Pandas1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to pandas Data Structures\n",
    "To get started with pandas, you will need to get comfortable with its two workhorse\n",
    "data structures:<b> Series and DataFrame</b>. While they are not a universal solution for\n",
    "every problem, they provide a solid, easy-to-use basis for most applications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Series Object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>A Series</b> is the primary building block of pandas.\n",
    "\n",
    "Series represents a one-dimensional labeled indexed array based on the NumPy ndarray.\n",
    "\n",
    "Like an array, a Series can hold zero or more values of any single data type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Series\n",
    "A Series can be created and initialized by passing either a <b>scalar value,\n",
    "a NumPy ndarray, a Python list, or a Python Dict</b> as the data parameter of\n",
    "the Series constructor. This is the default parameter and does not need to\n",
    "be specified if it is the first item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create one item Series\n",
    "s1 = pd.Series(2)\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a series of multiple items from a list\n",
    "s2 = pd.Series([1, 2, 3, 4, 5])\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the values in the Series\n",
    "s2.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5, step=1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the index of the Series\n",
    "s2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# explicitly create an index\n",
    "# index is alpha, not integer\n",
    "s3 = pd.Series([1, 2, 3], index=['a', 'b', 'c']) \n",
    "s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vlaue by label 's3['c']' is 3 and vlaue by index 's3[2]'is 3\n"
     ]
    }
   ],
   "source": [
    "# lookup by label value, not integer position\n",
    "print(f\"vlaue by label 's3['c']' is {s3['c']} and vlaue by index 's3[2]'is {s3[2]}\")\n",
    "# access both by label and index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    A\n",
       "1    B\n",
       "2    C\n",
       "3    D\n",
       "4    E\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create Series from an existing index\n",
    "# scalar value with be copied at each index label\n",
    "s4 = pd.Series([\"A\",\"B\",\"C\",\"D\",\"E\"], index=s2.index)\n",
    "s4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create Series from dict\n",
    "s4 = pd.Series({'a': 1,\n",
    "                'b': 2,\n",
    "                'c': 3, \n",
    "                'd': 4})\n",
    "s4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    33\n",
       "2    44\n",
       "3    55\n",
       "4    66\n",
       "dtype: int32"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s5 = pd.Series(np.array([22,33,44,55,66]))\n",
    "s5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Size, shape, uniqueness, and counts of values</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example series, which also contains a NaN\n",
    "s = pd.Series([0, 1, 1, 2, 3, 4, 4, 5, 6, 7,np.nan]) \n",
    "# numpy nan property is used here to create an NaN \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "11\n",
      "(11,)\n",
      "10\n",
      "[ 0.  1.  2.  3.  4.  5.  6.  7. nan]\n",
      "4.0    2\n",
      "1.0    2\n",
      "7.0    1\n",
      "6.0    1\n",
      "5.0    1\n",
      "3.0    1\n",
      "2.0    1\n",
      "0.0    1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(len(s))\n",
    "print(s.size)\n",
    "print(s.shape)\n",
    "print(s.count()) # count return not null values\n",
    "print(s.unique())\n",
    "print(s.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Peeking at data with heads, tails, and take</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    1.0\n",
       "2    1.0\n",
       "3    2.0\n",
       "4    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first five\n",
    "s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    1.0\n",
       "2    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first three\n",
    "s.head(n = 3) # s.head(3) is equivalent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6     4.0\n",
       "7     5.0\n",
       "8     6.0\n",
       "9     7.0\n",
       "10    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# last five\n",
    "s.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     6.0\n",
       "9     7.0\n",
       "10    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# last 3\n",
    "s.tail(n = 3) # equivalent to s.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9    7.0\n",
       "3    2.0\n",
       "9    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The .take() method will return the rows in a series that correspond to the zero-based positions:\n",
    "\n",
    "# only take specific items\n",
    "\n",
    "s.take([9, 3, 9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looking up values in Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1\n",
      "b    2\n",
      "c    3\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# single item lookup\n",
    "\n",
    "print(s3)\n",
    "s3['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accessing this Series using an integer value will perform a zero-based position lookup of the value:\n",
    "\n",
    "# lookup by position since the index is not an integer\n",
    "s3[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    3\n",
       "a    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multiple items\n",
    "s3[['c', 'a']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1\n",
       "3    2\n",
       "4    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# series with an integer index, but not starting with 0\n",
