Python Data Science basics with Numpy, Pandas and Matplotlib

Covers all Essential Python topics and Libraries for Data Science or Machine Learning Beginner.

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Course Overview

In this course, we will learn the basics of Python Data Structures and the most important Data Science libraries like NumPy and Pandas with step by step examples!

The first session will be a theory session in which, we will have an introduction to python, its applications and the libraries.

In the next session, we will proceed with installing python on your computer. We will install and configure anaconda which is a platform you can use for quick and easy installation of python and its libraries. We will get ourselves familiar with Jupiter notebook, which is the IDE that we are using throughout this course for python coding.

Then we will go ahead with the basic python data types like strings, numbers and its operations. We will deal with different types of ways to assign and access strings, string slicing, replacement, concatenation, formatting and f strings.

Dealing with numbers, we will discuss the assignment, accessing and different operations with integers and floats. The operations include basic ones and also advanced ones like exponents. Also, we will check the order of operations, increments and decrements, rounding values and typecasting.

Then we will proceed with basic data structures in python like Lists tuples and set. For lists, we will try different assignment, access and slicing options. Along with popular list methods, we will also see list extension, removal, reversing, sorting, min and max, existence check, list looping, slicing, and also inter-conversion of list and strings.

For Tuples also we will do the assignment and access options and the proceed with different options with set in python.

After that, we will deal with python dictionaries. Different assignment and access methods. Value update and delete methods and also looping through the values in the dictionary.

And after learning all of these basic data types and data structures, its time for us to proceed with the popular libraries for data-science in python. We will start with the NumPy library. We will check different ways to create a new NumPy array, reshaping, transforming list to arrays, zero arrays and one array, different array operations, array indexing, slicing, copying. we will also deal with creating and reshaping multi-dimensional NumPy arrays, array transpose, and statistical operations like mean-variance etc. using NumPy

Later we will go ahead with the next popular python library called Pandas. At first, we will deal with the one-dimensional labelled array in pandas called as the series.  We will create assign and access the series using different methods.

Then will go ahead with the Pandas Data frames, which is a 2-dimensional labelled data structure with columns of potentially different types. We will convert NumPy arrays and also pandas series to data frames. We will try column-wise and row-wise access options, dropping rows and columns, getting the summary of data frames with methods like min, max etc. Also, we will convert a python dictionary into a pandas data frame. In large datasets, it’s common to have empty or missing data. We will see how we can manage missing data within data frames. We will see sorting and indexing operations for data frames.

Most times, external data will be coming in either a CSV file or a JSON file. We will check how we can import CSV and JSON file data as a data frame so that we can do the operations and later convert this data frame to either CSV and JSON objects and write it into the respective files. 

Also, we will see how we can concatenate, join and merge two pandas data frames. Then we will deal with data stacking and pivoting using the data frame and also deal with duplicate values within the data-frame and to remove them selectively.

We can group data within a data-frame using group by methods for the pandas data frame. We will check the steps we need to follow for grouping. Similarly, we can do aggregation of data in the data-frame using different methods available and also using custom functions. We will also see other grouping techniques like Binning and bucketing based on data in the data-frame

At times we may need to use custom indexing for our dataframe. We will see methods to re-index rows and columns of a dataframe and also rename column indexes and rows. We will also check methods to do collective replacement of values in a dataframe and also to find the count of all or unique values in a dataframe.

Then we will proceed with implementing random permutation using both the NumPy and Pandas library and the steps to follow. Since an excel sheet and a dataframe are similar 2d arrays, we will see how we can load values in a dataframe from an excel sheet by parsing it. Then we will do a condition-based selection of values in a dataframe, also by using lambda functions and also finding rank based on columns.

Then we will go ahead with cross Tabulation of our dataframe using contingency tables. The steps we need to proceed with to create the cross-tabulation contingency table.

