Pandas Introduction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "lis = [1,2,3,4]\n",
    "labels = \"A B C D\".split()\n",
    "dic = {'a':1, 'b':2, 'c':3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series(lis)\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series(dic)\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1\n",
       "B    2\n",
       "C    3\n",
       "D    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series(lis, index=labels)\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    A\n",
       "1    B\n",
       "2    C\n",
       "0    D\n",
       "1    E\n",
       "2    F\n",
       "dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = pd.Series(\"A B C\".split())\n",
    "s3 = pd.Series(\"D E F\".split())\n",
    "s2.append(s3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    A\n",
       "1    B\n",
       "2    C\n",
       "3    D\n",
       "4    E\n",
       "5    F\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2.append(s3, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1\n",
       "B    2\n",
       "C    3\n",
       "D    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Tom</th>\n",
       "      <th>Dick</th>\n",
       "      <th>Harry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Tom  Dick  Harry\n",
       "A    1     4      7\n",
       "B    2     5      8\n",
       "C    3     6      9"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic = {\n",
    "    'Tom': [1,2,3],\n",
    "    'Dick': [4,5,6],\n",
    "    'Harry':[7,8,9]\n",
    "}\n",
    "df = pd.DataFrame(dic, index=['A', 'B', 'C'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1\n",
       "B    2\n",
       "C    3\n",
       "Name: Tom, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Tom']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'A'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2888\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2889\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2890\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'A'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-62-c31f9b0c4302>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'A'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2897\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2898\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2899\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2900\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2901\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2889\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2890\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2891\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2893\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'A'"
     ]
    }
   ],
   "source": [
    "df['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tom      1\n",
       "Dick     4\n",
       "Harry    7\n",
       "Name: A, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tom      1\n",
       "Dick     4\n",
       "Harry    7\n",
       "Name: A, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Pokemon</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>Fire</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pokemon   Type\n",
       "0   Bulbasaur  Grass\n",
       "1     Ivysaur  Grass\n",
       "2    Venusaur  Grass\n",
       "3  Charmander   Fire\n",
       "4  Charmeleon   Fire"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon = pd.read_csv('Datasets/pokemon.csv')\n",
    "pokemon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "\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>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "\n",
       "             College     Salary  \n",
       "0              Texas  7730337.0  \n",
       "1          Marquette  6796117.0  \n",
       "2  Boston University        NaN  \n",
       "3      Georgia State  1148640.0  \n",
       "4                NaN  5000000.0  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba = pd.read_csv('Datasets/Set1/nba.csv')\n",
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Team', 'Number', 'Position', 'Age', 'Height', 'Weight',\n",
       "       'College', 'Salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 458 entries, 0 to 457\n",
      "Data columns (total 9 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Name      457 non-null    object \n",
      " 1   Team      457 non-null    object \n",
      " 2   Number    457 non-null    float64\n",
      " 3   Position  457 non-null    object \n",
      " 4   Age       457 non-null    float64\n",
      " 5   Height    457 non-null    object \n",
      " 6   Weight    457 non-null    float64\n",
      " 7   College   373 non-null    object \n",
      " 8   Salary    446 non-null    float64\n",
      "dtypes: float64(4), object(5)\n",
      "memory usage: 32.3+ KB\n"
     ]
    }
   ],
   "source": [
    "nba.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New Orleans Pelicans      19\n",
       "Memphis Grizzlies         18\n",
       "Milwaukee Bucks           16\n",
       "New York Knicks           16\n",
       "San Antonio Spurs         15\n",
       "Phoenix Suns              15\n",
       "Dallas Mavericks          15\n",
       "Oklahoma City Thunder     15\n",
       "Utah Jazz                 15\n",
       "Toronto Raptors           15\n",
       "Portland Trail Blazers    15\n",
       "Indiana Pacers            15\n",
       "Washington Wizards        15\n",
       "Atlanta Hawks             15\n",
       "Houston Rockets           15\n",
       "Boston Celtics            15\n",
       "Brooklyn Nets             15\n",
       "Detroit Pistons           15\n",
       "Golden State Warriors     15\n",
       "Miami Heat                15\n",
       "Denver Nuggets            15\n",
       "Los Angeles Lakers        15\n",
       "Los Angeles Clippers      15\n",
       "Philadelphia 76ers        15\n",
       "Chicago Bulls             15\n",
       "Charlotte Hornets         15\n",
       "Cleveland Cavaliers       15\n",
       "Sacramento Kings          15\n",
       "Minnesota Timberwolves    14\n",
       "Orlando Magic             14\n",
       "Name: Team, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba['Team'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditional Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       True\n",
       "1       True\n",
       "2       True\n",
       "3       True\n",
       "4       True\n",
       "       ...  \n",
       "453    False\n",
       "454    False\n",
       "455    False\n",
       "456    False\n",
       "457    False\n",
