Day 3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "Think of pandas as an extremely powerful version of Excel, with a **lot more features.**\n",
    "\n",
    "* Series\n",
    "* DataFrames\n",
    "* Missing Data\n",
    "* GroupBy\n",
    "* Merging,Joining,and Concatenating\n",
    "* Operations\n",
    "* Data Input and Output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "A Series can have **axis labels**, meaning it can be indexed by a label, instead of just a number location. It also doesn't need to hold numeric data, it can hold any arbitrary Python Object.\n",
    "\n",
    "**Key features:**\n",
    "* **Homogeneous data**\n",
    "* **Size Immutable –size cannot be changed**\n",
    "* **Values of Data Mutable**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Series\n",
    "\n",
    "You can convert a list,numpy array, or dictionary to a Series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['a','b','c','d']\n",
    "my_list = [10,20,30,40]\n",
    "arr = np.array([10,20,30,40])\n",
    "d = {'a':10,'b':20,'c':30,'d':40}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Dealing with Series:Using Lists**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "3    40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(data=my_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "d    40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(data=my_list,index=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "d    40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(my_list,labels) #no need to add specify like above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Dealing with series: NumPy Arrays **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "3    40\n",
       "dtype: int32"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "d    40\n",
       "dtype: int32"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(arr,labels)#data first then index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Dictionary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 10, 'b': 20, 'c': 30, 'd': 40}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "d    40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data in a Series\n",
    "\n",
    "A pandas Series can hold a variety of object types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "3    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(data=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      <built-in function sum>\n",
       "1    <built-in function print>\n",
       "2      <built-in function len>\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Even functions (although unlikely that you will use this)\n",
    "pd.Series([sum,print,len])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using an Index\n",
    "\n",
    "The key to using a Series is understanding its index. Pandas makes use of these index names or numbers by allowing for fast look ups of information (works like a hash table or dictionary).\n",
    "\n",
    "Let's see some examples of how to grab information from a Series. Let us create two sereis, ser1 and ser2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser1 = pd.Series([1,2,3,4],index = ['USA', 'Germany','USSR', 'Japan'])                                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USA        1\n",
       "Germany    2\n",
       "USSR       3\n",
       "Japan      4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser2 = pd.Series([1,2,5,4],index = ['USA', 'Germany','Italy', 'Japan'])                                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USA        1\n",
       "Germany    2\n",
       "Italy      5\n",
       "Japan      4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1['USA']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Operations are then also done based off of index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Germany    4.0\n",
       "Italy      NaN\n",
       "Japan      8.0\n",
       "USA        2.0\n",
       "USSR       NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1 + ser2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DataFrames\n",
    "\n",
    "* Think of a DataFrame as a bunch of Series objects put together to share the same index.\n",
    "* The data is represented in rows and columns.\n",
    "\n",
    "**Key Features of a DataFrame:**\n",
    "* **Heterogeneous data**\n",
    "* **Size Mutable**\n",
    "* **Data Mutable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import randn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(randn(5,4),index='A B C D E'.split(),columns='W X Y Z'.split())# data, index=row,coloumn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.023574</td>\n",
       "      <td>-0.317857</td>\n",
       "      <td>1.910203</td>\n",
       "      <td>-0.969245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.737793</td>\n",
       "      <td>-0.194093</td>\n",
       "      <td>-0.342460</td>\n",
       "      <td>0.769201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.542481</td>\n",
       "      <td>-0.604787</td>\n",
       "      <td>1.049819</td>\n",
       "      <td>-0.117467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.500218</td>\n",
       "      <td>-0.239240</td>\n",
       "      <td>-2.023448</td>\n",
       "      <td>-0.434992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-1.074238</td>\n",
