pandas_DF_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0e44bcc7",
   "metadata": {},
   "source": [
    "# PAndas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d62b3737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\KIRAN Sonkamble\\\\python_by_afsan'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "633f4802",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load file into \n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "829c14ef",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   col1  col2  col3\n",
       "0     1     3     6\n",
       "1     2     4     7\n",
       "2     3     5     8"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d={'col1':[1,2,3],'col2':[3,4,5],'col3':[6,7,8]}\n",
    "df=pd.DataFrame(d,copy=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "405512c8",
   "metadata": {},
   "outputs": [
    {
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       "   0\n",
       "0  1\n",
       "1  2\n",
       "2  3"
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     "execution_count": 18,
     "metadata": {},
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   ],
   "source": [
    "pd.DataFrame([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b380833e",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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      "text/plain": [
       "   0  1  2\n",
       "0  1  2  3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([[1,2,3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "03cfca23",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <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",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1    2    3    4\n",
       "0  1  2  3.0  4.0  NaN\n",
       "1  4  3  NaN  NaN  NaN\n",
       "2  0  6  9.0  8.0  7.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([[1,2,3,4],[4,3],[0,6,9,8,7]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6091f069",
   "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>7</th>\n",
       "      <th>5</th>\n",
       "      <th>3</th>\n",
       "      <th>0</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   7  5    3    0    9\n",
       "0  1  2  3.0  4.0  NaN\n",
       "1  4  3  NaN  NaN  NaN\n",
       "2  0  6  9.0  8.0  7.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([[1,2,3,4],[4,3],[0,6,9,8,7]],columns=[7,5,3,0,9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "77972942",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>z</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
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       "  <tbody>\n",
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      "text/plain": [
       "   z  x  y\n",
       "0  1  3  1\n",
       "1  2  2  1\n",
       "2  3  1  1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "d1=pd.DataFrame({'z':(1,2,3),'x':[3,2,1],'y':1},dtype=np.int8)\n",
    "# d1.info()\n",
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "493a53e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>!</th>\n",
       "      <th>@</th>\n",
       "      <th>#</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
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       "      <td>4</td>\n",
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       "      <td>7</td>\n",
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      "text/plain": [
       "   !  @  #\n",
       "9  1  2  3\n",
       "4  4  5  6\n",
       "6  7  8  9"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# Constructing data frame using ndarray\n",
    "\n",
    "df2= pd.DataFrame(np.array([[1,2,3],[4,5,6],[7,8,9]]),columns=['!','@','#'],index=[9,4,6])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7add3df5",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>l</th>\n",
       "      <th>d</th>\n",
       "      <th>w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>89</td>\n",
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      "text/plain": [
       "    a   l   d    w\n",
       "1   1   2   3  NaN\n",
       "3   0   7   4  1.0\n",
       "5  12  89  34  NaN"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([[1,2,3],[0,7,4,1],[12,89,34]],columns=('a','l','d','w'),index=[1,3,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7fac3bfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [0]\n",
       "Index: []"
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     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df2=pd.DataFrame(np.array([]))\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c2c60ee4",
   "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>!</th>\n",
       "      <th>@</th>\n",
       "      <th>#</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
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      "text/plain": [
       "   !  @  #\n",
       "9  1  2  3\n",
       "4  4  5  6\n",
       "6  7  8  9"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df3= pd.DataFrame(np.array([(1,2,3),(4,5,6),(7,8,9)]),columns=['!','@','#'],index=[9,4,6])\n",
    "df3\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ae594a49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n",
       "      dtype=[('a', '<i4'), ('b', '<i4'), ('c', '<i4')])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n",
    "                dtype=[(\"a\", \"i4\"), (\"b\", \"i4\"), (\"c\", \"i4\")])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4e6c15c3",
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "0  1  2  3\n",
       "1  4  5  6\n",
       "2  7  8  9"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3= pd.DataFrame(np.array([(1,2,3),(4,5,6),(7,8,9)],dtype=[(\"a\", \"i4\"), (\"b\", \"i4\"), (\"c\", \"i4\")]))\n",
    "df3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b1dd44e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cd923f6d",
   "metadata": {},
   "source": [
