PW_pivot_all.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 29036 entries, 0 to 29035\n",
      "Data columns (total 8 columns):\n",
      "matched word    29035 non-null object\n",
      "freq in pws     29036 non-null int64\n",
      "freq in web     29036 non-null int64\n",
      "POS freq        29036 non-null int64\n",
      "POS             29036 non-null object\n",
      "analysis        29036 non-null object\n",
      "finnish word    29036 non-null bool\n",
      "finnish name    29036 non-null bool\n",
      "dtypes: bool(2), int64(3), object(3)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# give the datafile name\n",
    "datafile= 'all_words_pivot.csv'\n",
    "addin = '_all'\n",
    "\n",
    "df = pd.read_csv(datafile, header=0, sep=',', encoding='utf-8')\n",
    "df.info() # could also use df_final.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matched word</th>\n",
       "      <th>freq in pws</th>\n",
       "      <th>freq in web</th>\n",
       "      <th>POS freq</th>\n",
       "      <th>POS</th>\n",
       "      <th>analysis</th>\n",
       "      <th>finnish word</th>\n",
       "      <th>finnish name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>salasana</td>\n",
       "      <td>1313</td>\n",
       "      <td>16553</td>\n",
       "      <td>16173</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kissa</td>\n",
       "      <td>954</td>\n",
       "      <td>54393</td>\n",
       "      <td>52191</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>koira</td>\n",
       "      <td>835</td>\n",
       "      <td>138611</td>\n",
       "      <td>137680</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nalle</td>\n",
       "      <td>659</td>\n",
       "      <td>8667</td>\n",
       "      <td>5053</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>johanna</td>\n",
       "      <td>567</td>\n",
       "      <td>39837</td>\n",
       "      <td>39280</td>\n",
       "      <td>PROPN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  matched word  freq in pws  freq in web  POS freq    POS  \\\n",
       "0     salasana         1313        16553     16173   NOUN   \n",
       "1        kissa          954        54393     52191   NOUN   \n",
       "2        koira          835       138611    137680   NOUN   \n",
       "3        nalle          659         8667      5053   NOUN   \n",
       "4      johanna          567        39837     39280  PROPN   \n",
       "\n",
       "               analysis  finnish word  finnish name  \n",
       "0  Case=Nom|Number=Sing          True         False  \n",
       "1  Case=Nom|Number=Sing          True         False  \n",
       "2  Case=Nom|Number=Sing          True         False  \n",
       "3  Case=Nom|Number=Sing          True          True  \n",
       "4  Case=Nom|Number=Sing         False          True  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rename columns due to printing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.rename(columns={'matched word': 'word', 'finnish word': 'Finnish word', 'finnish name': 'Finnish name'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function for rendering dataframe table to figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#https://stackoverflow.com/questions/19726663/how-to-save-the-pandas-dataframe-series-data-as-a-figure\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import six\n",
    "\n",
    "\n",
    "def render_mpl_table(data, rows=None, col_width=3.0, row_height=0.625, font_size=14,\n",
    "                     header_color='#40466e', row_colors=['#f1f1f2', 'w'], edge_color='#40466e',\n",
    "                     bbox=[0, 0, 1, 1], header_columns=0,\n",
    "                     ax=None, **kwargs):\n",
    "    if ax is None:\n",
    "        size = (np.array(data.shape[::-1]) + np.array([0, 1])) * np.array([col_width, row_height])#+np.array([8, 0])\n",
    "        fig, ax = plt.subplots(figsize=size)\n",
    "        ax.axis('off')\n",
    "\n",
    "    mpl_table = ax.table(cellText=data.values, bbox=bbox, colLabels=data.columns, rowLabels=rows, **kwargs)\n",
    "\n",
    "    mpl_table.auto_set_font_size(False)\n",
    "    mpl_table.set_fontsize(font_size)\n",
    "\n",
    "    for k, cell in six.iteritems(mpl_table._cells):\n",
    "        cell.set_edgecolor(edge_color)\n",
    "        if k[0] == 0 or k[1] < header_columns:\n",
    "            cell.set_text_props(weight='bold', color='w')\n",
    "            cell.set_facecolor(header_color)\n",
    "        else:\n",
    "            cell.set_facecolor(row_colors[k[0]%len(row_colors) ])\n",
    "    return ax\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finnish names and words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### freq in pws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.20114033229931"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Finnish name, percentage of all words used in passwords'''\n",
    "(df[df['Finnish name']==True]['freq in pws'].sum()/float(df['freq in pws'].sum()))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.634109381457499"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Finnish name, percentage of unique words used in passwords'''\n",
    "(df[df['Finnish name']==True]['freq in pws'].count()/float(df['freq in pws'].count()))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45.990772693484821"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Finnish word, percentage of all words used in passwords'''\n",
    "(df[df['Finnish word']==True]['freq in pws'].sum()/float(df['freq in pws'].sum()))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46.376911420305831"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Finnish word, percentage of unique words used in passwords'''\n",
    "(df[df['Finnish word']==True]['freq in pws'].count()/float(df['freq in pws'].count()))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dff = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### freq in web"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.242450703088011"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Finnish name, percentage of all words used in web'''\n",
