EX_07/ex_07_pandas_2.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Hello Pandas AGAIN!\n"
    }
   ],
   "source": [
    "print('Hello Pandas AGAIN!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATAFRAMES\n",
    "\n",
    "The primary Pandas data structure!\n",
    "\n",
    "Can be thought of as a dict-like container for Series objects.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import randn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets seed it, so our data is the same (in case you want to folow me:)\n",
    "np.random.seed(101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How To Create a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For the purpose of our studing, here is how: DataFrame(Data, Label, Label)\n",
    "df=pd.DataFrame(randn(5,4), ['A','B','C','D','E'], ['W','X','Y','Z' ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          W         X         Y         Z\nA  2.706850  0.628133  0.907969  0.503826\nB  0.651118 -0.319318 -0.848077  0.605965\nC -2.018168  0.740122  0.528813 -0.589001\nD  0.188695 -0.758872 -0.933237  0.955057\nE  0.190794  1.978757  2.605967  0.683509",
      "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>W</th>\n      <th>X</th>\n      <th>Y</th>\n      <th>Z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>A</th>\n      <td>2.706850</td>\n      <td>0.628133</td>\n      <td>0.907969</td>\n      <td>0.503826</td>\n    </tr>\n    <tr>\n      <th>B</th>\n      <td>0.651118</td>\n      <td>-0.319318</td>\n      <td>-0.848077</td>\n      <td>0.605965</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>-2.018168</td>\n      <td>0.740122</td>\n      <td>0.528813</td>\n      <td>-0.589001</td>\n    </tr>\n    <tr>\n      <th>D</th>\n      <td>0.188695</td>\n      <td>-0.758872</td>\n      <td>-0.933237</td>\n      <td>0.955057</td>\n    </tr>\n    <tr>\n      <th>E</th>\n      <td>0.190794</td>\n      <td>1.978757</td>\n      <td>2.605967</td>\n      <td>0.683509</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Each of this columns and row are Series themselves!\n",
    "\n",
    "# INDEXING & SELECTION IN PANDAS\n",
    "\n",
    " ### Using Brackets Notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "A    2.706850\nB    0.651118\nC   -2.018168\nD    0.188695\nE    0.190794\nName: W, dtype: float64"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "# just pass in the column name, ie 'W'\n",
    "df['W']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "pandas.core.series.Series"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "type(df['W'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## See 'W' is just a Series!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "pandas.core.frame.DataFrame"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The df itself is the DataFrame!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using SQL Notation: \n",
    "\n",
    "Note: not recommended, because we can confuse with the real method of df object!\n",
    "\n",
    "So, always use the bracket Notation when it comes to rescuing series from df :)\n",
    "\n",
    "Anyway, here you have it:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "A    2.706850\nB    0.651118\nC   -2.018168\nD    0.188695\nE    0.190794\nName: W, dtype: float64"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "df.W"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Multiple Columns back!\n",
    "Pass in a List, please!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          W         Z\nA  2.706850  0.503826\nB  0.651118  0.605965\nC -2.018168 -0.589001\nD  0.188695  0.955057\nE  0.190794  0.683509",
      "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>W</th>\n      <th>Z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>A</th>\n      <td>2.706850</td>\n      <td>0.503826</td>\n    </tr>\n    <tr>\n      <th>B</th>\n      <td>0.651118</td>\n      <td>0.605965</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>-2.018168</td>\n      <td>-0.589001</td>\n    </tr>\n    <tr>\n      <th>D</th>\n      <td>0.188695</td>\n      <td>0.955057</td>\n    </tr>\n    <tr>\n      <th>E</th>\n      <td>0.190794</td>\n      <td>0.683509</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "# WHICH IS GOING TO RESULT IN ANOTHER SET OF BRACKETS HERE!\n",