    "# 1st way to create a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "44f28455",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\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>name_speaker</th>\n",
       "      <th>speaker_occupation</th>\n",
       "      <th>title</th>\n",
       "      <th>views</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ken Robinson</td>\n",
       "      <td>Author/educator</td>\n",
       "      <td>Do schools kill creativity?</td>\n",
       "      <td>47227110</td>\n",
       "      <td>4553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Al Gore</td>\n",
       "      <td>Climate advocate</td>\n",
       "      <td>Averting the climate crisis</td>\n",
       "      <td>3200520</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>David Pogue</td>\n",
       "      <td>Technology columnist</td>\n",
       "      <td>Simplicity sells</td>\n",
       "      <td>1636292</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Majora Carter</td>\n",
       "      <td>Activist for environmental justice</td>\n",
       "      <td>Greening the ghetto</td>\n",
       "      <td>1697550</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hans Rosling</td>\n",
       "      <td>Global health expert; data visionary</td>\n",
       "      <td>The best stats you've ever seen</td>\n",
       "      <td>12005869</td>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tony Robbins</td>\n",
       "      <td>Life coach; expert in leadership psychology</td>\n",
       "      <td>Why we do what we do</td>\n",
       "      <td>20685401</td>\n",
       "      <td>672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Julia Sweeney</td>\n",
       "      <td>Actor, comedian, playwright</td>\n",
       "      <td>Letting go of God</td>\n",
       "      <td>3769987</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Joshua Prince-Ramus</td>\n",
       "      <td>Architect</td>\n",
       "      <td>Behind the design of Seattle's library</td>\n",
       "      <td>967741</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Dan Dennett</td>\n",
       "      <td>Philosopher, cognitive scientist</td>\n",
       "      <td>Let's teach religion -- all religion -- in sch...</td>\n",
       "      <td>2567958</td>\n",
       "      <td>582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Rick Warren</td>\n",
       "      <td>Pastor, author</td>\n",
       "      <td>A life of purpose</td>\n",
       "      <td>3095993</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name_speaker                           speaker_occupation  \\\n",
       "0         Ken Robinson                              Author/educator   \n",
       "1              Al Gore                             Climate advocate   \n",
       "2          David Pogue                         Technology columnist   \n",
       "3        Majora Carter           Activist for environmental justice   \n",
       "4         Hans Rosling         Global health expert; data visionary   \n",
       "5         Tony Robbins  Life coach; expert in leadership psychology   \n",
       "6        Julia Sweeney                  Actor, comedian, playwright   \n",
       "7  Joshua Prince-Ramus                                    Architect   \n",
       "8          Dan Dennett             Philosopher, cognitive scientist   \n",
       "9          Rick Warren                               Pastor, author   \n",
       "\n",
       "                                               title     views  comments  \n",
       "0                        Do schools kill creativity?  47227110      4553  \n",
       "1                        Averting the climate crisis   3200520       265  \n",
       "2                                   Simplicity sells   1636292       124  \n",
       "3                                Greening the ghetto   1697550       200  \n",
       "4                    The best stats you've ever seen  12005869       593  \n",
       "5                               Why we do what we do  20685401       672  \n",
       "6                                  Letting go of God   3769987       919  \n",
       "7             Behind the design of Seattle's library    967741        46  \n",
       "8  Let's teach religion -- all religion -- in sch...   2567958       582  \n",
       "9                                  A life of purpose   3095993       900  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('datasets/ted_data.csv')\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f5da091a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>model</th>\n",
       "      <th>max_resolution</th>\n",
       "      <th>low_resolution</th>\n",
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       "      <th>storage</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Canon PowerShot A460</td>\n",
       "      <td>2592</td>\n",
       "      <td>2048</td>\n",
       "      <td>38</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Kodak C763</td>\n",
       "      <td>3072</td>\n",
       "      <td>2592</td>\n",
       "      <td>35</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Nikon Coolpix P500</td>\n",
       "      <td>3648</td>\n",
       "      <td>2560</td>\n",
       "      <td>36</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Samsung S850</td>\n",
       "      <td>3264</td>\n",
       "      <td>2558</td>\n",
       "      <td>39</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Sony DSC</td>\n",
       "      <td>3228</td>\n",
       "      <td>2624</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                 model  max_resolution  low_resolution  \\\n",
       "0           0  Canon PowerShot A460            2592            2048   \n",
       "1           1            Kodak C763            3072            2592   \n",
       "2           2    Nikon Coolpix P500            3648            2560   \n",
