Data Analysis With Pandas: A Complete Tutorial

From Beginner To Advanced Level

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Course Overview

Python is one of the most popular programming languages in today’s technical world. Python offers both object-oriented and structural programming features. Hence, it makes very interesting to study Data Analysis with Python. Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. The features provided in pandas automate and simplify a lot of the common tasks that would take many lines of code to write in the basic Python language. Pandas is best suited for structured, labelled data, in other words, tabular data, that has headings associated with each column of data.

This course is for those who are ready to take their data analysis skill to the next higher level with Python data analysis toolkit, i.e. "Pandas". This tutorial is designed for beginners and intermediates but that doesn't mean that we will not talk about the advanced stuff as well. In this course, you will learn the basic things to know about the 'Pandas' to become a data analyst or data scientist. During the course, you will have many projects as a part of large research-oriented industry projects to provide you with hands-on training.

What are the requirements?

  • Students must be willing to learn the Data Analysis with Python language
  • If you know basics of Python that is well and good
  • Basic Or intermediate experience with Microsoft Excel or another spreadsheet software, but not necessary
  • Basic knowledge of data types (strings, integers, floating points, Booleans) etc
  • Basic Programming knowledge Or knowing any other programming languages will also helps

What am I going to get from this course?

  • Build a Solid Foundation in Data Analysis with Python
  • Learn hundreds of methods and attributes across numerous pandas objects
  • You can prepare real world messy data files for AI and ML
  • You will learn almost all the Pandas basics necessary to become a 'Data Analyst'
  • You will be able to work with the Pandas Data Structures: Series, DataFrame and Index Objects
  • You will be able to analyze a large and messy data files
  • Manipulate data quickly and efficiently

What is the target audience?

  • Beginner Python developers - Curious to learn about Data Science Or Data Analysis
  • Data Analysis Beginners
  • Aspiring data scientists who want to add Python to their tool arsenal
  • Students and Other Professionals
  • AI and ML aspirants to upgrade their knowledge in Data Preprocessing before applying the machine learning algorithms to their projects
  • Data Analyst job seekers who wants to update their Resume with Python's data analysis toolkit

