pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Pandas\n",
    "----------------------------------------------------------------------------\n",
    "## Goals:\n",
    "* Learn how to use pandas dataframes\n",
    "* Plot basic charts using dataframes and matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference: \n",
    "* https://pandas.pydata.org/pandas-docs/stable/getting_started/overview.html\n",
    "* https://pandas.pydata.org/pandas-docs/stable/reference/frame.html\n",
    "* https://pandas.pydata.org/pandas-docs/stable/reference/series.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive.It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import pandas library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is well suited for many different kinds of data:\n",
    "* Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet\n",
    "* Ordered and unordered (not necessarily fixed-frequency) time series data.\n",
    "* Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels\n",
    "* Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure\n",
    "\n",
    "Data structures in pandas are:\n",
    "\n",
    "* Series objects: 1D array, similar to a column in a spreadsheet\n",
    "* DataFrame objects: 2D table, similar to a spreadsheet\n",
    "* Panel objects: Dictionary of DataFrames, similar to sheet in MS Excel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Serie\n",
    "A 1D array similar to a column in spreadsheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    a\n",
      "1    b\n",
      "2    c\n",
      "3    d\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "ndarray = np.array(['a','b','c','d'])\n",
    "serie = pd.Series(ndarray)\n",
    "print(serie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a data frame\n",
    "A dataframe is the tabular representation of data. Think of a dataframe as a spreadsheet with column headers and rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dog_data=[\n",
    "    ['Pedro','Doberman',3],\\\n",
    "    ['Clementine','Golden Retriever',8],\\\n",
    "    ['Norah','Great Dane',6],\\\n",
    "    ['Mabel','Austrailian Shepherd',1],\\\n",
    "    ['Bear','Maltese',4],\\\n",
    "    ['Bill','Great Dane',10]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>breed</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clementine</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norah</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>Austrailian Shepherd</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bear</td>\n",
       "      <td>Maltese</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bill</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name                 breed  age\n",
       "0       Pedro              Doberman    3\n",
       "1  Clementine      Golden Retriever    8\n",
       "2       Norah            Great Dane    6\n",
       "3       Mabel  Austrailian Shepherd    1\n",
       "4        Bear               Maltese    4\n",
       "5        Bill            Great Dane   10"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df=pd.DataFrame(dog_data,columns=['name','breed','age'])\n",
    "dog_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.int64'>\n"
     ]
    }
   ],
   "source": [
    "print(type(dog_df['age'].iloc[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Previewing the data frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[**DataFrame.head(n=5)**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html#pandas.DataFrame.head)\n",
    "* This function returns the first n rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>breed</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clementine</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norah</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>Austrailian Shepherd</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bear</td>\n",
       "      <td>Maltese</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name                 breed  age\n",
       "0       Pedro              Doberman    3\n",
       "1  Clementine      Golden Retriever    8\n",
       "2       Norah            Great Dane    6\n",
       "3       Mabel  Austrailian Shepherd    1\n",
       "4        Bear               Maltese    4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[**DataFrame.tail(n=5)**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.tail.html#pandas.DataFrame.tail)\n",
    "* This function returns last n rows from the object based on position. It is useful for quickly verifying data, for example, after sorting or appending rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>breed</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>Austrailian Shepherd</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bear</td>\n",
       "      <td>Maltese</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bill</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name                 breed  age\n",
       "3  Mabel  Austrailian Shepherd    1\n",
       "4   Bear               Maltese    4\n",
       "5   Bill            Great Dane   10"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[**DataFrame.shape**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.shape.html#pandas.DataFrame.shape)\n",
    "* Return a tuple representing the dimensionality of the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dog_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[**DataFrame.columns**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.columns.html#pandas.DataFrame.columns)\n",
    "* The column labels of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'breed', 'age'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[**DataFrame.dtypes**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dtypes.html#pandas.DataFrame.dtypes)\n",
    "* Return the dtypes in the DataFrame.\n",
    "* This returns a Series with the data type of each column.\n",