       "3           3          Samsung S850            3264            2558   \n",
       "4           4              Sony DSC            3228            2624   \n",
       "\n",
       "   zoom_width  storage  \n",
       "0          38       16  \n",
       "1          35       32  \n",
       "2          36       16  \n",
       "3          39       16  \n",
       "4          31       32  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load a data into pandas\n",
    "\n",
    "df1=pd.read_excel('datasets/automobile_camera.xlsx',sheet_name=1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c7241e5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0:    Unnamed: 0                   car_name  mpg  horsepower origin_country\n",
       " 0           0     Toyota Corolla Mark ii   24          95          Japan\n",
       " 1           1  Chevrolet Chevelle Malibu   18         130             US\n",
       " 2           2                Audi 100 LS   24          90        Germany\n",
       " 3           3                   BMW 2002   26         113        Germany\n",
       " 4           4               Datsun PL510   27          88          Japan,\n",
       " 1:    Unnamed: 0                 model  max_resolution  low_resolution  \\\n",
       " 0           0  Canon PowerShot A460            2592            2048   \n",
       " 1           1            Kodak C763            3072            2592   \n",
       " 2           2    Nikon Coolpix P500            3648            2560   \n",
       " 3           3          Samsung S850            3264            2558   \n",
       " 4           4              Sony DSC            3228            2624   \n",
       " \n",
       "    zoom_width  storage  \n",
       " 0          38       16  \n",
       " 1          35       32  \n",
       " 2          36       16  \n",
       " 3          39       16  \n",
       " 4          31       32  }"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df1=pd.read_excel('datasets/automobile_camera.xlsx',sheet_name=[0,1])\n",
    "df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b09c0a20",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>Canon PowerShot A460</td>\n",
       "      <td>2592</td>\n",
       "      <td>2048</td>\n",
       "      <td>38</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Kodak C763</td>\n",
       "      <td>3072</td>\n",
       "      <td>2592</td>\n",
       "      <td>35</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Nikon Coolpix P500</td>\n",
       "      <td>3648</td>\n",
       "      <td>2560</td>\n",
       "      <td>36</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Samsung S850</td>\n",
       "      <td>3264</td>\n",
       "      <td>2558</td>\n",
       "      <td>39</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Sony DSC</td>\n",
       "      <td>3228</td>\n",
       "      <td>2624</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                 model  max_resolution  low_resolution  \\\n",
       "0           0  Canon PowerShot A460            2592            2048   \n",
       "1           1            Kodak C763            3072            2592   \n",
       "2           2    Nikon Coolpix P500            3648            2560   \n",
       "3           3          Samsung S850            3264            2558   \n",
       "4           4              Sony DSC            3228            2624   \n",
       "\n",
       "   zoom_width  storage  \n",
       "0          38       16  \n",
       "1          35       32  \n",
       "2          36       16  \n",
       "3          39       16  \n",
       "4          31       32  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[1]\n",
    "# or\n",
    "# df1=pd.read_excel('datasets/automobile_camera.xlsx',sheet_name=['Automobile','Camera'])\n",
    "# df1['Automobile']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "93b3efd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2nd way to create df\n",
    "# Using dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c864cc54",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dict = {'Year' : [1990, 1994, 1998, 2002],\n",
    "           'Country' : ['Italy', 'USA', 'France', 'Japan'],\n",
    "           'Winner' : ['Germany', 'Brazil', 'France', 'Brazil'],\n",
    "           'GoalScored' : [115, 141, 171, 161]\n",
    "          }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "52d3d257",
   "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>Year</th>\n",
       "      <th>Country</th>\n",
       "      <th>Winner</th>\n",
       "      <th>GoalScored</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Germany</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1994</td>\n",
       "      <td>USA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1998</td>\n",
       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year Country   Winner  GoalScored\n",
       "0  1990   Italy  Germany         115\n",
       "1  1994     USA   Brazil         141\n",
       "2  1998  France   France         171\n",
       "3  2002   Japan   Brazil         161"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.DataFrame(df_dict)\n",
    "df\n",
    "\n",
    "# KEY becomes col\n",
    "# Array must be same length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c2639b46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3rd way\n",
    "\n",
    "df_dict_udindex = {'Winner' : pd.Series(['Germany', 'Brazil', 'France']),               \n",
    "         'GoalScored' : pd.Series([115, 141, 171, 161])} \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "46c9cf7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Winner</th>\n",
       "      <th>GoalScored</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Germany</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Winner  GoalScored\n",
       "0  Germany         115\n",
       "1   Brazil         141\n",
