Hands-On-With-Pandas.ipynb

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   "source": [
    "<h1 align='center'><i> Hands On with Pandas </i></h1>"
   ]
  },
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   "source": [
    "1. **Introduction**\n",
    "\n",
    "2. **Creating and Viewing DataFrames and Series:**\n",
    "   - `pd.DataFrame()`\n",
    "   - `pd.Series()`\n",
    "   - `df.head()`\n",
    "   - `df.tail()`\n",
    "   - `df.shape`\n",
    "   - `df.dtypes`\n",
    "   - `df.sample()`\n",
    "\n",
    "3. **Data Aggregation and Statistics**\n",
    "   - `df.sum()` \n",
    "   - `df.mean()`\n",
    "   - `df.median()`\n",
    "   - `df.max()`\n",
    "   - `df.min()`\n",
    "   - `df.count()`\n",
    "   - `df.nunique()`\n",
    "   - `df.info()`\n",
    "   - `df.describe()`\n",
    "   - `df.value_counts()`\n",
    "\n",
    "3. **Indexing and Selection:**\n",
    "   - `df['column_name']` \n",
    "   - `df.column_name` \n",
    "   - `df[['column_name']]`\n",
    "   - `df.loc[row_label]`\n",
    "   - `df.iloc[row_index]`\n",
    "   - `df[df['column'] > value]`\n",
    "   - `df[mask]`\n",
    "   - `df.ix[]`\n",
    "   - `df.where()`\n",
    "   - `df.query()`\n",
    "   - `df.isin()`\n",
    "\n",
    "4. **Data Manipulation:**\n",
    "   - `df.drop()`\n",
    "   - `df.rename()`\n",
    "   - `df.sort_values()`\n",
    "   - `df.groupby()`\n",
    "   - `df.get_group()`\n",
    "   - `df['a'].astype('data_type')`\n",
    "   - `df.set_index()`\n",
    "   - `df.reset_index()`\n",
    "   - `del df['col']`\n",
    "   - `df.agg()`\n",
    "   - `df.map()`\n",
    "   - `df.rank()`\n",
    "\n",
    "5. **Missing Data Handling:**\n",
    "   - `df.isna()`\n",
    "   - `df.dropna()`\n",
    "   - `df.fillna()`\n",
    "   - `df.isnull()`\n",
    "   - `df.notnull()`\n",
    "\n",
    "6. **Data Cleaning and Transformation:**\n",
    "   - `df.apply()`\n",
    "   - `df.replace()`\n",
    "   - `df.duplicated()`\n",
    "   - `df.drop_duplicates()`\n",
    "\n",
    "8. **Merging and Joining DataFrames:**\n",
    "   - `pd.concat()`\n",
    "   - `pd.merge()`\n",
    "   - `pd.join()`\n",
    "\n",
    "9. **Working with Dates and Times:**\n",
    "   - `pd.to_datetime()`\n",
    "   - `df.resample()`\n",
    "\n",
    "10. **Data Visualization:**\n",
    "   - `df.plot()`\n",
    "   - `df.hist()`, `df.boxplot()`\n",
    "\n",
    "11. **IO Operations:**\n",
    "   - `pd.read_csv()`, `pd.read_excel()`\n",
    "   - `df.to_csv()`, `df.to_excel()`"
   ]
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  {
   "cell_type": "markdown",
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   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is a popular open-source Python library widely used for data manipulation and analysis. It provides powerful data structures and functions that make working with structured data, such as CSV files, Excel sheets, SQL tables, and more, efficient and straightforward. The name *Pandas* is derived from \"Panel Data,\" which is a term used in statistics and econometrics.\n",
    "\n",
    "**DataFrame:** The core data structure in Pandas is the DataFrame, a two-dimensional labeled data structure with columns of potentially different data types. It resembles a spreadsheet or SQL table and allows data to be organized and analyzed effectively.\n",
    "\n",
    "**Series:** Another essential data structure in Pandas is the Series, which is a one-dimensional labeled array. Series can hold data of any type and are used to represent a single column or row of data in a DataFrame."
   ]
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   "source": [
    "### Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (1.4.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from pandas) (2021.3)\n",
      "Requirement already satisfied: numpy>=1.18.5 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from pandas) (1.21.5)\n",
      "Requirement already satisfied: six>=1.5 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating and Viewing DataFrames and Series"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame\n",
    "\n",
    "**From external file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "df_ext = pd.read_csv(\"data/nba.csv\")\n",
    "\n",
    "print(type(df_ext))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**From python dictonary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ajit</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Name  Age\n",
       "0  Ajit   23"
      ]
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   ],
   "source": [
    "d = {\"Name\":[\"Ajit\", \"Sujit\"], \"Age\":[23, 22]}\n",
    "\n",
    "df_dict = pd.DataFrame(d)\n",
    "\n",
    "df_dict.head(1)"
   ]
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       "      <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",
       "  </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",
       "\n",
       "             College     Salary  \n",
       "0              Texas  7730337.0  \n",
       "1          Marquette  6796117.0  \n",
       "2  Boston University        NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "df_ext.head(3)"
   ]
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   "execution_count": 17,
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       "(458, 9)"
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     "execution_count": 17,
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   "source": [
    "df_ext.shape"
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   "cell_type": "code",
   "execution_count": 18,
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       "      <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>453</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>2433333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-3</td>\n",
       "      <td>256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name       Team  Number Position   Age Height  Weight College  \\\n",
       "453  Shelvin Mack  Utah Jazz     8.0       PG  26.0    6-3   203.0  Butler   \n",
       "454     Raul Neto  Utah Jazz    25.0       PG  24.0    6-1   179.0     NaN   \n",
       "455  Tibor Pleiss  Utah Jazz    21.0        C  26.0    7-3   256.0     NaN   \n",
       "456   Jeff Withey  Utah Jazz    24.0        C  26.0    7-0   231.0  Kansas   \n",
       "457           NaN        NaN     NaN      NaN   NaN    NaN     NaN     NaN   \n",
       "\n",
       "        Salary  \n",
       "453  2433333.0  \n",
       "454   900000.0  \n",
       "455  2900000.0  \n",
       "456   947276.0  \n",
       "457        NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(458, 9)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_ext.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**From python list**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = [1, 2 , 3]\n",
    "\n",
    "lst_series = pd.Series(lst)\n",
    "\n",
    "type(lst_series)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Aggregation and Statistics"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**EDA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Team', 'Number', 'Position', 'Age', 'Height', 'Weight',\n",
       "       'College', 'Salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=458, step=1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(458, 9)"
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     "execution_count": 30,
     "metadata": {},
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   ],
   "source": [
    "df_ext.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name        457\n",
       "Team        457\n",
       "Number      457\n",
       "Position    457\n",
       "Age         457\n",
       "Height      457\n",
       "Weight      457\n",
       "College     373\n",
       "Salary      446\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name        457\n",
       "Team         30\n",
       "Number       53\n",
       "Position      5\n",
       "Age          22\n",
       "Height       18\n",
       "Weight       87\n",
       "College     118\n",
       "Salary      309\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": [
    "df_ext.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
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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>Number</th>\n",
       "      <th>Age</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>457.000000</td>\n",
       "      <td>457.000000</td>\n",
       "      <td>457.000000</td>\n",
       "      <td>4.460000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.678337</td>\n",
       "      <td>26.938731</td>\n",
       "      <td>221.522976</td>\n",
       "      <td>4.842684e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.966090</td>\n",
       "      <td>4.404016</td>\n",
       "      <td>26.368343</td>\n",