    "(df[df['Finnish name']==True]['freq in web'].sum()/float(df['freq in web'].sum()))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.634109381457499"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Finnish name, percentage of unique words used in web'''\n",
    "(df[df['Finnish name']==True]['freq in web'].count()/float(df['freq in web'].count()))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.137309440220875"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Finnish word, percentage of all words used in web'''\n",
    "(df[df['Finnish word']==True]['freq in web'].sum()/float(df['freq in web'].sum()))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46.376911420305831"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Finnish word, percentage of unique words used in web'''\n",
    "(df[df['Finnish word']==True]['freq in web'].count()/float(df['freq in web'].count()))*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As a small dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percent in pws</th>\n",
       "      <th>percent in web</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Finnish word</th>\n",
       "      <td>46.0</td>\n",
       "      <td>89.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finnish name</th>\n",
       "      <td>52.2</td>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             percent in pws percent in web\n",
       "Finnish word           46.0           89.1\n",
       "Finnish name           52.2           17.2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff.loc['Finnish word','percent in pws'] = \\\n",
    "'{0:.3}'.format(float((df[df['Finnish word']==True]['freq in pws'].sum()/float(df['freq in pws'].sum()))*100))\n",
    "dff.loc['Finnish word','percent in web'] = \\\n",
    "'{0:.3}'.format(float((df[df['Finnish word']==True]['freq in web'].sum()/float(df['freq in web'].sum()))*100))\n",
    "dff.loc['Finnish name','percent in pws'] = \\\n",
    "'{0:.3}'.format(float((df[df['Finnish name']==True]['freq in pws'].sum()/float(df['freq in pws'].sum()))*100))\n",
    "dff.loc['Finnish name','percent in web'] = \\\n",
    "'{0:.3}'.format(float((df[df['Finnish name']==True]['freq in web'].sum()/float(df['freq in web'].sum()))*100))\n",
    "\n",
    "dff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x4aba320>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xceedc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dft=dff\n",
    "rows = [i for i in dft.index]\n",
    "fign = 'finnish_percent'\n",
    "pngfile = fign + '_' + addin + '.png'\n",
    "epsfile = fign + '_' + addin + '.eps'\n",
    "\n",
    "ax = plt.subplot(111, frame_on=False) # no visible frame\n",
    "ax.xaxis.set_visible(False)  # hide the x axis\n",
    "ax.yaxis.set_visible(False)  # hide the y axis\n",
    "\n",
    "render_mpl_table(dft, header_columns=0, col_width=2.5, rows = rows, header_color='grey',  edge_color='w')\n",
    "\n",
    "#plt.tight_layout()\n",
    "#plt.savefig('posmorphtable_grey.png')\n",
    "plt.savefig(pngfile, format='png', dpi=300, bbox_inches='tight')\n",
    "plt.savefig(epsfile, format='eps', dpi=300, bbox_inches='tight');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>freq in pws</th>\n",
       "      <th>freq in web</th>\n",
       "      <th>POS freq</th>\n",
       "      <th>POS</th>\n",
       "      <th>analysis</th>\n",
       "      <th>Finnish word</th>\n",
       "      <th>Finnish name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>aurinko</td>\n",
       "      <td>567</td>\n",
       "      <td>84299</td>\n",
       "      <td>71522</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>batman</td>\n",
       "      <td>53</td>\n",
       "      <td>5080</td>\n",
       "      <td>3027</td>\n",
       "      <td>PROPN</td>\n",
       "      <td>Case=Gen|Number=Sing</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        word  freq in pws  freq in web  POS freq    POS              analysis  \\\n",
       "5    aurinko          567        84299     71522   NOUN  Case=Nom|Number=Sing   \n",
       "804   batman           53         5080      3027  PROPN  Case=Gen|Number=Sing   \n",
       "\n",
       "     Finnish word  Finnish name  \n",
       "5            True          True  \n",
       "804         False         False  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['word'].isin(['batman','aurinko'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### POS and morph frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mypos = pd.DataFrame([str(val) for i, val in enumerate(df.POS)])\n",
    "myanalysis = pd.DataFrame([str(val) for i, val in enumerate(df.analysis)])\n",
    "df['label'] = mypos + ' ' +  myanalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>freq in pws</th>\n",
       "      <th>freq in web</th>\n",
       "      <th>POS freq</th>\n",
       "      <th>POS</th>\n",
       "      <th>analysis</th>\n",
       "      <th>Finnish word</th>\n",
       "      <th>Finnish name</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>salasana</td>\n",
       "      <td>1313</td>\n",
       "      <td>16553</td>\n",
       "      <td>16173</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NOUN Case=Nom|Number=Sing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kissa</td>\n",
       "      <td>954</td>\n",
       "      <td>54393</td>\n",
       "      <td>52191</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NOUN Case=Nom|Number=Sing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>koira</td>\n",
       "      <td>835</td>\n",
       "      <td>138611</td>\n",
       "      <td>137680</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NOUN Case=Nom|Number=Sing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nalle</td>\n",
       "      <td>659</td>\n",
       "      <td>8667</td>\n",