       "2   France         171\n",
       "3      NaN         161"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.DataFrame(df_dict_udindex)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fccb1aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nan - Not a number or missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9bfe5075",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4 the way\n",
    "\n",
    "df_lotuples = [(2002, 'Japan', 'Brazil', 161), \n",
    "                (2006, 'Germany', 'Italy', 147), \n",
    "                (2010, 'South Africa', 'Spain', 145),\n",
    "                (2014, 'Brazil', 'Germany', 171)\n",
    "              ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "efecb209",
   "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",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Italy</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Spain</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Germany</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0             1        2    3\n",
       "0  2002         Japan   Brazil  161\n",
       "1  2006       Germany    Italy  147\n",
       "2  2010  South Africa    Spain  145\n",
       "3  2014        Brazil  Germany  171"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(df_lotuples)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "302004dc",
   "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>9</th>\n",
       "      <th>8</th>\n",
       "      <th>7</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Italy</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Spain</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Germany</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      9             8        7    6\n",
       "0  2002         Japan   Brazil  161\n",
       "1  2006       Germany    Italy  147\n",
       "2  2010  South Africa    Spain  145\n",
       "3  2014        Brazil  Germany  171"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4=pd.DataFrame(df_lotuples,columns=[9,8,7,6])\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "eca1f3c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5th way\n",
    "\n",
    "df_lodict = [\n",
    "             {'year' : 2002, 'HostCountry' : 'Japan', 'Winner' : 'Brazil'},\n",
    "             {'year' : 2006, 'HostCountry' : 'Germany', 'Winner' : 'Italy'},\n",
    "             {'year' : 2010, 'HostCountry' : 'South Africa', 'Winner' : 'Spain'},\n",
    "             {'year' : 2014, 'HostCountry' : 'Brazil', 'Winner' : 'Germany'},\n",
    "            ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d33f9bce",
   "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>year</th>\n",
       "      <th>HostCountry</th>\n",
       "      <th>Winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   HostCountry   Winner\n",
       "0  2002         Japan   Brazil\n",
       "1  2006       Germany    Italy\n",
       "2  2010  South Africa    Spain\n",
       "3  2014        Brazil  Germany"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(df_lodict)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13df6bab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "78976022",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Datframe basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "21c4e0e3",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"datasets/weather_data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d55ae11b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 4)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "aef4bcdc",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you want to see top data only\n",
    "\n",
    "df.head(3)     #--> show only top 3 data   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "eacca3ac",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tail\n",
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "322a55e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['day', 'temperature', 'windspeed', 'event'], dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cc21625b",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30.333333</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.829708</td>\n",
       "      <td>2.338090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.750000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>6.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temperature  windspeed\n",
       "count     6.000000   6.000000\n",
       "mean     30.333333   4.666667\n",
       "std       3.829708   2.338090\n",
       "min      24.000000   2.000000\n",
       "25%      28.750000   2.500000\n",
       "50%      31.500000   5.000000\n",
       "75%      32.000000   6.750000\n",
       "max      35.000000   7.000000"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get the basic statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "9e9bfa49",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>event</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
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       "      <td>1/1/2017</td>\n",
       "      <td>Rain</td>\n",
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       "    <tr>\n",
       "      <th>freq</th>\n",
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       "      <td>2</td>\n",
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      "text/plain": [
       "             day event\n",
       "count          6     6\n",
       "unique         6     3\n",
       "top     1/1/2017  Rain\n",
       "freq           1     2"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d9578ead",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>6</td>\n",
       "      <td>6.000000</td>\n",
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       "      <th>top</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.829708</td>\n",