       "      <td>5.229238e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>3.088800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1.044792e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>2.839073e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>6.500000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>307.000000</td>\n",
       "      <td>2.500000e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Number         Age      Weight        Salary\n",
       "count  457.000000  457.000000  457.000000  4.460000e+02\n",
       "mean    17.678337   26.938731  221.522976  4.842684e+06\n",
       "std     15.966090    4.404016   26.368343  5.229238e+06\n",
       "min      0.000000   19.000000  161.000000  3.088800e+04\n",
       "25%      5.000000   24.000000  200.000000  1.044792e+06\n",
       "50%     13.000000   26.000000  220.000000  2.839073e+06\n",
       "75%     25.000000   30.000000  240.000000  6.500000e+06\n",
       "max     99.000000   40.000000  307.000000  2.500000e+07"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Aggregation methods**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/xf/gcbfh98s6n53wh5f2hpb84nh0000gn/T/ipykernel_11849/2378096269.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df_ext.sum()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Number    8.079000e+03\n",
       "Age       1.231100e+04\n",
       "Weight    1.012360e+05\n",
       "Salary    2.159837e+09\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/xf/gcbfh98s6n53wh5f2hpb84nh0000gn/T/ipykernel_11849/2263698965.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df_ext.sum(axis = 0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Number    8.079000e+03\n",
       "Age       1.231100e+04\n",
       "Weight    1.012360e+05\n",
       "Salary    2.159837e+09\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext.sum(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/xf/gcbfh98s6n53wh5f2hpb84nh0000gn/T/ipykernel_11849/4103256542.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df_ext.min()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Number        0.0\n",
       "Age          19.0\n",
       "Weight      161.0\n",
       "Salary    30888.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25000000.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext['Salary'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/xf/gcbfh98s6n53wh5f2hpb84nh0000gn/T/ipykernel_11849/2486152843.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df_ext.median()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Number         13.0\n",
       "Age            26.0\n",
       "Weight        220.0\n",
       "Salary    2839073.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4842684.105381166"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext['Salary'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                    Team                   Number  Position  Age   Height  Weight  College         Salary    \n",
       "Aaron Brooks            Chicago Bulls          0.0     PG        31.0  6-0     161.0   Oregon          2250000.0     1\n",
       "Mike Muscala            Atlanta Hawks          31.0    PF        24.0  6-11    240.0   Bucknell        947276.0      1\n",
       "Mike Dunleavy           Chicago Bulls          34.0    SG        35.0  6-9     230.0   Duke            4500000.0     1\n",
       "Mike Conley             Memphis Grizzlies      11.0    PG        28.0  6-1     175.0   Ohio State      9588426.0     1\n",
       "Michael Kidd-Gilchrist  Charlotte Hornets      14.0    SF        22.0  6-7     232.0   Kentucky        6331404.0     1\n",
       "                                                                                                                    ..\n",
       "Hassan Whiteside        Miami Heat             21.0    C         26.0  7-0     265.0   Marshall        981348.0      1\n",
       "Harrison Barnes         Golden State Warriors  40.0    SF        24.0  6-8     225.0   North Carolina  3873398.0     1\n",
       "Greivis Vasquez         Milwaukee Bucks        21.0    PG        29.0  6-6     217.0   Maryland        6600000.0     1\n",
       "Greg Monroe             Milwaukee Bucks        15.0    C         26.0  6-11    265.0   Georgetown      16407500.0    1\n",
       "Zach Randolph           Memphis Grizzlies      50.0    PF        34.0  6-9     260.0   Michigan State  9638555.0     1\n",
       "Length: 364, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New Orleans Pelicans      19\n",
       "Memphis Grizzlies         18\n",
       "New York Knicks           16\n",
       "Milwaukee Bucks           16\n",
       "Boston Celtics            15\n",
       "Brooklyn Nets             15\n",
       "Portland Trail Blazers    15\n",
       "Oklahoma City Thunder     15\n",
       "Denver Nuggets            15\n",
       "Washington Wizards        15\n",
       "Miami Heat                15\n",
       "Charlotte Hornets         15\n",
       "Atlanta Hawks             15\n",
       "San Antonio Spurs         15\n",
       "Houston Rockets           15\n",
       "Dallas Mavericks          15\n",
       "Indiana Pacers            15\n",
       "Detroit Pistons           15\n",
       "Cleveland Cavaliers       15\n",
       "Chicago Bulls             15\n",
       "Sacramento Kings          15\n",
       "Phoenix Suns              15\n",
       "Los Angeles Lakers        15\n",
       "Los Angeles Clippers      15\n",
       "Golden State Warriors     15\n",
       "Toronto Raptors           15\n",
       "Philadelphia 76ers        15\n",
       "Utah Jazz                 15\n",
       "Orlando Magic             14\n",
       "Minnesota Timberwolves    14\n",
       "Name: Team, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext['Team'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing and Selections\n",
    "\n",
    "Indexing and selection in Pandas refer to the process of accessing specific rows, columns, or elements from a DataFrame or Series. Pandas provides various methods for indexing and selection, allowing you to extract the data you need for analysis and manipulation. Here are the main techniques for indexing and selection in Pandas:\n",
    "\n",
    "**Indexing and Selection in DataFrame:**\n",
    "\n",
    "1. **Bracket `[]` Operator:**\n",
    "   - Selecting a Single Column: `df['column_name']` or `df.column_name`\n",
    "   - Selecting Multiple Columns: `df[['col1', 'col2']]`\n",
    "\n",
    "2. **`.loc[]` Operator:**\n",
    "   - Selection by Label (Row and Column): `df.loc[row_label, column_label]`\n",
    "   - Slicing Rows: `df.loc[start_row:end_row]`\n",
    "   - Selecting Rows and Columns: `df.loc[row_labels, column_labels]`\n",
    "\n",
    "3. **`.iloc[]` Operator:**\n",
    "   - Selection by Integer Position (Row and Column): `df.iloc[row_index, col_index]`\n",
    "   - Slicing Rows: `df.iloc[start_row:end_row]`\n",
    "   - Selecting Rows and Columns: `df.iloc[row_indices, col_indices]`\n",
    "\n",
    "4. **Boolean Indexing:**\n",
    "   - Selecting Rows Based on Condition: `df[df['column'] > value]`\n",
    "   - Combining Conditions: `df[(df['col1'] > value1) & (df['col2'] < value2)]`\n",
    "\n",
    "5. **`.at[]` and `.iat[]`:**\n",
    "   - Fast scalar value access by label or integer position: `df.at[row_label, column_label]` or `df.iat[row_index, col_index]`\n",
    "\n",
    "**Indexing and Selection in Series:**\n",
    "\n",
    "1. **Bracket `[]` Operator:**\n",
    "   - Selecting by Label: `series[label]`\n",
    "   - Selecting by Integer Position: `series[position]`\n",
    "   - Selecting Multiple Elements: `series[start:end]`\n",
    "\n",
    "2. **`.loc[]` Operator:**\n",
    "   - Selection by Label: `series.loc[label]`\n",
    "   - Slicing: `series.loc[start:end]`\n",
    "\n",
    "3. **`.iloc[]` Operator:**\n",
    "   - Selection by Integer Position: `series.iloc[position]`\n",
    "   - Slicing: `series.iloc[start:end]`\n",
    "\n",
    "4. **Boolean Indexing:**\n",
    "   - Selecting Elements Based on Condition: `series[series > value]`\n",
    "\n",
    "These indexing and selection methods allow you to retrieve specific data from a DataFrame or Series based on your needs. The selection techniques are flexible and powerful, making data extraction and analysis in Pandas efficient and intuitive. Remember that proper indexing and selection are essential for efficient data manipulation and analysis in Pandas.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Frames"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Bracket `[]` Operator**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>2433333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>2900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Salary\n",
       "0    7730337.0\n",
       "1    6796117.0\n",
       "2          NaN\n",
       "3    1148640.0\n",
       "4    5000000.0\n",
       "..         ...\n",
       "453  2433333.0\n",
       "454   900000.0\n",
       "455  2900000.0\n",
       "456   947276.0\n",
       "457        NaN\n",
       "\n",
       "[458 rows x 1 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#single column\n",