       "      <td>5053</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NOUN Case=Nom|Number=Sing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>johanna</td>\n",
       "      <td>567</td>\n",
       "      <td>39837</td>\n",
       "      <td>39280</td>\n",
       "      <td>PROPN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>PROPN Case=Nom|Number=Sing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       word  freq in pws  freq in web  POS freq    POS              analysis  \\\n",
       "0  salasana         1313        16553     16173   NOUN  Case=Nom|Number=Sing   \n",
       "1     kissa          954        54393     52191   NOUN  Case=Nom|Number=Sing   \n",
       "2     koira          835       138611    137680   NOUN  Case=Nom|Number=Sing   \n",
       "3     nalle          659         8667      5053   NOUN  Case=Nom|Number=Sing   \n",
       "4   johanna          567        39837     39280  PROPN  Case=Nom|Number=Sing   \n",
       "\n",
       "   Finnish word  Finnish name                       label  \n",
       "0          True         False   NOUN Case=Nom|Number=Sing  \n",
       "1          True         False   NOUN Case=Nom|Number=Sing  \n",
       "2          True         False   NOUN Case=Nom|Number=Sing  \n",
       "3          True          True   NOUN Case=Nom|Number=Sing  \n",
       "4         False          True  PROPN Case=Nom|Number=Sing  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correction to PRON _label\n",
    "label 'PROPN _' --> 'PROPN Case=Nom|Number=Sing'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>freq in pws</th>\n",
       "      <th>freq in web</th>\n",
       "      <th>POS freq</th>\n",
       "      <th>POS</th>\n",
       "      <th>analysis</th>\n",
       "      <th>Finnish word</th>\n",
       "      <th>Finnish name</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24413</th>\n",
       "      <td>metabo</td>\n",
       "      <td>1</td>\n",
       "      <td>430</td>\n",
       "      <td>300</td>\n",
       "      <td>PROPN</td>\n",
       "      <td>_</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>PROPN _</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23651</th>\n",
       "      <td>fatal</td>\n",
       "      <td>1</td>\n",
       "      <td>359</td>\n",
       "      <td>265</td>\n",
       "      <td>PROPN</td>\n",
       "      <td>_</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>PROPN _</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23375</th>\n",
       "      <td>steveco</td>\n",
       "      <td>1</td>\n",
       "      <td>216</td>\n",
       "      <td>125</td>\n",
       "      <td>PROPN</td>\n",
       "      <td>_</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>PROPN _</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          word  freq in pws  freq in web  POS freq    POS analysis  \\\n",
       "24413   metabo            1          430       300  PROPN        _   \n",
       "23651    fatal            1          359       265  PROPN        _   \n",
       "23375  steveco            1          216       125  PROPN        _   \n",
       "\n",
       "       Finnish word  Finnish name    label  \n",
       "24413         False         False  PROPN _  \n",
       "23651         False         False  PROPN _  \n",
       "23375         False         False  PROPN _  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['label']=='PROPN _')].sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>freq in pws</th>\n",
       "      <th>freq in web</th>\n",
       "      <th>POS freq</th>\n",
       "      <th>POS</th>\n",
       "      <th>analysis</th>\n",
       "      <th>Finnish word</th>\n",
       "      <th>Finnish name</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10815</th>\n",
       "      <td>catariina</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>PROPN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>PROPN Case=Nom|Number=Sing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9504</th>\n",
       "      <td>jippu</td>\n",
       "      <td>4</td>\n",
       "      <td>555</td>\n",
       "      <td>453</td>\n",
       "      <td>PROPN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>PROPN Case=Nom|Number=Sing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6722</th>\n",
       "      <td>duty</td>\n",
       "      <td>6</td>\n",
       "      <td>3630</td>\n",
       "      <td>2904</td>\n",
       "      <td>PROPN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>PROPN Case=Nom|Number=Sing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            word  freq in pws  freq in web  POS freq    POS  \\\n",
       "10815  catariina            3            8         5  PROPN   \n",
       "9504       jippu            4          555       453  PROPN   \n",
       "6722        duty            6         3630      2904  PROPN   \n",
       "\n",
       "                   analysis  Finnish word  Finnish name  \\\n",
       "10815  Case=Nom|Number=Sing         False          True   \n",
       "9504   Case=Nom|Number=Sing         False          True   \n",
       "6722   Case=Nom|Number=Sing         False         False   \n",
       "\n",
       "                            label  \n",
       "10815  PROPN Case=Nom|Number=Sing  \n",
       "9504   PROPN Case=Nom|Number=Sing  \n",
       "6722   PROPN Case=Nom|Number=Sing  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['label']=='PROPN Case=Nom|Number=Sing')].sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1458, 11969, 13427)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['label']=='PROPN _')]), len(df[(df['label']=='PROPN Case=Nom|Number=Sing')]), len(df[(df['label']=='PROPN _')])+ len(df[(df['label']=='PROPN Case=Nom|Number=Sing')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['label'] = np.where((df['label']=='PROPN _'), 'PROPN Case=Nom|Number=Sing', df['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 13427)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['label']=='PROPN _')]), len(df[(df['label']=='PROPN Case=Nom|Number=Sing')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mycols = ['label','freq in pws','freq in web']\n",
