Tabular Notebooks/01_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intro to Tabular and Pandas\n",
    "\n",
    "Before we begin doing tabular modeling, let's learn about the `Pandas` library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas? Like the bear? No!\n",
    "\n",
    "`pandas` is a library we can use for reading and analyzing any bit of Tabular data. We'll work out of the newly released 1.0 version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pandas --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do this by creating what are called `DataFrames`. These can come from a variety of forms. Most commonly:\n",
    "\n",
    "* `read_csv`\n",
    "* `read_excel`\n",
    "* `read_feather`\n",
    "* `read_html`\n",
    "* `read_json`\n",
    "* `read_pickle`\n",
    "* `read_sql`\n",
    "* `read_table`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll focus on the `read_csv` function. We'll use the Titanic dataset for today's tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'kaggle-titanic'...\n",
      "remote: Enumerating objects: 19, done.\u001b[K\n",
      "remote: Counting objects: 100% (19/19), done.\u001b[K\n",
      "remote: Compressing objects: 100% (13/13), done.\u001b[K\n",
      "remote: Total 19 (delta 0), reused 19 (delta 0), pack-reused 0\u001b[K\n",
      "Unpacking objects: 100% (19/19), done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/pcsanwald/kaggle-titanic.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the `Pathlib` library to read our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.basic_data import pathlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = pathlib.Path('kaggle-titanic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And look at what we grabbed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('kaggle-titanic/train.csv'),\n",
       " PosixPath('kaggle-titanic/doc'),\n",
       " PosixPath('kaggle-titanic/.git'),\n",
       " PosixPath('kaggle-titanic/test.csv'),\n",
       " PosixPath('kaggle-titanic/.gitignore'),\n",
       " PosixPath('kaggle-titanic/README.md'),\n",
       " PosixPath('kaggle-titanic/test'),\n",
       " PosixPath('kaggle-titanic/project.clj'),\n",
       " PosixPath('kaggle-titanic/src')]"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path.ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that we have a `train.csv` and `test.csv` file. We'll work out of the `train` file today. Let's make our `DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(path/'train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now for some common functionalities:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Functionalities:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Head and Tail\n",
    "\n",
    "`.head()` and `.tail()` will show the first and last few rows of a `DataFrame`. You can pass in `n` rows to look at (the default is 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   survived  pclass  ... cabin embarked\n",
       "0         0       3  ...   NaN        S\n",
       "1         1       1  ...   C85        C\n",
       "2         1       3  ...   NaN        S\n",
       "3         1       1  ...  C123        S\n",
       "\n",
       "[4 rows x 11 columns]"
      ]
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   "source": [
    "df.head(n=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "     survived  pclass  ... cabin embarked\n",
       "888         0       3  ...   NaN        S\n",
       "889         1       1  ...  C148        C\n",
       "890         0       3  ...   NaN        Q\n",
       "\n",
       "[3 rows x 11 columns]"
      ]
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     "execution_count": null,
     "metadata": {
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   ],
   "source": [
    "df.tail(n=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting\n",
    "\n",