    "s5 = pd.Series([1, 2, 3], index=[2, 3, 4])  \n",
    "s5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# label-based lookup versus position-based lookup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s5[2]  # 2 is considered as label based look up\n",
    "       # coz label also has 2 init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-25-001a9f7426c3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms5\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m   \u001b[1;31m# now see in this case we have integer label lookup,position lookup is not working\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1066\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1067\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1068\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1069\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1070\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4728\u001b[0m         \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"getitem\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4729\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4730\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"tz\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4731\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4732\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_boolean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "s5[0]   # now see in this case we have integer label lookup,position lookup is not working"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s5.loc[2]  # loc also works on label based look up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s5.iloc[0]  #iLoc forcefully works on position based look up even you dont specify position based index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# multiple items by label (loc)\n",
    "s5.loc[[4, 3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s5[[0,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s5.iloc[[0,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "positional indexers are out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_list_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   2129\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2130\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2131\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36mtake\u001b[1;34m(self, indices, axis, is_copy, **kwargs)\u001b[0m\n\u001b[0;32m   4431\u001b[0m         \u001b[0mindices\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_platform_int\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4432\u001b[1;33m         \u001b[0mnew_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4433\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mtake\u001b[1;34m(self, indices, axis, allow_fill, fill_value, **kwargs)\u001b[0m\n\u001b[0;32m    901\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 902\u001b[1;33m             \u001b[0mtaken\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    903\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_shallow_copy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtaken\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 3 is out of bounds for size 3",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-416bee078d1d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms5\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m  \u001b[1;31m# integr location will throw exception\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1422\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1423\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1424\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1425\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1426\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   2146\u001b[0m         \u001b[1;31m# a list of integers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2147\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_list_like_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2148\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_list_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2149\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2150\u001b[0m         \u001b[1;31m# a single integer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_list_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   2131\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2132\u001b[0m             \u001b[1;31m# re-raise with different error message\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2133\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"positional indexers are out-of-bounds\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2134\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2135\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: positional indexers are out-of-bounds"
     ]
    }
   ],
   "source": [
    "s5.iloc[[0,2,3]]  # integr location will throw exception"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alignment via index labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A fundamental difference between a NumPy ndarray and a pandas Series is the\n",
    "ability of a Series to automatically align data from another Series based on label\n",
    "values before performing an operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s6 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])\n",
    "s6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4\n",
       "c    3\n",
       "b    2\n",
       "a    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s7 = pd.Series([4, 3, 2, 1], index=['d', 'c', 'b', 'a'])\n",
    "s7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 's6' 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-28-21b7fca61bd2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# add them\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0ms6\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0ms7\u001b[0m    \u001b[1;31m#it first alligns the data as per label then performs operation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 's6' is not defined"