    "salary_series = df_ext['Salary']\n",
    "salary_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name     Salary\n",
       "0  Avery Bradley  7730337.0\n",
       "1    Jae Crowder  6796117.0\n",
       "2   John Holland        NaN"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#multiple columns\n",
    "name_team_df = df_ext[[\"Name\", \"Salary\"]]\n",
    "name_team_df.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`.loc[]` operator**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7730337.0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get salary for Name =  Avery Bradley\n",
    "df_ext_name_salary = df_ext.loc[0, \"Salary\"]\n",
    "df_ext_name_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Team', 'Number', 'Position', 'Age', 'Height', 'Weight',\n",
       "       'College', 'Salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LSU</td>\n",
       "      <td>1170960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Louisville</td>\n",
       "      <td>1824360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ohio State</td>\n",
       "      <td>2569260.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              College     Salary\n",
       "0               Texas  7730337.0\n",
       "2   Boston University        NaN\n",
       "4                 NaN  5000000.0\n",
       "6                 LSU  1170960.0\n",
       "8          Louisville  1824360.0\n",
       "10         Ohio State  2569260.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get College and Salary for alternate Names upto index = 10\n",
    "df_ext_name_salary = df_ext.loc[0:10:2, \"College\":\"Salary\"]\n",
    "df_ext_name_salary"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`.iloc[]` Operator**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>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": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'6-10'"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get entry at 4th row and 5th column\n",
    "result = df_ext.iloc[4, 5]\n",
    "result"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Boolean Indexing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Amir Johnson</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>90.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Jarrett Jack</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>2.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Georgia Tech</td>\n",
       "      <td>6300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Arron Afflalo</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>4.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>8000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Lou Amundson</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>17.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>220.0</td>\n",
       "      <td>UNLV</td>\n",
       "      <td>1635476.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name             Team  Number Position   Age Height  Weight  \\\n",
       "4   Jonas Jerebko   Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "5    Amir Johnson   Boston Celtics    90.0       PF  29.0    6-9   240.0   \n",
       "19   Jarrett Jack    Brooklyn Nets     2.0       PG  32.0    6-3   200.0   \n",
       "30  Arron Afflalo  New York Knicks     4.0       SG  30.0    6-5   210.0   \n",
       "31   Lou Amundson  New York Knicks    17.0       PF  33.0    6-9   220.0   \n",
       "\n",
       "         College      Salary  \n",
       "4            NaN   5000000.0  \n",
       "5            NaN  12000000.0  \n",
       "19  Georgia Tech   6300000.0  \n",
       "30          UCLA   8000000.0  \n",
       "31          UNLV   1635476.0  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get all the players where age > 28\n",
    "mask = df_ext[\"Age\"] > 28\n",
    "res_df = df_ext[mask]\n",
    "res_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ext.dropna(subset=[\"Name\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Name, Team, Number, Position, Age, Height, Weight, College, Salary]\n",
       "Index: []"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get all the players where Name starts with A and Salary is higher than the minimum\n",
    "mask = df_ext[\"Name\"].str.startswith(\"J\") | df_ext[\"Salary\"] > df_ext[\"Salary\"].min()\n",
    "\n",
    "res_df = df_ext[mask]\n",
    "res_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0      False\n",
       " 1       True\n",
       " 2       True\n",
       " 3      False\n",
       " 4       True\n",
       "        ...  \n",
       " 452    False\n",
       " 453    False\n",
       " 454    False\n",
       " 455    False\n",
       " 456     True\n",
       " Name: Name, Length: 457, dtype: bool,\n",
       " 0       True\n",
       " 1       True\n",
       " 2      False\n",
       " 3       True\n",
       " 4       True\n",
       "        ...  \n",
       " 452     True\n",
       " 453     True\n",
       " 454     True\n",
       " 455     True\n",
       " 456     True\n",
       " Name: Salary, Length: 457, dtype: bool)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext[\"Name\"].str.startswith(\"J\"), df_ext[\"Salary\"] > df_ext[\"Salary\"].min()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`.at[]` and `.iat[]`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Avery Bradley'"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext.at[0,\"Name\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Avery Bradley'"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext.iat[0, 0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Avery Bradley    7730337.0\n",
       "Jae Crowder      6796117.0\n",
       "John Holland           NaN\n",
       "R.J. Hunter      1148640.0\n",
       "Jonas Jerebko    5000000.0\n",
       "                   ...    \n",
       "Trey Lyles       2239800.0\n",
       "Shelvin Mack     2433333.0\n",
       "Raul Neto         900000.0\n",
       "Tibor Pleiss     2900000.0\n",
       "Jeff Withey       947276.0\n",
       "Length: 457, dtype: float64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series = pd.Series(list(df_ext[\"Salary\"]), index=df_ext[\"Name\"])\n",
    "series"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Bracket `[]` Operator**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7730337.0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series[\"Avery Bradley\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7730337.0"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`loc[]` operator**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Shelvin Mack    2433333.0\n",
       "Raul Neto        900000.0\n",
       "Tibor Pleiss    2900000.0\n",
       "Jeff Withey      947276.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.loc[\"Shelvin Mack\":\"Jeff Withey\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`iloc[]` operator**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Avery Bradley    7730337.0\n",
       "Jae Crowder      6796117.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.iloc[0:2]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Boolean Indexing**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Carmelo Anthony    22875000.0\n",
       "Chris Paul         21468695.0\n",
       "Kobe Bryant        25000000.0\n",
       "Derrick Rose       20093064.0\n",
       "LeBron James       22970500.0\n",
       "Dwight Howard      22359364.0\n",
       "Chris Bosh         22192730.0\n",
       "Kevin Durant       20158622.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get all the records where salary is greater than 20000000\n",
    "mask = series > 20000000\n",
    "\n",
    "series[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Manipulation\n",
    "\n",
    "1. **`df.drop()`:** Remove specified rows or columns from the DataFrame.\n",
    "\n",
    "2. **`df.rename()`:** Rename columns or index labels.\n",
    "\n",
    "3. **`df.sort_values()`:** Sort the DataFrame by specified columns.\n",
    "\n",
    "4. **`df.groupby()`:** Group data based on one or more columns.\n",
    "\n",
    "5. **`df.get_group()`:** Get a specific group from a grouped DataFrame using the group key.\n",
    "\n",
    "6. **`df['a'].astype('data_type')`:** Convert the data type of a specific column 'a' to the specified data type.\n",
    "\n",
    "7. **`df.set_index()`:** Set the DataFrame's index to a specific column.\n",
    "\n",
    "8. **`df.reset_index()`:** Reset the DataFrame's index, optionally dropping the current index.\n",
    "\n",
    "10. **`df.agg()`:** Perform aggregation operations on the DataFrame, like mean, sum, etc.\n",
    "\n",
    "11. **`df.map()`:** Apply a function element-wise on a Series.\n",
    "\n",
    "12. **`df.rank()`:** Compute numerical rank of elements in the DataFrame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#read the data\n",
    "df = pd.read_csv(\"data/nba.csv\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**df.drop()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop Heigh column\n",