    "We can select a variety of ways in pandas: row, value, even by a column:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Row:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two different ways we can select rows, `loc` and `iloc`. Each work a little differently"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `loc`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`loc` is used to get rows (or columns) with a particular **label** from an index. IE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
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       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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      "text/plain": [
       "   survived  pclass  ... cabin embarked\n",
       "0         0       3  ...   NaN        S\n",
       "1         1       1  ...   C85        C\n",
       "2         1       3  ...   NaN        S\n",
       "3         1       1  ...  C123        S\n",
       "\n",
       "[4 rows x 11 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {
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    }
   ],
   "source": [
    "df.loc[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we grabbed rows 0 through 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `iloc`\n",
    "`iloc` is used when we want to get rows (or columns) from a *position* on an index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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      "text/plain": [
       "   survived  pclass  ... cabin embarked\n",
       "0         0       3  ...   NaN        S\n",
       "1         1       1  ...   C85        C\n",
       "2         1       3  ...   NaN        S\n",
       "\n",
       "[3 rows x 11 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
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   ],
   "source": [
    "df.iloc[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whereas here we grabbed the first three rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Column\n",
    "\n",
    "We can pass in either a string, an index, or multiple columns to select:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: survived, dtype: int64"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['survived'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To select multiple columns, pass in a double array of your names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sex   age  survived\n",
       "0    male  22.0         0\n",
       "1  female  38.0         1\n",
       "2  female  26.0         1\n",
       "3  female  35.0         1\n",
       "4    male  35.0         0"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['sex', 'age', 'survived']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And to pass as an index, first do the number of rows followed by the column index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    1\n",
       "888    0\n",
       "889    1\n",
       "890    0\n",
       "Name: survived, Length: 891, dtype: int64"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass                                               name\n",
       "0           0       3                            Braund, Mr. Owen Harris\n",
       "1           1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "2           1       3                             Heikkinen, Miss. Laina\n",
       "3           1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "4           0       3                           Allen, Mr. William Henry\n",
       "..        ...     ...                                                ...\n",
       "886         0       2                              Montvila, Rev. Juozas\n",
       "887         1       1                       Graham, Miss. Margaret Edith\n",
       "888         0       3           Johnston, Miss. Catherine Helen \"Carrie\"\n",
       "889         1       1                              Behr, Mr. Karl Howell\n",
       "890         0       3                                Dooley, Mr. Patrick\n",
       "\n",
       "[891 rows x 3 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Value\n",
    "\n",
    "We can select based on a value a few different ways, most involving a boolean argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.loc[df['column_name'] == some_value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass  ... cabin embarked\n",