    "df2 = df[mycols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>label</th>\n",
       "      <th>freq in pws</th>\n",
       "      <th>freq in web</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NOUN Case=Nom|Number=Sing</td>\n",
       "      <td>1313</td>\n",
       "      <td>16553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NOUN Case=Nom|Number=Sing</td>\n",
       "      <td>954</td>\n",
       "      <td>54393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NOUN Case=Nom|Number=Sing</td>\n",
       "      <td>835</td>\n",
       "      <td>138611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NOUN Case=Nom|Number=Sing</td>\n",
       "      <td>659</td>\n",
       "      <td>8667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PROPN Case=Nom|Number=Sing</td>\n",
       "      <td>567</td>\n",
       "      <td>39837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        label  freq in pws  freq in web\n",
       "0   NOUN Case=Nom|Number=Sing         1313        16553\n",
       "1   NOUN Case=Nom|Number=Sing          954        54393\n",
       "2   NOUN Case=Nom|Number=Sing          835       138611\n",
       "3   NOUN Case=Nom|Number=Sing          659         8667\n",
       "4  PROPN Case=Nom|Number=Sing          567        39837"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "percentpws = (df2['freq in pws']/df2['freq in pws'].sum())*100\n",
    "df2.insert(2, 'percent in pws', percentpws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>freq in pws</th>\n",
       "      <th>percent in pws</th>\n",
       "      <th>freq in web</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NOUN Case=Nom|Number=Sing</td>\n",
       "      <td>954</td>\n",
       "      <td>0.396168</td>\n",
       "      <td>54393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NOUN Case=Nom|Number=Sing</td>\n",
       "      <td>835</td>\n",
       "      <td>0.346751</td>\n",
       "      <td>138611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       label  freq in pws  percent in pws  freq in web\n",
       "0  NOUN Case=Nom|Number=Sing         1313        0.545250        16553\n",
       "1  NOUN Case=Nom|Number=Sing          954        0.396168        54393\n",
       "2  NOUN Case=Nom|Number=Sing          835        0.346751       138611"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "percentweb = df2['freq in pws']/df2['freq in web'].sum()*100\n",
    "df2.insert(4, 'percent in web', percentweb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.081847957040398239, 99.999999999999986)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['percent in web'].sum(), df2['percent in pws'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# can't get this working here, for some reason, takes place later\n",
    "#df2['percent in web'] = df2['percent in web'].map(lambda x: '{0:.3}'.format(x))\n",
    "# df2['percent in pws'] = df2['percent in pws'].map(lambda x: '{0:.2}'.format(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>freq in pws</th>\n",
       "      <th>percent in pws</th>\n",
       "      <th>freq in web</th>\n",
       "      <th>percent in web</th>\n",
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       "      <td>16553</td>\n",
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       "      <th>1</th>\n",
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       "      <td>954</td>\n",
       "      <td>0.396168</td>\n",
       "      <td>54393</td>\n",
       "      <td>0.000324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NOUN Case=Nom|Number=Sing</td>\n",
       "      <td>835</td>\n",
       "      <td>0.346751</td>\n",
       "      <td>138611</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NOUN Case=Nom|Number=Sing</td>\n",
       "      <td>659</td>\n",
       "      <td>0.273663</td>\n",
       "      <td>8667</td>\n",
       "      <td>0.000224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PROPN Case=Nom|Number=Sing</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        label  freq in pws  percent in pws  freq in web  \\\n",
       "0   NOUN Case=Nom|Number=Sing         1313        0.545250        16553   \n",
       "1   NOUN Case=Nom|Number=Sing          954        0.396168        54393   \n",
       "2   NOUN Case=Nom|Number=Sing          835        0.346751       138611   \n",
       "3   NOUN Case=Nom|Number=Sing          659        0.273663         8667   \n",
       "4  PROPN Case=Nom|Number=Sing          567        0.235458        39837   \n",
       "\n",
       "   percent in web  \n",
       "0        0.000446  \n",
       "1        0.000324  \n",
       "2        0.000284  \n",
       "3        0.000224  \n",
       "4        0.000193  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# sort the data\n",
    "# dfa by word frequency in passwords\n",
    "# dfw by word frequency in web\n",
    "dfa = df2.groupby(['label']).sum().sort_values(['freq in pws'],ascending=False)\n",
    "dfw = df2.groupby(['label']).sum().sort_values(['freq in web'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By word frequency in pw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                                     freq in pws  percent in pws  freq in web  \\\n",
       "label                                                                           \n",
       "PROPN Case=Nom|Number=Sing                133327       55.366746     29579349   \n",
       "NOUN Case=Nom|Number=Sing                  73821       30.655670     47737381   \n",
       "PROPN Case=Gen|Number=Sing                  4492        1.865394      5710400   \n",
       "ADJ Case=Nom|Degree=Pos|Number=Sing         4054        1.683506     14385913   \n",
       "NOUN Case=Nom|Number=Plur                   3486        1.447632      7228301   \n",
       "\n",
       "                                     percent in web  \n",
       "label                                                \n",
