DataFrames 1&2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          W         X         Y         Z\n",
      "A  2.706850  0.628133  0.907969  0.503826\n",
      "B  0.651118 -0.319318 -0.848077  0.605965\n",
      "C -2.018168  0.740122  0.528813 -0.589001\n",
      "D  0.188695 -0.758872 -0.933237  0.955057\n",
      "E  0.190794  1.978757  2.605967  0.683509\n",
      "F  0.302665  1.693723 -1.706086 -1.159119 <class 'pandas.core.frame.DataFrame'> \n",
      "\n",
      "A    2.706850\n",
      "B    0.651118\n",
      "C   -2.018168\n",
      "D    0.188695\n",
      "E    0.190794\n",
      "F    0.302665\n",
      "Name: W, dtype: float64 <class 'pandas.core.series.Series'> \n",
      "\n",
      "          W         Z\n",
      "A  2.706850  0.503826\n",
      "B  0.651118  0.605965\n",
      "C -2.018168 -0.589001\n",
      "D  0.188695  0.955057\n",
      "E  0.190794  0.683509\n",
      "F  0.302665 -1.159119 \n",
      "\n",
      "          W         X         Y         Z       sum\n",
      "A  2.706850  0.628133  0.907969  0.503826  3.334983\n",
      "B  0.651118 -0.319318 -0.848077  0.605965  0.331800\n",
      "C -2.018168  0.740122  0.528813 -0.589001 -1.278046\n",
      "D  0.188695 -0.758872 -0.933237  0.955057 -0.570177\n",
      "E  0.190794  1.978757  2.605967  0.683509  2.169552\n",
      "F  0.302665  1.693723 -1.706086 -1.159119  1.996388\n",
      "          W         X         Y         Z\n",
      "A  2.706850  0.628133  0.907969  0.503826\n",
      "B  0.651118 -0.319318 -0.848077  0.605965\n",
      "C -2.018168  0.740122  0.528813 -0.589001\n",
      "D  0.188695 -0.758872 -0.933237  0.955057\n",
      "E  0.190794  1.978757  2.605967  0.683509\n",
      "F  0.302665  1.693723 -1.706086 -1.159119\n",
      "          W         X         Y         Z\n",
      "A  2.706850  0.628133  0.907969  0.503826\n",
      "B  0.651118 -0.319318 -0.848077  0.605965\n",
      "C -2.018168  0.740122  0.528813 -0.589001\n",
      "D  0.188695 -0.758872 -0.933237  0.955057\n",
      "F  0.302665  1.693723 -1.706086 -1.159119\n",
      "(5, 4)\n",
      "W    2.706850\n",
      "X    0.628133\n",
      "Y    0.907969\n",
      "Z    0.503826\n",
      "Name: A, dtype: float64 <class 'pandas.core.series.Series'>\n",
      "W   -2.018168\n",
      "X    0.740122\n",
      "Y    0.528813\n",
      "Z   -0.589001\n",
      "Name: C, dtype: float64\n",
      "0.907969446477\n",
      "          W         Y\n",
      "A  2.706850  0.907969\n",
      "B  0.651118 -0.848077\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from numpy.random import randn\n",
    "#same set of random num as in video -> using seed(101)\n",
    "np.random.seed(101)\n",
    "df = pd.DataFrame(randn(6,4),['A','B','C','D','E','F'],['W','X','Y','Z'])\n",
    "print(df,type(df),'\\n')\n",
    "\n",
    "#SINGLE COLUMN WE GET SERIES\n",
    "print(df['W'],type(df['W']),'\\n')\n",
    "\n",
    "#MULTIPLE COLUMNS WE GET DATAFRAME\n",
    "print(df[['W','Z']],'\\n')\n",
    "\n",
    "#adding new column\n",
    "df['sum'] =  df['W']+df['X']\n",
    "print(df)\n",
    "\n",
    "#deleting col\n",
    "#axis = 0 refers index, 1 refers col\n",
    "# for changes to occur use inplace= true\n",
    "df.drop('sum',axis = 1,inplace= True)\n",
    "print(df)\n",
    "\n",
    "#drop index\n",
    "df.drop('E',inplace = True)\n",
    "print(df)\n",
    "\n",
    "#shape to see axis 0 ,1\n",
    "print(df.shape)\n",
    "\n",
    "#rows or index\n",
    "print(df.loc['A'],type(df.loc['A']))\n",
    "print(df.iloc[2])\n",
    "\n",
    "#getting values\n",
    "print(df.loc['A','Y'])\n",
    "\n",
    "print(df.loc[['A','B'],['W','Y']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       W      X      Y      Z\n",
      "A   True   True   True   True\n",
      "B   True  False  False   True\n",
      "C  False   True   True  False\n",
      "D   True  False  False   True\n",
      "F   True   True  False  False\n",
      "       W      X      Y      Z\n",
      "A   True   True   True   True\n",
      "B   True  False  False   True\n",
      "C  False   True   True  False\n",
      "D   True  False  False   True\n",
      "F   True   True  False  False\n",
      "          W         X         Y         Z\n",
      "A  2.706850  0.628133  0.907969  0.503826\n",
      "B  0.651118       NaN       NaN  0.605965\n",
      "C       NaN  0.740122  0.528813       NaN\n",
      "D  0.188695       NaN       NaN  0.955057\n",
      "F  0.302665  1.693723       NaN       NaN\n",
      "          W         X         Y         Z\n",
      "A  2.706850  0.628133  0.907969  0.503826\n",
      "B  0.651118       NaN       NaN  0.605965\n",
      "C       NaN  0.740122  0.528813       NaN\n",
      "D  0.188695       NaN       NaN  0.955057\n",
      "F  0.302665  1.693723       NaN       NaN\n"
     ]
    }
   ],
   "source": [
    "#selecting values in entire dataframe\n",
    "\n",
    "print(df > 0)\n",
    "booldf = df > 0\n",
    "print(booldf)\n",
    "#displays values that are true & Nan for false\n",
    "print(df[booldf])\n",
    "print(df[df>0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A     True\n",
      "B     True\n",
      "C    False\n",
      "D     True\n",
      "F     True\n",
      "Name: W, dtype: bool\n",
      "          W         X         Y         Z\n",
      "A  2.706850  0.628133  0.907969  0.503826\n",
      "B  0.651118 -0.319318 -0.848077  0.605965\n",
      "D  0.188695 -0.758872 -0.933237  0.955057\n",
      "F  0.302665  1.693723 -1.706086 -1.159119\n",
      "          W         X         Y         Z\n",