    "\n",
    "df_new = df.drop(columns=[\"Height\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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>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>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>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>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>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  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   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": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**df.rename()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>University</th>\n",
       "      <th>Wages</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",
       "  </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",
       "\n",
       "          University      Wages  \n",
       "0              Texas  7730337.0  \n",
       "1          Marquette  6796117.0  \n",
       "2  Boston University        NaN  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename College to University and Salary to Wages -> column renaming\n",
    "df_renamed = df.rename(columns= {\"College\":\"University\", \"Salary\":\"Wages\"})\n",
    "\n",
    "df_renamed.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>Avery Bradley</td>\n",
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       "      <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>y</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "x  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "y    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",
       "\n",
       "             College     Salary  \n",
       "x              Texas  7730337.0  \n",
       "y          Marquette  6796117.0  \n",
       "2  Boston University        NaN  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#index renaming -> 0 and 1 to x and y\n",
    "df_renamed = df.rename(index= {0:\"x\", 1:\"y\"})\n",
    "\n",
    "df_renamed.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
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       "      <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",
       "  </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",
       "\n",
       "             College     Salary  \n",
       "0              Texas  7730337.0  \n",
       "1          Marquette  6796117.0  \n",
       "2  Boston University        NaN  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#index -> map all index to string\n",
    "df_renamed = df.rename(index = str)\n",
    "\n",
    "df_renamed.head(3)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`sort_values()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>109</th>\n",
       "      <td>Kobe Bryant</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>212.0</td>\n",
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       "      <td>25000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>23.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22970500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Carmelo Anthony</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>7.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>240.0</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>22875000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name                 Team  Number Position   Age Height  \\\n",
       "109      Kobe Bryant   Los Angeles Lakers    24.0       SF  37.0    6-6   \n",
       "169     LeBron James  Cleveland Cavaliers    23.0       SF  31.0    6-8   \n",
       "33   Carmelo Anthony      New York Knicks     7.0       SF  32.0    6-8   \n",
       "\n",
       "     Weight   College      Salary  \n",
       "109   212.0       NaN  25000000.0  \n",
       "169   250.0       NaN  22970500.0  \n",
       "33    240.0  Syracuse  22875000.0  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sort the values in by salary in descending and by name ascending\n",
    "df_sorted = df.sort_values(by=[\"Salary\", \"Name\"], ascending=[False, True])\n",
    "\n",
    "df_sorted.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`groupby()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>Team</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>4.860197e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>4.181505e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>3.501898e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>5.222728e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Team        Salary\n",
       "0      Atlanta Hawks  4.860197e+06\n",
       "1     Boston Celtics  4.181505e+06\n",
       "2      Brooklyn Nets  3.501898e+06\n",
       "3  Charlotte Hornets  5.222728e+06"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find mean of salary for each Team\n",
    "\n",
    "df_grpd = df.groupby(by=[\"Team\"], as_index=False)[\"Salary\"].mean()\n",
    "\n",
    "df_grpd.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>&lt;lambda_0&gt;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>24.733333</td>\n",
       "      <td>20.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>26.133333</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>27.400000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Team        Age                       \n",
       "                           mean   min   max <lambda_0>\n",
       "0      Atlanta Hawks  28.200000  22.0  35.0       13.0\n",
       "1     Boston Celtics  24.733333  20.0  29.0        9.0\n",
       "2      Brooklyn Nets  25.600000  21.0  32.0       11.0\n",
       "3  Charlotte Hornets  26.133333  21.0  31.0       10.0\n",
       "4      Chicago Bulls  27.400000  21.0  35.0       14.0"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find avg, min, max and range of age of Age column for each team\n",
    "\n",
    "df_grpd = df.groupby(by=[\"Team\"], as_index=False).agg({\"Age\" : [\"mean\", \"min\", \"max\", lambda x : x.max()- x.min()]})\n",
    "\n",
    "df_grpd.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`get_group`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'f'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mUntitled-1.ipynb Cell 89\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:Untitled-1.ipynb?jupyter-notebook#Y201sdW50aXRsZWQ%3D?line=0'>1</a>\u001b[0m df_grpd \u001b[39m=\u001b[39m df\u001b[39m.\u001b[39mgroupby(\u001b[39m\"\u001b[39m\u001b[39mTeam\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m----> <a href='vscode-notebook-cell:Untitled-1.ipynb?jupyter-notebook#Y201sdW50aXRsZWQ%3D?line=2'>3</a>\u001b[0m df_grpd\u001b[39m.\u001b[39;49mget_group(\u001b[39m\"\u001b[39;49m\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m)\u001b[39m.\u001b[39mmean()\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/groupby/groupby.py:747\u001b[0m, in \u001b[0;36mBaseGroupBy.get_group\u001b[0;34m(self, name, obj)\u001b[0m\n\u001b[1;32m    745\u001b[0m inds \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_index(name)\n\u001b[1;32m    746\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mlen\u001b[39m(inds):\n\u001b[0;32m--> 747\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(name)\n\u001b[1;32m    749\u001b[0m \u001b[39mreturn\u001b[39;00m obj\u001b[39m.\u001b[39m_take_with_is_copy(inds, axis\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxis)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'f'"
     ]
    }
   ],
   "source": [
    "df_grpd = df.groupby(\"Team\")\n",
    "\n",
    "df_grpd.get_group(\"f\").mean()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df['a'].astype('data_type')`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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>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</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</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>0</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</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</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  \n",
       "1          Marquette  6796117  \n",
       "2  Boston University        0  \n",
       "3      Georgia State  1148640  \n",
       "4                NaN  5000000  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert salary to nearest integer\n",
    "\n",
    "df[\"Salary\"] = df[\"Salary\"].fillna(0).astype('int64')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df.set_index()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>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",
       "    <tr>\n",
       "      <th>Team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Boston Celtics</th>\n",
       "      <td>Avery Bradley</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boston Celtics</th>\n",
       "      <td>Jae Crowder</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boston Celtics</th>\n",
       "      <td>John Holland</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boston Celtics</th>\n",