       "1         1       1  ...   C85        C\n",
       "2         1       3  ...   NaN        S\n",
       "3         1       1  ...  C123        S\n",
       "8         1       3  ...   NaN        S\n",
       "9         1       2  ...   NaN        C\n",
       "\n",
       "[5 rows x 11 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['sex'] == 'female'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or even a series of values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>female</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Miss. Augusta Maria</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>345764</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ahlin, Mrs. Johan (Johanna Persdotter Larsson)</td>\n",
       "      <td>female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7546</td>\n",
       "      <td>9.4750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    survived  pclass  ... cabin embarked\n",
       "14         0       3  ...   NaN        S\n",
       "18         0       3  ...   NaN        S\n",
       "24         0       3  ...   NaN        S\n",
       "38         0       3  ...   NaN        S\n",
       "40         0       3  ...   NaN        S\n",
       "\n",
       "[5 rows x 11 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['sex'] == 'female') & (df['survived'] == 0)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the basic selects done, let's go into some more complex ideas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting\n",
    "\n",
    "We can plot out any data we want from our `DataFrames`, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc8906b2518>"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6jsNZNL8UTfIO4AJ6H12wq9u8gt4rcm6uqmtGNduxJslFVfWxUc+xEJK8FbgE2AGcBlxW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\nTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['pclass'].iloc[:5].plot(kind='bar', title='pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc890642518>"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kaFXe8tYG/Oz1znhx62gdBxa4JqmdQFudT7xvFU1t4QdWKGK5u/N2omYT77eJ\nP8/dgVuU0ScBYMzDX+HhfxdEfkeVUso1r7Xc1TKa76i+LUpkLIW+HvQWvxPS4aN3Rot7UVUjrnlx\nFR6bv9M0jdM6VnupaV99i3XulEjeNnnFe4GFEHhu4Z6IW0DPP9YfxMbiGsNtS3ZVRAlfIo3MiYVo\n9LEvpseoQXbxWqGvLI/vg5aK1GEN0th5RKzE3+5haXatyutbo/J2S9ydtI2OYAivrYieunDOlsMx\n6YzWAeYPWm0ZosNYY40FPeobhB1mPa0/UNyoZsfobJtkWlfBkMDTX8Z+IK1uasfjX+yMvNGmgowW\n92plCNStpeYRME5vA/UpbSU6XnViWlt0FAWH67CvqhHPL96L2/9uHI9976fb8KNZXxtuu/Xt/IiP\nF0hs7HUnboTOtNoPmtH7G93g8UaQSFttJhgJpJn4atfaWd9Wm+0eZLbtScjlI4vZuRitf2dNMR61\nMKKMsHrQG6UzGrrCDQvYLIsZOjeq3kDXtgezKv963xGsP6AfVxF4eO4OvLK8CAt3VDgpqqtktLjL\nEG/bkLGOwp0zBArN3B0Oj33Dq2tx9QurIg2pRbLTi+xYL05xYiEaWu66NMZumTgK5gJG9WImmvG6\nZWTeXKK2W2+OYNbT1SlqUetbOnD2w19hgyJS6jloxc7px3g9Tl+UOkMh7Xl5+T789JU15vnG6XRX\n2wOR+b1g1mZUV1qi31kSwfczMTnpYr25pDbyZdzsY6KWdUXVaGoL4LH5xmFR8fp8VbeKrNUiO9aL\nE4QQjsRdPY7V2DJGuaVq/B2j4+rP17C8NnVi6ZZxYPVbfph1KcJILc+W0lrUNHfghcV78e60CYb5\n58Tjd7Z4izNMH5Um1lgw6yn6xBfWYYnxtjDtQ06mnUZNI6n661P4OZYtdw3XvbQ60ohl3AVzthyO\nEfaQheWmxeoGlZ2pJ3JMiwPFq50y+hFl0SrVFeXC0IulQZ5HPYwWko2DVukwueZakbITVivxj7bq\nBR6ZuyMqgkXbfd7qMO753MP/9QJklL9dF/xInibr9eduhGFsugunKmsk6U8xKGG5m6Gmj+eh6BYZ\nLe5e2Hxan7v2RpUdNEpruenLp72JZF7X1BTr91djnkVHGi8mxZDZT3vcYGSoAu12/R6xeX7yTWnM\numRgdH5B3duakSi4FS3z9ILdeH3V/ih3wpur90vlY+dz/7rQeBAyQvit8PEvwr5zs4evsbhbHtIQ\nbVV1aPL8ep9x5zkjl54bg5upOZTXtxqO72P6QNJY37PXHIjZ/s1B46AGILFIG7fIaHFX2WPR28xp\n0+iMlhFo1ziEH/i8wGyXKEQNYAwAABcySURBVKx8rlpqmiS66wvgptfX4ievrMGd75sPdmVtoTq/\nOUIhOZeM1tCNWO5Wbpk479Mfzfoa33tqaXw7m2Ak3DI+d7tzCBkIlIq2TtUJoo0sYiHkH9j689h+\nqC4qHFTPHe99g1eWF0WVVVuEYEhofM2dG3ISVClt1MhNJuXT9y4uqW5GjWZYC6NrZhZRpkW7X/6B\naEEOaow4AqGsriXSh0JruT9lEBHz//5mHNQQfS7hSJtUjBjpC3FvaAtg0rPL0BYw+ABp0CCsKjpi\nuQejxb1FE6Nu52658/1v8MLivZZWx8Rnl5lnosFqiAA1ft5tt8xJ982X+mintWLV5WhXjb0PW4aN\nxTWR+UOdYHU8Q8td4ga89sVV2Kfrzh8KCUyeuQLztpY5fosyem0XsH64ah9Carr2QAgPzilA0ZEm\ns91Mfe5qkWua23HyffPx+soiZX3nHrLuhdrmDoyYMQ9zthyOOp7120Z42/2fbdesEbj4qaW47Jll\nlse76En7h772kujbdUcwBO0L2wWPL8G5jy4C0Fm38RhI6r4Hq5vwizfW46T75jvOI1F8Ie4AsK+q\nCXsrYqNWjC6LZZij8j8YEhHxjN5iTVAIzNtahr8s3GMprM1x9qTU3nCn3v8FFhSUe/JBVSYgQytA\n6gNTe7TYaJHkWy9mGNWZ/sY3Km1lQxue130TaQ+GsKu8Af/9z80xonzDq51uF6Nj5hiophDW7h+t\n+0hN98X2Mrz99QE87iBcsTOWPPy/Qomn/2BDSUxapx8G7/5gU3SZJcJMo8vm6HCWaLPSl6M9GLKM\nYQ//d35MtSk9Nn8XVilusmRb7xkt7jIf7Iw7zphfrS1KzHwgJNAWiE7X3B7AiBnzIhfLrkyJXkqj\n/fXtY/HOCksBj7c9yVju0T73WCtHJlrGS6zkSCZaxow8nSCrPaTbgyF8V9exZm1RZwy0Uf65hm4Z\n69d4I8tdxUn4rLqrWfPRumWcemV6dc2LytfpSIiuinuU5R6dcUcgZHrdO8XdubobPZyTHRaZ0eKu\nvyhGlWfkGukI2FdyMBSKstyJgIp6+xEcoxpPghdTb1EY+cJziKImJtATr+Uu0xADwViRsYoJT7bh\nbnU4mTj3SKy1Lq3e2g6Yhc0K+/ZpZrlbR8t0XnC1zOqgbPqPwlq051zT3BHjlrG6Pk597t3ycqLK\nuaDAWWceN6fHtIp26ggK07pOZCrIeN1+bpLZ4q4XPyNxN6jPjlAIj83ficfm7zR9JQvo3DKyTdtL\ny701EDQYjIusLfc4G5RMnH+U5a57xQcM3DJxlcQbErn5Yix3E8tOn51RWzP2uctHy6jXVy2TWVm0\naQFg3J8XaoYitvcttxp9z7IgEBJSbcgMNwyBkIHBEWO5B0OmkS1qvejf4KPLaeJuMvrWl2TrJqM7\nMcVaWkaWu8F+QYFXlXEyRg7sZZx3UMRY7jJEhUImeC31Y9q0tAfRvUtu1DoiOws1vmM3S/RI1ApQ\np0CYp0+2z93qcIbuOhMx0qc9cLQJzy3cg99ePgpEZCpi+rA7ebeMtRBoy6neA3nKWPmmbxGINYYa\n28KRKKpbqcZkwpVluysNo0WsyM2hqICEVNAeDKF7Tq7O5x6KSWNmAKnltxJ37Slu0ETiGGWZbMs9\no8Vd/wqqvwhFVY1YajA9mnYUyblbjQc6CvvcO29OAkldnFbNVGqyYnbB44tx0SkDbdO1dASRpxsT\nPYeshSBea6FBRty1/vWIAzeuwxnS2hGMiUxxCxnL3exU1hZVY21RNaZ+dwQG9OpqOjiUfhRQo+Zj\nGAppEy3TqLk2+o9+VsEC+jzVsZnMRFgt2TKD6ePs5oO9eNRA6eGXjZqoG1ZuRzCE7l1yo+5DvUs2\nbLmHl/XDLdwhMdeumT/e6Pq1WzwkvCCzxV1Yi/vEZ5cb7mf1QapzfSjmYlgNLayiHYNDn7OZ9V9W\n1xo1/rQZLe1B9OoafckINm6ZOG8SmbFEggYWpJthmQ98tl2qXuJhTdFRnDCgZ5TP2+wjslmx1fRm\nlrvM4GbaiS8ixxPWVp62J6uaTqZtbtMNsKeKu92+RvWinQjdiJCQK5NZm1Bj8aPS2uYWjdF10YdL\nB4JC41LspEay06LZw9ToPrB6A/CCzPa56yp2wfZyTJ65wtQaV9FaWmYuykBIoC0Y7ZaRaazaaAW3\nvRAP/rsAm0tro9blkPlxHvhsO9aY9AZUuefjLYbrGyWEKcpyV5YtQ/gcvpZuMBhtz60ef/d+ug2v\nrowWELPymfWibetQxd24XTQkMHKl1UMyXnF/fdX+qN9qZx0zi/KoMmxtm8HE3uqDwYyOQEj6u43R\nNZ1nMF6/0/Bho34geoHVhkJqq9wuvl5FP0GPiozlvqCgHMv3eDepdmZb7roKVONz73p/EwoO15vu\n12EQJ6xn/f5qnH/SsZHfX2wvxwqJC6FtgG572FYXHo3p1ERk7C4KhQTeXVtsm+eH+aV46voxMetl\n3DL/9e5GrP/DJBx3THepyZsTfdV+8stdOFgda+nGy56K6J7NeitMLa/ZeP6qFfhRvrH417fEN2m4\n3aBtWnEPRMTded2qs4BZPXONLGgZAqGQlM89JOSNIKcjU6oPPG3++odGa0ew07Wldd9I1qfZA9zO\nch8xY15kefMfr0C/nl2ljucEX4m7FqvZyQMGoWR6iqqaYp60TRKWg/ZDpJnPfdrbGwzXx8PbXx/A\nuSMGxKx3MuvT959bjjl3XRS17p6Pt0rt+9mmQ/hs02GMPaGfbdp1+2MtcSc4nXH+SGMbHpxTgAd/\neCYAYJNuLBBVLJ79arfhpMVW4/kAwOV/WYFBx3QznPIPkH+11zN7TbH0QF3BkEBTWyCuSSHq4nz4\nAPaC3B4UUm8TRxrbsNjgu5gRTi139R7QlnXTweg335+9tg6Tz/wWgPDbhlsY6YphD3qE3/bt7x7n\n+MotI4v2qXzE5MZs6QhGfbiSxaoBltW1Iv9AtXRjliW/OFY0nZR9T0UjKiVi+I14bP4u7Cirx3KD\nj26J4sabz9tfH4gs/4duLJAFBRX4fPOhhGajNxN2AKhOYMRLbbkfvHa0abo1RUdx5p8WYOlud9uU\nHf/ML7F0zQSCISlx95LJM1fi882HDF08Wr4sKAdg/mE5HkoMhsv44V+N5zD2at7frBT3LzQX2+iD\nlko8gqe1hoysm+tfNp9UwIyBvbuiT3fzl6wDBuOJyPjMtTS0xW/FAdY9Wt+YOh5D+/WQzkt2pisn\nTDaZB/et1QfcO4gOM9dW3x5dHOVz1beH4NoxxxtuW7Mv3FvaaSchr+kIxgYkpIK7P9iMJZLGlNG3\nhXhxIk2yvYqdktHiHu90Y/oPS2ZUNrQ6zlsr7m5NiD371vOQazFy0wEDn/CUl4ytBDMS+fgHWI+/\nceEpA9G9i7OmFu4e7466B5SxX4zYXFJruN5L3pg63lH6vBwydRkkO3Zalo5gYp2YUoGZ28Rr2HI3\nwOuxGiotXrnN0Ir7Vy5YU8f26oozj+9rKe77DSx3p/7Jm99c77hsWqxuDLuu6+/cel7UbyGA22bn\no7TGnY+n2tEGZejZtbOjWK+uuVjxf5fho19d4EpZAKBbXq59Ig15OTkxb1Y9lM5s8Q5A5zXpYrk7\nIVWdrthyNyDo8cUwnRvVAq247ygzj9iR5aji1zQSyAeuMffFOsXoRjznxP7S+1tZ/nbDxX7v1EEx\n69z8LmE0yqEVZw3tG1luag/ihGN7JjyeuZZuDt9ienbLxbeHhj+5qQ+eEUrP6pV7zQexSyUFh+tx\npDG+7zipotVFt4wT2HI3wMu3vu5dcrD/SBOIwtazFVqrWjYCQT8+idP0L//8HNx64QhHeTjlk9u/\n60o+RCQd/QEA14093pGP3m2MuqO7OV1a11z52+7AE1ejS24Ofvf9U/HF3RdHHjwnmQybkU446bTz\nzQNXeFgSOWQm/vACttwN0Hf9PXeEvKVpx4UnD1TyHGDr++2S23nny368eXfahJh1Y4ebB0SN14U7\ntgWCjgTTjmu+M8S1vPQQgP495T8izrzhbNceLFYcd0w3w/W/njQqZl0XB4JsRY8uuegj+UFV2666\n5ObgjCF90Eux3IcNcP/h98ovzrFNY+UeVDH6vjLGom0DwIBeXXHrhSMt0wzuY3y93GD4gB5So746\nYc8jV+H2S0823X75GccB8M5y9yTOnYgmA3geQC6A14UQT3hxnF7dwsW//IzB+O3lo3DisT3x+0+2\n4qqzhuCjjaXo0SUHJwzoiddW7sePxg3D4D7dsH5/NaZ/7yR8ve8odhyux/H9uuOMIX1w+ejBeHTe\nTizZVYmnrv8OLjjpWJw+5BjccO4JWFV4BFtL63DJqYOwuaQW3z9zMF5etg/BkMCRpnY8cPUZmLu1\nDNVN7ejeJQcnD+oNANhb2YgrRg9GbXM7TvtWH/xo1tcIhgQuPW0QLjj5WGx/6Eqc9acFAIA/XTsa\nV575LUx5aTUaWwN48cazsfFgDa5UYnAf/Y+z0NgWwLLdlQgJoKkt3CD+dtM4rCs6ipOP640FBeU4\n7pju+NemQ/jNxFPwgibE789TzsTW0jp065KDlvYQjumehz7d81DZ0IbbLh6JxrYglu+uwtD+PXDy\noN74/pmDAQBzf30RrnlxFQBgzLC+kfHuVcYM74ezju+DjcU1uPG8E1Ba04zXVu7Hd4b1xVYlbU4O\n4cUbx+F/P9qCqd8dgffWFWPi6cfhj58X4PyTwg+t528Yi+Kjzbj8jPBxv9W3O+bcdWEkfOy2i8I3\nvv5jeJ/ueZFu/pedNgiXnnYcNhbXgAjonpeLbw/ri6ON7eial4P31xejRNMJ6p7Jp2PJrgpsOFCD\nUcf1xreH9sUn35TinBP7Y/0fJuF3H27BTRNOAACcOvgY3HXZKRjcpxvmbi1DXi5FOpTdfMGJuGnC\niZj+bn6kw9Oi/7kEzyzYjf1HmnDHZSejoTWAWcv24e5Jo9C/Zxf8ZuIpKDrSFHmgPzIvPMnGj8YN\nw86yegRDAu//MtYA+NmEE3GksR3XjR2KPt27YOGOCggA+yobccdlJ6Oyvg3XnzMMGw5U45rvHI+j\nTW14bP4urNhThTOG9MFOxVU4ZezxGN6/J47r0w1//LwA9151Oq4881tYe+8kfJRfgrOG9sUtmv4Y\nz/x4DEJCYOzwfrj17Q0Y2q9HpN/CrJvG4fb3vsElpw7C8f164KqzvhX5hvPyz8/BqMG9MbRfD8xc\ntBejjuuNvyzcg5EDe+GY7nkYPqAnzhhyDADgj9eOxrG9u6JbXg4embcT0y4aiS65OXh9ZRECIYFB\nx3TDT8cPx+jj+6CqoQ1jh/fHnC2HMLhPd+woq0dpdQuKq5vQ2BrARaMGory+DVtKatGzay6a24MY\nM7wfqpvacOmpx6FbXg5GDuqFHYfrUd8awMmDekUmpp9x1el44ovoie8B4OrvDEG33By0B0M4a2hf\n7ClvwKebDgEAnvrRd3C4rgV3TxqFV1cUoayuFV3zcvD7yadj/f5qbCwO97E4/6QBOLZXNywoKMet\nF45EDhGGePSWSm6P1EdEuQD2ALgCQCmADQBuFELsMNtn/PjxIj8/39Vy+JWH/l2At1YfwB9+cAZ+\n+b2TYrYLIVDfEkBfB5ayHRMeW4SK+jYs/t0lmKQbr+fAE1cb7vPl9nL86u8bccXowXjtZuPokLK6\nFvTv2TVmpEsZgiGBlo4gjja24ZKnl+H5G8Ziytihtvst3FGBX76Tj8tOG4S3bjnPNr0Z87eV4Y73\nvok6PyEERt4bnk7NrF5SycvL9+GJL3bhtotG4n6J7zVtgSBOu/9LAMbnEwqFhw7Qv0HWtXRgzENf\nme7nlPwD1bj+5TUYd0I/fHrHhQnnZ0QwJHDyfZ3XTu1Bqho07047DxePiv02VN3UjgE2btv61g48\n/O8duP/qM1zviUpEG4UQhjeYF5b7eQAKhRBFysE/ADAFgKm4M/L0VxqHWRgoEbkq7EDnMLLH95W3\nMFSjwSqme4iD/PTk5hB6d8tD72552PPIVeiaJ+c2UYcEGNa/Z9zHBjpdBNHDQqdwqnsJ1Mia4yUt\nRfXbgNY9pMVoohEAOKabNx3fZd1Z8WDmbjq+X4+Yt1UtdsIOAH26d8EzP44d4sNrvLgKQwFowxNK\nAcS8XxLRdADTAeCEE07woBj+5JcXn4TGtgCmfvfEpB3z77dNwKq9R9Cjay7+dtM4dO+Sg2AIll3e\nLx89GP91yUm4/RJzn6NbyAo7AFwzZgh2VzTg1xNPSeiYZw/vj99MGoWfnjs8av0D14zGhJGxw0Gk\nA7ddPBKtHUH8bILc/UZEuP/qM3DRKPvhqLXk5IT3047NlAjjTuiPOy49Gf/53RGu5GfGcz8dgwG9\nwg/tl39+DoQQOHfkAJx4bC9c4NK5JBMv3DLXA5gshLhN+f0LABOEEHeZ7cNuGYZhGOdYuWW8iJY5\nBEBrzgxT1jEMwzBJwgtx3wBgFBGNJKKuAG4AMMeD4zAMwzAmuO5zF0IEiOguAAsQDoV8UwhR4PZx\nGIZhGHM8+awthJgPYL4XeTMMwzD2ZHQPVYZhGMYYFneGYRgfwuLOMAzjQ1jcGYZhfIjrnZjiKgRR\nFYDiOHcfCCA9B7VOHVwn0XB9RMP1EUum1smJQojYQW+QJuKeCESUb9ZDK1vhOomG6yMaro9Y/Fgn\n7JZhGIbxISzuDMMwPsQP4v5qqguQhnCdRMP1EQ3XRyy+q5OM97kzDMMwsfjBcmcYhmF0sLgzDMP4\nkIwWdyKaTES7iaiQiGakujzJgIiGE9FSItpBRAVEdLeyfgARLSSivcr//sp6IqIXlDraSkTjUnsG\n3kBEuUS0iYjmKr9HEtE65bz/qQw/DSLqpvwuVLaPSGW5vYKI+hHRx0S0i4h2EtEF2dxGiOi/lftl\nOxH9g4i6+72NZKy4KxNxvwTgKgCjAdxIRPaz/mY+AQC/E0KMBnA+gDuV854BYLEQYhSAxcpvIFw/\no5S/6QBmJb/ISeFuADs1v58E8JwQ4hQANQCmKeunAahR1j+npPMjzwP4UghxOoAxCNdNVrYRIhoK\n4DcAxgshzkJ4KPIb4Pc2IoTIyD8AFwBYoPl9L4B7U12uFNTD5wCuALAbwBBl3RAAu5XlVwDcqEkf\nSeeXP4Rn+1oMYCKAuQAI4d6Gefq2gvA8Axcoy3lKOkr1ObhcH30B7NefV7a2EXTO6zxAueZzAVzp\n9zaSsZY7jCfiHpqisqQE5XXxbADrAAwWQpQpm8oBDFaWs6GeZgK4B4A6Y/exAGqFEAHlt/acI/Wh\nbK9T0vuJkQCqALyluKpeJ6JeyNI2IoQ4BOAZAAcBlCF8zTfC520kk8U9qyGi3gA+AfBbIUS9dpsI\nmxxZEeNKRNcAqBRCbEx1WdKIPADjAMwSQpwNoAmdLhgAWddG+gOYgvBD73gAvQBMTmmhkkAmi3vW\nTsRNRF0QFvb3hBCfKqsriGiIsn0IgEplvd/r6UIAPySiAwA+QNg18zyAfkSkzjSmPedIfSjb+wI4\nmswCJ4FSAKVCiHXK748RFvtsbSOXA9gvhKgSQnQA+BThduPrNpLJ4p6VE3ETEQF4A8BOIcRfNJvm\nAJiqLE9F2Bevrr9ZiYg4H0Cd5tU84xFC3CuEGCaEGIFwG1gihLgJwFIA1yvJ9PWh1tP1SnpfWbBC\niHIAJUR0mrJqEoAdyNI2grA75nwi6qncP2p9+LuNpNrpn+CHkh8A2ANgH4A/pLo8STrnixB+nd4K\nYLPy9wOEfYKLAewFsAjAACU9IRxVtA/ANoQjBlJ+Hh7VzaUA5irLJwFYD6AQwEcAuinruyu/C5Xt\nJ6W63B7VxVgA+Uo7+QxA/2xuIwAeArALwHYA7wLo5vc2wsMPMAzD+JBMdsswDMMwJrC4MwzD+BAW\nd4ZhGB/C4s4wDONDWNwZhmF8CIs7wzCMD2FxZxiG8SH/H9+IKO/KN5KPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['fare'].plot(title='fare')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot multiple columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc890129828>"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['survived', 'pclass']][:20].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dealing with multiple `DataFrames`\n",
    "\n",
    "Sometimes, we have multiple dataframes of data. How do we combine them?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df[['sibsp', 'parch', 'ticket', 'fare', 'cabin', 'embarked', 'name']].iloc[:50]\n",
    "df2 = df[df.columns[:6]].iloc[:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge\n",
    "\n",