       "      <td>-0.371593</td>\n",
       "      <td>1.174365</td>\n",
       "      <td>-1.922123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  1.023574 -0.317857  1.910203 -0.969245\n",
       "B -1.737793 -0.194093 -0.342460  0.769201\n",
       "C -0.542481 -0.604787  1.049819 -0.117467\n",
       "D -0.500218 -0.239240 -2.023448 -0.434992\n",
       "E -1.074238 -0.371593  1.174365 -1.922123"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1.023574\n",
       "B   -1.737793\n",
       "C   -0.542481\n",
       "D   -0.500218\n",
       "E   -1.074238\n",
       "Name: W, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['W']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.023574</td>\n",
       "      <td>-0.969245</td>\n",
       "      <td>1.910203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.737793</td>\n",
       "      <td>0.769201</td>\n",
       "      <td>-0.342460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.542481</td>\n",
       "      <td>-0.117467</td>\n",
       "      <td>1.049819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.500218</td>\n",
       "      <td>-0.434992</td>\n",
       "      <td>-2.023448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-1.074238</td>\n",
       "      <td>-1.922123</td>\n",
       "      <td>1.174365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         Z         Y\n",
       "A  1.023574 -0.969245  1.910203\n",
       "B -1.737793  0.769201 -0.342460\n",
       "C -0.542481 -0.117467  1.049819\n",
       "D -0.500218 -0.434992 -2.023448\n",
       "E -1.074238 -1.922123  1.174365"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pass a list of column names\n",
    "df[['W','Z','Y']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1.023574\n",
       "B   -1.737793\n",
       "C   -0.542481\n",
       "D   -0.500218\n",
       "E   -1.074238\n",
       "Name: W, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.W #dont use this using column name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['W'])#DataFrame Columns are just Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating a new column:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['new'] = df['W'] + df['Y'] #Creating a new column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.023574</td>\n",
       "      <td>-0.317857</td>\n",
       "      <td>1.910203</td>\n",
       "      <td>-0.969245</td>\n",
       "      <td>2.933777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.737793</td>\n",
       "      <td>-0.194093</td>\n",
       "      <td>-0.342460</td>\n",
       "      <td>0.769201</td>\n",
       "      <td>-2.080253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.542481</td>\n",
       "      <td>-0.604787</td>\n",
       "      <td>1.049819</td>\n",
       "      <td>-0.117467</td>\n",
       "      <td>0.507338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.500218</td>\n",
       "      <td>-0.239240</td>\n",
       "      <td>-2.023448</td>\n",
       "      <td>-0.434992</td>\n",
       "      <td>-2.523666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-1.074238</td>\n",
       "      <td>-0.371593</td>\n",
       "      <td>1.174365</td>\n",
       "      <td>-1.922123</td>\n",
       "      <td>0.100127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z       new\n",
       "A  1.023574 -0.317857  1.910203 -0.969245  2.933777\n",
       "B -1.737793 -0.194093 -0.342460  0.769201 -2.080253\n",
       "C -0.542481 -0.604787  1.049819 -0.117467  0.507338\n",
       "D -0.500218 -0.239240 -2.023448 -0.434992 -2.523666\n",
       "E -1.074238 -0.371593  1.174365 -1.922123  0.100127"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "</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>1.023574</td>\n",
       "      <td>-0.317857</td>\n",
       "      <td>1.910203</td>\n",
       "      <td>-0.969245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.737793</td>\n",
       "      <td>-0.194093</td>\n",
       "      <td>-0.342460</td>\n",
       "      <td>0.769201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.542481</td>\n",
       "      <td>-0.604787</td>\n",
       "      <td>1.049819</td>\n",
       "      <td>-0.117467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.500218</td>\n",
       "      <td>-0.239240</td>\n",
       "      <td>-2.023448</td>\n",
       "      <td>-0.434992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-1.074238</td>\n",
       "      <td>-0.371593</td>\n",
       "      <td>1.174365</td>\n",
       "      <td>-1.922123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  1.023574 -0.317857  1.910203 -0.969245\n",
       "B -1.737793 -0.194093 -0.342460  0.769201\n",
       "C -0.542481 -0.604787  1.049819 -0.117467\n",
       "D -0.500218 -0.239240 -2.023448 -0.434992\n",
       "E -1.074238 -0.371593  1.174365 -1.922123"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('new',axis=1) #axis =0 is index # does not drop the coloumn from the Df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('new',axis=1,inplace=True) #remove the coloum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.023574</td>\n",
       "      <td>-0.317857</td>\n",
       "      <td>1.910203</td>\n",
       "      <td>-0.969245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.737793</td>\n",
       "      <td>-0.194093</td>\n",
