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Master Pandas for Data Analysis and Visualisation

Name: Master Pandas for Data Analysis and Visualisation
Rating: 4.4 (9 reviews)

Data Analysis & Visualisation in Pandas, Pandas Plotting Lib, Numpy, Python, Streamlit, Problem Solving & 5 EDA Projects

Created byLutfor Rahman

Last updated 7/2025

English

What you'll learn

Basic, Intermediate & Object Oriented Programming in Python
Basic & Intermediate of Numpy
Basic to Advanced of Pandas Series
Basic to Advanced of Pandas DataFrame
Indexing, Slicing & Sorting a Pandas DataFrame
Joining, Merging, Concatenating, Updating & Combining a Pandas DataFrame
Filtering, Group by and Aggregation in Pandas DataFrame
String Operations in Pandas DataFrame
Multi-Indexing in Pandas DataFrame
Pivot & Reshaping a Pandas DataFrame
Working with Datetime & Timeseries in Pandas DataFrame
Resampling & Rolling
Styling a Pandas DataFrame
Options & Settings in Pandas DataFrame
Plotting & Visualisation of Pandas DataFrame
Data Cleaning & Preprocessing in Pandas DataFrame
Pandas Plotting Library
Streamlit Basics
Streamlit Dashboard
EDA projects on Kaggle Dataset

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

32 sections • 198 lectures • 44h 47m total length

Introduction5:07

01. Welcome to my Universe4:25
02. Variables & Value assignments3:59
03. Python - Data types & Operators8:13
04. Python - Operators11:02
Explore seven kinds of Python operators—arithmetic, assignment, comparison, logical, identity, membership, and bitwise—and practice with addition, subtraction, multiplication, division, modulus, exponentiation, and floor division.
05. Python Statements - if, else7:21
06. Solution of Homework on Python Statements7:10
07. Python For Loop6:19
08. Python - While Loop3:27
09. Python List & Array - Part 110:51
10. Python Function & Method6:42
11. Python List & Array part 28:18
12. Python List & Array - Part 38:05
Explore two-dimensional lists, learning to declare a list of lists, print elements, access with indices (including negative), and traverse with nested for loops for multi-dimensional arrays.
13. Python Tuple, Set and Dictionary13:26
14. Python String8:46
15. Python Dates & Math Modules8:31
16. Custom type in Python3:47
17. Class, Object, Object properties, Constructor and methods10:18
18. Python Scope, Private fields and methods15:36
19. Python Inheritance14:39
Explore Python inheritance by building a base person class and derived student, teacher, and peon classes, using super constructors, get info methods, and introducing polymorphism.
20. Python Polymorphism & Abstraction14:22
Explore Python polymorphism and abstraction, implement polymorphic methods, use built-in functions like len, define abstract methods with ABC to enforce subclass implementation, and apply inheritance in practical examples.
21. Python Iterator6:32
22. Python Lambda2:45
23. Python Files10:01
24. Python Try Except6:59

How many types of data in Python
Get Remainders
Solve age problem
Write a python function to add two numbers
How many logical operators in Python
Get first & last item of a list
Get single item from a dictionary
Display current date & time using Python
Calculate the number of days between two dates
Check if the number is odd or even
Check if the letter is Vowel or Consonant
Get the sum of 1 to 50
Sort a list using for loop
Get the sum up to the number if it's odd or get the multiplied value by PI

01. Creating Numpy arrays8:54
02. Numpy Array Attributes & Functions6:16
Explore NumPy array attributes such as ndim, size, shape, and dtype, and apply functions like array, arange, sort, concat, split, and min/max with argmin/argmax to prep indexing and slicing.
03. Indexing and slicing Numpy array10:51
04. Numpy Copy & View5:59
05. Reshape, Resize, Ravel & Flatten7:46
06. Arithmetic Operations & Aggregations6:36

Pandas Series - Create a basic Pandas Series12:58
Pandas Series - Get, Show, Add and Update a Series8:11
Learn to create and inspect a pandas series using range, access data with square bracket indexing, get and loc/iloc methods, and add or update items by index.
Series - sample() info(), describe(), head(), tail(), first(), last(), take()4:06
Pandas Series - Data types in Pandas Series7:38
Explore data types in pandas series, including integer, int64 and int32, float64 and float32, string (object), boolean, datetime, and the categorical type, with dtype checks and astype conversions.
Pandas Series - Python functions in use5:28
Pandas Series - nlargest(), nsmallest(), keys() and items()7:31
Pandas Series - unique(), nunique(), duplicated() and item()3:30
Pandas Series - Important Attributes5:05
Pandas Series - Reading CSV files10:50
Pandas Series - Important Parameters9:00
Pandas Series - Get and Slice using [] and .get() method15:03
Learn indexing and slicing in pandas series using square brackets and the get method, load data with read_csv, squeeze to series, and handle unique and duplicate indices and out-of-bounds access.
Pandas Series - Get and Slice using .loc[] and .iloc[]12:28
Pandas Series - .index, .reindex() and .reset_index()6:38
Pandas Series - Sorting using sort_values()7:02
Pandas Series - Sorting using sort_index()8:09
Sort a pandas series by its index with sort_index, explore ascending and in-place options, and compare with sort_values while choosing a sorting algorithm such as mergesort, quicksort, or heapsort.
Pandas Series - add(), sub(), mul(), div()6:18
Pandas Series - abs(), round(), ceil(), floor()8:27
Explore absolute values with pandas series using abs, practice rounding with round, and learn ceil and floor operations via numpy in an apply, with decimal precision control.
Series - max(), min(), argmax(), argmin() mean(), median(), sum(), std(), var()5:17
Explore essential pandas series operations by computing max, min, argmax, argmin, mean, median, sum, standard deviation, and variance, then apply them to a salaries dataset read from csv.
Pandas Series - Filtering using comparison operators and the filter() method9:27
Learn to filter data in Pandas Series using comparison operators and the filter method on an employees.csv salary dataset, including equals, greater than, regex filtering, and starts-with criteria.
Pandas Series - fillna(), drop(), dropna(), drop_duplicates()8:42
Pandas Series - isna(), isnull(), notna(), notnull()5:21
Filter a pandas series with isna and isnull, identify non-null values with notna and notnull, and count nan and non-nan entries using sum and boolean indexing.
Pandas Series - The where(), mask(), between() and apply() method9:26
Pandas Series - The replace() method5:01
Pandas Series - The groupby() method6:12
Pandas Series - The agg() & aggregate() method12:35

