data-analysis/029-pandas-series/intro-to-pands-series.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Series\n",
    "\n",
    "This is a notebook for the medium article [A Practical Introduction to Pandas Series](https://bindichen.medium.com/a-practical-introduction-to-pandas-series-9915521cdc69)\n",
    "\n",
    "Please check out article for instructions\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Creating a Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 from a Python list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Google\n",
       "1    Microsoft\n",
       "2     Facebook\n",
       "3        Apple\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies = ['Google', 'Microsoft', 'Facebook', 'Apple']\n",
    "\n",
    "pd.Series(companies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100       Google\n",
       "101    Microsoft\n",
       "102     Facebook\n",
       "103        Apple\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Custom Index\n",
    "pd.Series(companies,index=[100,101,102,103])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GOOGL       Google\n",
       "MSFT     Microsoft\n",
       "FB        Facebook\n",
       "AAPL         Apple\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Custom Index\n",
    "pd.Series(companies,index=['GOOGL','MSFT','FB','AAPL'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 from a dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a       Google\n",
       "b    Microsoft\n",
       "c     Facebook\n",
       "d        Apple\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies = {\n",
    "    'a': 'Google',\n",
    "    'b': 'Microsoft',\n",
    "    'c': 'Facebook',\n",
    "    'd': 'Apple'\n",
    "}\n",
    "pd.Series(companies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a       Google\n",
       "b    Microsoft\n",
       "d        Apple\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# When index is specified\n",
    "pd.Series(\n",
    "    companies, \n",
    "    index=['a', 'b', 'd']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 from a scalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100    10\n",
       "101    10\n",
       "102    10\n",
       "103    10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(10, index=[100, 101, 102, 103])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 with the read_csv()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the Pandas `read_csv()` function will import data as a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "      <th>cost</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019/1/1</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019/1/2</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019/1/3</td>\n",
       "      <td>C</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019/1/4</td>\n",
       "      <td>D</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date product  price  cost  profit\n",
       "0  2019/1/1       A     10     5       1\n",
       "1  2019/1/2       B     20    12       2\n",
       "2  2019/1/3       C     30    20       3\n",
       "3  2019/1/4       D     40    30       4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want the data to be imported into a Series instead of a DataFrame, we can provide additional arguments `usecols` and `squeeze`. The `squeeze=True` will convert a DataFrame of one column into a Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    A\n",
       "1    B\n",
       "2    C\n",
       "3    D\n",
       "Name: product, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data.csv', usecols=['product'], squeeze=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Retrieving data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 with position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])\n",
    "\n",
    "s[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    3\n",
       "d    4\n",
       "e    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[-3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    3\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieving elements within a range\n",
    "s[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "c    3\n",
       "e    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieving elements by step\n",
    "s[::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 with index/label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[['b','c','d']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "companies = ['Google', 'Microsoft', 'Facebook', 'Apple']\n",
    "s = pd.Series(companies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Values and indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Google', 'Microsoft', 'Facebook', 'Apple'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The values attribute returns an array of all the values within the series.\n",
    "s.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The index attribute returns a RangeIndex object. \n",
    "# We can see it starts at 0 and stops at 5. \n",
    "# The last part is called step and that's telling us that it's incrementing by 1\n",
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The is_unique attribute returns a boolean (True or False). \n",
    "# It is a really convenient way to check if every series value is unique or not.\n",