    "df[['W','Z']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a New Collumn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          W         X         Y         Z       new\nA  2.706850  0.628133  0.907969  0.503826  3.614819\nB  0.651118 -0.319318 -0.848077  0.605965 -0.196959\nC -2.018168  0.740122  0.528813 -0.589001 -1.489355\nD  0.188695 -0.758872 -0.933237  0.955057 -0.744542\nE  0.190794  1.978757  2.605967  0.683509  2.796762",
      "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>W</th>\n      <th>X</th>\n      <th>Y</th>\n      <th>Z</th>\n      <th>new</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>A</th>\n      <td>2.706850</td>\n      <td>0.628133</td>\n      <td>0.907969</td>\n      <td>0.503826</td>\n      <td>3.614819</td>\n    </tr>\n    <tr>\n      <th>B</th>\n      <td>0.651118</td>\n      <td>-0.319318</td>\n      <td>-0.848077</td>\n      <td>0.605965</td>\n      <td>-0.196959</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>-2.018168</td>\n      <td>0.740122</td>\n      <td>0.528813</td>\n      <td>-0.589001</td>\n      <td>-1.489355</td>\n    </tr>\n    <tr>\n      <th>D</th>\n      <td>0.188695</td>\n      <td>-0.758872</td>\n      <td>-0.933237</td>\n      <td>0.955057</td>\n      <td>-0.744542</td>\n    </tr>\n    <tr>\n      <th>E</th>\n      <td>0.190794</td>\n      <td>1.978757</td>\n      <td>2.605967</td>\n      <td>0.683509</td>\n      <td>2.796762</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "# Just make some arithmetic on the right side with the series you want to create your column:\n",
    "df['new'] = df['W'] + df['Y']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas requires that you specify that you really want to modify your data in place (affect the original DB);\n",
    "#vIt is like so you do not accidentally lose information;\n",
    "# In case you’ve done a bunch of adjustments to your data, you don’t want to accidentally lose it, right?\n",
    "# This is like ‘commit’ in DB!\n",
    "df.drop('new', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          W         X         Y         Z\nA  2.706850  0.628133  0.907969  0.503826\nB  0.651118 -0.319318 -0.848077  0.605965\nC -2.018168  0.740122  0.528813 -0.589001\nD  0.188695 -0.758872 -0.933237  0.955057\nE  0.190794  1.978757  2.605967  0.683509",
      "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>W</th>\n      <th>X</th>\n      <th>Y</th>\n      <th>Z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>A</th>\n      <td>2.706850</td>\n      <td>0.628133</td>\n      <td>0.907969</td>\n      <td>0.503826</td>\n    </tr>\n    <tr>\n      <th>B</th>\n      <td>0.651118</td>\n      <td>-0.319318</td>\n      <td>-0.848077</td>\n      <td>0.605965</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>-2.018168</td>\n      <td>0.740122</td>\n      <td>0.528813</td>\n      <td>-0.589001</td>\n    </tr>\n    <tr>\n      <th>D</th>\n      <td>0.188695</td>\n      <td>-0.758872</td>\n      <td>-0.933237</td>\n      <td>0.955057</td>\n    </tr>\n    <tr>\n      <th>E</th>\n      <td>0.190794</td>\n      <td>1.978757</td>\n      <td>2.605967</td>\n      <td>0.683509</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          W         X         Y         Z\nA  2.706850  0.628133  0.907969  0.503826\nB  0.651118 -0.319318 -0.848077  0.605965\nC -2.018168  0.740122  0.528813 -0.589001\nD  0.188695 -0.758872 -0.933237  0.955057",
      "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>W</th>\n      <th>X</th>\n      <th>Y</th>\n      <th>Z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>A</th>\n      <td>2.706850</td>\n      <td>0.628133</td>\n      <td>0.907969</td>\n      <td>0.503826</td>\n    </tr>\n    <tr>\n      <th>B</th>\n      <td>0.651118</td>\n      <td>-0.319318</td>\n      <td>-0.848077</td>\n      <td>0.605965</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>-2.018168</td>\n      <td>0.740122</td>\n      <td>0.528813</td>\n      <td>-0.589001</td>\n    </tr>\n    <tr>\n      <th>D</th>\n      <td>0.188695</td>\n      <td>-0.758872</td>\n      <td>-0.933237</td>\n      <td>0.955057</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "# This time I am not doing this in place!\n",