About the Author

Course Curriculum

Getting Started
7 Video Lectures | 00:50:54

  • Course Introduction
    03:35
     
  • How To Get Most Out Of This Course
    02:06
     
  • Better To Know These Things
    02:57
     
  • How To Install Python IPython And Jupyter Notebook
    08:27
     
  • How To Install Anaconda For macOS And Linux Users
    06:37
     
  • How To Work With The Jupyter Notebook Part-1
    16:13
     
  • How To Work With The Jupyter Notebook Part-2
    10:59
     

Pandas building blocks
2 Video Lectures | 00:19:11

  • How To Work With The Tabular Data
    05:23
     
  • How To Read The Documentation In Pandas
    13:48
     

Pandas DataStructures
6 Video Lectures | 01:09:28

  • Theory On Pandas Data Structures
    05:43
     
  • How To Construct The Pandas Series
    12:18
     
  • How To Construct The DataFrame Objects
    13:01
     
  • How To Construct The Pandas Index Objects
    12:17
     
  • Practice Part 01
    04:10
     
  • Practice Part 01 Solution
    21:59
     

Data Indexing and Selection
9 Video Lectures | 00:58:28

  • Theory On Data Indexing And Selection
    05:49
     
  • Data Selection In Series Part 1
    05:43
     
  • Data Selection In Series Part 2
    02:15
     
  • Indexers Loc And Iloc In Series
    12:12
     
  • Data Selection In DataFrame Part 1
    04:34
     
  • Data Selection In DataFrame Part 2
    03:27
     
  • Accessing Values Using Loc Iloc And Ix In DataFrame Objects
    09:01
     
  • Practice Part 02
    02:38
     
  • Practice Part 02 Solution
    12:49
     

Essential Functionalities
13 Video Lectures | 02:03:18

  • Theory On Essential Functionalities
    10:02
     
  • How To Reindex Pandas Objects
    11:44
     
  • How To Drop Entries From An Axis
    08:12
     
  • Arithmetic And Data Alignment
    07:21
     
  • Arithmetic Methods With Fill Values
    15:25
     
  • Broadcasting In Pandas
    06:57
     
  • Apply And Applymap In Pandas
    07:52
     
  • How To Sort And Rank In Pandas
    13:22
     
  • How To Work With The Duplicated Indices
    04:06
     
  • Summarising And Computing Descriptive Statistics
    07:03
     
  • Unique Values Value Counts And Membership
    12:01
     
  • Practice_Part_03
    02:16
     
  • Practice_Part_03 Solution
    16:57
     

Data Handling
8 Video Lectures | 01:24:44

  • Theory On Data Handling
    04:33
     
  • How To Read The Csv Files Part - 1
    19:28
     
  • How To Read The Csv Files Part - 2
    14:38
     
  • How To Read Text Files In Pieces
    07:25
     
  • How To Export Data In Text Format
    09:48
     
  • How To Use Python's Csv Module
    10:40
     
  • Practice_Part_04
    02:42
     
  • Practice_Part_04 Solution
    15:30
     

Data Cleaning and preparation
17 Video Lectures | 02:54:19

  • Theory On Data Preprocessing
    10:53
     
  • How To Handle Missing Values
    09:34
     
  • How To Filter The Missing Values
    09:02
     
  • How To Filter The Missing Values Part 2
    09:08
     
  • How To Remove Duplicate Rows And Values
    12:25
     
  • How To Replace The Non Null Values
    09:05
     
  • How To Rename The Axis Labels
    06:41
     
  • How To Descretize And Bin The Data Part - 1
    22:04
     
  • How To Filter And Detect The Outliers
    03:47
     
  • How To Reorder And Select Randomly
    07:07
     
  • Converting The Categorical Variables Into Dummy Variables
    09:49
     
  • How To Use 'map' Method
    06:53
     
  • How To Manipulate With Strings
    12:24
     
  • Using Regular Expressions
    20:10
     
  • Working With The Vectorized String Functions
    08:07
     
  • Practice_Part_05
    02:33
     
  • Practice_Part_05 Solution
    14:37
     

Data Wrangling
12 Video Lectures | 01:45:16

  • Theory On Data Wrangling
    07:43
     
  • Hierarchical Indexing
    08:13
     
  • Hierarchical Indexing Reordering And Sorting
    06:48
     
  • Summary Statistics By Level
    02:47
     
  • Hierarchical Indexing With DataFrame Columns
    05:04
     
  • How To Merge The Pandas Objects
    19:41
     
  • Merging On Row Index
    13:11
     
  • How To Concatenate Along An Axis
    18:37
     
  • How To Combine With Overlap
    06:47
     
  • How To Reshape And Pivot Data In Pandas
    08:51
     
  • Practice_Part_06
    01:22
     
  • Practice_Part_06 Solution
    06:12
     

Data grouping and aggregation
9 Video Lectures | 01:03:13

  • Thoery On Data Groupby And Aggregation
    03:59
     
  • Groupby Operation
    15:38
     
  • How To Iterate Over Groupby Object
    05:45
     
  • How To Select Columns In Groupby Method
    03:00
     
  • Grouping Using Dictionaries And Series
    02:57
     
  • Grouping Using Functions And Index Level
    05:29
     
  • Data Aggregation
    10:20
     
  • Practice_Part_07
    02:54
     
  • Practice_Part_07 Solution
    13:11
     

Time Series Analysis
10 Video Lectures | 01:40:51

  • Theory On Time Series Analysis
    06:29
     
  • Introduction To Time Series Data Types
    10:13
     
  • How To Convert Between String And Datetime
    14:40
     
  • Time Series Basics With Pandas Objects
    12:53
     
  • Date Ranges Frequencies And Shifting - Part 1
    11:22
     
  • Date Ranges Frequencies And Shifting Part - 2
    10:42
     
  • Time Zone Handling
    08:51
     
  • Periods And Period Arithmetic’s
    10:47
     
  • Practice_Part_08
    02:42
     
  • Practice_Part_08 Solution
    12:12
     

How to Analyse with the part of real life project
1 Document Lectures | 9 Video Lectures | 01:35:26

  • A Brief Introduction To The Pandas Projects
    10:30
     
  • Project_1 Description
    04:42
     
  • Project_1 Solution Part - 1
    17:29
     
  • Project_1 Solution Part - 2
    13:47
     
  • Project_2 Description
    02:19
     
  • Project_2 Solution
    19:30
     
  • Project_3 Description
    02:37
     
  • Project_3 Solution Part - 1
    12:07
     
  • Project_3 Solution Part - 2
    12:25
     
  • Project Assignment
    12 Page

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