     ]
    }
   ],
   "source": [
    "# add them\n",
    "s6 + s7    #it first alligns the data as per label then performs operation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <h3>Nan + number = NaN </h3>      (NaN added to a number results in NaN)\n",
    "    \n",
    "<h3>number + NaN = NaN</h3>        (Number added to a Nan results in NaN)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s8 = pd.Series({'a': 1, 'b': 2, 'c': 3, 'd': 5})\n",
    "s8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b     6\n",
       "c     7\n",
       "d     9\n",
       "e    10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s9 = pd.Series({'b': 6, 'c': 7, 'd': 9, 'e': 10})\n",
    "s9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     NaN\n",
       "b     8.0\n",
       "c    10.0\n",
       "d    14.0\n",
       "e     NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NaN's result for a and e\n",
    "# demonstrates alignment\n",
    "s8 + s9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "a    2.0\n",
       "b    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s10 = pd.Series([1.0, 2.0, 3.0], index=['a', 'a', 'b'])\n",
    "s10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    4.0\n",
       "a    5.0\n",
       "c    6.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s11 = pd.Series([4.0, 5.0, 6.0], index=['a', 'a', 'c'])\n",
    "s11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the two Series objects are added (or any other operation performed), the\n",
    "resulting Series has four 'a' index labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    5.0\n",
       "a    6.0\n",
       "a    6.0\n",
       "a    7.0\n",
       "b    NaN\n",
       "c    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s10 + s11"
   ]
  },
  {
   "attachments": {
    "catesion.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![catesion.png](attachment:catesion.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The special case of Not-A-Number (NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean of numpy array values\n",
    "nda = np.array([1, 2, 3, 4, 5])\n",
    "nda.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean of numpy array values with a NaN\n",
    "nda = np.array([1, 2, 3, 4, np.NaN])\n",
    "nda.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ignores NaN values\n",
    "s = pd.Series(nda)      \n",
    "s.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# handle NaN values like NumPy\n",
    "s.mean(skipna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boolean Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6     True\n",
       "7     True\n",
       "8     True\n",
       "9     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# which rows have values that are > 5?\n",
    "s = pd.Series(np.arange(0, 10))\n",
    "\n",
    "s > 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    6\n",
       "7    7\n",
       "8    8\n",
       "9    9\n",
       "dtype: int32"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select rows where values are > 5\n",
    "logicalResults = s > 5\n",
    "s[logicalResults]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    6\n",
       "7    7\n",
       "8    8\n",
       "9    9\n",
       "dtype: int32"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a little shorter version\n",
    "s[s > 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    6\n",
       "7    7\n",
       "dtype: int32"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# commented as it throws an exception\n",
    "# s[s > 5 and s < 8]\n",
    "\n",
    "# correct syntax\n",
    "s[(s > 5) & (s < 8)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, True)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([True, False, False, True, True]).all(),pd.Series([True, False, False, True, True]).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.array([True,False,True,True])).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# are all items >= 0?\n",
    "(s >= 0).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6    False\n",
       "7    False\n",
       "8    False\n",
       "9    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s < 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# any items < 2?\n",
    "(s[s < 2].any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many values < 2?\n",
    "(s < 2).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reindexing a Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reindexing in pandas is a process that makes the data in a Series or DataFrame\n",
    "match a given set of labels. This is core to the functionality of pandas as it enables\n",
    "label alignment across multiple objects, which may originally have different\n",
    "indexing schemes.\n",
    "This process of performing a reindex includes the following steps:\n",
    "1. Reordering existing data to match a set of labels.\n",
    "2. Inserting NaN markers where no data exists for a label.\n",
    "3. Possibly, filling missing data for a label using some type of logic (defaulting\n",
    "to adding NaN values)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.493330\n",
       "1    1.734023\n",
       "2    0.632139\n",
       "3   -0.830808\n",
       "4   -0.556488\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample series of five items\n",
    "s = pd.Series(np.random.randn(5))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.493330\n",
       "b    1.734023\n",
       "c    0.632139\n",
       "d   -0.830808\n",
       "e   -0.556488\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change the index\n",