       "      <td>-0.342460</td>\n",
       "      <td>0.769201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.542481</td>\n",
       "      <td>-0.604787</td>\n",
       "      <td>1.049819</td>\n",
       "      <td>-0.117467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.500218</td>\n",
       "      <td>-0.239240</td>\n",
       "      <td>-2.023448</td>\n",
       "      <td>-0.434992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-1.074238</td>\n",
       "      <td>-0.371593</td>\n",
       "      <td>1.174365</td>\n",
       "      <td>-1.922123</td>\n",
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       "  </tbody>\n",
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       "          W         X         Y         Z\n",
       "A  1.023574 -0.317857  1.910203 -0.969245\n",
       "B -1.737793 -0.194093 -0.342460  0.769201\n",
       "C -0.542481 -0.604787  1.049819 -0.117467\n",
       "D -0.500218 -0.239240 -2.023448 -0.434992\n",
       "E -1.074238 -0.371593  1.174365 -1.922123"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>A</th>\n",
       "      <td>1.023574</td>\n",
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       "      <td>1.910203</td>\n",
       "      <td>-0.969245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.737793</td>\n",
       "      <td>-0.194093</td>\n",
       "      <td>-0.342460</td>\n",
       "      <td>0.769201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.542481</td>\n",
       "      <td>-0.604787</td>\n",
       "      <td>1.049819</td>\n",
       "      <td>-0.117467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.500218</td>\n",
       "      <td>-0.239240</td>\n",
       "      <td>-2.023448</td>\n",
       "      <td>-0.434992</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  1.023574 -0.317857  1.910203 -0.969245\n",
       "B -1.737793 -0.194093 -0.342460  0.769201\n",
       "C -0.542481 -0.604787  1.049819 -0.117467\n",
       "D -0.500218 -0.239240 -2.023448 -0.434992"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('E')# no inplace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.023574</td>\n",
       "      <td>-0.317857</td>\n",
       "      <td>1.910203</td>\n",
       "      <td>-0.969245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.737793</td>\n",
       "      <td>-0.194093</td>\n",
       "      <td>-0.342460</td>\n",
       "      <td>0.769201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.542481</td>\n",
       "      <td>-0.604787</td>\n",
       "      <td>1.049819</td>\n",
       "      <td>-0.117467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.500218</td>\n",
       "      <td>-0.239240</td>\n",
       "      <td>-2.023448</td>\n",
       "      <td>-0.434992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-1.074238</td>\n",
       "      <td>-0.371593</td>\n",
       "      <td>1.174365</td>\n",
       "      <td>-1.922123</td>\n",
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      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  1.023574 -0.317857  1.910203 -0.969245\n",
       "B -1.737793 -0.194093 -0.342460  0.769201\n",
       "C -0.542481 -0.604787  1.049819 -0.117467\n",
       "D -0.500218 -0.239240 -2.023448 -0.434992\n",
       "E -1.074238 -0.371593  1.174365 -1.922123"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 4)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "W    1.023574\n",
       "X   -0.317857\n",
       "Y    1.910203\n",
       "Z   -0.969245\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['A'] # selecting Rows _> series too!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "W   -0.542481\n",
       "X   -0.604787\n",
       "Y    1.049819\n",
       "Z   -0.117467\n",
       "Name: C, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3424601318460833"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['B','Y'] #Selecting subset of rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>W</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.023574</td>\n",
       "      <td>1.910203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.737793</td>\n",
       "      <td>-0.342460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         Y\n",
       "A  1.023574  1.910203\n",
       "B -1.737793 -0.342460"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[['A','B'],['W','Y']] #Subset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditional Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.023574</td>\n",
       "      <td>-0.317857</td>\n",
       "      <td>1.910203</td>\n",
       "      <td>-0.969245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.737793</td>\n",
       "      <td>-0.194093</td>\n",
       "      <td>-0.342460</td>\n",
       "      <td>0.769201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.542481</td>\n",
       "      <td>-0.604787</td>\n",
       "      <td>1.049819</td>\n",
       "      <td>-0.117467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.500218</td>\n",
       "      <td>-0.239240</td>\n",
       "      <td>-2.023448</td>\n",
       "      <td>-0.434992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-1.074238</td>\n",
       "      <td>-0.371593</td>\n",
       "      <td>1.174365</td>\n",
       "      <td>-1.922123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  1.023574 -0.317857  1.910203 -0.969245\n",