       "PROPN Case=Nom|Number=Sing                 0.045317  \n",
       "NOUN Case=Nom|Number=Sing                  0.025091  \n",
       "PROPN Case=Gen|Number=Sing                 0.001527  \n",
       "ADJ Case=Nom|Degree=Pos|Number=Sing        0.001378  \n",
       "NOUN Case=Nom|Number=Plur                  0.001185  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# format the output into a cleaner precision\n",
    "dfa['percent in web'] = dfa['percent in web'].map(lambda x: '{0:.3}'.format(x))\n",
    "dfa['percent in pws'] = dfa['percent in pws'].map(lambda x: '{0:.3}'.format(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                                     freq in pws percent in pws  freq in web  \\\n",
       "label                                                                          \n",
       "PROPN Case=Nom|Number=Sing                133327           55.4     29579349   \n",
       "NOUN Case=Nom|Number=Sing                  73821           30.7     47737381   \n",
       "PROPN Case=Gen|Number=Sing                  4492           1.87      5710400   \n",
       "ADJ Case=Nom|Degree=Pos|Number=Sing         4054           1.68     14385913   \n",
       "NOUN Case=Nom|Number=Plur                   3486           1.45      7228301   \n",
       "\n",
       "                                    percent in web  \n",
       "label                                               \n",
       "PROPN Case=Nom|Number=Sing                  0.0453  \n",
       "NOUN Case=Nom|Number=Sing                   0.0251  \n",
       "PROPN Case=Gen|Number=Sing                 0.00153  \n",
       "ADJ Case=Nom|Degree=Pos|Number=Sing        0.00138  \n",
       "NOUN Case=Nom|Number=Plur                  0.00118  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13, 10])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([13, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc6b0940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe7dd438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dff = dfa.copy()\n",
    "# dff.reset_index(level=0, inplace=True)\n",
    "# dft = dff[['label','percent in pws','percent in web']].head()\n",
    "fign = 'posmorphtable_blue'\n",
    "pngfile = fign + '_' + addin + '.png'\n",
    "epsfile = fign + '_' + addin + '.eps'\n",
    "\n",
    "dft=dfa[['percent in pws','percent in web']].head()\n",
    "rows = [i for i in dft.index]\n",
    "\n",
    "ax = plt.subplot(111, frame_on=False) # no visible frame\n",
    "ax.xaxis.set_visible(False)  # hide the x axis\n",
    "ax.yaxis.set_visible(False)  # hide the y axis\n",
    "\n",
    "render_mpl_table(dft, header_columns=0, col_width=2.5, rows = rows)\n",
    "\n",
    "\n",
    "#plt.tight_layout()\n",
    "#plt.savefig('posmorphtable_blue.png')\n",
    "plt.savefig(pngfile, bbox_inches='tight')\n",
    "#plt.savefig('posmorphtable_blue.png', format='png', dpi=300, bbox_inches='tight')\n",
    "plt.savefig(epsfile, format='eps', dpi=300, bbox_inches='tight');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe7c6b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeb500b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dff = dfa.copy()\n",
    "# dff.reset_index(level=0, inplace=True)\n",
    "# dft = dff[['label','percent in pws','percent in web']].head()\n",
    "fign = 'posmorphtable_grey'\n",
    "pngfile = fign + '_' + addin + '.png'\n",
    "epsfile = fign + '_' + addin + '.eps'\n",
    "\n",
    "dft=dfa[['percent in pws','percent in web']].head()\n",
    "rows = [i for i in dft.index]\n",
    "\n",
    "ax = plt.subplot(111, frame_on=False) # no visible frame\n",
    "ax.xaxis.set_visible(False)  # hide the x axis\n",
    "ax.yaxis.set_visible(False)  # hide the y axis\n",
    "\n",
    "render_mpl_table(dft, header_columns=0, col_width=2.5, rows = rows, header_color='grey',  edge_color='w')\n",
    "\n",
    "#plt.tight_layout()\n",
    "#plt.savefig('posmorphtable_grey.png')\n",
    "plt.savefig(pngfile, format='png', dpi=300, bbox_inches='tight')\n",
    "plt.savefig(epsfile, format='eps', dpi=300, bbox_inches='tight');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 5 word examples from the top 5 pos+morph categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>freq in pws</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>salasana</td>\n",
       "      <td>1313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kissa</td>\n",
       "      <td>954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>koira</td>\n",
       "      <td>835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nalle</td>\n",
       "      <td>659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>aurinko</td>\n",
       "      <td>567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       word  freq in pws\n",
       "0  salasana         1313\n",
       "1     kissa          954\n",
       "2     koira          835\n",
       "3     nalle          659\n",
       "5   aurinko          567"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['word','freq in pws']][df['label']=='NOUN Case=Nom|Number=Sing'].sort_values(['freq in pws'],ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>freq in pws</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>johanna</td>\n",
       "      <td>567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>antti</td>\n",
       "      <td>493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ville</td>\n",
       "      <td>426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>emilia</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>mikko</td>\n",
       "      <td>382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       word  freq in pws\n",
       "4   johanna          567\n",
       "7     antti          493\n",
       "10    ville          426\n",
       "11   emilia          425\n",
       "18    mikko          382"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['word','freq in pws']][df['label']=='PROPN Case=Nom|Number=Sing'].sort_values(['freq in pws'],ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>freq in pws</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [word, freq in pws]\n",