    "## Note: axis=0 is the default, so you don’t need to specify it here:)\n",
    "df.drop('E', axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(5, 4)"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "# See that our DataFrame has not been affected yet by the last drop! We didn’t make it in place, remember?\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          W         X         Y         Z\nA  2.706850  0.628133  0.907969  0.503826\nB  0.651118 -0.319318 -0.848077  0.605965\nC -2.018168  0.740122  0.528813 -0.589001\nD  0.188695 -0.758872 -0.933237  0.955057\nE  0.190794  1.978757  2.605967  0.683509",
      "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>W</th>\n      <th>X</th>\n      <th>Y</th>\n      <th>Z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>A</th>\n      <td>2.706850</td>\n      <td>0.628133</td>\n      <td>0.907969</td>\n      <td>0.503826</td>\n    </tr>\n    <tr>\n      <th>B</th>\n      <td>0.651118</td>\n      <td>-0.319318</td>\n      <td>-0.848077</td>\n      <td>0.605965</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>-2.018168</td>\n      <td>0.740122</td>\n      <td>0.528813</td>\n      <td>-0.589001</td>\n    </tr>\n    <tr>\n      <th>D</th>\n      <td>0.188695</td>\n      <td>-0.758872</td>\n      <td>-0.933237</td>\n      <td>0.955057</td>\n    </tr>\n    <tr>\n      <th>E</th>\n      <td>0.190794</td>\n      <td>1.978757</td>\n      <td>2.605967</td>\n      <td>0.683509</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "# See, df isn't affected yet\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting Rows\n",
    "\n",
    "There are two methods:\n",
    "\n",
    "1. LOC  ->  label-BASE index\n",
    "\n",
    "2. ILOC ->  numerical-BASE index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "W    2.706850\nX    0.628133\nY    0.907969\nZ    0.503826\nName: A, dtype: float64"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "# iT'S LITTLE WEIRD HOW THE METHODS ARE CALLED IN PANDAS: IT USE SQUARED BRACKET!\n",
    "# But that's the way it works for Pandas! \n",
    "# This returns a series of that 'A' row!\n",
    "df.loc['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "W    2.706850\nX    0.628133\nY    0.907969\nZ    0.503826\nName: A, dtype: float64"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "# or alternativelly, type the index of the row required!\n",
    "# This is a numerical-BASE index locator = iloc\n",
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Returning a Single Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "-0.8480769834036315"
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "# iNDEXING\n",
    "df.loc['B', 'Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "-0.8480769834036315"
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "# Returnning the same as previous, just locatting it\n",
    "# Grab the element on the second row ('B) and in the third column ('Y'), right?\n",
    "df.iloc[1,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Returning a SUB-SET of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          W         Y\nA  2.706850  0.907969\nB  0.651118 -0.848077",
      "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>W</th>\n      <th>Y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>A</th>\n      <td>2.706850</td>\n      <td>0.907969</td>\n    </tr>\n    <tr>\n      <th>B</th>\n      <td>0.651118</td>\n      <td>-0.848077</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "# Just pass two list of the rows and columns you want!\n",
    "# Please, get used to the SQUARED BRACKET :/\n",
    "df.loc[['A', 'B'],['W', 'Y']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Ok, we're going to stop here for now and continue the discussion in the next PySeries Episode!\n"
    }
   ],
   "source": [
    "print(\"Ok, we're going to stop here for now and continue the discussion in the next PySeries Episode!\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Thank You for reading This post!. Bye!\n"
    }
   ],
   "source": [
    "# https://medium.com/jungletronics/pandas-dataframes-7ba872dcbc30\n",
    "print('Thank You for reading This post!. Bye!')"
   ]
  }
 ]
}