pandas-intro.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Year       WHO region          Country Beverage Types  Display Value\n0  1986  Western Pacific         Viet Nam           Wine           0.00\n1  1986         Americas          Uruguay          Other           0.50\n2  1985           Africa     Cet d'Ivoire           Wine           1.62\n3  1986         Americas         Columbia           Beer           4.27\n4  1987         Americas  Saint Kitts and           Beer           1.98\n"
     ]
    }
   ],
   "source": [
    "alcohols = pd.read_csv('world_alcohol.csv')\n",
    "print(alcohols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>WHO region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Beverage Types</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Cet d'Ivoire</td>\n",
       "      <td>Wine</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Columbia</td>\n",
       "      <td>Beer</td>\n",
       "      <td>4.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Saint Kitts and</td>\n",
       "      <td>Beer</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>WHO region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Beverage Types</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Cet d'Ivoire</td>\n",
       "      <td>Wine</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Columbia</td>\n",
       "      <td>Beer</td>\n",
       "      <td>4.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Saint Kitts and</td>\n",
       "      <td>Beer</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alcohols.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>WHO region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Beverage Types</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Cet d'Ivoire</td>\n",
       "      <td>Wine</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Columbia</td>\n",
       "      <td>Beer</td>\n",
       "      <td>4.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Saint Kitts and</td>\n",
       "      <td>Beer</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>WHO region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Beverage Types</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Cet d'Ivoire</td>\n",
       "      <td>Wine</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Columbia</td>\n",
       "      <td>Beer</td>\n",
       "      <td>4.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Saint Kitts and</td>\n",
       "      <td>Beer</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alcohols.loc[1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1986\n1    1986\n2    1985\n3    1986\n4    1987\nName: Year, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alcohols['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1986\n1    1986\n2    1985\n3    1986\n4    1987\nName: Year, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alcohols.Year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "China   2000    123\n        2001    124\nUS      2000    125\n        2001    126\nCanada  2001    127\n        2004    128\ndtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populations = [123, 124, 125, 126, 127, 128]\n",
    "index = pd.MultiIndex.from_tuples([('China', 2000), ('China', 2001), ('US', 2000), ('US', 2001), ('Canada', 2001), ('Canada', 2004)])\n",
    "pop = pd.Series(populations, index=index)\n",
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>0.040243</td>\n",
       "      <td>0.026031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.448145</td>\n",
       "      <td>0.356229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>0.491886</td>\n",
       "      <td>0.105252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.516016</td>\n",
       "      <td>0.271970</td>\n",
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       "      <th>data1</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>0.040243</td>\n",
       "      <td>0.026031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.448145</td>\n",
       "      <td>0.356229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>0.491886</td>\n",
       "      <td>0.105252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.516016</td>\n",
       "      <td>0.271970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(4, 2), index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]], columns=['data1', 'data2'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA   2000    1\n     2001    2\nNYC  2005    8\nTX   2000    3\n     2002    4\ndtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.Series({('CA', 2000): 1, ('CA', 2001): 2, ('TX', 2000): 3, ('TX', 2002): 4, ('NYC', 2005): 8})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiIndex(levels=[['a', 'b'], [1, 2]],\n           labels=[[0, 0, 1, 1], [0, 1, 0, 1]])\nMultiIndex(levels=[['a', 'b'], [1, 2]],\n           labels=[[0, 0, 1, 1], [0, 1, 0, 1]])\nMultiIndex(levels=[['a', 'b'], [1, 2]],\n           labels=[[0, 0, 1, 1], [0, 1, 0, 1]])\na  1    0.527758\n   2    0.186724\nb  1    0.932946\n   2    0.939352\ndtype: float64\n1    0.527758\n2    0.186724\ndtype: float64\n"