    "* The result’s index is the original DataFrame’s columns.\n",
    "* Columns with mixed types are stored with the object dtype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name     object\n",
       "breed    object\n",
       "age       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[**DataFrame.values**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.values.html#pandas.DataFrame.values)\n",
    "* Return a Numpy representation of the DataFrame.\n",
    "* Python documentation recommends using DataFrame.to_numpy() instead.\n",
    "* Only the values in the DataFrame will be returned, the axes labels will be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Pedro', 'Doberman', 3],\n",
       "       ['Clementine', 'Golden Retriever', 8],\n",
       "       ['Norah', 'Great Dane', 6],\n",
       "       ['Mabel', 'Austrailian Shepherd', 1],\n",
       "       ['Bear', 'Maltese', 4],\n",
       "       ['Bill', 'Great Dane', 10]], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[**DataFrame.describe(percentiles=None, include=None, exclude=None)**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html#pandas.DataFrame.describe)\n",
    "* Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.\n",
    "* Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types. The output will vary depending on what is provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.326660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             age\n",
       "count   6.000000\n",
       "mean    5.333333\n",
       "std     3.326660\n",
       "min     1.000000\n",
       "25%     3.250000\n",
       "50%     5.000000\n",
       "75%     7.500000\n",
       "max    10.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[**Series.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.value_counts.html)\n",
    "* Return a Series containing counts of unique values.\n",
    "* The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Great Dane              2\n",
       "Golden Retriever        1\n",
       "Maltese                 1\n",
       "Doberman                1\n",
       "Austrailian Shepherd    1\n",
       "Name: breed, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df['breed'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting/Querying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>name</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clementine</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norah</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bear</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bill</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name  age\n",
       "0       Pedro    3\n",
       "1  Clementine    8\n",
       "2       Norah    6\n",
       "3       Mabel    1\n",
       "4        Bear    4\n",
       "5        Bill   10"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df[['name','age']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[**DataFrame.iloc**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iloc.html#pandas.DataFrame.iloc)\n",
    "* Purely integer-location based indexing for selection by position.\n",
    "* .iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.\n",
    "\n",
    "Allowed inputs are:\n",
    "\n",
    "* An integer, e.g. 5.\n",
    "* A list or array of integers, e.g. [4, 3, 0].\n",
    "* A slice object with ints, e.g. 1:7.\n",
    "* A boolean array.\n",
    "* A callable function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing (one of the above). This is useful in method chains, when you don’t have a reference to the calling object, but would like to base your selection on some value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>breed</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norah</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>Austrailian Shepherd</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name                 breed  age\n",
       "2  Norah            Great Dane    6\n",
       "3  Mabel  Austrailian Shepherd    1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df.iloc[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>breed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clementine</td>\n",
       "      <td>Golden Retriever</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norah</td>\n",
       "      <td>Great Dane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>Austrailian Shepherd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name                 breed\n",
       "1  Clementine      Golden Retriever\n",
       "2       Norah            Great Dane\n",
       "3       Mabel  Austrailian Shepherd"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df.iloc[1:4, 0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>breed</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norah</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bear</td>\n",
       "      <td>Maltese</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bill</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name       breed  age\n",
       "2  Norah  Great Dane    6\n",
       "4   Bear     Maltese    4\n",
       "5   Bill  Great Dane   10"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df[dog_df['breed'].isin(['Great Dane', 'Maltese'])]"
   ]
  },
  {
   "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>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norah</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name       breed  age\n",
       "2  Norah  Great Dane    6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df[dog_df['name']=='Norah']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>breed</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bill</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name       breed  age\n",
       "5  Bill  Great Dane   10"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df[(dog_df['name']=='Bill') & (dog_df['breed']=='Great Dane')]"
   ]
  },
  {