    "s.is_unique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Data type and size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.ndim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A method as mentioned actually does something to the object. It may be manipulating it, or adding value, or do some calculation with the object's values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = [10, 5, 3, 2.5, 8, 11]\n",
    "\n",
    "s = pd.Series(prices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Showing rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10.0\n",
       "1     5.0\n",
       "2     3.0\n",
       "3     2.5\n",
       "4     8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10.0\n",
       "1     5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4     8.0\n",
       "5    11.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Performing aggregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.583333333333333"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39.5"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33000.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.product()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean           6.583333\n",
       "sum           39.500000\n",
       "product    33000.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.agg(['mean','sum','product'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Counting values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series(['a','b','b','a','a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a', 'b'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    3\n",
       "b    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Sorting by values or index labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = [10, 5, 3, 2.5, 8, 11]\n",
    "\n",
    "s = pd.Series(prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     2.5\n",
       "2     3.0\n",
       "1     5.0\n",
       "4     8.0\n",
       "0    10.0\n",
       "5    11.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ascending by default\n",
    "s.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    11.0\n",
       "0    10.0\n",
       "4     8.0\n",
       "1     5.0\n",
       "2     3.0\n",
       "3     2.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To sort it in descenting order\n",
    "s.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     2.5\n",
       "2     3.0\n",
       "1     5.0\n",
       "4     8.0\n",
       "0    10.0\n",
       "5    11.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To modify the original series\n",
    "s.sort_values(inplace=True)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10.0\n",
       "1     5.0\n",
       "2     3.0\n",
       "3     2.5\n",
       "4     8.0\n",
       "5    11.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ascending by default\n",
    "s.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    11.0\n",
       "4     8.0\n",
       "3     2.5\n",
       "2     3.0\n",
       "1     5.0\n",
       "0    10.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To sort it in descenting order\n",
    "s.sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To modify the original series\n",
    "s.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 Working with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([1, 2, 3, np.nan, np.nan])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6 Searching values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = [10, 5, 3, 2.5, 8, 11]\n",
    "\n",
    "s = pd.Series(prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    11.0\n",
       "0    10.0\n",
       "4     8.0\n",
       "1     5.0\n",
       "2     3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.nlargest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    11.0\n",
       "0    10.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.nlargest(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2     True\n",
       "3     True\n",
       "4    False\n",
       "5    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.le(5, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2     True\n",
       "3     True\n",
       "4    False\n",
       "5    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s <= 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Working with Python built-in functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = [10, 5, 3, 2.5, 8, 11]\n",
    "\n",
    "s = pd.Series(prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['T',\n",
       " '_AXIS_LEN',\n",
       " '_AXIS_NAMES',\n",
       " '_AXIS_NUMBERS',\n",
       " '_AXIS_ORDERS',\n",
       " '_AXIS_REVERSED',\n",
       " '_AXIS_TO_AXIS_NUMBER',\n",
       " '_HANDLED_TYPES',\n",
       " '__abs__',\n",
       " '__add__',\n",
       " '__and__',\n",
       " '__annotations__',\n",
       " '__array__',\n",
       " '__array_priority__',\n",
       " '__array_ufunc__',\n",
       " '__array_wrap__',\n",
       " '__bool__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__copy__',\n",
       " '__deepcopy__',\n",
       " '__delattr__',\n",
       " '__delitem__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__div__',\n",
       " '__divmod__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__finalize__',\n",