       "Index: []"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['word','freq in pws']][df['label']=='PROPN _'].sort_values(['freq in pws'],ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>freq in pws</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>man</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>pokemon</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>batman</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>sebastian</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>iron</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          word  freq in pws\n",
       "82         man          249\n",
       "516    pokemon           79\n",
       "804     batman           53\n",
       "829  sebastian           52\n",
       "979       iron           45"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['word','freq in pws']][df['label']=='PROPN Case=Gen|Number=Sing'].sort_values(['freq in pws'],ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>freq in pws</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9426</th>\n",
       "      <td>erkin</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5158</th>\n",
       "      <td>horn</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19186</th>\n",
       "      <td>hellgren</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16587</th>\n",
       "      <td>niken</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8655</th>\n",
       "      <td>minjan</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27961</th>\n",
       "      <td>alden</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1759</th>\n",
       "      <td>merlin</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23664</th>\n",
       "      <td>distortion</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3399</th>\n",
       "      <td>villen</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14683</th>\n",
       "      <td>ulvilan</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             word  freq in pws\n",
       "9426        erkin            4\n",
       "5158         horn            8\n",
       "19186    hellgren            1\n",
       "16587       niken            2\n",
       "8655       minjan            4\n",
       "27961       alden            1\n",
       "1759       merlin           25\n",
       "23664  distortion            1\n",
       "3399       villen           13\n",
       "14683     ulvilan            2"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For batman categorie, check a random sample of 10\n",
    "df[['word','freq in pws']][df['label']=='PROPN Case=Gen|Number=Sing'].sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>freq in pws</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>rakas</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>oma</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>hullu</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>terve</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>salainen</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         word  freq in pws\n",
       "13      rakas          405\n",
       "97        oma          223\n",
       "106     hullu          218\n",
       "234     terve          140\n",
       "263  salainen          131"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['word','freq in pws']][df['label']=='ADJ Case=Nom|Degree=Pos|Number=Sing'].sort_values(['freq in pws'],ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By word frequency in web data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>freq in pws</th>\n",
       "      <th>percent in pws</th>\n",
       "      <th>freq in web</th>\n",
       "      <th>percent in web</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADV _</th>\n",
       "      <td>2021</td>\n",
       "      <td>0.839261</td>\n",
       "      <td>61336613</td>\n",
       "      <td>0.000687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOUN Case=Nom|Number=Sing</th>\n",
       "      <td>73821</td>\n",
       "      <td>30.655670</td>\n",
       "      <td>47737381</td>\n",
       "      <td>0.025091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROPN Case=Nom|Number=Sing</th>\n",
       "      <td>133327</td>\n",
       "      <td>55.366746</td>\n",
       "      <td>29579349</td>\n",
       "      <td>0.045317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADJ Case=Nom|Degree=Pos|Number=Sing</th>\n",
       "      <td>4054</td>\n",
       "      <td>1.683506</td>\n",
       "      <td>14385913</td>\n",
       "      <td>0.001378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VERB Mood=Ind|Number=Sing|Person=3|Tense=Past|VerbForm=Fin|Voice=Act</th>\n",
       "      <td>974</td>\n",
       "      <td>0.404473</td>\n",
       "      <td>11718459</td>\n",
       "      <td>0.000331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    freq in pws  \\\n",
       "label                                                             \n",
       "ADV _                                                      2021   \n",
       "NOUN Case=Nom|Number=Sing                                 73821   \n",
       "PROPN Case=Nom|Number=Sing                               133327   \n",
       "ADJ Case=Nom|Degree=Pos|Number=Sing                        4054   \n",
       "VERB Mood=Ind|Number=Sing|Person=3|Tense=Past|V...          974   \n",
       "\n",
       "                                                    percent in pws  \\\n",
       "label                                                                \n",
       "ADV _                                                     0.839261   \n",
       "NOUN Case=Nom|Number=Sing                                30.655670   \n",
       "PROPN Case=Nom|Number=Sing                               55.366746   \n",
       "ADJ Case=Nom|Degree=Pos|Number=Sing                       1.683506   \n",
       "VERB Mood=Ind|Number=Sing|Person=3|Tense=Past|V...        0.404473   \n",