    "s.index = ['a', 'b', 'c', 'd', 'e']\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's examine a slightly more practical example. The following code concatenates\n",
    "two Series objects resulting in duplicate index labels, which may not be desired in the\n",
    "resulting Series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.469112\n",
       "1   -0.282863\n",
       "2   -1.509059\n",
       "0   -1.135632\n",
       "1    1.212112\n",
       "2   -0.173215\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat copies index values verbatim (as it is),\n",
    "# potentially making duplicates since we have some or all label index same\n",
    "np.random.seed(123456)\n",
    "\n",
    "s1 = pd.Series(np.random.randn(3))\n",
    "\n",
    "s2 = pd.Series(np.random.randn(3))\n",
    "\n",
    "combined = pd.concat([s1, s2])\n",
    "combined\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.469112\n",
       "1   -0.282863\n",
       "2   -1.509059\n",
       "3   -1.135632\n",
       "4    1.212112\n",
       "5   -0.173215\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reset the index so that duplication of index may be removed\n",
    "combined.index =np.arange(0, len(combined))\n",
    "combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reindexing using the .index property in-place modifies the Series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# reindex() method\n",
    "Greater flexibility in creating a new index is provided using the .reindex() method.\n",
    "An example of the flexibility of .reindex() over assigning the .index property\n",
    "directly is that the list provided to .reindex() can be of a different length than the\n",
    "number of rows in the Series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    0.469112\n",
      "b   -0.282863\n",
      "c   -1.509059\n",
      "d   -1.135632\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "a    0.469112\n",
       "c   -1.509059\n",
       "g         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(123456)\n",
    "s1 = pd.Series(np.random.randn(4), ['a', 'b', 'c', 'd'])\n",
    "print(s1)\n",
    "# reindex with different number of labels\n",
    "# results in dropped rows and/or NaN's\n",
    "\n",
    "\n",
    "s2 = s1.reindex(['a', 'c', 'g'])\n",
    "s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Things to be noted: \n",
    "    1. reindex() donot re-index inplace, it will return a new series original will not be modified\n",
    "    2. if any index not matching the previous index will be assigned NaN\n",
    "    3. The index present in previous indexes, if not included in re-index \n",
    "        then the row will not be added in new series. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9          NaN\n",
       "5    -0.173215\n",
       "3    -1.135632\n",
       "4     1.212112\n",
       "0     0.469112\n",
       "1    -0.282863\n",
       "2    -1.509059\n",
       "6          NaN\n",
       "11         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.reindex([9,5,3,4,0,1,2,6,11])  # not in place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.469112\n",
       "1   -0.282863\n",
       "2   -1.509059\n",
       "3   -1.135632\n",
       "4    1.212112\n",
       "5   -0.173215\n",
       "dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined   # last indexing is still there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reindexing is also useful when you want to align two Series to perform an\n",
    "operation on matching elements from each series; however, for some reason,\n",
    "the two Series had index labels that will not initially align.\n",
    "The following example demonstrates this, where the first Series has indexes as\n",
    "sequential integers, but the second has a string representation of what would be\n",
    "the same values:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   NaN\n",
       "1   NaN\n",
       "2   NaN\n",
       "0   NaN\n",
       "1   NaN\n",
       "2   NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# different types for the same values of labels\n",
    "# causes big trouble\n",
    "s1 = pd.Series([0, 1, 2], index=[0, 1, 2])\n",
    "s2 = pd.Series([3, 4, 5], index=['0', '1', '2'])\n",
    "s1 + s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can easily guess what had happened here.\n",
    "\n",
    "all values are NaN because the operation tries to add the item in the\n",
    "first series with the integer label 0, which has a value of 0, but can't find the\n",
    "item in the other series and therefore, the result is NaN (and this fails six times\n",
    "in this case)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Once this situation is identified:</h5>\n",
    "it becomes a fairly trivial situation to fix by\n",
    "reindexing the second series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    5\n",
       "2    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reindex by casting the label types and we will get the desired result\n",
    "\n",
    "s2.index = s2.index.values.astype(int)\n",
    "s1 + s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overriding the default action of inserting <b>NaN</b> as a missing value during reindexing can\n",
    "be changed by using the <b>fill_value</b> parameter of the method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.493330\n",
       "b    1.734023\n",
       "c    0.632139\n",
       "d   -0.830808\n",
       "e   -0.556488\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill with 0 instead of NaN\n",
    "s2 = s.copy()\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.49333\n",
       "f    0.00000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2_reindexed = s2.reindex(['a', 'f'], fill_value=0)\n",