       "      <td>R.J. Hunter</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</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Name  Number Position   Age Height  Weight  \\\n",
       "Team                                                                  \n",
       "Boston Celtics  Avery Bradley     0.0       PG  25.0    6-2   180.0   \n",
       "Boston Celtics    Jae Crowder    99.0       SF  25.0    6-6   235.0   \n",
       "Boston Celtics   John Holland    30.0       SG  27.0    6-5   205.0   \n",
       "Boston Celtics    R.J. Hunter    28.0       SG  22.0    6-5   185.0   \n",
       "\n",
       "                          College   Salary  \n",
       "Team                                        \n",
       "Boston Celtics              Texas  7730337  \n",
       "Boston Celtics          Marquette  6796117  \n",
       "Boston Celtics  Boston University        0  \n",
       "Boston Celtics      Georgia State  1148640  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set Team as index\n",
    "\n",
    "df_index = df.set_index(keys=[\"Team\"])\n",
    "\n",
    "df_index.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df.reset_index()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\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>Team</th>\n",
       "      <th>Name</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>Boston Celtics</td>\n",
       "      <td>Avery Bradley</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>Jae Crowder</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>John Holland</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Team           Name  Number Position   Age Height  Weight  \\\n",
       "0  Boston Celtics  Avery Bradley     0.0       PG  25.0    6-2   180.0   \n",
       "1  Boston Celtics    Jae Crowder    99.0       SF  25.0    6-6   235.0   \n",
       "2  Boston Celtics   John Holland    30.0       SG  27.0    6-5   205.0   \n",
       "\n",
       "             College   Salary  \n",
       "0              Texas  7730337  \n",
       "1          Marquette  6796117  \n",
       "2  Boston University        0  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_non_index = df_index.reset_index(level=None)\n",
    "df_non_index.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df.agg()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <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",
       "      <th>mean_age</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</td>\n",
       "      <td>26.879913</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</td>\n",
       "      <td>26.879913</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>0</td>\n",
       "      <td>26.879913</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",
       "\n",
       "             College   Salary   mean_age  \n",
       "0              Texas  7730337  26.879913  \n",
       "1          Marquette  6796117  26.879913  \n",
       "2  Boston University        0  26.879913  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find avg age of the players\n",
    "\n",
    "df[\"mean_age\"] = df[\"Age\"].fillna(0).agg(\"mean\")\n",
    "\n",
    "df.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df.map()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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",
       "      <th>mean_age</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>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337</td>\n",
       "      <td>26.879913</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>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117</td>\n",
       "      <td>26.879913</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>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>0</td>\n",
       "      <td>26.879913</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      NaN  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0      NaN  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0      NaN  27.0    6-5   205.0   \n",
       "\n",
       "             College   Salary   mean_age  \n",
       "0              Texas  7730337  26.879913  \n",
       "1          Marquette  6796117  26.879913  \n",
       "2  Boston University        0  26.879913  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert position to smaller case letters\n",
    "df[\"Position\"] = df[\"Position\"].map({\"PG\":\"pg\"})\n",
    "\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df.rank()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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",
       "      <th>mean_age</th>\n",
       "      <th>rank</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>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337</td>\n",
       "      <td>26.879913</td>\n",
       "      <td>2</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>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117</td>\n",
       "      <td>26.879913</td>\n",
       "      <td>4</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>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>0</td>\n",
       "      <td>26.879913</td>\n",
       "      <td>15</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>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640</td>\n",
       "      <td>26.879913</td>\n",
       "      <td>14</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>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000</td>\n",
       "      <td>26.879913</td>\n",
       "      <td>5</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      NaN  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0      NaN  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0      NaN  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0      NaN  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0      NaN  29.0   6-10   231.0   \n",
       "\n",
       "             College   Salary   mean_age  rank  \n",
       "0              Texas  7730337  26.879913     2  \n",
       "1          Marquette  6796117  26.879913     4  \n",
       "2  Boston University        0  26.879913    15  \n",
       "3      Georgia State  1148640  26.879913    14  \n",
       "4                NaN  5000000  26.879913     5  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add rank column for each team wrt Salary\n",
    "\n",
    "df[\"rank\"] = df.groupby(\"Team\")[\"Salary\"].rank(ascending=False).astype('int')\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Missing Values\n",
    "\n",
    "1. **`df.isna()`:**\n",
    "   - Purpose: This method returns a DataFrame of the same shape as the original DataFrame `df`, where each element is a Boolean value indicating whether the corresponding value is NaN (Not a Number) or missing.\n",
    "   - Usage: `df.isna()`\n",
    "\n",
    "2. **`df.dropna()`:**\n",
    "   - Purpose: This method is used to remove rows or columns containing any missing values (NaN) from the DataFrame `df`.\n",
    "   - Usage:\n",
    "     - Drop rows with missing values: `df.dropna()`\n",
    "     - Drop columns with missing values: `df.dropna(axis=1)`\n",
    "\n",
    "3. **`df.fillna()`:**\n",
    "   - Purpose: This method is used to fill missing values in the DataFrame `df` with specified values.\n",
    "   - Usage: `df.fillna(value)`, where `value` is the value you want to use to fill the missing values.\n",
    "\n",
    "4. **`df.isnull()`:**\n",
    "   - Purpose: This method is similar to `df.isna()` and returns a DataFrame of the same shape as `df` with Boolean values indicating whether the corresponding values are NaN (missing).\n",
    "   - Usage: `df.isnull()`\n",
    "\n",
    "5. **`df.notnull()`:**\n",
    "   - Purpose: This method is the complement of `df.isnull()` and returns a DataFrame of the same shape as `df` with Boolean values indicating whether the corresponding values are not NaN (not missing).\n",
    "   - Usage: `df.notnull()`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df.isna()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <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",
       "      <th>mean_age</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name   Team  Number  Position    Age  Height  Weight  College  Salary  \\\n",
       "0  False  False   False      True  False   False   False    False   False   \n",
       "1  False  False   False      True  False   False   False    False   False   \n",
       "2  False  False   False      True  False   False   False    False   False   \n",
       "3  False  False   False      True  False   False   False    False   False   \n",
       "\n",
       "   mean_age   rank  \n",
       "0     False  False  \n",
       "1     False  False  \n",
       "2     False  False  \n",
       "3     False  False  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na = df.isna()\n",
    "\n",
    "df_na.head(4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df.isnull()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>College</th>\n",