       "\n",
       "                                                    freq in web  \\\n",
       "label                                                             \n",
       "ADV _                                                  61336613   \n",
       "NOUN Case=Nom|Number=Sing                              47737381   \n",
       "PROPN Case=Nom|Number=Sing                             29579349   \n",
       "ADJ Case=Nom|Degree=Pos|Number=Sing                    14385913   \n",
       "VERB Mood=Ind|Number=Sing|Person=3|Tense=Past|V...     11718459   \n",
       "\n",
       "                                                    percent in web  \n",
       "label                                                               \n",
       "ADV _                                                     0.000687  \n",
       "NOUN Case=Nom|Number=Sing                                 0.025091  \n",
       "PROPN Case=Nom|Number=Sing                                0.045317  \n",
       "ADJ Case=Nom|Degree=Pos|Number=Sing                       0.001378  \n",
       "VERB Mood=Ind|Number=Sing|Person=3|Tense=Past|V...        0.000331  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfw['percent in web'] = dfw['percent in web'].map(lambda x: '{0:.3}'.format(x))\n",
    "dfw['percent in pws'] = dfw['percent in pws'].map(lambda x: '{0:.3}'.format(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>freq in pws</th>\n",
       "      <th>percent in pws</th>\n",
       "      <th>freq in web</th>\n",
       "      <th>percent in web</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADV _</th>\n",
       "      <td>2021</td>\n",
       "      <td>0.839</td>\n",
       "      <td>61336613</td>\n",
       "      <td>0.000687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOUN Case=Nom|Number=Sing</th>\n",
       "      <td>73821</td>\n",
       "      <td>30.7</td>\n",
       "      <td>47737381</td>\n",
       "      <td>0.0251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROPN Case=Nom|Number=Sing</th>\n",
       "      <td>133327</td>\n",
       "      <td>55.4</td>\n",
       "      <td>29579349</td>\n",
       "      <td>0.0453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADJ Case=Nom|Degree=Pos|Number=Sing</th>\n",
       "      <td>4054</td>\n",
       "      <td>1.68</td>\n",
       "      <td>14385913</td>\n",
       "      <td>0.00138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VERB Mood=Ind|Number=Sing|Person=3|Tense=Past|VerbForm=Fin|Voice=Act</th>\n",
       "      <td>974</td>\n",
       "      <td>0.404</td>\n",
       "      <td>11718459</td>\n",
       "      <td>0.000331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    freq in pws  \\\n",
       "label                                                             \n",
       "ADV _                                                      2021   \n",
       "NOUN Case=Nom|Number=Sing                                 73821   \n",
       "PROPN Case=Nom|Number=Sing                               133327   \n",
       "ADJ Case=Nom|Degree=Pos|Number=Sing                        4054   \n",
       "VERB Mood=Ind|Number=Sing|Person=3|Tense=Past|V...          974   \n",
       "\n",
       "                                                   percent in pws  \\\n",
       "label                                                               \n",
       "ADV _                                                       0.839   \n",
       "NOUN Case=Nom|Number=Sing                                    30.7   \n",
       "PROPN Case=Nom|Number=Sing                                   55.4   \n",
       "ADJ Case=Nom|Degree=Pos|Number=Sing                          1.68   \n",
       "VERB Mood=Ind|Number=Sing|Person=3|Tense=Past|V...          0.404   \n",
       "\n",
       "                                                    freq in web percent in web  \n",
       "label                                                                           \n",
       "ADV _                                                  61336613       0.000687  \n",
       "NOUN Case=Nom|Number=Sing                              47737381         0.0251  \n",
       "PROPN Case=Nom|Number=Sing                             29579349         0.0453  \n",
       "ADJ Case=Nom|Degree=Pos|Number=Sing                    14385913        0.00138  \n",
       "VERB Mood=Ind|Number=Sing|Person=3|Tense=Past|V...     11718459       0.000331  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xef14630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = 10\n",
    "title = 'Frequencies of top ' + str(sample) + ' morphological forms used in passwords'\n",
    "dfa['freq in pws'][:sample].plot(legend=True, title = title);\n",
    "dfa['freq in web'][:sample].plot(secondary_y=True, legend=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3a04160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfb = dfa[:5]\n",
    "dfb.plot(y = 'freq in pws',x=dfb.index,legend=True, title = title);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examples of the most used words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Most used words, sort the original dataframe by frequency used in pws\n",
    "dfsort = df.sort_values(['freq in pws'],ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>freq in pws</th>\n",
       "      <th>freq in web</th>\n",
       "      <th>POS freq</th>\n",
       "      <th>POS</th>\n",
       "      <th>analysis</th>\n",
       "      <th>Finnish word</th>\n",
       "      <th>Finnish name</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>salasana</td>\n",
       "      <td>1313</td>\n",
       "      <td>16553</td>\n",
       "      <td>16173</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NOUN Case=Nom|Number=Sing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kissa</td>\n",
       "      <td>954</td>\n",
       "      <td>54393</td>\n",
       "      <td>52191</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NOUN Case=Nom|Number=Sing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>koira</td>\n",