       "B -1.737793 -0.194093 -0.342460  0.769201\n",
       "C -0.542481 -0.604787  1.049819 -0.117467\n",
       "D -0.500218 -0.239240 -2.023448 -0.434992\n",
       "E -1.074238 -0.371593  1.174365 -1.922123"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
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       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       W      X      Y      Z\n",
       "A   True  False   True  False\n",
       "B  False  False  False   True\n",
       "C  False  False   True  False\n",
       "D  False  False  False  False\n",
       "E  False  False   True  False"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df>0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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>1.023574</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.910203</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.769201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.049819</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.174365</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "          W   X         Y         Z\n",
       "A  1.023574 NaN  1.910203       NaN\n",
       "B       NaN NaN       NaN  0.769201\n",
       "C       NaN NaN  1.049819       NaN\n",
       "D       NaN NaN       NaN       NaN\n",
       "E       NaN NaN  1.174365       NaN"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df>0.1] ## checking id any >0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.910203</td>\n",
       "      <td>-0.969245</td>\n",
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       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.737793</td>\n",
       "      <td>-0.194093</td>\n",
       "      <td>-0.342460</td>\n",
       "      <td>0.769201</td>\n",
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       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.542481</td>\n",
       "      <td>-0.604787</td>\n",
       "      <td>1.049819</td>\n",
       "      <td>-0.117467</td>\n",
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       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.500218</td>\n",
       "      <td>-0.239240</td>\n",
       "      <td>-2.023448</td>\n",
       "      <td>-0.434992</td>\n",
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       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-1.074238</td>\n",
       "      <td>-0.371593</td>\n",
       "      <td>1.174365</td>\n",
       "      <td>-1.922123</td>\n",
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       "          W         X         Y         Z\n",
       "A  1.023574 -0.317857  1.910203 -0.969245\n",
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       "E -1.074238 -0.371593  1.174365 -1.922123"
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     "execution_count": 67,
     "metadata": {},
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   "source": [
    "df"
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  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    False\n",
       "B     True\n",
       "C     True\n",
       "D     True\n",
       "E     True\n",
       "Name: W, dtype: bool"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['W']<0"
   ]
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  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A     True\n",
       "B    False\n",
       "C    False\n",
       "D    False\n",
       "E    False\n",
       "Name: W, dtype: bool"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['W']>0"
   ]
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  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "df[df['W']>0] #Checking if there is W>0"
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   "execution_count": 73,
   "metadata": {},
   "outputs": [
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       "      <td>-1.737793</td>\n",
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       "      <td>-0.342460</td>\n",
       "      <td>0.769201</td>\n",
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       "      <td>1.049819</td>\n",
       "      <td>-0.117467</td>\n",
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       "      <th>D</th>\n",
       "      <td>-0.500218</td>\n",
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       "      <td>-0.434992</td>\n",
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       "      <th>E</th>\n",
       "      <td>-1.074238</td>\n",
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       "          W         X         Y         Z\n",
       "B -1.737793 -0.194093 -0.342460  0.769201\n",
       "C -0.542481 -0.604787  1.049819 -0.117467\n",
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   "source": [
    "df[df['W']<0] #Checking if there is W<0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   "source": [
    "df[df['Z']>0] #Checking if there is Z>0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "B   -0.342460\n",
       "C    1.049819\n",
       "D   -2.023448\n",
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       "Name: Y, dtype: float64"
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     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df[df['W']<0]['Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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