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       "      <th>rank</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name   Team  Number  Position    Age  Height  Weight  College  Salary  \\\n",
       "0  False  False   False      True  False   False   False    False   False   \n",
       "1  False  False   False      True  False   False   False    False   False   \n",
       "2  False  False   False      True  False   False   False    False   False   \n",
       "3  False  False   False      True  False   False   False    False   False   \n",
       "4  False  False   False      True  False   False   False     True   False   \n",
       "\n",
       "   mean_age   rank  \n",
       "0     False  False  \n",
       "1     False  False  \n",
       "2     False  False  \n",
       "3     False  False  \n",
       "4     False  False  "
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_null = df.isnull()\n",
    "\n",
    "df_null.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df.notnull`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <th>4</th>\n",
       "      <td>True</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Name  Team  Number  Position   Age  Height  Weight  College  Salary  \\\n",
       "0  True  True    True     False  True    True    True     True    True   \n",
       "1  True  True    True     False  True    True    True     True    True   \n",
       "2  True  True    True     False  True    True    True     True    True   \n",
       "3  True  True    True     False  True    True    True     True    True   \n",
       "4  True  True    True     False  True    True    True    False    True   \n",
       "\n",
       "   mean_age  rank  \n",
       "0      True  True  \n",
       "1      True  True  \n",
       "2      True  True  \n",
       "3      True  True  \n",
       "4      True  True  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_not_null = df.notnull()\n",
    "\n",
    "df_not_null.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df.dropna()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
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       "      <td>25.0</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
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       "      <td>26.879913</td>\n",
       "      <td>4</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>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>0</td>\n",
       "      <td>26.879913</td>\n",
       "      <td>15</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>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640</td>\n",
       "      <td>26.879913</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jordan Mickey</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>235.0</td>\n",
       "      <td>LSU</td>\n",
       "      <td>1170960</td>\n",
       "      <td>26.879913</td>\n",
       "      <td>13</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      NaN  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0      NaN  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0      NaN  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0      NaN  22.0    6-5   185.0   \n",
       "6  Jordan Mickey  Boston Celtics    55.0      NaN  21.0    6-8   235.0   \n",
       "\n",
       "             College   Salary   mean_age  rank  \n",
       "0              Texas  7730337  26.879913     2  \n",
       "1          Marquette  6796117  26.879913     4  \n",
       "2  Boston University        0  26.879913    15  \n",
       "3      Georgia State  1148640  26.879913    14  \n",
       "6                LSU  1170960  26.879913    13  "
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop column where a null value is there\n",
    "df_col_drp = df.dropna(subset=[\"College\"], axis=0, how=\"all\", thresh=0.2)\n",
    "\n",
    "df_col_drp.head(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df.fillna()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Marquette</td>\n",
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       "      <td>4</td>\n",
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       "      <td>John Holland</td>\n",
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       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>0</td>\n",
       "      <td>26.879913</td>\n",
       "      <td>15</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>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640</td>\n",
       "      <td>26.879913</td>\n",
       "      <td>14</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>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000</td>\n",
       "      <td>26.879913</td>\n",
       "      <td>5</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      NaN  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0      NaN  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0      NaN  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0      NaN  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0      NaN  29.0   6-10   231.0   \n",
       "\n",
       "             College   Salary   mean_age  rank  \n",
       "0              Texas  7730337  26.879913     2  \n",
       "1          Marquette  6796117  26.879913     4  \n",
       "2  Boston University        0  26.879913    15  \n",
       "3      Georgia State  1148640  26.879913    14  \n",
       "4                NaN  5000000  26.879913     5  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fill with null values\n",
    "df[\"Number\"] = df[\"Number\"].fillna(df[\"Number\"].mean(), axis=0)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**Data Cleaning and Transformation**\n",
    "\n",
    "1. **`df.apply()`:**\n",
    "   - Purpose: This method applies a function along either the rows or columns of the DataFrame.\n",
    "   - Usage: `df.apply(func, axis=0, ...)` or `df.apply(func, axis=1, ...)`\n",
    "   - Parameters:\n",
    "     - `func`: The function to be applied to each row or column.\n",
    "     - `axis`: Specifies the axis along which the function is applied. Use `axis=0` for column-wise and `axis=1` for row-wise application.\n",
    "\n",
    "2. **`df.replace()`:**\n",
    "   - Purpose: This method is used to replace specific values in the DataFrame with other values.\n",
    "   - Usage: `df.replace(to_replace, value, inplace=False, ...)`\n",
    "   - Parameters:\n",
    "     - `to_replace`: The value or list of values to be replaced.\n",
    "     - `value`: The value or list of values to replace `to_replace` with.\n",
    "     - `inplace`: If True, the DataFrame is modified in place (i.e., no new DataFrame is created). If False (default), a new DataFrame with replaced values is returned.\n",
    "\n",
    "3. **`df.duplicated()`:**\n",
    "   - Purpose: This method identifies duplicate rows in the DataFrame and returns a boolean Series indicating whether each row is a duplicate.\n",
    "   - Usage: `df.duplicated(subset=None, keep='first')`\n",
    "   - Parameters:\n",
    "     - `subset`: A list of column names to consider for identifying duplicates. If not provided, all columns are used.\n",
    "     - `keep`: Determines which duplicates, if any, to mark as `True`. It can take the following values:\n",
    "       - 'first' (default): Mark all duplicates as `True` except the first occurrence.\n",
    "       - 'last': Mark all duplicates as `True` except the last occurrence.\n",
    "       - False: Mark all duplicates as `True`.\n",
    "\n",
    "4. **`df.drop_duplicates()`:**\n",
    "   - Purpose: This method drops duplicate rows from the DataFrame and returns a new DataFrame with the duplicates removed.\n",
    "   - Usage: `df.drop_duplicates(subset=None, keep='first', inplace=False)`\n",
    "   - Parameters:\n",
    "     - `subset`: A list of column names to consider for identifying duplicates. If not provided, all columns are used.\n",
    "     - `keep`: Determines which duplicates, if any, to keep. It can take the same values as in `df.duplicated()`.\n",
    "     - `inplace`: If True, the DataFrame is modified in place (i.e., no new DataFrame is created). If False (default), a new DataFrame with duplicates removed is returned.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/nba.csv\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df.apply`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>boston celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>boston celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>boston celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>boston celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>boston celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>utah jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>utah jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>utah jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>utah jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Team\n",
       "0    boston celtics\n",
       "1    boston celtics\n",
       "2    boston celtics\n",
       "3    boston celtics\n",
       "4    boston celtics\n",
       "..              ...\n",