   "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",
       "    }\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>breed</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>Austrailian Shepherd</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bear</td>\n",
       "      <td>Maltese</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name                 breed  age\n",
       "0  Pedro              Doberman    3\n",
       "3  Mabel  Austrailian Shepherd    1\n",
       "4   Bear               Maltese    4"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df[dog_df['age']<5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>breed</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clementine</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norah</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bill</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name             breed  age\n",
       "1  Clementine  Golden Retriever    8\n",
       "2       Norah        Great Dane    6\n",
       "5        Bill        Great Dane   10"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_df[dog_df['breed'].str.contains('G')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "owner_data=[['Bilbo','Pedro'],['Gandalf','Bear'],['Sam','Bill']]\n",
    "owner_df=pd.DataFrame(owner_data,columns=['owner_name','dog_name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[DataFrame.merge(right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html#pandas.DataFrame.merge)**\n",
    "* Merge DataFrame or named Series objects with a database-style join.\n",
    "* The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.merge(owner_df,dog_df,left_on='dog_name',right_on='name',how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>owner_name</th>\n",
       "      <th>dog_name</th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bilbo</td>\n",
       "      <td>Pedro</td>\n",
       "      <td>Pedro</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gandalf</td>\n",
       "      <td>Bear</td>\n",
       "      <td>Bear</td>\n",
       "      <td>Maltese</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sam</td>\n",
       "      <td>Bill</td>\n",
       "      <td>Bill</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  owner_name dog_name   name       breed  age\n",
       "0      Bilbo    Pedro  Pedro    Doberman    3\n",
       "1    Gandalf     Bear   Bear     Maltese    4\n",
       "2        Sam     Bill   Bill  Great Dane   10"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More details on merge parameters:\n",
    "* **right** : DataFrame\n",
    "* **how** : {‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘inner’\n",
    "  * left: use only keys from left frame, similar to a SQL left outer join; preserve key order\n",
    "  * right: use only keys from right frame, similar to a SQL right outer join; preserve key order\n",
    "  * outer: use union of keys from both frames, similar to a SQL full outer join; sort keys lexicographically\n",
    "  * inner: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys\n",
    "* **on** : label or list. Column or index level names to join on. These must be found in both DataFrames. If on is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames.\n",
    "* **left_on** : label or list, or array-like. Column or index level names to join on in the left DataFrame. Can also be an array or list of arrays of the length of the left DataFrame. These arrays are treated as if they are columns.\n",
    "* **right_on** : label or list, or array-like Column or index level names to join on in the right DataFrame. Can also be an array or list of arrays of the length of the right DataFrame. These arrays are treated as if they are columns.\n",
    "\n",
    "Reference: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Merge method | SQL Join Name | Description |\n",
    "| -------------|---------------|-------------|\n",
    "| left | LEFT OUTER JOIN | Use keys from left frame only | \n",
    "| right | RIGHT OUTER JOIN | Use keys from right frame only |\n",
    "| outer | FULL OUTER JOIN | Use union of keys from both frames |\n",
    "| inner | INNER JOIN | Use intersection of keys from both frames |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inner Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "inner_df = owner_df.merge(dog_df, left_on='dog_name', right_on='name', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>owner_name</th>\n",
       "      <th>dog_name</th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bilbo</td>\n",
       "      <td>Pedro</td>\n",
       "      <td>Pedro</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gandalf</td>\n",
       "      <td>Bear</td>\n",
       "      <td>Bear</td>\n",
       "      <td>Maltese</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sam</td>\n",
       "      <td>Bill</td>\n",
       "      <td>Bill</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  owner_name dog_name   name       breed  age\n",
       "0      Bilbo    Pedro  Pedro    Doberman    3\n",
       "1    Gandalf     Bear   Bear     Maltese    4\n",
       "2        Sam     Bill   Bill  Great Dane   10"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inner_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "inner_df=inner_df.drop(['name'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>owner_name</th>\n",
       "      <th>dog_name</th>\n",
       "      <th>breed</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bilbo</td>\n",
       "      <td>Pedro</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gandalf</td>\n",
       "      <td>Bear</td>\n",
       "      <td>Maltese</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sam</td>\n",
       "      <td>Bill</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  owner_name dog_name       breed  age\n",
       "0      Bilbo    Pedro    Doberman    3\n",
       "1    Gandalf     Bear     Maltese    4\n",