After all these operations in the data, we have, now its time to visualize the data. We will do exercises in which we can generate graphs and plots. We will be using another popular Python library called Matplotlib to generate graphs and plots. We will do tweaking of the graphs and plots by adjusting the plot types, its parameters, labels, titles etc.

Then we will use another visualization option called histogram which can be used to groups numbers into ranges. We will also be trying different options provided by matplotlib library

What are the requirements?

  • A decent configuration computer and the willingness to lay the corner stone for your big data journey.

What am I going to get from this course?

  • Essential Python data types and data structure basics with Libraries like NumPy and Pandas for Data Science or Machine Learning Beginner.

What is the target audience?

  • Data science enthusiasts who want to begin their career

About the Author

I  am a pioneering, talented and security-oriented Android/iOS Mobile and PHP/Python Web Developer Application Developer offering more than eight years’ overall IT experience which involves designing, implementing, integrating, testing and supporting impact-full web and mobile applications. I am a Post Graduate Masters Degree holder in Computer Science and Engineering. My experience with PHP/Python Programming is an added advantage for server based Android and iOS Client Applications. I am currently serving full time as a Senior Solution Architect managing my client's projects from start to finish to ensure high quality, innovative and functional design.

Course Curriculum

Course Introduction and Table of Contents
1 Video Lectures | 00:09:29

  • Course Introduction and Table of Contents
    09:29
     

Introduction to Python, Pandas and Numpy
1 Video Lectures | 00:06:53

  • Introduction to Python, Pandas and Numpy
    06:53
     

System and Environment Setup
1 Video Lectures | 00:08:25

  • System and Environment Setup
    08:25
     

Python Strings
2 Video Lectures | 00:19:37

  • Python Strings - Part 1
    10:47
     
  • Python Strings - Part 2
    08:50
     

Python Numbers and Operators
2 Video Lectures | 00:13:29

  • Python Numbers and Operators - Part 1
    06:29
     
  • Python Numbers and Operators - Part 2
    07:00
     

Python Lists
5 Video Lectures | 00:31:07

  • Python Lists - Part 1
    05:09
     
  • Python Lists - Part 2
    06:08
     
  • Python Lists - Part 3
    05:28
     
  • Python Lists - Part 4
    06:54
     
  • Python Lists - Part 5
    07:28
     

Tuples in Python
1 Video Lectures | 00:05:35

  • Tuples in Python
    05:35
     

Sets in Python
2 Video Lectures | 00:08:59

  • Sets in Python - Part 1
    05:01
     
  • Sets in Python - Part 2
    03:58
     

Python Dictionary
2 Video Lectures | 00:13:31

  • Python Dictionary - Part 1
    06:36
     
  • Python Dictionary - Part 2
    06:55
     

NumPy Library - Introduction
3 Video Lectures | 00:16:44

  • NumPy Library Intro - Part 1
    04:51
     
  • NumPy Library Intro - Part 2
    05:25
     
  • NumPy Library Intro - Part 3
    06:28
     

NumPy Array Operations and Indexing
2 Video Lectures | 00:09:45

  • NumPy Array Operations and Indexing - Part 1
    04:11
     
  • NumPy Array Operations and Indexing - Part 2
    05:34
     

NumPy Multi-Dimensional Arrays
3 Video Lectures | 00:18:08

  • NumPy Multi-Dimensional Arrays - Part 1
    07:15
     
  • NumPy Multi-Dimensional Arrays - Part 2
    05:34
     
  • NumPy Multi-Dimensional Arrays - Part 3
    05:19
     

Introduction to Pandas Series
1 Video Lectures | 00:08:10