      "C -2.018168  0.740122  0.528813 -0.589001\n",
      "F  0.302665  1.693723 -1.706086 -1.159119\n",
      "A    0.628133\n",
      "B   -0.319318\n",
      "D   -0.758872\n",
      "F    1.693723\n",
      "Name: X, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#selecting values inside a col & displaying df\n",
    "print(df['W']>0)\n",
    "#displays only true values NO null values \n",
    "print(df[df['W']>0])\n",
    "print(df[df['Z']<0])\n",
    "#selecting & displaying particular col\n",
    "print( df[df['W']>0]['X'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A     True\n",
      "B     True\n",
      "C    False\n",
      "D     True\n",
      "F     True\n",
      "Name: W, dtype: bool\n",
      "          W         X         Y         Z\n",
      "A  2.706850  0.628133  0.907969  0.503826\n",
      "B  0.651118 -0.319318 -0.848077  0.605965\n",
      "D  0.188695 -0.758872 -0.933237  0.955057\n",
      "F  0.302665  1.693723 -1.706086 -1.159119\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Y</th>\n",
       "      <th>X</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.628133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.848077</td>\n",
       "      <td>-0.319318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.933237</td>\n",
       "      <td>-0.758872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>-1.706086</td>\n",
       "      <td>1.693723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Y         X\n",
       "A  0.907969  0.628133\n",
       "B -0.848077 -0.319318\n",
       "D -0.933237 -0.758872\n",
       "F -1.706086  1.693723"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# same as above more clearly\n",
    "boolser = df['W']>0\n",
    "print(boolser)\n",
    "result = df[boolser]\n",
    "print(result)\n",
    "mycols = ['Y','X']\n",
    "result[mycols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          W         X         Y         Z\n",
      "F  0.302665  1.693723 -1.706086 -1.159119\n",
      "          W         X         Y         Z\n",
      "A  2.706850  0.628133  0.907969  0.503826\n",
      "B  0.651118 -0.319318 -0.848077  0.605965\n",
      "D  0.188695 -0.758872 -0.933237  0.955057\n",
      "F  0.302665  1.693723 -1.706086 -1.159119\n"
     ]
    }
   ],
   "source": [
    "#more than 1 condition\n",
    "# df[(df['W']>0) and (df['Y']>1)] -> and cant compare series. use &\n",
    "print(  df[(df['W']>0) & (df['X']>1)]  )\n",
    "print(  df[(df['W']>0) | (df['X']>1)]  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</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>0</th>\n",
       "      <td>A</td>\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>1</th>\n",
       "      <td>B</td>\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>2</th>\n",
       "      <td>C</td>\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>3</th>\n",
       "      <td>D</td>\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>4</th>\n",
       "      <td>F</td>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  index         W         X         Y         Z\n",
       "0     A  2.706850  0.628133  0.907969  0.503826\n",
       "1     B  0.651118 -0.319318 -0.848077  0.605965\n",
       "2     C -2.018168  0.740122  0.528813 -0.589001\n",
       "3     D  0.188695 -0.758872 -0.933237  0.955057\n",
       "4     F  0.302665  1.693723 -1.706086 -1.159119"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index reset to a col\n",
    "df.reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CA', 'NY', 'WY', 'OR', 'CO']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#while adding length of value should match with len of index\n",
    "newind = 'CA NY WY OR CO'.split() \n",
    "newind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "<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>states</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>CA</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>NY</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>WY</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>OR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z states\n",
       "A  2.706850  0.628133  0.907969  0.503826     CA\n",
       "B  0.651118 -0.319318 -0.848077  0.605965     NY\n",
       "C -2.018168  0.740122  0.528813 -0.589001     WY\n",
       "D  0.188695 -0.758872 -0.933237  0.955057     OR\n",
       "F  0.302665  1.693723 -1.706086 -1.159119     CO"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['states'] = newind\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "<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",
       "    <tr>\n",
       "      <th>states</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CA</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>NY</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>WY</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>OR</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>CO</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               W         X         Y         Z\n",
       "states                                        \n",
       "CA      2.706850  0.628133  0.907969  0.503826\n",
       "NY      0.651118 -0.319318 -0.848077  0.605965\n",
       "WY     -2.018168  0.740122  0.528813 -0.589001\n",
       "OR      0.188695 -0.758872 -0.933237  0.955057\n",
       "CO      0.302665  1.693723 -1.706086 -1.159119"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('states')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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