       "453       utah jazz\n",
       "454       utah jazz\n",
       "455       utah jazz\n",
       "456       utah jazz\n",
       "457             NaN\n",
       "\n",
       "[458 rows x 1 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x : x[[\"Team\"]].str.lower(), axis = \"columns\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df.replace()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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</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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#replace Boston with BOSTON in Team column\n",
    "\n",
    "df[\"Team\"] = df['Team'].str.replace(\"Boston\", \"BOSTON\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df.duplicated()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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",
       "      <th>duplicate</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",
       "      <td>False</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",
       "      <td>True</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",
       "      <td>True</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",
       "\n",
       "             College     Salary  duplicate  \n",
       "0              Texas  7730337.0      False  \n",
       "1          Marquette  6796117.0       True  \n",
       "2  Boston University        NaN       True  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['duplicate'] = df.duplicated(subset=[\"Team\"], keep='first')\n",
    "\n",
    "df.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df.drop_duplicates()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(458, 10)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop duplicates on all columns\n",
    "df_distinct = df.drop_duplicates()\n",
    "df_distinct.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(458, 10)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 10)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop duplicates on column Team \n",
    "\n",
    "df_distinct = df.drop_duplicates(subset=[\"Team\"], keep='first')\n",
    "\n",
    "df_distinct.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging and Joining DataFrames\n",
    "\n",
    "1. **`pd.concat()`**:\n",
    "   - Purpose: Concatenates DataFrames along a specified axis (rows or columns).\n",
    "   - Usage: `pd.concat(objs, axis=0, join='outer', ignore_index=False)`\n",
    "   - Parameters:\n",
    "     - `objs`: Sequence of DataFrames to concatenate.\n",
    "     - `axis`: Concatenate along rows (`axis=0`) or columns (`axis=1`).\n",
    "     - `join`: Determines handling of columns not present in all DataFrames.\n",
    "     - `ignore_index`: Create a new integer index if True.\n",
    "\n",
    "2. **`pd.merge()`**:\n",
    "   - Purpose: Merges DataFrames based on a common column or index.\n",
    "   - Usage: `pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None)`\n",
    "   - Parameters:\n",
    "     - `left` and `right`: DataFrames to merge.\n",
    "     - `how`: Type of merge - 'inner', 'outer', 'left', or 'right'.\n",
    "     - `on`, `left_on`, `right_on`: Columns or indexes on which to merge.\n",
    "\n",
    "3. **`pd.join()`**:\n",
    "   - Purpose: Joins DataFrames based on their indexes.\n",
    "   - Usage: `df.join(other, on=None, how='left')`\n",
    "   - Parameters:\n",
    "     - `other`: DataFrame to join with.\n",
    "     - `on`: Column name or index level on which to join.\n",
    "     - `how`: Type of join - 'left', 'right', or 'inner'.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`pd.concat()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "week1 = pd.read_csv(\"data/Restaurant - Week 1 Sales.csv\")\n",
    "week2 = pd.read_csv(\"data/Restaurant - Week 2 Sales.csv\")\n",
    "customers = pd.read_csv(\"data/Restaurant - Customers.csv\")\n",
    "foods = pd.read_csv(\"data/Restaurant - Foods.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
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       "      <th>0</th>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Customer ID  Food ID\n",
       "0          537        9"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week1.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
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      "text/plain": [
       "   Customer ID  Food ID\n",
       "0          688       10"
      ]
     },
     "execution_count": 36,
     "metadata": {},
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   ],
   "source": [
    "week2.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company</th>\n",
       "      <th>Occupation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Joseph</td>\n",
       "      <td>Perkins</td>\n",
       "      <td>Male</td>\n",
       "      <td>Dynazzy</td>\n",
       "      <td>Community Outreach Specialist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Alvarez</td>\n",
       "      <td>Female</td>\n",
       "      <td>DabZ</td>\n",
       "      <td>Senior Quality Engineer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID First Name Last Name  Gender  Company                     Occupation\n",
       "0   1     Joseph   Perkins    Male  Dynazzy  Community Outreach Specialist\n",
       "1   2   Jennifer   Alvarez  Female     DabZ        Senior Quality Engineer"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>3.99</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>9.99</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Food ID Food Item  Price\n",
       "0        1     Sushi   3.99\n",
       "1        2   Burrito   9.99"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foods.head(2)"
   ]
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   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">A</th>\n",
       "      <th>0</th>\n",
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       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     Customer ID  Food ID\n",
       "A 0          537        9\n",
       "  1           97        4\n",
       "  2          658        1\n",
       "  3          202        2\n",
       "  4          155        9"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sales fortwo weeks\n",
    "sales = pd.concat([week1, week2], keys=[\"A\", \"B\"])\n",
    "sales.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "734"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.loc[(\"B\", 240), \"Customer ID\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df.append()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/xf/gcbfh98s6n53wh5f2hpb84nh0000gn/T/ipykernel_16298/2471712667.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  sales = week1.append(week2, ignore_index=True)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "   Customer ID  Food ID\n",
       "0          537        9\n",
       "1           97        4"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "sales = week1.append(week2, ignore_index=True)\n",
    "\n",
    "sales.head(2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`df.merge()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>3</td>\n",
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      "text/plain": [
       "   Customer ID  Food ID\n",
       "0          304        3\n",
       "1          540        3"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_week = week1.merge(week2, how=\"inner\", on=[\"Customer ID\", \"Food ID\"])\n",
    "\n",
    "res_week.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 2)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_week.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>_merge</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>97</td>\n",
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       "      <td>left_only</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID - Week 1  Food ID - Week 2     _merge\n",
       "0          537               9.0               5.0       both\n",
       "1           97               4.0               NaN  left_only"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_week = week1.merge(week2, how=\"outer\", on=[\"Customer ID\"], suffixes = [\" - Week 1\", \" - Week 2\"], indicator = True)\n",
    "\n",
    "res_week.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "right_only    197\n",
       "left_only     195\n",
       "both           62\n",
       "Name: _merge, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_week[\"_merge\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Customer ID</th>\n",
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       "      <th>Food ID - Week 2</th>\n",
       "      <th>_merge</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>449</th>\n",
       "      <td>855</td>\n",
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       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>276</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>252</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>392 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Food ID - Week 1  Food ID - Week 2      _merge\n",