       "      <td>835</td>\n",
       "      <td>138611</td>\n",
       "      <td>137680</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NOUN Case=Nom|Number=Sing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nalle</td>\n",
       "      <td>659</td>\n",
       "      <td>8667</td>\n",
       "      <td>5053</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NOUN Case=Nom|Number=Sing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>johanna</td>\n",
       "      <td>567</td>\n",
       "      <td>39837</td>\n",
       "      <td>39280</td>\n",
       "      <td>PROPN</td>\n",
       "      <td>Case=Nom|Number=Sing</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>PROPN Case=Nom|Number=Sing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       word  freq in pws  freq in web  POS freq    POS              analysis  \\\n",
       "0  salasana         1313        16553     16173   NOUN  Case=Nom|Number=Sing   \n",
       "1     kissa          954        54393     52191   NOUN  Case=Nom|Number=Sing   \n",
       "2     koira          835       138611    137680   NOUN  Case=Nom|Number=Sing   \n",
       "3     nalle          659         8667      5053   NOUN  Case=Nom|Number=Sing   \n",
       "4   johanna          567        39837     39280  PROPN  Case=Nom|Number=Sing   \n",
       "\n",
       "   Finnish word  Finnish name                       label  \n",
       "0          True         False   NOUN Case=Nom|Number=Sing  \n",
       "1          True         False   NOUN Case=Nom|Number=Sing  \n",
       "2          True         False   NOUN Case=Nom|Number=Sing  \n",
       "3          True          True   NOUN Case=Nom|Number=Sing  \n",
       "4         False          True  PROPN Case=Nom|Number=Sing  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfsort.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([u'salasana', u'kissa', u'koira', u'nalle', u'johanna', u'aurinko',\n",
       "       u'perkele', u'antti', u'qwerty', u'paska'], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfsort['word'][:sample].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf546240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#https://stackoverflow.com/questions/11640243/pandas-plot-multiple-y-axes\n",
    "\n",
    "sample = 5\n",
    "title = 'Frequencies of top ' + str(sample) + ' words used in passwords'\n",
    "x = [str(i) for i in dfsort['word'][:sample]]\n",
    "dfsort['freq in pws'][:sample].plot(legend=True, title = title, x=x);\n",
    "dfsort['freq in web'][:sample].plot(secondary_y=True, legend=True, sharex = True);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['salasana', 'kissa', 'koira', 'nalle', 'johanna']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf65a080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#https://matplotlib.org/gallery/api/two_scales.html\n",
    "\n",
    "fign = 'wordfreq'\n",
    "pngfile = fign + '_' + addin + '.png'\n",
    "epsfile = fign + '_' + addin + '.eps'\n",
    "\n",
    "title = 'all sources combined'\n",
    "\n",
    "sample = 15\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(10,4))\n",
    "x = [i for i in dfsort[:sample].index]\n",
    "x_ticks_labels = [str(i) for i in dfsort['word'][:sample]]\n",
    "y1 = dfsort['freq in pws'][:sample]\n",
    "# print len(x), len(y1)\n",
    "ax1.plot(x, y1, 'b-')\n",
    "#ax1.set_xlabel('words')\n",
    "# Make the y-axis label, ticks and tick labels match the line color.\n",
    "ax1.set_ylabel('freq in pws', color='b')\n",
    "ax1.tick_params('y', colors='b')\n",
    "# Set number of ticks for x-axis\n",
    "ax1.set_xticks(x)\n",
    "# Set ticks labels for x-axis\n",
    "ax1.set_xticklabels(x_ticks_labels, rotation='vertical', fontsize=18)\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "y2 = dfsort['freq in web'][:sample]\n",
    "ax2.plot(x, y2, 'g--')\n",
    "ax2.set_ylabel('freq in web', color='g')\n",
    "ax2.tick_params('y', colors='g')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.title(title)\n",
    "#plt.show()\n",
    "plt.savefig(pngfile, format='png', dpi=300)\n",
    "plt.savefig(epsfile, format='eps', dpi=300);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "top_freqs_all.csv\n"
     ]
    }
   ],
   "source": [
    "# Save smaller sample for combining figures in another notebook\n",
    "sample = 15\n",
    "filename = 'top_freqs' + addin + '.csv'\n",
    "print filename\n",
    "\n",
    "mydf=pd.DataFrame()\n",
    "mydf['word'] = [str(i) for i in dfsort['word'][:sample]]\n",
    "mydf['pwfreq'] = [str(i) for i in dfsort['freq in pws'][:sample]]\n",
    "mydf['webfreq'] = [str(i) for i in dfsort['freq in web'][:sample]]\n",
    "#mydf['fn'] = [str(i) for i in dfsort['Finnish name'][:sample]]\n",
    "mydf.to_csv(filename, sep=',', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf66c748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#https://matplotlib.org/gallery/api/two_scales.html\n",
    "\n",
    "sample = 8\n",
    "#title = 'Frequencies of top ' + str(sample) + ' words used in passwords'\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(10,4))\n",
    "x = [i for i, val in enumerate(dfa[:sample].index)]\n",
    "x_ticks_labels = [str(i) for i in dfa[:sample].index]\n",
    "y1 = dfa['freq in pws'][:sample]\n",
    "print len(x), len(y1)\n",
    "ax1.plot(x, y1, 'b-')\n",
    "ax1.set_xlabel('')\n",
    "# Make the y-axis label, ticks and tick labels match the line color.\n",
    "ax1.set_ylabel('freq in pws', color='b')\n",
    "ax1.tick_params('y', colors='b')\n",
    "# Set number of ticks for x-axis\n",
    "ax1.set_xticks(x)\n",
    "# Set ticks labels for x-axis\n",
    "ax1.set_xticklabels(x_ticks_labels, rotation='vertical', fontsize=18)\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "y2 = dfa['freq in web'][:sample]\n",
    "ax2.plot(x, y2, 'g--')\n",
    "ax2.set_ylabel('freq in web', color='g')\n",
    "ax2.tick_params('y', colors='g')\n",
    "\n",
    "#fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}