0. 10 minutes to Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pylab\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas data frames make data easy to see, references, and manipulate\n",
    "\n",
    "very much copied from\n",
    "http://pandas.pydata.org/pandas-docs/stable/10min.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can create a data frame with a dictionary of lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "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>phone</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Melanie</th>\n",
       "      <td>555-1212</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bob</th>\n",
       "      <td>555-1234</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vidhya</th>\n",
       "      <td>555-1111</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ming</th>\n",
       "      <td>555-2222</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            phone  age\n",
       "Melanie  555-1212   17\n",
       "Bob      555-1234   17\n",
       "Vidhya   555-1111   18\n",
       "Ming     555-2222   18"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students = pd.DataFrame({'phone': ['555-1212', '555-1234', '555-1111', '555-2222'], 'age':[17, 17, 18, 18]}, index = ['Melanie', 'Bob', 'Vidhya', 'Ming'])\n",
    "students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Melanie', 'Bob', 'Vidhya', 'Ming'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can also create a data frame with a numpy array and some column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jenny</th>\n",
       "      <td>-0.358926</td>\n",
       "      <td>0.247822</td>\n",
       "      <td>-0.683742</td>\n",
       "      <td>1.866706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frank</th>\n",
       "      <td>2.072176</td>\n",
       "      <td>-0.062526</td>\n",
       "      <td>0.152750</td>\n",
       "      <td>0.366829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wenfei</th>\n",
       "      <td>0.371840</td>\n",
       "      <td>-0.186373</td>\n",
       "      <td>-1.392546</td>\n",
       "      <td>-0.398626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arun</th>\n",
       "      <td>0.721424</td>\n",
       "      <td>-0.356202</td>\n",
       "      <td>1.192430</td>\n",
       "      <td>-0.714051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mary</th>\n",
       "      <td>1.325236</td>\n",
       "      <td>-0.948726</td>\n",
       "      <td>-0.871586</td>\n",
       "      <td>0.075613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ivan</th>\n",
       "      <td>-0.261533</td>\n",
       "      <td>1.124442</td>\n",
       "      <td>-0.207939</td>\n",
       "      <td>-0.032490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A         B         C         D\n",
       "Jenny  -0.358926  0.247822 -0.683742  1.866706\n",
       "Frank   2.072176 -0.062526  0.152750  0.366829\n",
       "Wenfei  0.371840 -0.186373 -1.392546 -0.398626\n",
       "Arun    0.721424 -0.356202  1.192430 -0.714051\n",
       "Mary    1.325236 -0.948726 -0.871586  0.075613\n",
       "Ivan   -0.261533  1.124442 -0.207939 -0.032490"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,4), index=['Jenny', 'Frank', 'Wenfei', 'Arun', 'Mary', 'Ivan'], columns=list('ABCD'))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are also Series, which gets you all the functionality of a data frame when you have a 1-dimensional set of data with an index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    3.0\n",
       "c    5.0\n",
       "d    NaN\n",
       "e    6.0\n",
       "f    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index = ['a', 'b', 'c', 'd', 'e', 'f']\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    False\n",
       "b    False\n",
       "c    False\n",
       "d     True\n",
       "e    False\n",
       "f    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataframes and series play nice with plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Once you have a data frame, it's easy to see portions of your data that match what you want (same goes for series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    NaN\n",
       "e    6.0\n",
       "f    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[s.index > 'c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    3.0\n",
       "c    5.0\n",
       "e    6.0\n",
       "f    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[s.isnull() == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Melanie    17\n",
       "Bob        17\n",
       "Vidhya     18\n",
       "Ming       18\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Melanie    17\n",
       "Bob        17\n",
       "Vidhya     18\n",
       "Ming       18\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "phone    555-1212\n",
       "age            17\n",
       "Name: Melanie, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'selection by label'\n",
    "students.loc['Melanie']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age       17\n",
       "grade    100\n",
       "Name: Melanie, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.loc['Melanie', ['age', 'grade']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "phone    555-1234\n",
       "age            17\n",
       "grade          97\n",
       "Name: Bob, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select by position\n",
    "students.iloc[1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.iloc[1, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "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>phone</th>\n",
       "      <th>age</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Vidhya</th>\n",
       "      <td>555-1111</td>\n",
       "      <td>18</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ming</th>\n",
       "      <td>555-2222</td>\n",
       "      <td>18</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           phone  age  grade\n",
       "Vidhya  555-1111   18     80\n",
       "Ming    555-2222   18     85"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students[students['age'] > 17]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It's also easy to summarize your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.age.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "students.age.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "students.age.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can add information to an existing data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "students['grade'] = [100, 97, 80, 85]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "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>phone</th>\n",
       "      <th>age</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Melanie</th>\n",
       "      <td>555-1212</td>\n",
       "      <td>17</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            phone  age  grade\n",
       "Melanie  555-1212   17    100"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students[students['grade'] == students['grade'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can also easily aggregate information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age\n",
       "17    98.5\n",
       "18    82.5\n",
       "Name: grade, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.groupby('age').grade.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can even create categories to aggregate with on-the-fly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bins = np.linspace(70, 100, 3)\n",
    "bins\n",
    "students.groupby(np.digitize(students.grades, bins)).age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finally applying functions is also quite straight-forward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# First let's see what a lambda function looks like / does\n",
    "f = lambda x: x + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "students.age.apply(lambda age: age + 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's take a look at some built methods we might want to apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "students.age.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "students.age.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "students.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "students.cummax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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