       " '__float__',\n",
       " '__floordiv__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattr__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__getstate__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__iadd__',\n",
       " '__iand__',\n",
       " '__ifloordiv__',\n",
       " '__imod__',\n",
       " '__imul__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__int__',\n",
       " '__invert__',\n",
       " '__ior__',\n",
       " '__ipow__',\n",
       " '__isub__',\n",
       " '__iter__',\n",
       " '__itruediv__',\n",
       " '__ixor__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__long__',\n",
       " '__lt__',\n",
       " '__matmul__',\n",
       " '__mod__',\n",
       " '__module__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__neg__',\n",
       " '__new__',\n",
       " '__nonzero__',\n",
       " '__or__',\n",
       " '__pos__',\n",
       " '__pow__',\n",
       " '__radd__',\n",
       " '__rand__',\n",
       " '__rdiv__',\n",
       " '__rdivmod__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__rfloordiv__',\n",
       " '__rmatmul__',\n",
       " '__rmod__',\n",
       " '__rmul__',\n",
       " '__ror__',\n",
       " '__round__',\n",
       " '__rpow__',\n",
       " '__rsub__',\n",
       " '__rtruediv__',\n",
       " '__rxor__',\n",
       " '__setattr__',\n",
       " '__setitem__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__sub__',\n",
       " '__subclasshook__',\n",
       " '__truediv__',\n",
       " '__weakref__',\n",
       " '__xor__',\n",
       " '_accessors',\n",
       " '_add_numeric_operations',\n",
       " '_add_series_or_dataframe_operations',\n",
       " '_agg_by_level',\n",
       " '_agg_examples_doc',\n",
       " '_agg_see_also_doc',\n",
       " '_aggregate',\n",
       " '_aggregate_multiple_funcs',\n",
       " '_align_frame',\n",
       " '_align_series',\n",
       " '_binop',\n",
       " '_builtin_table',\n",
       " '_can_hold_na',\n",
       " '_check_inplace_setting',\n",
       " '_check_is_chained_assignment_possible',\n",
       " '_check_label_or_level_ambiguity',\n",
       " '_check_setitem_copy',\n",
       " '_clear_item_cache',\n",
       " '_clip_with_one_bound',\n",
       " '_clip_with_scalar',\n",
       " '_consolidate',\n",
       " '_consolidate_inplace',\n",
       " '_construct_axes_dict',\n",
       " '_construct_axes_from_arguments',\n",
       " '_construct_result',\n",
       " '_constructor',\n",
       " '_constructor_expanddim',\n",
       " '_constructor_sliced',\n",
       " '_convert',\n",
       " '_convert_dtypes',\n",
       " '_cython_table',\n",
       " '_data',\n",
       " '_deprecations',\n",
       " '_dir_additions',\n",
       " '_dir_deletions',\n",
       " '_drop_axis',\n",
       " '_drop_labels_or_levels',\n",
       " '_find_valid_index',\n",
       " '_get_axis',\n",
       " '_get_axis_name',\n",
       " '_get_axis_number',\n",
       " '_get_axis_resolvers',\n",
       " '_get_block_manager_axis',\n",
       " '_get_bool_data',\n",
       " '_get_cacher',\n",
       " '_get_cleaned_column_resolvers',\n",
       " '_get_cython_func',\n",
       " '_get_index_resolvers',\n",
       " '_get_item_cache',\n",
       " '_get_label_or_level_values',\n",
       " '_get_numeric_data',\n",
       " '_get_value',\n",
       " '_get_values',\n",
       " '_get_values_tuple',\n",
       " '_get_with',\n",
       " '_gotitem',\n",
       " '_index',\n",
       " '_indexed_same',\n",
       " '_info_axis',\n",
       " '_info_axis_name',\n",
       " '_info_axis_number',\n",
       " '_init_dict',\n",
       " '_init_mgr',\n",
       " '_internal_names',\n",
       " '_internal_names_set',\n",
       " '_is_builtin_func',\n",
       " '_is_cached',\n",
       " '_is_copy',\n",
       " '_is_label_or_level_reference',\n",
       " '_is_label_reference',\n",
       " '_is_level_reference',\n",
       " '_is_mixed_type',\n",
       " '_is_view',\n",
       " '_iset_item',\n",
       " '_ix',\n",
       " '_ixs',\n",
       " '_map_values',\n",
       " '_maybe_cache_changed',\n",
       " '_maybe_update_cacher',\n",
       " '_metadata',\n",
       " '_needs_reindex_multi',\n",
       " '_obj_with_exclusions',\n",
       " '_protect_consolidate',\n",
       " '_reduce',\n",
       " '_reindex_axes',\n",
       " '_reindex_indexer',\n",
       " '_reindex_multi',\n",
       " '_reindex_with_indexers',\n",
       " '_repr_data_resource_',\n",
       " '_repr_latex_',\n",
       " '_reset_cache',\n",
       " '_reset_cacher',\n",
       " '_selected_obj',\n",
       " '_selection',\n",
       " '_selection_list',\n",
       " '_selection_name',\n",
       " '_set_as_cached',\n",
       " '_set_axis',\n",
       " '_set_axis_name',\n",
       " '_set_is_copy',\n",
       " '_set_item',\n",
       " '_set_labels',\n",
       " '_set_name',\n",
       " '_set_value',\n",
       " '_set_values',\n",
       " '_set_with',\n",
       " '_set_with_engine',\n",
       " '_slice',\n",
       " '_stat_axis',\n",
       " '_stat_axis_name',\n",
       " '_stat_axis_number',\n",
       " '_take_with_is_copy',\n",
       " '_to_dict_of_blocks',\n",
       " '_try_aggregate_string_function',\n",
       " '_typ',\n",
       " '_update_inplace',\n",
       " '_validate_dtype',\n",
       " '_values',\n",