     ]
    }
   ],
   "source": [
    "df = pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'], [1, 2, 1, 2]])\n",
    "print(df)\n",
    "df = pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b', 2)])\n",
    "print(df)\n",
    "df = pd.MultiIndex.from_product([['a', 'b'], [1, 2]])\n",
    "print(df)\n",
    "data = pd.Series(np.random.rand(4), index=df)\n",
    "print(data)\n",
    "print(data['a'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# concat, join, merge, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1  2  3\n1  a  b  c\n2  a  b  c\n3  a  b  c\n    1  4\n4  a1  d\n    1    2    3    4\n1   a    b    c  NaN\n2   a    b    c  NaN\n3   a    b    c  NaN\n4  a1  NaN  NaN    d\n    1    2    3    4\n1   a    b    c  NaN\n2   a    b    c  NaN\n3   a    b    c  NaN\n4  a1  NaN  NaN    d\n"
     ]
    }
   ],
   "source": [
    "df1 = pd.DataFrame({1: 'a', 2: 'b', 3: 'c'}, index=[1, 2, 3])\n",
    "print(df1)\n",
    "df2 = pd.DataFrame({1: 'a1', 4: 'd'}, index=[4])\n",
    "print(df2)\n",
    "print(pd.concat([df1, df2]))\n",
    "print(df1.append(df2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  employee        group\n0      Bob   Accounting\n1     Jake  Engineering\n2     Lisa     Platform\n3      Sue           HR\n  employee  hire_year\n0     Lisa       2004\n1      Bob       2008\n2     Jake       2012\n3      Sue       2014\n  employee        group  hire_year\n0      Bob   Accounting       2008\n1     Jake  Engineering       2012\n2     Lisa     Platform       2004\n3      Sue           HR       2014\n"
     ]
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'employee': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "                    'group': ['Accounting', 'Engineering', 'Platform', 'HR']})\n",
    "df2 = pd.DataFrame({'employee': ['Lisa', 'Bob', 'Jake', 'Sue'],\n",
    "                    'hire_year': [2004, 2008, 2012, 2014]})\n",
    "print(df1)\n",
    "print(df2)\n",
    "print(pd.merge(df1, df2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  employee        group\n0      Bob   Accounting\n1     Jake  Engineering\n2     Lisa     Platform\n3      Sue           HR\n   hire_year  name\n0       2004  Lisa\n1       2008   Bob\n2       2012  Jake\n3       2014   Sue\n  employee        group  hire_year  name\n0      Bob   Accounting       2008   Bob\n1     Jake  Engineering       2012  Jake\n2     Lisa     Platform       2004  Lisa\n3      Sue           HR       2014   Sue\n  employee        group  hire_year  name\n0      Bob   Accounting       2004  Lisa\n1     Jake  Engineering       2008   Bob\n2     Lisa     Platform       2012  Jake\n3      Sue           HR       2014   Sue\n  employee        group  hire_year  name\n0      Bob   Accounting       2004  Lisa\n1     Jake  Engineering       2008   Bob\n2     Lisa     Platform       2012  Jake\n3      Sue           HR       2014   Sue\n"
     ]
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'employee': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "                    'group': ['Accounting', 'Engineering', 'Platform', 'HR']})\n",
    "df2 = pd.DataFrame({'name': ['Lisa', 'Bob', 'Jake', 'Sue'],\n",
    "                    'hire_year': [2004, 2008, 2012, 2014]})\n",
    "print(df1)\n",
    "print(df2)\n",
    "print(pd.merge(df1, df2, left_on='employee', right_on='name'))\n",
    "df1.set_index('employee')\n",
    "df2.set_index('name')\n",
    "print(pd.merge(df1, df2, left_index=True, right_index=True))\n",
    "print(df1.join(df2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Group by, aggregation, filter, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  key  data1  data2\n0   a      0      5\n1   b      1      0\n2   c      2      3\n3   a      3      3\n4   b      4      7\n5   c      5      9\n"
     ]
    }
   ],
   "source": [
    "rng = np.random.RandomState(0)\n",
    "df = pd.DataFrame({'key': ['a', 'b', 'c', 'a', 'b', 'c'], 'data1': range(6), 'data2': rng.randint(0, 10, 6)}, columns=['key', 'data1', 'data2'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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     "execution_count": 43,
     "metadata": {},
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   ],
   "source": [
    "df.groupby('key').aggregate(['min', np.median, max])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 44,
     "metadata": {},
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   ],
   "source": [
    "df.groupby('key').transform(lambda x: x - x.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
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  }
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