       "Name: Team, Length: 458, dtype: bool"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba['Team'] == 'Boston Celtics'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>Kent Bazemore</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>201.0</td>\n",
       "      <td>Old Dominion</td>\n",
       "      <td>2000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>Tim Hardaway Jr.</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>10.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>1304520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Kirk Hinrich</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>12.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6-4</td>\n",
       "      <td>190.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>2854940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Al Horford</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>15.0</td>\n",
       "      <td>C</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>245.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Kris Humphries</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>43.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>1000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>Kyle Korver</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>26.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6-7</td>\n",
       "      <td>212.0</td>\n",
       "      <td>Creighton</td>\n",
       "      <td>5746479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>Paul Millsap</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>4.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>246.0</td>\n",
       "      <td>Louisiana Tech</td>\n",
       "      <td>18671659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>Mike Muscala</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>31.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>240.0</td>\n",
       "      <td>Bucknell</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>Lamar Patterson</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>13.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>225.0</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>525093.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>Dennis Schroder</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>17.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>172.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1763400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Mike Scott</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>32.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>237.0</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>3333333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>Thabo Sefolosha</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>25.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6-7</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Tiago Splitter</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>11.0</td>\n",
       "      <td>C</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>245.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9756250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>Walter Tavares</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>22.0</td>\n",
       "      <td>C</td>\n",
       "      <td>24.0</td>\n",
       "      <td>7-3</td>\n",
       "      <td>260.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>Jeff Teague</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>186.0</td>\n",
       "      <td>Wake Forest</td>\n",
       "      <td>8000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name           Team  Number Position   Age Height  Weight  \\\n",
       "309     Kent Bazemore  Atlanta Hawks    24.0       SF  26.0    6-5   201.0   \n",
       "310  Tim Hardaway Jr.  Atlanta Hawks    10.0       SG  24.0    6-6   205.0   \n",
       "311      Kirk Hinrich  Atlanta Hawks    12.0       SG  35.0    6-4   190.0   \n",
       "312        Al Horford  Atlanta Hawks    15.0        C  30.0   6-10   245.0   \n",
       "313    Kris Humphries  Atlanta Hawks    43.0       PF  31.0    6-9   235.0   \n",
       "314       Kyle Korver  Atlanta Hawks    26.0       SG  35.0    6-7   212.0   \n",
       "315      Paul Millsap  Atlanta Hawks     4.0       PF  31.0    6-8   246.0   \n",
       "316      Mike Muscala  Atlanta Hawks    31.0       PF  24.0   6-11   240.0   \n",
       "317   Lamar Patterson  Atlanta Hawks    13.0       SG  24.0    6-5   225.0   \n",
       "318   Dennis Schroder  Atlanta Hawks    17.0       PG  22.0    6-1   172.0   \n",
       "319        Mike Scott  Atlanta Hawks    32.0       PF  27.0    6-8   237.0   \n",
       "320   Thabo Sefolosha  Atlanta Hawks    25.0       SF  32.0    6-7   220.0   \n",
       "321    Tiago Splitter  Atlanta Hawks    11.0        C  31.0   6-11   245.0   \n",
       "322    Walter Tavares  Atlanta Hawks    22.0        C  24.0    7-3   260.0   \n",
       "323       Jeff Teague  Atlanta Hawks     0.0       PG  27.0    6-2   186.0   \n",
       "\n",
       "            College      Salary  \n",
       "309    Old Dominion   2000000.0  \n",
       "310        Michigan   1304520.0  \n",
       "311          Kansas   2854940.0  \n",
       "312         Florida  12000000.0  \n",
       "313       Minnesota   1000000.0  \n",
       "314       Creighton   5746479.0  \n",
       "315  Louisiana Tech  18671659.0  \n",
       "316        Bucknell    947276.0  \n",
       "317      Pittsburgh    525093.0  \n",
       "318             NaN   1763400.0  \n",
       "319        Virginia   3333333.0  \n",
       "320             NaN   4000000.0  \n",
       "321             NaN   9756250.0  \n",
       "322             NaN   1000000.0  \n",
       "323     Wake Forest   8000000.0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hawks = nba[nba['Team'] == 'Atlanta Hawks']\n",
    "hawks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "309    26.0\n",
       "310    24.0\n",
       "311    35.0\n",
       "312    30.0\n",
       "313    31.0\n",
       "314    35.0\n",
       "315    31.0\n",
       "316    24.0\n",
       "317    24.0\n",
       "318    22.0\n",
       "319    27.0\n",
       "320    32.0\n",
       "321    31.0\n",
       "322    24.0\n",
       "323    27.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hawks['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.2"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hawks['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.2"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[nba['Team'] == 'Atlanta Hawks']['Age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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