Master Pandas For Data Analysis And Visualisation

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

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

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 & PandasI’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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Installation and Configuration

Lecture 2 Installing & configuring Anaconda

Lecture 3 Online Compiler & Google CoLab

Lecture 4 Installing PyCharm

Section 3: Python

Lecture 5 01. Welcome to my Universe

Lecture 6 02. Variables & Value assignments

Lecture 7 03. Python - Data types & Operators

Lecture 8 04. Python - Operators

Lecture 9 05. Python Statements - if, else

Lecture 10 06. Solution of Homework on Python Statements

Lecture 11 07. Python For Loop

Lecture 12 08. Python - While Loop

Lecture 13 09. Python List & Array - Part 1

Lecture 14 10. Python Function & Method

Lecture 15 11. Python List & Array part 2

Lecture 16 12. Python List & Array - Part 3

Lecture 17 13. Python Tuple, Set and Dictionary

Lecture 18 14. Python String

Lecture 19 15. Python Dates & Math Modules

Lecture 20 16. Custom type in Python

Lecture 21 17. Class, Object, Object properties, Constructor and methods

Lecture 22 18. Python Scope, Private fields and methods

Lecture 23 19. Python Inheritance

Lecture 24 20. Python Polymorphism & Abstraction

Lecture 25 21. Python Iterator

Lecture 26 22. Python Lambda

Lecture 27 23. Python Files

Lecture 28 24. Python Try Except

Section 4: Coding exercises and Problem solving on Python

Section 5: Numpy

Lecture 29 01. Creating Numpy arrays

Lecture 30 02. Numpy Array Attributes & Functions

Lecture 31 03. Indexing and slicing Numpy array

Lecture 32 04. Numpy Copy & View

Lecture 33 05. Reshape, Resize, Ravel & Flatten

Lecture 34 06. Arithmetic Operations & Aggregations

Section 6: Coding exercises and Problem solving on Numpy

Section 7: Pandas Series

Lecture 35 Pandas Series - Create a basic Pandas Series

Lecture 36 Pandas Series - Get, Show, Add and Update a Series

Lecture 37 Series - sample() info(), describe(), head(), tail(), first(), last(), take()

Lecture 38 Pandas Series - Data types in Pandas Series

Lecture 39 Pandas Series - Python functions in use

Lecture 40 Pandas Series - nlargest(), nsmallest(), keys() and items()

Lecture 41 Pandas Series - unique(), nunique(), duplicated() and item()

Lecture 42 Pandas Series - Important Attributes

Lecture 43 Pandas Series - Reading CSV files

Lecture 44 Pandas Series - Important Parameters

Lecture 45 Pandas Series - Get and Slice using [] and .get() method

Lecture 46 Pandas Series - Get and Slice using .loc[] and .iloc[]

Lecture 47 Pandas Series - .index, .reindex() and .reset_index()

Lecture 48 Pandas Series - Sorting using sort_values()

Lecture 49 Pandas Series - Sorting using sort_index()

Lecture 50 Pandas Series - add(), sub(), mul(), div()

Lecture 51 Pandas Series - abs(), round(), ceil(), floor()

Lecture 52 Series - max(), min(), argmax(), argmin() mean(), median(), sum(), std(), var()

Lecture 53 Pandas Series - Filtering using comparison operators and the filter() method

Lecture 54 Pandas Series - fillna(), drop(), dropna(), drop_duplicates()

Lecture 55 Pandas Series - isna(), isnull(), notna(), notnull()

Lecture 56 Pandas Series - The where(), mask(), between() and apply() method

Lecture 57 Pandas Series - The replace() method

Lecture 58 Pandas Series - The groupby() method

Lecture 59 Pandas Series - The agg() & aggregate() method

Section 8: Coding exercises and Problem solving on Python

Section 9: Pandas DataFrame

Lecture 60 Pandas DataFrame - Creating a basic Pandas DataFrame

Lecture 61 Pandas DataFrame - Get, Show, Add and Update Pandas DataFrame

Lecture 62 DataFrame - sample() info(), describe(), head(), tail(), first(), last(), take()