       "      <td>2.338090</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.750000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "             day  temperature  windspeed event\n",
       "count          6     6.000000   6.000000     6\n",
       "unique         6          NaN        NaN     3\n",
       "top     1/1/2017          NaN        NaN  Rain\n",
       "freq           1          NaN        NaN     2\n",
       "mean         NaN    30.333333   4.666667   NaN\n",
       "std          NaN     3.829708   2.338090   NaN\n",
       "min          NaN    24.000000   2.000000   NaN\n",
       "25%          NaN    28.750000   2.500000   NaN\n",
       "50%          NaN    31.500000   5.000000   NaN\n",
       "75%          NaN    32.000000   6.750000   NaN\n",
       "max          NaN    35.000000   7.000000   NaN"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cbcff03b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6 entries, 0 to 5\n",
      "Data columns (total 4 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   day          6 non-null      object\n",
      " 1   temperature  6 non-null      int64 \n",
      " 2   windspeed    6 non-null      int64 \n",
      " 3   event        6 non-null      object\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 320.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7b3cfb2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1c9300e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selection/slicing and indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6738908a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>name_localizedValue</th>\n",
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       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>33558</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>33593</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>232</td>\n",
       "      <td>2018-05-01 01:31:29</td>\n",
       "      <td>2018-05-01 01:35:22</td>\n",
       "      <td>3194</td>\n",
       "      <td>McGinley Square</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>29217</td>\n",
       "      <td>FREE Bonus Month with Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190</td>\n",
       "      <td>2018-05-01 02:03:29</td>\n",
       "      <td>2018-05-01 02:06:40</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>29662</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>303</td>\n",
       "      <td>2018-05-01 04:27:12</td>\n",
       "      <td>2018-05-01 04:32:16</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>15271</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32423</th>\n",
       "      <td>396</td>\n",
       "      <td>2018-05-30 13:49:18</td>\n",
       "      <td>2018-05-30 13:55:55</td>\n",
       "      <td>3184</td>\n",
       "      <td>Paulus Hook</td>\n",
       "      <td>3279</td>\n",
       "      <td>Dixon Mills</td>\n",
       "      <td>29639</td>\n",
       "      <td>Join Citi Bike for $14.95/month</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32424</th>\n",
       "      <td>313</td>\n",
       "      <td>2018-05-30 13:49:21</td>\n",
       "      <td>2018-05-30 13:54:35</td>\n",
       "      <td>3202</td>\n",
       "      <td>Newport PATH</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>26301</td>\n",
       "      <td>$25 Off Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32425</th>\n",
       "      <td>316</td>\n",
       "      <td>2018-05-30 13:49:51</td>\n",
       "      <td>2018-05-30 13:55:08</td>\n",
       "      <td>3220</td>\n",
       "      <td>5 Corners Library</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>29260</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32426</th>\n",
       "      <td>1130</td>\n",
       "      <td>2018-05-30 13:50:52</td>\n",
       "      <td>2018-05-30 14:09:42</td>\n",
       "      <td>3281</td>\n",
       "      <td>Leonard Gordon Park</td>\n",
       "      <td>3213</td>\n",
       "      <td>Van Vorst Park</td>\n",
       "      <td>26239</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32427</th>\n",
       "      <td>369</td>\n",
       "      <td>2018-05-30 13:50:58</td>\n",
       "      <td>2018-05-30 13:57:08</td>\n",
       "      <td>3220</td>\n",
       "      <td>5 Corners Library</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>33660</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32428 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tripduration            starttime             stoptime  \\\n",
       "0               338  2018-05-01 00:04:47  2018-05-01 00:10:25   \n",
       "1              1482  2018-05-01 01:31:10  2018-05-01 01:55:53   \n",
       "2               232  2018-05-01 01:31:29  2018-05-01 01:35:22   \n",
       "3               190  2018-05-01 02:03:29  2018-05-01 02:06:40   \n",
       "4               303  2018-05-01 04:27:12  2018-05-01 04:32:16   \n",
       "...             ...                  ...                  ...   \n",
       "32423           396  2018-05-30 13:49:18  2018-05-30 13:55:55   \n",
       "32424           313  2018-05-30 13:49:21  2018-05-30 13:54:35   \n",
       "32425           316  2018-05-30 13:49:51  2018-05-30 13:55:08   \n",
       "32426          1130  2018-05-30 13:50:52  2018-05-30 14:09:42   \n",
       "32427           369  2018-05-30 13:50:58  2018-05-30 13:57:08   \n",
       "\n",
       "       start station id   start station name  end station id end station name  \\\n",
       "0                  3639           Harborside            3199     Newport Pkwy   \n",
       "1                  3681             Grand St            3185        City Hall   \n",
       "2                  3194      McGinley Square            3193     Lincoln Park   \n",
       "3                  3185            City Hall            3186    Grove St PATH   \n",
       "4                  3207          Oakland Ave            3195          Sip Ave   \n",
