Seaborn-Distribution-plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tips = sns.load_dataset('tips')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc6ab240>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fe3Db2vKZKs2YiGA9/RjT0NFHRlL8OYMxEq0oy0SBfY2dTpdiTFiz0I8xjR39lEbR0M6o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\neBhaNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc6aba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(tips['total_bill'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The curve is Kernal Density Estimate, To remove this set the kde = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xec73860>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7QpJ/An4NePWgfkcCX02yFngT+JUFvMaWqnoJIMmd3Wsa6DrsGOha7gbdeP0z9DZOO4ve\nUuPPFvAaB2945AZIOiy5hq7l6DXg2O7+A8DHul8NmgJ+E3jwoD4AvwDsqaq3gE/S2x1xUBd3P/p7\nDL2fEvvhUgcgjYIzdC07VfVSkh92XxX8B+BHwGP0Zs7XVtV/JnkJ2J/kMXo/4v3XwB1JrqS3V/d/\nLeAlfwD8LfBeetujutyiw5Lb50pSI1xykaRGuOQiAUl+C7jhoOZ/q6rfHUc90mK45CJJjXDJRZIa\nYaBLUiMMdElqhIEuSY0w0CWpEf8LHtWqGe6VC0QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xec732b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(tips['total_bill'],kde=False)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "we can change the number of histogram bars by setting bins=?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **A histogram represents the distribution of data by forming bins along the range of the data and then drawing bars to show the number of observations that fall in each bin.**\n",
    "* **A rug is a line instead of point at each observation point.**\n",
    "* **Drawing a KDE is more computationally involved than drawing a histogram. What happens is that each observation is first replaced with a normal (Gaussian) curve centered at that value..For each point there is a curve of normal distribution.Next, these curves are summed to compute the value of the density at each point in the support grid. The resulting curve is then normalized so that the area under it is equal to 1:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can show only kde curve,histogram or rug(line instead of point at observation) by setting corresponding values as :\n",
    "## True/False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc90a6d8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wZuERjnfqdMtK9ef/DaAwxrC70jn9QqC4amYqbZ09vFWkF1dRqj8t/QBQ3dJBbWsnuQFU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\nwHgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcb93828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(tips.total_bill,hist=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd4d6908>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aLPNf3h0pkOTpwKvoX2f5HHBJV/aknX9VvbuqVlTVFLABuK2qfp1FMv8kz0zy7MOPgQuA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\nkhoGgySpYTBIkhoGgySpYTBIkhr/AxQvnG2DKqBzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd4d6438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(tips.total_bill,kde=False,rug=True,hist=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jointplot()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "jointplot(x=col1,y=col2,data=?/dataset,kind=?)\n",
    "allows you to basically match up two distplots for bivariate data. With your choice of what 'kind' parameter to compare with:\n",
    "“scatter”\n",
    "“reg”\n",
    "“resid”\n",
    "“kde”\n",
    "“hex”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x112e8438>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fC7ocQQsydbjQa3W0NSlSN/JBHBllhh2bLbPO6zo3XTInY5m8aiOpn9SLee/8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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112e8358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " sns.jointplot(x=tips.total_bill,y=tips.tip,data=tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1027f4e0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EkTVPhdd14EvwTl27QXe9/EK4g2q+md15lohmhvb29rouVScqxQsdaE5buXIlV0nT6Otf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\nZrnhoGRmZrnhoGRmZrnhoGRmZrnx/41bA2jgsfqzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10decef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(tips.total_bill,tips.tip,kind='hex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pairplot\n",
    "\n",
    "pairplot will plot pairwise relationships across an entire dataframe (for the numerical columns) and supports a color hue argument (for categorical columns). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x117ffef0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B3AxgOxF9qYiXKgC+KYS4CMDHAdxJRB8D8G0AvxFCTALwm+RzhrETx9lqSpio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\n0IwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1180a940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x14bc4cc0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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om5bMw1/uXO9EFJ65J3ei/jP3wIKLypvrpeqGmCKWmorIYcBbQBD4B2Bf4EfG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11692ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(tips,hue='sex')#,palette='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## rugplot\n",
    "\n",
    "rugplots are actually a very simple concept, they just draw a dash mark for every point on a univariate distribution. They are the building block of a KDE plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15c6fc18>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uSQ0Zd0lq6H8AuWjzyFZ3KPgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15bcddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.rugplot(tips.total_bill)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## kdeplot\n",
    "\n",
    "kdeplots are [Kernel Density Estimation plots](http://en.wikipedia.org/wiki/Kernel_density_estimation#Practical_estimation_of_the_bandwidth). These KDE plots replace every single observation with a Gaussian (Normal) distribution centered around that value. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'NaN' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-c1bb0653f159>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mNaN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'NaN' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series ,DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = DataFrame(np.arange(25).reshape(5,5),index=list('abcde'),columns=list('vwxyz'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "      <th>x</th>\n",
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       "      <th>z</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    v   w   x   y   z\n",
       "a   0   1   2   3   4\n",
       "b   5   6   7   8   9\n",
       "c  10  11  12  13  14\n",
       "d  15  16  17  18  19\n",
       "e  20  21  22  23  24"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df.ix[:3,3]=np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:only-child th {\n",
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       "\n",
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       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    v   w   x     y   z\n",
       "a   0   1   2   NaN   4\n",
       "b   5   6   7   NaN   9\n",
       "c  10  11  12   NaN  14\n",
       "d  15  16  17  18.0  19\n",
       "e  20  21  22  23.0  24"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.iloc[:4,4]=np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "      <th>z</th>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
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       "      <td>24.0</td>\n",
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       "    v   w   x     y     z\n",
       "a   0   1   2   NaN   NaN\n",
       "b   5   6   7   NaN   NaN\n",
       "c  10  11  12   NaN   NaN\n",
       "d  15  16  17  18.0   NaN\n",
       "e  20  21  22  23.0  24.0"
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     "execution_count": 41,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
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   "source": [
    "df.loc[:2,'w']=np.nan"
   ]
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  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
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    "df.loc[:'c','v']=np.nan"
   ]
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  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
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       "e  20.0  21.0  22  23.0  24.0 NaN"
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