Lecture 63 Pandas DataFrame - nlargest(), nsmallest()

Lecture 64 Pandas DataFrame - unique(), nunique() & duplicated()

Lecture 65 Pandas DataFrame - Data types in Pandas DataFrame

Lecture 66 Pandas DataFrame - Python functions in use

Lecture 67 Pandas DataFrame - count() & value_counts()

Lecture 68 Reading CSV, Excel, JSON & TXT files

Lecture 69 Pandas DataFrame - Playing with Attributes

Lecture 70 Pandas DataFrame - Important Parameters

Lecture 71 Pandas DataFrame - add(), sub(), mul(), div(), mod()

Lecture 72 Pandas DataFrame - pow(), abs(), round(), ceil, floor, square, sqrt

Lecture 73 Pandas DataFrame - min(), max(), sum(), mean(), median(), std(), var()

Lecture 74 Pandas DataFrame - dot(), prod(), cumsum(), corr(), cov()

Section 10: Coding Exercises & Problem Solving

Section 11: Pandas DataFrame - indexing, slicing & sorting

Lecture 75 Pandas DataFrame - set_index() and reset_index(), .index, .index.names and more

Lecture 76 DataFrame - Get and Slice using [], .get(), .loc[] and .iloc[], .at[], .iat[]

Lecture 77 Pandas DataFrame - [] vs .get() vs .loc[] vs .iloc[] vs .at[] vs .iat[]

Lecture 78 Pandas DataFrame - sort_values()

Lecture 79 Pandas DataFrame - sort_index()

Section 12: Coding Exercises & Problem Solving

Section 13: Pandas DataFrame - Joining, Merging, Concatenating and Combining

Lecture 80 Pandas DataFrame - The join() & merge() method

Lecture 81 Pandas DataFrame - The update(), concat() & combine() method

Section 14: Pandas DataFrame - Filtering Pandas DataFrame

Lecture 82 Pandas DataFrame - Filtering using comparison and logical operators

Lecture 83 Pandas DataFrame - The filter() method

Lecture 84 DataFrame - isna(), notna(), fillna(), dropna(), drop(), drop_duplicates()

Lecture 85 Pandas DataFrame - The where(), mask(), between() and apply() method

Lecture 86 Pandas DataFrame - The query() method

Lecture 87 Pandas DataFrame - The replace() & map() method

Section 15: Coding Exercises & Problem Solving

Section 16: Pandas DataFrame - GroupBy & Aggregation

Lecture 88 Pandas Group by - Basics of Grouping by

Lecture 89 Pandas Group by - .groups, .ngroups, .indices and .get_group()

Lecture 90 Pandas Group by - Methods in use

Lecture 91 Pandas Group by - Basic aggregation operations

Lecture 92 Pandas Group by - Group by multiple columns

Lecture 93 Pandas Group by - Looping through the groupby object

Lecture 94 Pandas Group by - Multiple aggregations

Lecture 95 Pandas Group by - Using the .filter() method on groupby object

Lecture 96 Pandas Group by - Using the .pipe() method on groupby object

Lecture 97 Pandas Group by - Using the .transform() method on groupby object

Section 17: Pandas - String methods & working with text data

Lecture 98 Pandas - Python string methods

Lecture 99 Pandas - lower(), upper(), capitalize(), title() and swapcase() methods

Lecture 100 Pandas - find(), rfind(), findall(), index(), rindex() & count() methods

Lecture 101 Pandas - contains(), startswith(), endswith(), match() and fullmatch() methods

Lecture 102 Pandas - isalpha(), isdigit(), isalnum(), islower(), isupper() and more …

Lecture 103 Pandas - len(), strip(), rstrip(), lstrip() methods

Lecture 104 Pandas - str.pad(), str.center(), str.rjust() and str.ljust() methods