       " '_where',\n",
       " 'abs',\n",
       " 'add',\n",
       " 'add_prefix',\n",
       " 'add_suffix',\n",
       " 'agg',\n",
       " 'aggregate',\n",
       " 'align',\n",
       " 'all',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argsort',\n",
       " 'array',\n",
       " 'asfreq',\n",
       " 'asof',\n",
       " 'astype',\n",
       " 'at',\n",
       " 'at_time',\n",
       " 'attrs',\n",
       " 'autocorr',\n",
       " 'axes',\n",
       " 'backfill',\n",
       " 'between',\n",
       " 'between_time',\n",
       " 'bfill',\n",
       " 'bool',\n",
       " 'clip',\n",
       " 'combine',\n",
       " 'combine_first',\n",
       " 'compare',\n",
       " 'convert_dtypes',\n",
       " 'copy',\n",
       " 'corr',\n",
       " 'count',\n",
       " 'cov',\n",
       " 'cummax',\n",
       " 'cummin',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'describe',\n",
       " 'diff',\n",
       " 'div',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'drop',\n",
       " 'drop_duplicates',\n",
       " 'droplevel',\n",
       " 'dropna',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'duplicated',\n",
       " 'empty',\n",
       " 'eq',\n",
       " 'equals',\n",
       " 'ewm',\n",
       " 'expanding',\n",
       " 'explode',\n",
       " 'factorize',\n",
       " 'ffill',\n",
       " 'fillna',\n",
       " 'filter',\n",
       " 'first',\n",
       " 'first_valid_index',\n",
       " 'floordiv',\n",
       " 'ge',\n",
       " 'get',\n",
       " 'groupby',\n",
       " 'gt',\n",
       " 'hasnans',\n",
       " 'head',\n",
       " 'hist',\n",
       " 'iat',\n",
       " 'idxmax',\n",
       " 'idxmin',\n",
       " 'iloc',\n",
       " 'index',\n",
       " 'infer_objects',\n",
       " 'interpolate',\n",
       " 'is_monotonic',\n",
       " 'is_monotonic_decreasing',\n",
       " 'is_monotonic_increasing',\n",
       " 'is_unique',\n",
       " 'isin',\n",
       " 'isna',\n",
       " 'isnull',\n",
       " 'item',\n",
       " 'items',\n",
       " 'iteritems',\n",
       " 'keys',\n",
       " 'kurt',\n",
       " 'kurtosis',\n",
       " 'last',\n",
       " 'last_valid_index',\n",
       " 'le',\n",
       " 'loc',\n",
       " 'lt',\n",
       " 'mad',\n",
       " 'map',\n",
       " 'mask',\n",
       " 'max',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memory_usage',\n",
       " 'min',\n",
       " 'mod',\n",
       " 'mode',\n",
       " 'mul',\n",
       " 'multiply',\n",
       " 'name',\n",
       " 'nbytes',\n",
       " 'ndim',\n",
       " 'ne',\n",
       " 'nlargest',\n",
       " 'notna',\n",
       " 'notnull',\n",
       " 'nsmallest',\n",
       " 'nunique',\n",
       " 'pad',\n",
       " 'pct_change',\n",
       " 'pipe',\n",
       " 'plot',\n",
       " 'pop',\n",
       " 'pow',\n",
       " 'prod',\n",
       " 'product',\n",
       " 'quantile',\n",
       " 'radd',\n",
       " 'rank',\n",
       " 'ravel',\n",
       " 'rdiv',\n",
       " 'rdivmod',\n",
       " 'reindex',\n",
       " 'reindex_like',\n",
       " 'rename',\n",
       " 'rename_axis',\n",
       " 'reorder_levels',\n",
       " 'repeat',\n",
       " 'replace',\n",
       " 'resample',\n",
       " 'reset_index',\n",
       " 'rfloordiv',\n",
       " 'rmod',\n",
       " 'rmul',\n",
       " 'rolling',\n",
       " 'round',\n",
       " 'rpow',\n",
       " 'rsub',\n",
       " 'rtruediv',\n",
       " 'sample',\n",
       " 'searchsorted',\n",
       " 'sem',\n",
       " 'set_axis',\n",
       " 'shape',\n",
       " 'shift',\n",
       " 'size',\n",
       " 'skew',\n",
       " 'slice_shift',\n",
       " 'sort_index',\n",
       " 'sort_values',\n",
       " 'squeeze',\n",
       " 'std',\n",
       " 'sub',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'swaplevel',\n",
       " 'tail',\n",
       " 'take',\n",
       " 'to_clipboard',\n",
       " 'to_csv',\n",
       " 'to_dict',\n",
       " 'to_excel',\n",
       " 'to_frame',\n",
       " 'to_hdf',\n",
       " 'to_json',\n",
       " 'to_latex',\n",
       " 'to_list',\n",
       " 'to_markdown',\n",
       " 'to_numpy',\n",
       " 'to_period',\n",
       " 'to_pickle',\n",
       " 'to_sql',\n",
       " 'to_string',\n",
       " 'to_timestamp',\n",
       " 'to_xarray',\n",
       " 'transform',\n",
       " 'transpose',\n",
       " 'truediv',\n",
       " 'truncate',\n",
       " 'tz_convert',\n",
       " 'tz_localize',\n",
       " 'unique',\n",
       " 'unstack',\n",
       " 'update',\n",
       " 'value_counts',\n",
       " 'values',\n",
       " 'var',\n",
       " 'view',\n",
       " 'where',\n",
       " 'xs']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10.0, 5.0, 3.0, 2.5, 8.0, 11.0]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 10.0, 1: 5.0, 2: 3.0, 3: 2.5, 4: 8.0, 5: 11.0}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python `in` keyword returns a boolean value that compares the value you provide to the values in the list. It's going to return `True` if it exists among those values and `False` if it does not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by default Pandas is going to look among the index labels not the actual values within the Series. \n",
    "2.5 in s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2.5 in s.values"
   ]
  }
 ],
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