Pandas DataFrame - Creating a basic Pandas DataFrame13:04
Pandas DataFrame - Get, Show, Add and Update Pandas DataFrame9:42
Learn to create and display a pandas DataFrame and retrieve data by column and row with iloc, loc, and at. Update values and add new columns with numpy.
DataFrame - sample() info(), describe(), head(), tail(), first(), last(), take()5:21
Pandas DataFrame - nlargest(), nsmallest()5:00
Pandas DataFrame - unique(), nunique() & duplicated()5:43
Pandas DataFrame - Data types in Pandas DataFrame15:22
Pandas DataFrame - Python functions in use11:14
Pandas DataFrame - count() & value_counts()10:08
Reading CSV, Excel, JSON & TXT files11:50
Pandas DataFrame - Playing with Attributes5:45
Pandas DataFrame - Important Parameters20:20
Pandas DataFrame - add(), sub(), mul(), div(), mod()8:10
Pandas DataFrame - pow(), abs(), round(), ceil, floor, square, sqrt7:52
Pandas DataFrame - min(), max(), sum(), mean(), median(), std(), var()7:30
Explore how to compute min, max, sum, mean, median, std, and var for a Pandas DataFrame, per column and across the entire dataset, using DataFrame methods and Python built-ins.
Pandas DataFrame - dot(), prod(), cumsum(), corr(), cov()6:03

Requirements

No prior programming knowledge is required
You need a decent computer with a decent internet connection.
A very very important prerequisite: You are seriously willing to write codes with me.
A very very important prerequisite: You are seriously willing to learn data analysis and visualisation using Pandas only and do multiple EDA projects.

Description

Welcome to the course "Master Pandas for Data Analysis and Visualisation". The biggest and the best course on Pandas for Data Analysis and Visualisation. This is the only course based on Pandas Problem Solving & multiple EDA Projects.

First you will learn Python from scratch to object oriented Python. Then you will learn Numpy from very basic to intermediate level. After that you will learn Pandas Series from very beginning to advance level and then you will learn Pandas DataFrame in Details.

In Pandas DataFrame, you will learning everything from basic to advanced. You will learn how to create a Pandas DataFrame and run basic operations. You will learn indexing, slicing & sorting a Pandas DataFrame. You will learn joining, merging, concatenating, updating, combining, filtering, grouping by, aggregation, string operations, multiindexing, pivot & reshaping, datetime & series, resampling & rolling, styling, options & settings, plotting & visualisation and data cleaning & preprocessing.

You will also learn Solving Pandas Problems, Feature Engineering & EDA.

Finally, you will do multiple EDA projects using only Pandas & Pandas Plotting Library.

And at the end, you will learn to develop a basic dashboard using Streamlit & Pandas

I’ve already added about 45hrs of contents. There will be more than 10 hours of contents soon. So, what are you waiting for? Enrol into the course and suggest your favourite EDA projects to add into the course.

You will learn through developing projects and writing codes together. We will together develop about 5 projects. I've already added 5 projects and about 2 more projects I will add based on student's choice.

I promise to give you something which no instructor has ever given in any course.

Who this course is for:

For someone who wants to Learn Data Analysis & Visualisation in Pandas, Pandas Plotting Lib, Numpy, Python, Streamlit, Problem Solving & 5 EDA Projects

Master Pandas for Data Analysis and Visualisation

What you'll learn

Explore related topics

Coding Exercises

Course content

Introduction1 lecture • 5min

Installation and Configuration3 lectures • 15min

Python24 lectures • 3hr 22min

Coding exercises and Problem solving on Python0

Numpy6 lectures • 46min

Coding exercises and Problem solving on Numpy0

Pandas Series25 lectures • 3hr 20min

Coding exercises and Problem solving on Python0

Pandas DataFrame15 lectures • 2hr 23min

Coding Exercises & Problem Solving0

Requirements

Description

Who this course is for: