio</th>\n",
       "      <th>labor_market_women_ratio</th>\n",
       "      <th>labor_market_all</th>\n",
       "      <th>labor_market_women</th>\n",
       "      <th>labor_market_men</th>\n",
       "      <th>all_men</th>\n",
       "      <th>men_1940</th>\n",
       "      <th>men_1960</th>\n",
       "      <th>men_before1960</th>\n",
       "      <th>men_before1940</th>\n",
       "      <th>m_ratio_before1960</th>\n",
       "      <th>m_ratio_1960</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>men</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478654</td>\n",
       "      <td>0.960924</td>\n",
       "      <td>-0.097164</td>\n",
       "      <td>0.104775</td>\n",
       "      <td>-0.204663</td>\n",
       "      <td>-0.149762</td>\n",
       "      <td>-0.226497</td>\n",
       "      <td>0.891249</td>\n",
       "      <td>0.536587</td>\n",
       "      <td>0.413249</td>\n",
       "      <td>0.877298</td>\n",
       "      <td>0.826714</td>\n",
       "      <td>-0.161293</td>\n",
       "      <td>0.003774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>women</th>\n",
       "      <td>0.478654</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.652638</td>\n",
       "      <td>-0.854905</td>\n",
       "      <td>0.262048</td>\n",
       "      <td>-0.012283</td>\n",
       "      <td>0.087044</td>\n",
       "      <td>-0.079090</td>\n",
       "      <td>0.393198</td>\n",
       "      <td>0.368770</td>\n",
       "      <td>0.258299</td>\n",
       "      <td>0.459909</td>\n",
       "      <td>0.410242</td>\n",
       "      <td>-0.668435</td>\n",
       "      <td>-0.396500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all_persons</th>\n",
       "      <td>0.960924</td>\n",
       "      <td>0.652638</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.322694</td>\n",
       "      <td>0.164052</td>\n",
       "      <td>-0.166609</td>\n",
       "      <td>-0.094258</td>\n",
       "      <td>-0.205084</td>\n",
       "      <td>0.855821</td>\n",
       "      <td>0.525790</td>\n",
       "      <td>0.404626</td>\n",
       "      <td>0.857064</td>\n",
       "      <td>0.805237</td>\n",
       "      <td>-0.308602</td>\n",
       "      <td>-0.071326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>men_ratio</th>\n",
       "      <td>-0.097164</td>\n",
       "      <td>-0.854905</td>\n",
       "      <td>-0.322694</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.271342</td>\n",
       "      <td>-0.079568</td>\n",
       "      <td>-0.170079</td>\n",
       "      <td>-0.010439</td>\n",
       "      <td>0.024116</td>\n",
       "      <td>-0.081584</td>\n",
       "      <td>-0.029477</td>\n",
       "      <td>-0.119769</td>\n",
       "      <td>-0.079768</td>\n",
       "      <td>0.663679</td>\n",
       "      <td>0.551632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labor_market_women_ratio</th>\n",
       "      <td>0.104775</td>\n",
       "      <td>0.262048</td>\n",
       "      <td>0.164052</td>\n",
       "      <td>-0.271342</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003129</td>\n",
       "      <td>0.391813</td>\n",
       "      <td>-0.238664</td>\n",
       "      <td>0.019924</td>\n",
       "      <td>0.087003</td>\n",
       "      <td>-0.018906</td>\n",
       "      <td>0.037543</td>\n",
       "      <td>0.002325</td>\n",
       "      <td>-0.181602</td>\n",
       "      <td>-0.231789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labor_market_all</th>\n",
       "      <td>-0.204663</td>\n",
       "      <td>-0.012283</td>\n",
       "      <td>-0.166609</td>\n",
       "      <td>-0.079568</td>\n",
       "      <td>-0.003129</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.889263</td>\n",
       "      <td>0.961240</td>\n",
       "      <td>-0.262282</td>\n",
       "      <td>-0.135478</td>\n",
       "      <td>-0.126892</td>\n",
       "      <td>-0.190656</td>\n",
       "      <td>-0.176845</td>\n",
       "      <td>-0.060547</td>\n",
       "      <td>-0.040972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labor_market_women</th>\n",
       "      <td>-0.149762</td>\n",
       "      <td>0.087044</td>\n",
       "      <td>-0.094258</td>\n",
       "      <td>-0.170079</td>\n",
       "      <td>0.391813</td>\n",
       "      <td>0.889263</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.767696</td>\n",
       "      <td>-0.228731</td>\n",
       "      <td>-0.079496</td>\n",
       "      <td>-0.118199</td>\n",
       "      <td>-0.150917</td>\n",
       "      <td>-0.152046</td>\n",
       "      <td>-0.130239</td>\n",
       "      <td>-0.131264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labor_market_men</th>\n",
       "      <td>-0.226497</td>\n",
       "      <td>-0.079090</td>\n",
       "      <td>-0.205084</td>\n",
       "      <td>-0.010439</td>\n",
       "      <td>-0.238664</td>\n",
       "      <td>0.961240</td>\n",
       "      <td>0.767696</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.261784</td>\n",
       "      <td>-0.151637</td>\n",
       "      <td>-0.122913</td>\n",
       "      <td>-0.195084</td>\n",
       "      <td>-0.175817</td>\n",
       "      <td>-0.012266</td>\n",
       "      <td>-0.019308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all_men</th>\n",
       "      <td>0.891249</td>\n",
       "      <td>0.393198</td>\n",
       "      <td>0.855821</td>\n",
       "      <td>0.024116</td>\n",
       "      <td>0.019924</td>\n",
       "      <td>-0.262282</td>\n",
       "      <td>-0.228731</td>\n",
       "      <td>-0.261784</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.579572</td>\n",
       "      <td>0.461195</td>\n",
       "      <td>0.981266</td>\n",
       "      <td>0.922457</td>\n",
       "      <td>-0.155039</td>\n",
       "      <td>0.103083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>men_1940</th>\n",
       "      <td>0.536587</td>\n",
       "      <td>0.368770</td>\n",
       "      <td>0.525790</td>\n",
       "      <td>-0.081584</td>\n",
       "      <td>0.087003</td>\n",
       "      <td>-0.135478</td>\n",
       "      <td>-0.079496</td>\n",
       "      <td>-0.151637</td>\n",
       "      <td>0.579572</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696461</td>\n",
       "      <td>0.488580</td>\n",
       "      <td>0.293328</td>\n",
       "      <td>-0.175881</td>\n",
       "      <td>0.144509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>men_1960</th>\n",
       "      <td>0.413249</td>\n",
       "      <td>0.258299</td>\n",
       "      <td>0.404626</td>\n",
       "      <td>-0.029477</td>\n",
       "      <td>-0.018906</td>\n",
       "      <td>-0.126892</td>\n",
       "      <td>-0.118199</td>\n",
       "      <td>-0.122913</td>\n",
       "      <td>0.461195</td>\n",
       "      <td>0.696461</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.334969</td>\n",
       "      <td>0.276106</td>\n",
       "      <td>-0.200641</td>\n",
       "      <td>0.323050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>men_before1960</th>\n",
       "      <td>0.877298</td>\n",
       "      <td>0.459909</td>\n",
       "      <td>0.857064</td>\n",
       "      <td>-0.119769</td>\n",
       "      <td>0.037543</td>\n",
       "      <td>-0.190656</td>\n",
       "      <td>-0.150917</td>\n",
       "      <td>-0.195084</td>\n",
       "      <td>0.981266</td>\n",
       "      <td>0.488580</td>\n",
       "      <td>0.334969</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.949609</td>\n",
       "      <td>-0.339907</td>\n",
       "      <td>0.012360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>men_before1940</th>\n",
       "      <td>0.826714</td>\n",
       "      <td>0.410242</td>\n",
       "      <td>0.805237</td>\n",
       "      <td>-0.079768</td>\n",
       "      <td>0.002325</td>\n",
       "      <td>-0.176845</td>\n",
       "      <td>-0.152046</td>\n",
       "      <td>-0.175817</td>\n",
       "      <td>0.922457</td>\n",
       "      <td>0.293328</td>\n",
       "      <td>0.276106</td>\n",
       "      <td>0.949609</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.323123</td>\n",
       "      <td>0.013142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m_ratio_before1960</th>\n",
       "      <td>-0.161293</td>\n",
       "      <td>-0.668435</td>\n",
       "      <td>-0.308602</td>\n",
       "      <td>0.663679</td>\n",
       "      <td>-0.181602</td>\n",
       "      <td>-0.060547</td>\n",
       "      <td>-0.130239</td>\n",
       "      <td>-0.012266</td>\n",
       "      <td>-0.155039</td>\n",
       "      <td>-0.175881</td>\n",
       "      <td>-0.200641</td>\n",
       "      <td>-0.339907</td>\n",
       "      <td>-0.323123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.233725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m_ratio_1960</th>\n",
       "      <td>0.003774</td>\n",
       "      <td>-0.396500</td>\n",
       "      <td>-0.071326</td>\n",
       "      <td>0.551632</td>\n",
       "      <td>-0.231789</td>\n",
       "      <td>-0.040972</td>\n",
       "      <td>-0.131264</td>\n",
       "      <td>-0.019308</td>\n",
       "      <td>0.103083</td>\n",
       "      <td>0.144509</td>\n",
       "      <td>0.323050</td>\n",
       "      <td>0.012360</td>\n",
       "      <td>0.013142</td>\n",
       "      <td>0.233725</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               men     women  all_persons  men_ratio  \\\n",
       "men                       1.000000  0.478654     0.960924  -0.097164   \n",
       "women                     0.478654  1.000000     0.652638  -0.854905   \n",
       "all_persons               0.960924  0.652638     1.000000  -0.322694   \n",
       "men_ratio                -0.097164 -0.854905    -0.322694   1.000000   \n",
       "labor_market_women_ratio  0.104775  0.262048     0.164052  -0.271342   \n",
       "labor_market_all         -0.204663 -0.012283    -0.166609  -0.079568   \n",
       "labor_market_women       -0.149762  0.087044    -0.094258  -0.170079   \n",
       "labor_market_men         -0.226497 -0.079090    -0.205084  -0.010439   \n",
       "all_men                   0.891249  0.393198     0.855821   0.024116   \n",
       "men_1940                  0.536587  0.368770     0.525790  -0.081584   \n",
       "men_1960                  0.413249  0.258299     0.404626  -0.029477   \n",
       "men_before1960            0.877298  0.459909     0.857064  -0.119769   \n",
       "men_before1940            0.826714  0.410242     0.805237  -0.079768   \n",
       "m_ratio_before1960       -0.161293 -0.668435    -0.308602   0.663679   \n",
       "m_ratio_1960              0.003774 -0.396500    -0.071326   0.551632   \n",
       "\n",
       "                          labor_market_women_ratio  labor_market_all  \\\n",
       "men                                       0.104775         -0.204663   \n",
       "women                                     0.262048         -0.012283   \n",
       "all_persons                               0.164052         -0.166609   \n",
       "men_ratio                                -0.271342         -0.079568   \n",
       "labor_market_women_ratio                  1.000000         -0.003129   \n",
       "labor_market_all                         -0.003129          1.000000   \n",
       "labor_market_women                        0.391813          0.889263   \n",
       "labor_market_men                         -0.238664          0.961240   \n",
       "all_men                                   0.019924         -0.262282   \n",
       "men_1940                                  0.087003         -0.135478   \n",
       "men_1960                                 -0.018906         -0.126892   \n",
       "men_before1960                            0.037543         -0.190656   \n",
       "men_before1940                            0.002325         -0.176845   \n",
       "m_ratio_before1960                       -0.181602         -0.060547   \n",
       "m_ratio_1960                             -0.231789         -0.040972   \n",
       "\n",
       "                          labor_market_women  labor_market_men   all_men  \\\n",
       "men                                -0.149762         -0.226497  0.891249   \n",
       "women                               0.087044         -0.079090  0.393198   \n",
       "all_persons                        -0.094258         -0.205084  0.855821   \n",
       "men_ratio                          -0.170079         -0.010439  0.024116   \n",
       "labor_market_women_ratio            0.391813         -0.238664  0.019924   \n",
       "labor_market_all                    0.889263          0.961240 -0.262282   \n",
       "labor_market_women                  1.000000          0.767696 -0.228731   \n",
       "labor_market_men                    0.767696          1.000000 -0.261784   \n",
       "all_men                            -0.228731         -0.261784  1.000000   \n",
       "men_1940                           -0.079496         -0.151637  0.579572   \n",
       "men_1960                           -0.118199         -0.122913  0.461195   \n",
       "men_before1960                     -0.150917         -0.195084  0.981266   \n",
       "men_before1940                     -0.152046         -0.175817  0.922457   \n",
       "m_ratio_before1960                 -0.130239         -0.012266 -0.155039   \n",
       "m_ratio_1960                       -0.131264         -0.019308  0.103083   \n",
       "\n",
       "                          men_1940  men_1960  men_before1960  men_before1940  \\\n",
       "men                       0.536587  0.413249        0.877298        0.826714   \n",
       "women                     0.368770  0.258299        0.459909        0.410242   \n",
       "all_persons               0.525790  0.404626        0.857064        0.805237   \n",
       "men_ratio                -0.081584 -0.029477       -0.119769       -0.079768   \n",
       "labor_market_women_ratio  0.087003 -0.018906        0.037543        0.002325   \n",
       "labor_market_all         -0.135478 -0.126892       -0.190656       -0.176845   \n",
       "labor_market_women       -0.079496 -0.118199       -0.150917       -0.152046   \n",
       "labor_market_men         -0.151637 -0.122913       -0.195084       -0.175817   \n",
       "all_men                   0.579572  0.461195        0.981266        0.922457   \n",
       "men_1940                  1.000000  0.696461        0.488580        0.293328   \n",
       "men_1960                  0.696461  1.000000        0.334969        0.276106   \n",
       "men_before1960            0.488580  0.334969        1.000000        0.949609   \n",
       "men_before1940            0.293328  0.276106        0.949609        1.000000   \n",
       "m_ratio_before1960       -0.175881 -0.200641       -0.339907       -0.323123   \n",
       "m_ratio_1960              0.144509  0.323050        0.012360        0.013142   \n",
       "\n",
       "                          m_ratio_before1960  m_ratio_1960  \n",
       "men                                -0.161293      0.003774  \n",
       "women                              -0.668435     -0.396500  \n",
       "all_persons                        -0.308602     -0.071326  \n",
       "men_ratio                           0.663679      0.551632  \n",
       "labor_market_women_ratio           -0.181602     -0.231789  \n",
       "labor_market_all                   -0.060547     -0.040972  \n",
       "labor_market_women                 -0.130239     -0.131264  \n",
       "labor_market_men                   -0.012266     -0.019308  \n",
       "all_men                            -0.155039      0.103083  \n",
       "men_1940                           -0.175881      0.144509  \n",
       "men_1960                           -0.200641      0.323050  \n",
       "men_before1960                     -0.339907      0.012360  \n",
       "men_before1940                     -0.323123      0.013142  \n",
       "m_ratio_before1960                  1.000000      0.233725  \n",
       "m_ratio_1960                        0.233725      1.000000  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import spearmanr\n",
    "df_copy=df_copy[[\"men\",\"women\",\"all_persons\",\"men_ratio\",\"labor_market_women_ratio\",\n",
    "                 \"labor_market_all\",\"labor_market_women\",\"labor_market_men\",\"all_men\",\n",
    "                 \"men_1940\",\"men_1960\",\"men_before1960\",\"men_before1940\",\"m_ratio_before1960\",\"m_ratio_1960\"]]\n",
    "#construct two arrays, one of the correlation and the other of the p-vals\n",
    "rho = df_copy.corr(method='spearman')\n",
    "pval = np.zeros([df_copy.shape[1],df_copy.shape[1]])\n",
    "pval2=np.zeros([df_copy.shape[1],df_copy.shape[1]])\n",
    "spr=np.zeros([df_copy.shape[1],df_copy.shape[1]])\n",
    "for i in range(df_copy.shape[1]): # rows are the number of rows in the matrix.\n",
    "    for j in range(df_copy.shape[1]):\n",
    "        JonI        = pd.ols(y=df_copy.icol(i), x=df_copy.icol(j), intercept=True)\n",
    "        pval[i,j]  = JonI.f_stat['p-value']\n",
    "        a=df_copy[~((df_copy.icol(i).isnull())|(df_copy.icol(j).isnull()))]\n",
    "        pval2[i,j]= spearmanr(a.icol(i),a.icol(j))[1]\n",
    "        spr[i,j]= spearmanr(a.icol(i),a.icol(j))[0]\n",
    "\n",
    "rho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>men</th>\n",
       "      <th>women</th>\n",
       "      <th>all_persons</th>\n",
       "      <th>men_ratio</th>\n",
       "      <th>labor_market_women_ratio</th>\n",
       "      <th>labor_market_all</th>\n",
       "      <th>labor_market_women</th>\n",
       "      <th>labor_market_men</th>\n",
       "      <th>all_men</th>\n",
       "      <th>men_1940</th>\n",
       "      <th>men_1960</th>\n",
       "      <th>men_before1960</th>\n",
       "      <th>men_before1940</th>\n",
       "      <th>m_ratio_before1960</th>\n",
       "      <th>m_ratio_1960</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>men</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.343011e-25</td>\n",
       "      <td>2.936477e-230</td>\n",
       "      <td>4.901379e-02</td>\n",
       "      <td>3.527115e-02</td>\n",
       "      <td>3.399790e-05</td>\n",
       "      <td>2.545499e-03</td>\n",
       "      <td>4.258911e-06</td>\n",
       "      <td>1.626780e-88</td>\n",
       "      <td>2.427985e-20</td>\n",
       "      <td>6.751464e-12</td>\n",
       "      <td>1.794179e-78</td>\n",
       "      <td>7.002763e-62</td>\n",
       "      <td>1.096230e-02</td>\n",
       "      <td>9.747204e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>women</th>\n",
       "      <td>6.343011e-25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.069111e-51</td>\n",
       "      <td>1.238928e-118</td>\n",
       "      <td>9.068749e-08</td>\n",
       "      <td>8.055786e-01</td>\n",
       "      <td>8.055864e-02</td>\n",
       "      <td>1.124560e-01</td>\n",
       "      <td>8.069611e-11</td>\n",
       "      <td>1.333717e-09</td>\n",
       "      <td>3.083168e-05</td>\n",
       "      <td>4.543548e-14</td>\n",
       "      <td>3.061940e-11</td>\n",
       "      <td>1.789676e-33</td>\n",
       "      <td>5.151891e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all_persons</th>\n",
       "      <td>2.936477e-230</td>\n",
       "      <td>3.069111e-51</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.052580e-11</td>\n",
       "      <td>9.341434e-04</td>\n",
       "      <td>7.739650e-04</td>\n",
       "      <td>5.837129e-02</td>\n",
       "      <td>3.272947e-05</td>\n",
       "      <td>4.216856e-74</td>\n",
       "      <td>1.854747e-19</td>\n",
       "      <td>2.002574e-11</td>\n",
       "      <td>4.506047e-71</td>\n",
       "      <td>2.135382e-56</td>\n",
       "      <td>7.165122e-07</td>\n",
       "      <td>5.487360e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>men_ratio</th>\n",
       "      <td>4.901379e-02</td>\n",
       "      <td>1.238928e-118</td>\n",
       "      <td>2.052580e-11</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.002182e-08</td>\n",
       "      <td>1.102918e-01</td>\n",
       "      <td>5.969521e-04</td>\n",
       "      <td>8.343177e-01</td>\n",
       "      <td>7.020882e-01</td>\n",
       "      <td>1.949811e-01</td>\n",
       "      <td>6.400907e-01</td>\n",
       "      <td>6.285362e-02</td>\n",
       "      <td>2.162930e-01</td>\n",
       "      <td>7.314560e-33</td>\n",
       "      <td>4.231225e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labor_market_women_ratio</th>\n",
       "      <td>3.527115e-02</td>\n",
       "      <td>9.068749e-08</td>\n",
       "      <td>9.341434e-04</td>\n",
       "      <td>3.002182e-08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.500147e-01</td>\n",
       "      <td>2.831158e-16</td>\n",
       "      <td>1.219070e-06</td>\n",
       "      <td>7.539206e-01</td>\n",
       "      <td>1.702654e-01</td>\n",
       "      <td>7.661192e-01</td>\n",
       "      <td>5.643828e-01</td>\n",
       "      <td>9.715418e-01</td>\n",
       "      <td>4.429373e-03</td>\n",
       "      <td>5.351382e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labor_market_all</th>\n",
       "      <td>3.399790e-05</td>\n",
       "      <td>8.055786e-01</td>\n",
       "      <td>7.739650e-04</td>\n",
       "      <td>1.102918e-01</td>\n",
       "      <td>9.500147e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.223571e-138</td>\n",
       "      <td>4.801370e-227</td>\n",
       "      <td>2.668823e-05</td>\n",
       "      <td>3.225456e-02</td>\n",
       "      <td>4.502717e-02</td>\n",
       "      <td>3.147496e-03</td>\n",
       "      <td>6.229245e-03</td>\n",
       "      <td>3.463064e-01</td>\n",
       "      <td>7.362930e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labor_market_women</th>\n",
       "      <td>2.545499e-03</td>\n",
       "      <td>8.055864e-02</td>\n",
       "      <td>5.837129e-02</td>\n",
       "      <td>5.969521e-04</td>\n",
       "      <td>2.831158e-16</td>\n",
       "      <td>1.223571e-138</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.046999e-79</td>\n",
       "      <td>2.654258e-04</td>\n",
       "      <td>2.103407e-01</td>\n",
       "      <td>6.203224e-02</td>\n",
       "      <td>1.984125e-02</td>\n",
       "      <td>1.892680e-02</td>\n",
       "      <td>4.209096e-02</td>\n",
       "      <td>2.787403e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labor_market_men</th>\n",
       "      <td>4.258911e-06</td>\n",
       "      <td>1.124560e-01</td>\n",
       "      <td>3.272947e-05</td>\n",
       "      <td>8.343177e-01</td>\n",
       "      <td>1.219070e-06</td>\n",
       "      <td>4.801370e-227</td>\n",
       "      <td>1.046999e-79</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.768041e-05</td>\n",
       "      <td>1.641876e-02</td>\n",
       "      <td>5.225105e-02</td>\n",
       "      <td>2.503944e-03</td>\n",
       "      <td>6.541945e-03</td>\n",
       "      <td>8.488240e-01</td>\n",
       "      <td>8.739476e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all_men</th>\n",
       "      <td>1.626780e-88</td>\n",
       "      <td>8.069611e-11</td>\n",
       "      <td>4.216856e-74</td>\n",
       "      <td>7.020882e-01</td>\n",
       "      <td>7.539206e-01</td>\n",
       "      <td>2.668823e-05</td>\n",
       "      <td>2.654258e-04</td>\n",
       "      <td>2.768041e-05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.431943e-24</td>\n",
       "      <td>8.799232e-15</td>\n",
       "      <td>1.173051e-173</td>\n",
       "      <td>3.579205e-101</td>\n",
       "      <td>1.452453e-02</td>\n",
       "      <td>3.854724e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>men_1940</th>\n",
       "      <td>2.427985e-20</td>\n",
       "      <td>1.333717e-09</td>\n",
       "      <td>1.854747e-19</td>\n",
       "      <td>1.949811e-01</td>\n",
       "      <td>1.702654e-01</td>\n",
       "      <td>3.225456e-02</td>\n",
       "      <td>2.103407e-01</td>\n",
       "      <td>1.641876e-02</td>\n",
       "      <td>3.431943e-24</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.448833e-38</td>\n",
       "      <td>6.351203e-16</td>\n",
       "      <td>3.450553e-06</td>\n",
       "      <td>5.478476e-03</td>\n",
       "      <td>2.225458e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>men_1960</th>\n",
       "      <td>6.751464e-12</td>\n",
       "      <td>3.083168e-05</td>\n",
       "      <td>2.002574e-11</td>\n",
       "      <td>6.400907e-01</td>\n",
       "      <td>7.661192e-01</td>\n",
       "      <td>4.502717e-02</td>\n",
       "      <td>6.203224e-02</td>\n",
       "      <td>5.225105e-02</td>\n",
       "      <td>8.799232e-15</td>\n",
       "      <td>3.448833e-38</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.348774e-08</td>\n",
       "      <td>1.312664e-05</td>\n",
       "      <td>1.492719e-03</td>\n",
       "      <td>5.308943e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>men_before1960</th>\n",
       "      <td>1.794179e-78</td>\n",
       "      <td>4.543548e-14</td>\n",
       "      <td>4.506047e-71</td>\n",
       "      <td>6.285362e-02</td>\n",
       "      <td>5.643828e-01</td>\n",
       "      <td>3.147496e-03</td>\n",
       "      <td>1.984125e-02</td>\n",
       "      <td>2.503944e-03</td>\n",
       "      <td>1.173051e-173</td>\n",
       "      <td>6.351203e-16</td>\n",
       "      <td>9.348774e-08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.449579e-123</td>\n",
       "      <td>8.061494e-08</td>\n",
       "      <td>9.179293e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>men_before1940</th>\n",
       "      <td>7.002763e-62</td>\n",
       "      <td>3.061940e-11</td>\n",
       "      <td>2.135382e-56</td>\n",
       "      <td>2.162930e-01</td>\n",
       "      <td>9.715418e-01</td>\n",
       "      <td>6.229245e-03</td>\n",
       "      <td>1.892680e-02</td>\n",
       "      <td>6.541945e-03</td>\n",
       "      <td>3.579205e-101</td>\n",
       "      <td>3.450553e-06</td>\n",
       "      <td>1.312664e-05</td>\n",
       "      <td>6.449579e-123</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.672611e-07</td>\n",
       "      <td>9.127564e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m_ratio_before1960</th>\n",
       "      <td>1.096230e-02</td>\n",
       "      <td>1.789676e-33</td>\n",
       "      <td>7.165122e-07</td>\n",
       "      <td>7.314560e-33</td>\n",
       "      <td>4.429373e-03</td>\n",
       "      <td>3.463064e-01</td>\n",
       "      <td>4.209096e-02</td>\n",
       "      <td>8.488240e-01</td>\n",
       "      <td>1.452453e-02</td>\n",
       "      <td>5.478476e-03</td>\n",
       "      <td>1.492719e-03</td>\n",
       "      <td>8.061494e-08</td>\n",
       "      <td>3.672611e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.696500e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m_ratio_1960</th>\n",
       "      <td>9.747204e-01</td>\n",
       "      <td>5.151891e-04</td>\n",
       "      <td>5.487360e-01</td>\n",
       "      <td>4.231225e-07</td>\n",
       "      <td>5.351382e-02</td>\n",
       "      <td>7.362930e-01</td>\n",
       "      <td>2.787403e-01</td>\n",
       "      <td>8.739476e-01</td>\n",
       "      <td>3.854724e-01</td>\n",
       "      <td>2.225458e-01</td>\n",
       "      <td>5.308943e-03</td>\n",
       "      <td>9.179293e-01</td>\n",
       "      <td>9.127564e-01</td>\n",
       "      <td>5.696500e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    men          women    all_persons  \\\n",
       "men                        0.000000e+00   6.343011e-25  2.936477e-230   \n",
       "women                      6.343011e-25   0.000000e+00   3.069111e-51   \n",
       "all_persons               2.936477e-230   3.069111e-51   0.000000e+00   \n",
       "men_ratio                  4.901379e-02  1.238928e-118   2.052580e-11   \n",
       "labor_market_women_ratio   3.527115e-02   9.068749e-08   9.341434e-04   \n",
       "labor_market_all           3.399790e-05   8.055786e-01   7.739650e-04   \n",
       "labor_market_women         2.545499e-03   8.055864e-02   5.837129e-02   \n",
       "labor_market_men           4.258911e-06   1.124560e-01   3.272947e-05   \n",
       "all_men                    1.626780e-88   8.069611e-11   4.216856e-74   \n",
       "men_1940                   2.427985e-20   1.333717e-09   1.854747e-19   \n",
       "men_1960                   6.751464e-12   3.083168e-05   2.002574e-11   \n",
       "men_before1960             1.794179e-78   4.543548e-14   4.506047e-71   \n",
       "men_before1940             7.002763e-62   3.061940e-11   2.135382e-56   \n",
       "m_ratio_before1960         1.096230e-02   1.789676e-33   7.165122e-07   \n",
       "m_ratio_1960               9.747204e-01   5.151891e-04   5.487360e-01   \n",
       "\n",
       "                              men_ratio  labor_market_women_ratio  \\\n",
       "men                        4.901379e-02              3.527115e-02   \n",
       "women                     1.238928e-118              9.068749e-08   \n",
       "all_persons                2.052580e-11              9.341434e-04   \n",
       "men_ratio                  0.000000e+00              3.002182e-08   \n",
       "labor_market_women_ratio   3.002182e-08              0.000000e+00   \n",
       "labor_market_all           1.102918e-01              9.500147e-01   \n",
       "labor_market_women         5.969521e-04              2.831158e-16   \n",
       "labor_market_men           8.343177e-01              1.219070e-06   \n",
       "all_men                    7.020882e-01              7.539206e-01   \n",
       "men_1940                   1.949811e-01              1.702654e-01   \n",
       "men_1960                   6.400907e-01              7.661192e-01   \n",
       "men_before1960             6.285362e-02              5.643828e-01   \n",
       "men_before1940             2.162930e-01              9.715418e-01   \n",
       "m_ratio_before1960         7.314560e-33              4.429373e-03   \n",
       "m_ratio_1960               4.231225e-07              5.351382e-02   \n",
       "\n",
       "                          labor_market_all  labor_market_women  \\\n",
       "men                           3.399790e-05        2.545499e-03   \n",
       "women                         8.055786e-01        8.055864e-02   \n",
       "all_persons                   7.739650e-04        5.837129e-02   \n",
       "men_ratio                     1.102918e-01        5.969521e-04   \n",
       "labor_market_women_ratio      9.500147e-01        2.831158e-16   \n",
       "labor_market_all              0.000000e+00       1.223571e-138   \n",
       "labor_market_women           1.223571e-138        0.000000e+00   \n",
       "labor_market_men             4.801370e-227        1.046999e-79   \n",
       "all_men                       2.668823e-05        2.654258e-04   \n",
       "men_1940                      3.225456e-02        2.103407e-01   \n",
       "men_1960                      4.502717e-02        6.203224e-02   \n",
       "men_before1960                3.147496e-03        1.984125e-02   \n",
       "men_before1940                6.229245e-03        1.892680e-02   \n",
       "m_ratio_before1960            3.463064e-01        4.209096e-02   \n",
       "m_ratio_1960                  7.362930e-01        2.787403e-01   \n",
       "\n",
       "                          labor_market_men        all_men      men_1940  \\\n",
       "men                           4.258911e-06   1.626780e-88  2.427985e-20   \n",
       "women                         1.124560e-01   8.069611e-11  1.333717e-09   \n",
       "all_persons                   3.272947e-05   4.216856e-74  1.854747e-19   \n",
       "men_ratio                     8.343177e-01   7.020882e-01  1.949811e-01   \n",
       "labor_market_women_ratio      1.219070e-06   7.539206e-01  1.702654e-01   \n",
       "labor_market_all             4.801370e-227   2.668823e-05  3.225456e-02   \n",
       "labor_market_women            1.046999e-79   2.654258e-04  2.103407e-01   \n",
       "labor_market_men              0.000000e+00   2.768041e-05  1.641876e-02   \n",
       "all_men                       2.768041e-05   0.000000e+00  3.431943e-24   \n",
       "men_1940                      1.641876e-02   3.431943e-24  0.000000e+00   \n",
       "men_1960                      5.225105e-02   8.799232e-15  3.448833e-38   \n",
       "men_before1960                2.503944e-03  1.173051e-173  6.351203e-16   \n",
       "men_before1940                6.541945e-03  3.579205e-101  3.450553e-06   \n",
       "m_ratio_before1960            8.488240e-01   1.452453e-02  5.478476e-03   \n",
       "m_ratio_1960                  8.739476e-01   3.854724e-01  2.225458e-01   \n",
       "\n",
       "                              men_1960  men_before1960  men_before1940  \\\n",
       "men                       6.751464e-12    1.794179e-78    7.002763e-62   \n",
       "women                     3.083168e-05    4.543548e-14    3.061940e-11   \n",
       "all_persons               2.002574e-11    4.506047e-71    2.135382e-56   \n",
       "men_ratio                 6.400907e-01    6.285362e-02    2.162930e-01   \n",
       "labor_market_women_ratio  7.661192e-01    5.643828e-01    9.715418e-01   \n",
       "labor_market_all          4.502717e-02    3.147496e-03    6.229245e-03   \n",
       "labor_market_women        6.203224e-02    1.984125e-02    1.892680e-02   \n",
       "labor_market_men          5.225105e-02    2.503944e-03    6.541945e-03   \n",
       "all_men                   8.799232e-15   1.173051e-173   3.579205e-101   \n",
       "men_1940                  3.448833e-38    6.351203e-16    3.450553e-06   \n",
       "men_1960                  0.000000e+00    9.348774e-08    1.312664e-05   \n",
       "men_before1960            9.348774e-08    0.000000e+00   6.449579e-123   \n",
       "men_before1940            1.312664e-05   6.449579e-123    0.000000e+00   \n",
       "m_ratio_before1960        1.492719e-03    8.061494e-08    3.672611e-07   \n",
       "m_ratio_1960              5.308943e-03    9.179293e-01    9.127564e-01   \n",
       "\n",
       "                          m_ratio_before1960  m_ratio_1960  \n",
       "men                             1.096230e-02  9.747204e-01  \n",
       "women                           1.789676e-33  5.151891e-04  \n",
       "all_persons                     7.165122e-07  5.487360e-01  \n",
       "men_ratio                       7.314560e-33  4.231225e-07  \n",
       "labor_market_women_ratio        4.429373e-03  5.351382e-02  \n",
       "labor_market_all                3.463064e-01  7.362930e-01  \n",
       "labor_market_women              4.209096e-02  2.787403e-01  \n",
       "labor_market_men                8.488240e-01  8.739476e-01  \n",
       "all_men                         1.452453e-02  3.854724e-01  \n",
       "men_1940                        5.478476e-03  2.225458e-01  \n",
       "men_1960                        1.492719e-03  5.308943e-03  \n",
       "men_before1960                  8.061494e-08  9.179293e-01  \n",
       "men_before1940                  3.672611e-07  9.127564e-01  \n",
       "m_ratio_before1960              0.000000e+00  5.696500e-02  \n",
       "m_ratio_1960                    5.696500e-02  0.000000e+00  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names=[\"men\",\"women\",\"all_persons\",\"men_ratio\",\"labor_market_women_ratio\",\"labor_market_all\",\n",
    "    \"labor_market_women\",\"labor_market_men\",\"all_men\",\"men_1940\",\"men_1960\",\"men_before1960\",\"men_before1940\",\n",
    "       \"m_ratio_before1960\",\"m_ratio_1960\"]\n",
    "pd.DataFrame(pval2,index=names,columns=names)"
   ]
  }
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