Lecture 105 Pandas - split(), rsplit(), partition(), and rpartition() methods

Lecture 106 Pandas - More on the .split() method

Lecture 107 Pandas - str.replace() method

Lecture 108 Pandas - str.cat() method

Lecture 109 Pandas - str.slice() and str.slice_replace() methods

Section 18: Coding Exercises & Problem Solving

Section 19: MultiIndexing in Pandas

Lecture 110 Pandas - Creating MultiIndex Series

Lecture 111 Pandas - Creating MultiIndex DataFrame

Lecture 112 Pandas - Working with MultiIndex both on Rows & Columns

Lecture 113 Pandas - Working with MultiIndex "levels" on Rows

Lecture 114 Pandas - Working with MultiIndex "levels" on Columns

Lecture 115 Pandas - MultiIndex and Cross Section - .xs()

Lecture 116 Pandas - Sorting MultiIndex DataFrame

Lecture 117 Pandas - Slicing MultiIndex DataFrame

Section 20: Pandas DataFrame - Pivot & Reshaping

Lecture 118 Pivot & Reshape Pandas DataFrame using the .pivot() method

Lecture 119 Pivot & Reshape Pandas DataFrame - the .pivot_table() method

Lecture 120 Pivot & Reshaping Pandas DataFrame - .stack() & .unstack() methods

Lecture 121 Pivot & Reshape Pandas DataFrame - the unstack() method

Lecture 122 Pivot & Reshape Pandas DataFrame - the melt() method

Lecture 123 Pivot & Reshape Pandas DataFrame - transpose() & wide_to_long() methods

Section 21: Working with Pandas DateTime & Timeseries

Lecture 124 Pandas Datetime - Working with Python datetime

Lecture 125 Pandas Datetime - Working with Timestamp() object

Lecture 126 Pandas Datetime - pd.to_datetime() method - Part 1

Lecture 127 Pandas Datetime - pd.to_datetime() method - Part 2

Lecture 128 Pandas Datetime - Working with pd.date_range() method Part 1

Lecture 129 Pandas Datetime - Working with pd.date_range() method Part 2

Lecture 130 Pandas Datetime - Working with pd.date_range() method Part 3

Lecture 131 Pandas Datetime - Working with pd.date_range() method Part 4

Lecture 132 Pandas Datetime - Working with pd.date_range() method Part 5

Lecture 133 Pandas Datetime - Working with pd.date_range() method Part 6

Lecture 134 Pandas Datetime - Working with the DatetimeIndex object

Section 22: Coding Exercises & Problem Solving

Section 23: Pandas Grouper, Grouping, Resampling & Rolling

Lecture 135 Pandas - Rolling and Aggregation

Lecture 136 Pandas Group by & Grouper object

Lecture 137 Pandas Groupby & Resample

Section 24: Pandas - The Styler Object & Styling DataFrame

Lecture 138 Pandas Styling - style.applymap(), style.map() & style.apply() methods

Lecture 139 Pandas Styling - Specifying axis & subsets

Lecture 140 Pandas Styling - style.format()

Lecture 141 Pandas Styling - Built in Styles

Lecture 142 Pandas Styling - set_properties() method

Lecture 143 Pandas Styling - bar() & set_caption() methods

Lecture 144 Pandas Styling - style.background_gradient()

Section 25: Pandas DataFrame - Options & Settings

Lecture 145 Pandas Options & Settings - max_rows, min_rows & max_columns

Lecture 146 Pandas Options & Settings - get, set, reset & describe option

Lecture 147 Pandas Options & Settings - max_info_columns, max_info_rows & max_colwidth

Lecture 148 Pandas Options & Settings - large_repr, colheader_justify, multi_sparse

Lecture 149 Pandas Options & Settings - float_format, precision & chop_threshold

Section 26: Pandas - Plotting & Visualisations

Lecture 150 Pandas Plotting & Visualisation - Using .plot() method

Lecture 151 Pandas Plotting & Visualisation - Using .plot.bar() method

Lecture 152 Pandas Visualisation - Using .plot.line, .plot.area() & .plot.scatter() methods