       "...                 ...                  ...             ...              ...   \n",
       "32423              3184          Paulus Hook            3279      Dixon Mills   \n",
       "32424              3202         Newport PATH            3639       Harborside   \n",
       "32425              3220    5 Corners Library            3195          Sip Ave   \n",
       "32426              3281  Leonard Gordon Park            3213   Van Vorst Park   \n",
       "32427              3220    5 Corners Library            3207      Oakland Ave   \n",
       "\n",
       "       bikeid                      name_localizedValue    usertype  \n",
       "0       33558                        Annual Membership  Subscriber  \n",
       "1       33593                                  24 Hour    Customer  \n",
       "2       29217  FREE Bonus Month with Annual Membership  Subscriber  \n",
       "3       29662                                  24 Hour    Customer  \n",
       "4       15271                        Annual Membership  Subscriber  \n",
       "...       ...                                      ...         ...  \n",
       "32423   29639          Join Citi Bike for $14.95/month  Subscriber  \n",
       "32424   26301                $25 Off Annual Membership  Subscriber  \n",
       "32425   29260                        Annual Membership  Subscriber  \n",
       "32426   26239                        Annual Membership  Subscriber  \n",
       "32427   33660                        Annual Membership  Subscriber  \n",
       "\n",
       "[32428 rows x 10 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.read_csv('datasets/citibike_tripdata.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "50059d6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32428 entries, 0 to 32427\n",
      "Data columns (total 10 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   tripduration         32428 non-null  int64 \n",
      " 1   starttime            32428 non-null  object\n",
      " 2   stoptime             32428 non-null  object\n",
      " 3   start station id     32428 non-null  int64 \n",
      " 4   start station name   32428 non-null  object\n",
      " 5   end station id       32428 non-null  int64 \n",
      " 6   end station name     32428 non-null  object\n",
      " 7   bikeid               32428 non-null  int64 \n",
      " 8   name_localizedValue  32428 non-null  object\n",
      " 9   usertype             32428 non-null  object\n",
      "dtypes: int64(4), object(6)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5e9a3ce1",
   "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>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>name_localizedValue</th>\n",
       "      <th>usertype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>338</td>\n",
       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>33558</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>33593</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>232</td>\n",
       "      <td>2018-05-01 01:31:29</td>\n",
       "      <td>2018-05-01 01:35:22</td>\n",
       "      <td>3194</td>\n",
       "      <td>McGinley Square</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>29217</td>\n",
       "      <td>FREE Bonus Month with Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190</td>\n",
       "      <td>2018-05-01 02:03:29</td>\n",
       "      <td>2018-05-01 02:06:40</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>29662</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>303</td>\n",
       "      <td>2018-05-01 04:27:12</td>\n",
       "      <td>2018-05-01 04:32:16</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>15271</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32423</th>\n",
       "      <td>396</td>\n",
       "      <td>2018-05-30 13:49:18</td>\n",
       "      <td>2018-05-30 13:55:55</td>\n",
       "      <td>3184</td>\n",
       "      <td>Paulus Hook</td>\n",
       "      <td>3279</td>\n",
       "      <td>Dixon Mills</td>\n",
       "      <td>29639</td>\n",
       "      <td>Join Citi Bike for $14.95/month</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32424</th>\n",
       "      <td>313</td>\n",
       "      <td>2018-05-30 13:49:21</td>\n",
       "      <td>2018-05-30 13:54:35</td>\n",
       "      <td>3202</td>\n",
       "      <td>Newport PATH</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>26301</td>\n",
       "      <td>$25 Off Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32425</th>\n",
       "      <td>316</td>\n",
       "      <td>2018-05-30 13:49:51</td>\n",
       "      <td>2018-05-30 13:55:08</td>\n",
       "      <td>3220</td>\n",
       "      <td>5 Corners Library</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>29260</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32426</th>\n",
       "      <td>1130</td>\n",
       "      <td>2018-05-30 13:50:52</td>\n",
       "      <td>2018-05-30 14:09:42</td>\n",
       "      <td>3281</td>\n",
       "      <td>Leonard Gordon Park</td>\n",
       "      <td>3213</td>\n",
       "      <td>Van Vorst Park</td>\n",
       "      <td>26239</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32427</th>\n",
       "      <td>369</td>\n",
       "      <td>2018-05-30 13:50:58</td>\n",
       "      <td>2018-05-30 13:57:08</td>\n",
       "      <td>3220</td>\n",
       "      <td>5 Corners Library</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>33660</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32428 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tripduration            starttime             stoptime  \\\n",
       "0               338  2018-05-01 00:04:47  2018-05-01 00:10:25   \n",
       "1              1482  2018-05-01 01:31:10  2018-05-01 01:55:53   \n",
       "2               232  2018-05-01 01:31:29  2018-05-01 01:35:22   \n",
       "3               190  2018-05-01 02:03:29  2018-05-01 02:06:40   \n",