    "s2_reindexed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>ffill, bfill, & nearest</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       red\n",
       "8     green\n",
       "10     blue\n",
       "dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create example to demonstrate fills\n",
    "s3 = pd.Series(['red', 'green', 'blue' ], index=[0, 8, 10])\n",
    "s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       red\n",
       "1       red\n",
       "2       red\n",
       "3       red\n",
       "4       red\n",
       "5       red\n",
       "6       red\n",
       "7       red\n",
       "8     green\n",
       "9     green\n",
       "10     blue\n",
       "11     blue\n",
       "12     blue\n",
       "13     blue\n",
       "14     blue\n",
       "dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# forward fill example\n",
    "s3.reindex(np.arange(0,15), method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       red\n",
       "1     green\n",
       "2     green\n",
       "3     green\n",
       "4     green\n",
       "5     green\n",
       "6     green\n",
       "7     green\n",
       "8     green\n",
       "9      blue\n",
       "10     blue\n",
       "11      NaN\n",
       "12      NaN\n",
       "13      NaN\n",
       "14      NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# backwards fill example\n",
    "s3.reindex(np.arange(0,15), method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      red\n",
       "1      red\n",
       "2      red\n",
       "3      red\n",
       "4    green\n",
       "5    green\n",
       "6    green\n",
       "7    green\n",
       "8    green\n",
       "9     blue\n",
       "dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3.reindex(np.arange(0,10), method='nearest') #nearest: use nearest valid observations to fill gap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Slicing a Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    100\n",
       "11    101\n",
       "12    102\n",
       "13    103\n",
       "14    104\n",
       "15    105\n",
       "16    106\n",
       "17    107\n",
       "18    108\n",
       "19    109\n",
       "dtype: int32"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a Series to use for slicing\n",
    "# using index labels not starting at 0 to demonstrate\n",
    "# position based slicing\n",
    "\n",
    "s = pd.Series(np.arange(100, 110), index=np.arange(10, 20))\n",
    "\n",
    "# remember we pro \n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10    100\n",
      "12    102\n",
      "14    104\n",
      "dtype: int32\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10    100\n",
       "12    102\n",
       "14    104\n",
       "dtype: int32"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "print(s[0:6:2])  # [startofrow:endofrow:step]\n",
    "\n",
    "# # equivalent to\n",
    "s.iloc[[0, 2, 4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    100\n",
       "11    101\n",
       "12    102\n",
       "13    103\n",
       "14    104\n",
       "dtype: int32"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first five by slicing, same as .head(5)\n",
    "s[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Data in Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NaN values represent data is missing in the series\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio      35000\n",
       "Texas     71000\n",
       "Oregon    16000\n",
       "Utah       5000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdata = {'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}\n",
    "obj3 = pd.Series(sdata)\n",
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = ['California', 'Ohio', 'Oregon', 'Texas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California        NaN\n",
       "Ohio          35000.0\n",
       "Oregon        16000.0\n",
       "Texas         71000.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4 = pd.Series(sdata, index=states)\n",
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California     True\n",
       "Ohio          False\n",
       "Oregon        False\n",
       "Texas         False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(obj4)  # obj4.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    False\n",
       "Ohio           True\n",
       "Oregon         True\n",
       "Texas          True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.notnull(obj4)  #obj4.notnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "California        NaN\n",
       "Ohio          35000.0\n",
       "Oregon        16000.0\n",
       "Texas         71000.0\n",
       "Name: population, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4.name = 'population'\n",
    "obj4.index.name=\"state\"\n",
    "obj4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The pandas DataFrame Object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A DataFrame represents a rectangular table of data and contains an ordered collection of columns, each of which can be a different value type (numeric, string,\n",
    "boolean, etc.). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Creating a DataFrame from scratch</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3\n",
       "0  10  11  12  13\n",
       "1  20  21  22  23"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a DataFrame from a 2-d ndarray\n",
    "\n",
    "df = pd.DataFrame(np.array([[10, 11, 12, 13], [20, 21, 22, 23]]))\n",
    "df\n",
    "\n",
    "# default row and columns indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3   4\n",
       "0  10  11  12  13  14\n",
       "1  15  16  17  18  19"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a DataFrame for a list of Series objects\n",
    "\n",
    "df1 = pd.DataFrame([pd.Series(np.arange(10, 15)),\n",
    "                    pd.Series(np.arange(15, 20))])\n",