  • Introduction to Pandas Series
    08:10
     

Introduction to Pandas Dataframes
1 Video Lectures | 00:07:08

  • Introduction to Pandas Dataframes
    07:08
     

Pandas Dataframe conversion and drop
3 Video Lectures | 00:19:04

  • Pandas Dataframe conversion and drop - Part 1
    06:16
     
  • Pandas Dataframe conversion and drop - Part 2
    05:34
     
  • Pandas Dataframe conversion and drop - Part 3
    07:14
     

Pandas Dataframe summary and selection
3 Video Lectures | 00:18:14

  • Pandas Dataframe summary and selection - Part 1
    05:45
     
  • Pandas Dataframe summary and selection - Part 2
    05:31
     
  • Pandas Dataframe summary and selection - Part 3
    06:58
     

Pandas Missing Data Management and Sorting
2 Video Lectures | 00:13:19

  • Pandas Missing Data Management and Sorting - Part 1
    06:37
     
  • Pandas Missing Data Management and Sorting - Part 2
    06:42
     

Pandas Hierarchical-Multi Indexing
1 Video Lectures | 00:05:56

  • Pandas Hierarchical-Multi Indexing
    05:56
     

Pandas CSV File Read Write
2 Video Lectures | 00:12:20

  • Pandas CSV File Read Write - Part 1
    05:28
     
  • Pandas CSV File Read Write - Part 2
    06:52
     

Pandas JSON File Read Write
1 Video Lectures | 00:06:41

  • Pandas JSON File Read Write Operations
    06:41
     

Pandas Concatenation Merging and Joining
3 Video Lectures | 00:13:17

  • Pandas Concatenation Merging and Joining - Part 1
    04:39
     
  • Pandas Concatenation Merging and Joining - Part 2
    04:16
     
  • Pandas Concatenation Merging and Joining - Part 3
    04:22
     

Pandas Stacking and Pivoting
2 Video Lectures | 00:11:34

  • Pandas Stacking and Pivoting - Part 1
    05:22
     
  • Pandas Stacking and Pivoting - Part 2
    06:12
     

Pandas Duplicate Data Management
1 Video Lectures | 00:07:19

  • Pandas Duplicate Data Management
    07:19
     

Pandas Mapping
1 Video Lectures | 00:04:06

  • Pandas Mapping
    04:06
     

Pandas Grouping
1 Video Lectures | 00:05:45

  • Pandas Groupby
    05:45
     

Pandas Aggregation
1 Video Lectures | 00:08:33

  • Pandas Aggregation
    08:33
     

Pandas Binning or Bucketing
1 Video Lectures | 00:07:35

  • Pandas Binning or Bucketing
    07:35
     

Pandas Re-index and Rename
2 Video Lectures | 00:09:00

  • Pandas Re-index and Rename - Part 1
    04:04
     
  • Pandas Re-index and Rename - Part 2
    04:56
     

Pandas Replace Values
1 Video Lectures | 00:04:37

  • Pandas Replace Values
    04:37
     

Pandas Dataframe Metrics
1 Video Lectures | 00:06:48

  • Pandas Dataframe Metrics
    06:48
     

Pandas Random Permutation
1 Video Lectures | 00:08:15

  • Pandas Random Permutation
    08:15
     

Pandas Excel sheet Import
1 Video Lectures | 00:07:13

  • Pandas Excel sheet Import
    07:13
     

Pandas Condition Selection and Lambda Function
2 Video Lectures | 00:09:25

  • Pandas Condition Selection and Lambda Function - Part 1
    04:34
     
  • Pandas Condition Selection and Lambda Function - Part 2
    04:51
     

Pandas Ranks Min Max
1 Video Lectures | 00:06:02

  • Pandas Ranks Min Max
    06:02
     

Pandas Cross Tabulation
1 Video Lectures | 00:06:32

  • Pandas Cross Tabulation
    06:32
     

Matplotlib Graphs and plots
2 Video Lectures | 00:08:42

  • Graphs and plots using Matplotlib - Part 1
    06:25
     
  • Graphs and plots using Matplotlib - Part 2
    02:17
     

Matplotlib Histograms
1 Video Lectures | 00:03:21

  • Matplotlib Histograms
    03:21
     

Source Code Attached
1 Document Lectures

  • Source Code Download Link
    87 Page

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