       "4               303  2018-05-01 04:27:12  2018-05-01 04:32:16   \n",
       "...             ...                  ...                  ...   \n",
       "32423           396  2018-05-30 13:49:18  2018-05-30 13:55:55   \n",
       "32424           313  2018-05-30 13:49:21  2018-05-30 13:54:35   \n",
       "32425           316  2018-05-30 13:49:51  2018-05-30 13:55:08   \n",
       "32426          1130  2018-05-30 13:50:52  2018-05-30 14:09:42   \n",
       "32427           369  2018-05-30 13:50:58  2018-05-30 13:57:08   \n",
       "\n",
       "       start station id   start station name  end station id end station name  \\\n",
       "0                  3639           Harborside            3199     Newport Pkwy   \n",
       "1                  3681             Grand St            3185        City Hall   \n",
       "2                  3194      McGinley Square            3193     Lincoln Park   \n",
       "3                  3185            City Hall            3186    Grove St PATH   \n",
       "4                  3207          Oakland Ave            3195          Sip Ave   \n",
       "...                 ...                  ...             ...              ...   \n",
       "32423              3184          Paulus Hook            3279      Dixon Mills   \n",
       "32424              3202         Newport PATH            3639       Harborside   \n",
       "32425              3220    5 Corners Library            3195          Sip Ave   \n",
       "32426              3281  Leonard Gordon Park            3213   Van Vorst Park   \n",
       "32427              3220    5 Corners Library            3207      Oakland Ave   \n",
       "\n",
       "       bikeid                      name_localizedValue    usertype  \n",
       "0       33558                        Annual Membership  Subscriber  \n",
       "1       33593                                  24 Hour    Customer  \n",
       "2       29217  FREE Bonus Month with Annual Membership  Subscriber  \n",
       "3       29662                                  24 Hour    Customer  \n",
       "4       15271                        Annual Membership  Subscriber  \n",
       "...       ...                                      ...         ...  \n",
       "32423   29639          Join Citi Bike for $14.95/month  Subscriber  \n",
       "32424   26301                $25 Off Annual Membership  Subscriber  \n",
       "32425   29260                        Annual Membership  Subscriber  \n",
       "32426   26239                        Annual Membership  Subscriber  \n",
       "32427   33660                        Annual Membership  Subscriber  \n",
       "\n",
       "[32428 rows x 10 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "20c5a1b9",
   "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>bikeid</th>\n",
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       "  <tbody>\n",
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       "      <td>29662</td>\n",
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       "      <th>4</th>\n",
       "      <td>15271</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>32423</th>\n",
       "      <td>29639</td>\n",
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       "    <tr>\n",
       "      <th>32424</th>\n",
       "      <td>26301</td>\n",
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       "    <tr>\n",
       "      <th>32425</th>\n",
       "      <td>29260</td>\n",
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       "    <tr>\n",
       "      <th>32426</th>\n",
       "      <td>26239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32427</th>\n",
       "      <td>33660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32428 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bikeid\n",
       "0       33558\n",
       "1       33593\n",
       "2       29217\n",
       "3       29662\n",
       "4       15271\n",
       "...       ...\n",
       "32423   29639\n",
       "32424   26301\n",
       "32425   29260\n",
       "32426   26239\n",
       "32427   33660\n",
       "\n",
       "[32428 rows x 1 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting only one columns\n",
    "\n",
    "df[['bikeid']]\n",
    "# GETS Series data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "97ed916f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
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       "      <td>29217</td>\n",
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       "      <th>3</th>\n",
       "      <td>29662</td>\n",
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       "      <th>4</th>\n",
       "      <td>15271</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29298</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33619</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bikeid  tripduration\n",
       "0   33558           338\n",
       "1   33593          1482\n",
       "2   29217           232\n",
       "3   29662           190\n",
       "4   15271           303\n",
       "5   29298           176\n",
       "6   33619           577"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['bikeid','tripduration']].head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecd573ce",
   "metadata": {},
   "source": [
    "# @@ only for ROWS\n",
    "# Slicing operation   slicing operation willl always gives new dataf\n",
    "\n",
    "1. Normal\n",
    "2. loc\n",
    "3. iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cd72dfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normal:  only on rows\n",
    "\n",
    "datframe[start:stop:step]   ---> zero indexing\n",
    "\n",
    "datframe[start_name:stop_name:step]   --> custom indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3341dce8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
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       "      <td>3639</td>\n",