       "2        Sam     Bill  Great Dane   10"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inner_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Left Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "left_df = owner_df.merge(dog_df, left_on='dog_name', right_on='name', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>owner_name</th>\n",
       "      <th>dog_name</th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bilbo</td>\n",
       "      <td>Pedro</td>\n",
       "      <td>Pedro</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gandalf</td>\n",
       "      <td>Bear</td>\n",
       "      <td>Bear</td>\n",
       "      <td>Maltese</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sam</td>\n",
       "      <td>Bill</td>\n",
       "      <td>Bill</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  owner_name dog_name   name       breed  age\n",
       "0      Bilbo    Pedro  Pedro    Doberman    3\n",
       "1    Gandalf     Bear   Bear     Maltese    4\n",
       "2        Sam     Bill   Bill  Great Dane   10"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Right Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "right_df = owner_df.merge(dog_df, left_on='dog_name', right_on='name', how='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>owner_name</th>\n",
       "      <th>dog_name</th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bilbo</td>\n",
       "      <td>Pedro</td>\n",
       "      <td>Pedro</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gandalf</td>\n",
       "      <td>Bear</td>\n",
       "      <td>Bear</td>\n",
       "      <td>Maltese</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sam</td>\n",
       "      <td>Bill</td>\n",
       "      <td>Bill</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Clementine</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Norah</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mabel</td>\n",
       "      <td>Austrailian Shepherd</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  owner_name dog_name        name                 breed  age\n",
       "0      Bilbo    Pedro       Pedro              Doberman    3\n",
       "1    Gandalf     Bear        Bear               Maltese    4\n",
       "2        Sam     Bill        Bill            Great Dane   10\n",
       "3        NaN      NaN  Clementine      Golden Retriever    8\n",
       "4        NaN      NaN       Norah            Great Dane    6\n",
       "5        NaN      NaN       Mabel  Austrailian Shepherd    1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outer Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "outer_df = owner_df.merge(dog_df, left_on='dog_name', right_on='name', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>owner_name</th>\n",
       "      <th>dog_name</th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bilbo</td>\n",
       "      <td>Pedro</td>\n",
       "      <td>Pedro</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gandalf</td>\n",
       "      <td>Bear</td>\n",
       "      <td>Bear</td>\n",
       "      <td>Maltese</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sam</td>\n",
       "      <td>Bill</td>\n",
       "      <td>Bill</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Clementine</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Norah</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mabel</td>\n",
       "      <td>Austrailian Shepherd</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  owner_name dog_name        name                 breed  age\n",
       "0      Bilbo    Pedro       Pedro              Doberman    3\n",
       "1    Gandalf     Bear        Bear               Maltese    4\n",
       "2        Sam     Bill        Bill            Great Dane   10\n",
       "3        NaN      NaN  Clementine      Golden Retriever    8\n",
       "4        NaN      NaN       Norah            Great Dane    6\n",
       "5        NaN      NaN       Mabel  Austrailian Shepherd    1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outer_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[**DataFrame.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html#pandas.DataFrame.drop)\n",
    "* Drop specified labels from rows or columns.\n",
    "* Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index or column names. \n",
    "* When using a multi-index, labels on different levels can be removed by specifying the level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['name'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>owner_name</th>\n",
       "      <th>dog_name</th>\n",
       "      <th>breed</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bilbo</td>\n",
       "      <td>Pedro</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gandalf</td>\n",
       "      <td>Bear</td>\n",
       "      <td>Maltese</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sam</td>\n",
       "      <td>Bill</td>\n",
       "      <td>Great Dane</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  owner_name dog_name       breed  age\n",
       "0      Bilbo    Pedro    Doberman    3\n",
       "1    Gandalf     Bear     Maltese    4\n",
       "2        Sam     Bill  Great Dane   10"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Will allow us to embed images in the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_df = pd.DataFrame({\n",
    "    'col1': [1, 3, 2, 4],\n",
    "    'col2': [3, 6, 5, 1],\n",
    "    'col3': [4, 7, 6, 2],\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[**matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)**](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html)\n",
    "* Plot y versus x as lines and/or markers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f997c1d18d0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f997c1756a0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f997c0b8080>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}