       "1             97               4.0               NaN   left_only\n",
       "2            658               1.0               NaN   left_only\n",
       "3            202               2.0               NaN   left_only\n",
       "6            213               8.0               NaN   left_only\n",
       "7            600               1.0               NaN   left_only\n",
       "..           ...               ...               ...         ...\n",
       "449          855               NaN               4.0  right_only\n",
       "450          559               NaN              10.0  right_only\n",
       "451          276               NaN               4.0  right_only\n",
       "452          556               NaN              10.0  right_only\n",
       "453          252               NaN               9.0  right_only\n",
       "\n",
       "[392 rows x 4 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = res_week[\"_merge\"].isin([\"left_only\", \"right_only\"])\n",
    "res_week[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "      <th>Food Item</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>658</td>\n",
       "      <td>1</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>341</td>\n",
       "      <td>1</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID Food Item  Price\n",
       "0          658        1     Sushi   3.99\n",
       "1          600        1     Sushi   3.99\n",
       "2          155        1     Sushi   3.99\n",
       "3          341        1     Sushi   3.99"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_week = week1.merge(foods, how = \"left\", on = \"Food ID\", sort = True)\n",
    "\n",
    "res_week.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Customer ID - Week 1</th>\n",
       "      <th>Food ID - Week 1</th>\n",
       "      <th>Customer ID - Week 2</th>\n",
       "      <th>Food ID - Week 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "      <td>688</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "      <td>813</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID - Week 1  Food ID - Week 1  Customer ID - Week 2  \\\n",
       "0                   537                 9                   688   \n",
       "1                    97                 4                   813   \n",
       "\n",
       "   Food ID - Week 2  \n",
       "0                10  \n",
       "1                 7  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_week = week1.merge(week2, how = \"left\", left_index = True, \n",
    "                       right_index = True, suffixes = [\" - Week 1\", \" - Week 2\"])\n",
    "\n",
    "res_week.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "      <th>Satisfaction Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>537</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID  Satisfaction Rating\n",
       "0          537        9                    2\n",
       "1           97        4                    7\n",
       "2          658        1                    3\n",
       "3          202        2                    7\n",
       "4          155        9                   10"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "satisfaction = pd.read_csv(\"data/Restaurant - Week 1 Satisfaction.csv\")\n",
    "\n",
    "week1.join(satisfaction).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>Customer ID</th>\n",
       "      <th>Food ID</th>\n",
       "      <th>First Name_x</th>\n",
       "      <th>Last Name_x</th>\n",
       "      <th>Gender_x</th>\n",
       "      <th>Company_x</th>\n",
       "      <th>Occupation_x</th>\n",
       "      <th>ID</th>\n",
       "      <th>First Name_y</th>\n",
       "      <th>Last Name_y</th>\n",
       "      <th>Gender_y</th>\n",
       "      <th>Company_y</th>\n",
       "      <th>Occupation_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>Frances</td>\n",
       "      <td>Adams</td>\n",
       "      <td>Female</td>\n",
       "      <td>Dabshots</td>\n",
       "      <td>Developer III</td>\n",
       "      <td>8</td>\n",
       "      <td>Frances</td>\n",
       "      <td>Adams</td>\n",
       "      <td>Female</td>\n",
       "      <td>Dabshots</td>\n",
       "      <td>Developer III</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>Ruth</td>\n",
       "      <td>Alvarez</td>\n",
       "      <td>Female</td>\n",
       "      <td>Twitterlist</td>\n",
       "      <td>Mechanical Systems Engineer</td>\n",
       "      <td>13</td>\n",
       "      <td>Ruth</td>\n",
       "      <td>Alvarez</td>\n",
       "      <td>Female</td>\n",
       "      <td>Twitterlist</td>\n",
       "      <td>Mechanical Systems Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>Albert</td>\n",
       "      <td>Burns</td>\n",
       "      <td>Male</td>\n",
       "      <td>Rhynoodle</td>\n",
       "      <td>Junior Executive</td>\n",
       "      <td>21</td>\n",
       "      <td>Albert</td>\n",
       "      <td>Burns</td>\n",
       "      <td>Male</td>\n",
       "      <td>Rhynoodle</td>\n",
       "      <td>Junior Executive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>Donna</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>Female</td>\n",
       "      <td>Jaxbean</td>\n",
       "      <td>Chief Design Engineer</td>\n",
       "      <td>24</td>\n",
       "      <td>Donna</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>Female</td>\n",
       "      <td>Jaxbean</td>\n",
       "      <td>Chief Design Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>Bennett</td>\n",
       "      <td>Female</td>\n",
       "      <td>Twitternation</td>\n",
       "      <td>Account Executive</td>\n",
       "      <td>27</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>Bennett</td>\n",
       "      <td>Female</td>\n",
       "      <td>Twitternation</td>\n",
       "      <td>Account Executive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Food ID First Name_x Last Name_x Gender_x      Company_x  \\\n",
       "0            8        6      Frances       Adams   Female       Dabshots   \n",
       "1           13        2         Ruth     Alvarez   Female    Twitterlist   \n",
       "2           21        4       Albert       Burns     Male      Rhynoodle   \n",
       "3           24        8        Donna      Thomas   Female        Jaxbean   \n",
       "4           27        4      Jessica     Bennett   Female  Twitternation   \n",
       "\n",
       "                  Occupation_x  ID First Name_y Last Name_y Gender_y  \\\n",
       "0                Developer III   8      Frances       Adams   Female   \n",
       "1  Mechanical Systems Engineer  13         Ruth     Alvarez   Female   \n",
       "2             Junior Executive  21       Albert       Burns     Male   \n",
       "3        Chief Design Engineer  24        Donna      Thomas   Female   \n",
       "4            Account Executive  27      Jessica     Bennett   Female   \n",
       "\n",
       "       Company_y                 Occupation_y  \n",
       "0       Dabshots                Developer III  \n",
       "1    Twitterlist  Mechanical Systems Engineer  \n",
       "2      Rhynoodle             Junior Executive  \n",
       "3        Jaxbean        Chief Design Engineer  \n",
       "4  Twitternation            Account Executive  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_week = week2.merge(customers, how = \"left\", \n",
    "                       left_on = \"Customer ID\", \n",
    "                       right_on = \"ID\", sort = True)\n",
    "res_week.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Country  Price in US Dollars\n",
       "0 2016-01-01  Argentina                 2.39\n",
       "1 2016-01-01  Australia                 3.74\n",
       "2 2016-01-01     Brazil                 3.35"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac = pd.read_csv(\"data/bigmac.csv\", parse_dates = [\"Date\"])\n",
    "bigmac.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=652, step=1)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.index.get_level_values(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with Dates and Times\n",
    "\n",
    "- `pd.to_datetime()`\n",
    "- `df.resample()`\n",
    "- `pd.date_range()`\n",
    "- `.dt`\n",
    "- `pd.date_range()`\n",
    "- `pd.Timestamp()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization\n",
    "\n",
    "- `df.plot()`\n",
    "- `df.hist()`, `df.boxplot()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/nba.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Salary vs Age'}, xlabel='Age'>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='Age', y='Salary', kind='bar', title='Salary vs Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a histogram\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df.hist(column='Age', bins=10, color='skyblue', edgecolor='black')\n",
    "plt.title('Age Distribution')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}