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       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
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       "      <th>1</th>\n",
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       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>33593</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>232</td>\n",
       "      <td>2018-05-01 01:31:29</td>\n",
       "      <td>2018-05-01 01:35:22</td>\n",
       "      <td>3194</td>\n",
       "      <td>McGinley Square</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>29217</td>\n",
       "      <td>FREE Bonus Month with Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190</td>\n",
       "      <td>2018-05-01 02:03:29</td>\n",
       "      <td>2018-05-01 02:06:40</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>29662</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>303</td>\n",
       "      <td>2018-05-01 04:27:12</td>\n",
       "      <td>2018-05-01 04:32:16</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>15271</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "0           338  2018-05-01 00:04:47  2018-05-01 00:10:25              3639   \n",
       "1          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "2           232  2018-05-01 01:31:29  2018-05-01 01:35:22              3194   \n",
       "3           190  2018-05-01 02:03:29  2018-05-01 02:06:40              3185   \n",
       "4           303  2018-05-01 04:27:12  2018-05-01 04:32:16              3207   \n",
       "\n",
       "  start station name  end station id end station name  bikeid  \\\n",
       "0         Harborside            3199     Newport Pkwy   33558   \n",
       "1           Grand St            3185        City Hall   33593   \n",
       "2    McGinley Square            3193     Lincoln Park   29217   \n",
       "3          City Hall            3186    Grove St PATH   29662   \n",
       "4        Oakland Ave            3195          Sip Ave   15271   \n",
       "\n",
       "                       name_localizedValue    usertype  \n",
       "0                        Annual Membership  Subscriber  \n",
       "1                                  24 Hour    Customer  \n",
       "2  FREE Bonus Month with Annual Membership  Subscriber  \n",
       "3                                  24 Hour    Customer  \n",
       "4                        Annual Membership  Subscriber  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take 4 rows\n",
    "\n",
    "df[0:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "03e6fb62",
   "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>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>name_localizedValue</th>\n",
       "      <th>usertype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>338</td>\n",
       "      <td>2018-05-01 00:04:47</td>\n",
       "      <td>2018-05-01 00:10:25</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>33558</td>\n",
       "      <td>Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-05-01 01:31:10</td>\n",
       "      <td>2018-05-01 01:55:53</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>33593</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>232</td>\n",
       "      <td>2018-05-01 01:31:29</td>\n",
       "      <td>2018-05-01 01:35:22</td>\n",
       "      <td>3194</td>\n",
       "      <td>McGinley Square</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>29217</td>\n",
       "      <td>FREE Bonus Month with Annual Membership</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190</td>\n",
       "      <td>2018-05-01 02:03:29</td>\n",
       "      <td>2018-05-01 02:06:40</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>29662</td>\n",
       "      <td>24 Hour</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "0           338  2018-05-01 00:04:47  2018-05-01 00:10:25              3639   \n",
       "1          1482  2018-05-01 01:31:10  2018-05-01 01:55:53              3681   \n",
       "2           232  2018-05-01 01:31:29  2018-05-01 01:35:22              3194   \n",
       "3           190  2018-05-01 02:03:29  2018-05-01 02:06:40              3185   \n",
       "\n",
       "  start station name  end station id end station name  bikeid  \\\n",
       "0         Harborside            3199     Newport Pkwy   33558   \n",
       "1           Grand St            3185        City Hall   33593   \n",
       "2    McGinley Square            3193     Lincoln Park   29217   \n",
       "3          City Hall            3186    Grove St PATH   29662   \n",
       "\n",
       "                       name_localizedValue    usertype  \n",
       "0                        Annual Membership  Subscriber  \n",
       "1                                  24 Hour    Customer  \n",
       "2  FREE Bonus Month with Annual Membership  Subscriber  \n",
       "3                                  24 Hour    Customer  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:4:]\n",
    "# Empty dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f5cad57a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For custom indexing\n",
    "new_el= ['a',]\n",
    "new_data=df[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e727f4db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_data['a':'c':2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "b0e8a288",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 10)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data[::-1].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2dc1731",
   "metadata": {},
   "source": [
    "# above methode is only for rows but if we want both then comes\n",
    "\n",
    "loc\n",
    "iloc\n",
    "\n",
    "concepts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b73ac43",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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