Lecture 153 Pandas Plotting and Visualization - KDE Plotting

Lecture 154 Pandas Plotting and Visualization - Histogram Plotting

Lecture 155 Pandas Plotting and Visualization - Hexbin Plotting

Lecture 156 Pandas Plotting and Visualization - Pie Plotting

Section 27: Pandas - Data Cleaning and Preprocessing

Lecture 157 Pandas - Data Cleaning - Part 1

Lecture 158 Pandas - Data Cleaning - Part 2

Lecture 159 Pandas - Data Cleaning - Part 3

Lecture 160 Pandas - Data Cleaning - Part 4

Lecture 161 Pandas - Data Cleaning - Part 5

Section 28: Solve 101 Pandas Problems

Lecture 162 Solving Pandas Problems - from 1 to 13

Lecture 163 Solving Pandas Problems - from 14 to 19

Lecture 164 Solving Pandas Problems - from 19 to 25

Lecture 165 Solving Pandas Problems - from 26 to 30

Lecture 166 Solving Pandas Problems - from 31 to 32

Lecture 167 Solving Pandas Problems - from 33 to 37

Lecture 168 Solving Pandas Problems - from 38 to 43

Lecture 169 Solving Pandas Problems - from 44 to 50

Section 29: Pandas EDA - Exploratory Data Analysis

Lecture 170 What is EDA & Why Do We Need It?

Lecture 171 Types of Data in Data Analysis

Lecture 172 Univariate, Bivariate & Multivariate Analysis in Practice

Lecture 173 Type of Graphs & Charts used on EDA

Section 30: Feature Engineering in Data Analysis, Data Science & Machine Learning

Lecture 174 Feature Engineering in Data Analysis, Data Science & Machine Learning

Section 31: Pandas Exploratory Data Analysis Projects on Kaggle Dataset

Lecture 175 Pandas EDA Project 1 - IMDB Movie Data Analysis - Part 1

Lecture 176 Pandas EDA Project 1 - IMDB Movie Data Analysis - Part 2

Lecture 177 Pandas EDA Project 1 - IMDB Movie Data Analysis - Part 3

Lecture 178 Pandas EDA Project 1 - IMDB Movie Data Analysis - Part 4

Lecture 179 Pandas EDA Project 2 - Titanic survival dataset analysis - Part 1

Lecture 180 Pandas EDA Project 2 - Titanic survival dataset analysis - Part 2

Lecture 181 Pandas EDA Project 2 - Titanic survival dataset analysis - Part 3 I

Lecture 182 Pandas EDA Project 2 - Titanic survival dataset analysis - Part 3 II

Lecture 183 Pandas EDA Project 2 - Titanic survival dataset analysis - Part 4 I

Lecture 184 Pandas EDA Project 2 - Titanic survival dataset analysis - Part 4 II

Lecture 185 Pandas EDA Project 2 - Titanic survival dataset analysis - Part 5

Lecture 186 Pandas EDA Project 3 - Sales Analysis - Part 1

Lecture 187 Pandas EDA Project 3 - Sales Analysis - Part 2

Lecture 188 Pandas EDA Project 3 - Sales Analysis - Part 3

Lecture 189 Pandas EDA Project 3 - Sales Analysis - Part 4

Section 32: Pandas integration with Streamlit

Lecture 190 Streamlit - The Introduction

Lecture 191 Streamlit - Display elements

Lecture 192 Streamlit - Input Elements

Lecture 193 Streamlit - Markdown in Action

Lecture 194 Streamlit - Multipage Navigations

Lecture 195 Pandas & Streamlit Dashboard - Part 1

Lecture 196 Pandas & Streamlit Dashboard - Part 2

Lecture 197 Pandas & Streamlit Dashboard - Part 3

Lecture 198 Pandas & Streamlit Dashboard - Part 4

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