    "`merge` allows you to do standard database operations on a `DataFrame` or `Series`. Here is the doc line, let's break it down:\n",
    "```python\n",
    "pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None,\n",
    "         left_index=False, right_index=False, sort=True,\n",
    "         suffixes=('_x', '_y'), copy=True, indicator=False,\n",
    "         validate=None)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* `left` and `right` should be two `DataFrame` or `Series` objects. \n",
    "* `on` is a **column** or **index** name to join on and must be found in **both** dataframes. \n",
    "* `left_index` and `right_index` uses the respective input's index to use as a key instead\n",
    "* `how`: Either `left`, `right`, `outer`, or `inner`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sibsp  parch  ... embarked                                               name\n",
       "0      1      0  ...        S                            Braund, Mr. Owen Harris\n",
       "1      1      0  ...        C  Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "2      0      0  ...        S                             Heikkinen, Miss. Laina\n",
       "3      1      0  ...        S       Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "4      0      0  ...        S                           Allen, Mr. William Henry\n",
       "\n",
       "[5 rows x 7 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass  ...   age sibsp\n",
       "0         0       3  ...  22.0     1\n",
       "1         1       1  ...  38.0     1\n",
       "2         1       3  ...  26.0     0\n",
       "3         1       1  ...  35.0     1\n",
       "4         0       3  ...  35.0     0\n",
       "\n",
       "[5 rows x 6 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's practice merging on `name`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge = pd.merge(df1, df2, on='name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>sibsp_x</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>name</th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sibsp_x  parch            ticket     fare  ... pclass     sex   age  sibsp_y\n",
       "0        1      0         A/5 21171   7.2500  ...      3    male  22.0        1\n",
       "1        1      0          PC 17599  71.2833  ...      1  female  38.0        1\n",
       "2        0      0  STON/O2. 3101282   7.9250  ...      3  female  26.0        0\n",
       "3        1      0            113803  53.1000  ...      1  female  35.0        1\n",
       "4        0      0            373450   8.0500  ...      3    male  35.0        0\n",
       "\n",
       "[5 rows x 12 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appending\n",
    "\n",
    "We can tag one dataframe onto another:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_top = df.iloc[:5]\n",
    "df_bottom = df.iloc[5:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 5)"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_top), len(df_bottom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
       "        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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass  ... cabin embarked\n",
       "0         0       3  ...   NaN        S\n",
       "1         1       1  ...   C85        C\n",
       "2         1       3  ...   NaN        S\n",
       "3         1       1  ...  C123        S\n",
       "4         0       3  ...   NaN        S\n",
       "5         0       3  ...   NaN        Q\n",
       "6         0       1  ...   E46        S\n",
       "7         0       3  ...   NaN        S\n",
       "8         1       3  ...   NaN        S\n",
       "9         1       2  ...   NaN        C\n",
       "\n",
       "[10 rows x 11 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top.append(df_bottom)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropping\n",
    "\n",
    "You can drop a column or row, and by default `pandas` will return the dropped table. If you choose to do `inplace`, it will directly modify the dataframe. You can also pass in an `axis` parameter. By default it is `1`, which will drop a row. `0` drops a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass  ... cabin embarked\n",
       "1         1       1  ...   C85        C\n",
       "2         1       3  ...   NaN        S\n",
       "3         1       1  ...  C123        S\n",
       "4         0       3  ...   NaN        S\n",
       "\n",
       "[4 rows x 11 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top.drop(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>35.0</td>\n",
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       "      <td>S</td>\n",
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      ],
      "text/plain": [
       "   pclass                                               name  ... cabin  embarked\n",
       "0       3                            Braund, Mr. Owen Harris  ...   NaN         S\n",
       "1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  ...   C85         C\n",
       "2       3                             Heikkinen, Miss. Laina  ...   NaN         S\n",
       "3       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  ...  C123         S\n",
       "4       3                           Allen, Mr. William Henry  ...   NaN         S\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top.drop('survived', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>35.0</td>\n",
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      "text/plain": [
       "   survived  pclass  ... cabin embarked\n",
       "0         0       3  ...   NaN        S\n",
       "1         1       1  ...   C85        C\n",
       "2         1       3  ...   NaN        S\n",
       "3         1       1  ...  C123        S\n",
       "4         0       3  ...   NaN        S\n",
       "\n",
       "[5 rows x 11 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GroupBy\n",
    "\n",
    "GroupBy can be used to split our data into groups if they fall into some criteria. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "df_top.groupby(by=None, axis=0, level=None, as_index: bool=True, sort: bool=True, group_keys: bool=True, squeeze: bool=False, observed: bool=False)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameters:\n",
    "* `by`: mapping, function, string or some iterable\n",
    "* `axis`: default is 0\n",
    "* `level`: If the axis is heirarchical, group by levels or a particular level\n",
    "* `group_keys`: Add group keys to index to identify pieces"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For an example, let's group by `survived`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "surv = df.groupby('survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we call `first`, we'll see the first entry in each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>S</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>female</td>\n",
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      ],
      "text/plain": [
       "          pclass  ... embarked\n",
       "survived          ...         \n",
       "0              3  ...        S\n",
       "1              1  ...        C\n",
       "\n",
       "[2 rows x 10 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surv.first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To grab the group, we can call `get_group` and pass in either one of our classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass                                               name  ... cabin  embarked\n",
       "1         1  Cumings, Mrs. John Bradley (Florence Briggs Th...  ...   C85         C\n",
       "2         3                             Heikkinen, Miss. Laina  ...   NaN         S\n",
       "3         1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  ...  C123         S\n",
       "8         3  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  ...   NaN         S\n",
       "9         2                Nasser, Mrs. Nicholas (Adele Achem)  ...   NaN         C\n",
       "..      ...                                                ...  ...   ...       ...\n",
       "875       3                   Najib, Miss. Adele Kiamie \"Jane\"  ...   NaN         C\n",
       "879       1      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  ...   C50         C\n",
       "880       2       Shelley, Mrs. William (Imanita Parrish Hall)  ...   NaN         S\n",
       "887       1                       Graham, Miss. Margaret Edith  ...   B42         S\n",
       "889       1                              Behr, Mr. Karl Howell  ...  C148         C\n",
       "\n",
       "[342 rows x 10 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surv.get_group(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also group by multiple columns. Let's combine both `survived` and `sex`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "surs = df.groupby(['survived', 'sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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></th>\n",
       "      <th>pclass</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <th>sex</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>female</th>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>C49</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>female</th>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D56</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 pclass  ... embarked\n",
       "survived sex             ...         \n",
       "0        female       3  ...        S\n",
       "         male         3  ...        S\n",
       "1        female       1  ...        C\n",
       "         male         2  ...        S\n",
       "\n",
       "[4 rows x 9 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surs.first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can further analyze and split our data based on whatever queries we want to use!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Map\n",
    "\n",
    "`map` can be used to map values from two different `series` (column) that share a same column. Basically we can repeat our adjust values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: survived, dtype: int64"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['survived'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use a function or a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        dead\n",
       "1    survived\n",
       "2    survived\n",
       "3    survived\n",
       "4        dead\n",
       "Name: survived, dtype: object"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['survived'].map({0:'dead', 1:'survived'}).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    I am a 0\n",
       "1    I am a 1\n",
       "2    I am a 1\n",
       "3    I am a 1\n",
       "4    I am a 0\n",
       "Name: survived, dtype: object"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['survived'].map('I am a {}'.format).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# .apply"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply let's you pass in a function to apply to every value in a series. It takes in a function, `convert_dtype`, and some argumetns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a basic one that returns if someone survived:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lived(num):\n",
    "  if num == 0:\n",
    "    return \"Died\"\n",
    "  else:\n",
    "    return \"Lived\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = df['survived'].apply(lived)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Died\n",
       "1    Lived\n",
       "2    Lived\n",
       "3    Lived\n",
       "4     Died\n",
       "Name: survived, dtype: object"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use a lambda:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = df['age'].apply(lambda age: age + 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    22.0\n",
      "1    38.0\n",
      "2    26.0\n",
      "3    35.0\n",
      "4    35.0\n",
      "Name: age, dtype: float64\n",
      "0    27.0\n",
      "1    43.0\n",
      "2    31.0\n",
      "3    40.0\n",
      "4    40.0\n",
      "Name: age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(f'{df[\"age\"].head()}\\n{new.head()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
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