    "df1\n",
    "# default row and columns indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>boys</th>\n",
       "      <th>girls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   boys  girls\n",
       "0     1      6\n",
       "1     2      7\n",
       "2     3      8\n",
       "3     4      9\n",
       "4     5     10"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a DataFrame with two Series objects\n",
    "# and a dictionary\n",
    "s1 = pd.Series(np.arange(1, 6, 1))\n",
    "s2 = pd.Series(np.arange(6, 11, 1))\n",
    "df2= pd.DataFrame({'boys': s1, 'girls': s2})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>apples</th>\n",
       "      <th>oranges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   apples  oranges\n",
       "0      10       11\n",
       "1      20       21"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# specify column names\n",
    "df3 = pd.DataFrame(np.array([[10, 11], [20, 21]]),columns=['apples', 'oranges'])\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Mon</th>\n",
       "      <th>Tue</th>\n",
       "      <th>Wed</th>\n",
       "      <th>Thu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>apples</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>oranges</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mon  Tue  Wed  Thu\n",
       "apples    10   11   12   13\n",
       "oranges   20   21   22   23"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a DataFrame with named columns and rows\n",
    "\n",
    "df4 = pd.DataFrame(np.array([[10, 11, 12, 13], [20, 21, 22, 23]]),\n",
    "                  \n",
    "                                            index=['apples', 'oranges'],\n",
    "                                            columns=['Mon', 'Tue','Wed', 'Thu'])\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>c1</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   c1  c2    c3\n",
       "0   1   6   NaN\n",
       "1   2   7  12.0\n",
       "2   3   8  13.0\n",
       "3   4   9   NaN\n",
       "4   5  10   NaN"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# demonstrate alignment during creation\n",
    "s3 = pd.Series(np.arange(12, 14), index=[1, 2])\n",
    "df5 = pd.DataFrame({'c1': s1, 'c2': s2, 'c3': s3})\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Examples of creating data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>state</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2002</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2002</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2003</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  pop\n",
       "0    Ohio  2000  1.5\n",
       "1    Ohio  2001  1.7\n",
       "2    Ohio  2002  3.6\n",
       "3  Nevada  2001  2.4\n",
       "4  Nevada  2002  2.9\n",
       "5  Nevada  2003  3.2"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'],\n",
    "        'year': [2000, 2001, 2002, 2001, 2002, 2003],\n",
    "        'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]}\n",
    "\n",
    "frame = pd.DataFrame(data)\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Ohio</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   state  pop\n",
       "0  2000    Ohio  1.5\n",
       "1  2001    Ohio  1.7\n",
       "2  2002    Ohio  3.6\n",
       "3  2001  Nevada  2.4\n",
       "4  2002  Nevada  2.9\n",
       "5  2003  Nevada  3.2"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(frame, columns=['year', 'state', 'pop'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2000\n",
       "1    2001\n",
       "2    2002\n",
       "3    2001\n",
       "4    2002\n",
       "5    2003\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.5\n",
       "1    1.7\n",
       "2    3.6\n",
       "3    2.4\n",
       "4    2.9\n",
       "5    3.2\n",
       "Name: pop, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['pop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>six</td>\n",
       "      <td>2003</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop debt\n",
       "one    2000    Ohio  1.5  NaN\n",
       "two    2001    Ohio  1.7  NaN\n",
       "three  2002    Ohio  3.6  NaN\n",
       "four   2001  Nevada  2.4  NaN\n",
       "five   2002  Nevada  2.9  NaN\n",
       "six    2003  Nevada  3.2  NaN"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you pass a column that isn’t contained in the dict(debt), it will appear with missing values\n",
    "# in the result:\n",
    "frame2 = pd.DataFrame(data, columns=['year', 'state', 'pop', 'debt'],\n",
    "                              index=['one', 'two', 'three', 'four','five', 'six'])\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
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       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop debt\n",
       "one    2000    Ohio  1.5  100\n",
       "two    2001    Ohio  1.7  100\n",
       "three  2002    Ohio  3.6  100\n",
       "four   2001  Nevada  2.4  100\n",
       "five   2002  Nevada  2.9  100\n",
       "six    2003  Nevada  3.2  100"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.debt = \"100\"\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
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       "      <td>5</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    Ohio  1.5     0\n",
       "two    2001    Ohio  1.7     1\n",
       "three  2002    Ohio  3.6     2\n",
       "four   2001  Nevada  2.4     3\n",
       "five   2002  Nevada  2.9     4\n",
       "six    2003  Nevada  3.2     5"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2['debt']=np.arange(6)\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "val = pd.Series([-1.2, -1.5, -1.7], index=['two', 'four', 'five'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
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       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    Ohio  1.5   NaN\n",
       "two    2001    Ohio  1.7  -1.2\n",
       "three  2002    Ohio  3.6   NaN\n",
       "four   2001  Nevada  2.4  -1.5\n",
       "five   2002  Nevada  2.9  -1.7\n",
       "six    2003  Nevada  3.2   NaN"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2['debt'] = val\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
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       "      <td>2002</td>\n",
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       "      <td>False</td>\n",
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       "      <td>2003</td>\n",
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       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt  eastern\n",
       "one    2000    Ohio  1.5   NaN     True\n",
       "two    2001    Ohio  1.7  -1.2     True\n",
       "three  2002    Ohio  3.6   NaN     True\n",
       "four   2001  Nevada  2.4  -1.5    False\n",
       "five   2002  Nevada  2.9  -1.7    False\n",
       "six    2003  Nevada  3.2   NaN    False"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2['eastern'] = frame2.state == 'Ohio'\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>two</td>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>three</td>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>four</td>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>five</td>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>six</td>\n",
       "      <td>2003</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt  eastern  greaterThan2\n",
       "one    2000    Ohio  1.5   NaN     True         False\n",
       "two    2001    Ohio  1.7  -1.2     True         False\n",
       "three  2002    Ohio  3.6   NaN     True          True\n",
       "four   2001  Nevada  2.4  -1.5    False          True\n",
       "five   2002  Nevada  2.9  -1.7    False          True\n",
       "six    2003  Nevada  3.2   NaN    False          True"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2['greaterThan2']= frame2['pop'] > 2\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "del frame2['eastern']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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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       "        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>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "      <th>greaterThan2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>one</td>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>two</td>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>three</td>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>four</td>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>five</td>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>six</td>\n",
       "      <td>2003</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt  greaterThan2\n",
       "one    2000    Ohio  1.5   NaN         False\n",
       "two    2001    Ohio  1.7  -1.2         False\n",
       "three  2002    Ohio  3.6   NaN          True\n",
       "four   2001  Nevada  2.4  -1.5          True\n",
       "five   2002  Nevada  2.9  -1.7          True\n",
       "six    2003  Nevada  3.2   NaN          True"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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",
       "\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>Nevada</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2001</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2002</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Nevada  Ohio\n",
       "2001     2.4   1.7\n",
       "2002     2.9   3.6\n",
       "2000     NaN   1.5"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Another common form of data is a nested dict of dicts:\n",
    "pop = {'Nevada': {2001: 2.4, 2002: 2.9},\n",
    "       'Ohio':   {2000: 1.5, 2001: 1.7, 2002: 3.6}}\n",
    "df3 =pd.DataFrame(pop)\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>If the nested dict is passed to the DataFrame, pandas will interpret the outer dict keys\n",
    "as the columns and the inner keys as the row indices</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        2001  2002  2000\n",
       "Nevada   2.4   2.9   NaN\n",
       "Ohio     1.7   3.6   1.5"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Nevada</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2001</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2002</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Nevada  Ohio\n",
       "2001     2.4   1.7\n",
       "2002     2.9   3.6\n",
       "2000     NaN   1.5"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>Nevada</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2001</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2002</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Nevada  Ohio\n",
       "2001     2.4   1.7\n",
       "2002     2.9   3.6\n",
       "2003     NaN   NaN"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop1 =pd.DataFrame(pop, index=[2001, 2002, 2003])\n",
    "pop1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Ohio</th>\n",
       "      <th>Nevada</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2001</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2002</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Ohio  Nevada\n",
       "2001   1.7     2.4\n",
       "2002   3.6     2.9"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdata = {'Ohio': df3['Ohio'][:-1],\n",
    "        'Nevada': df3['Nevada'][:2]}\n",
    "pd.DataFrame(pdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.index.name = 'year'; df3.columns.name = 'state'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Index Objects"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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