Data Analysis Using Pandas In Python : Learn By Exercise

Published 11/2023
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.36 GB | Duration: 4h 10m

Learn to analyze data, run SQL commands, create pivot table on pandas dataframe, filter / sort dataframe, derive fields

What you’ll learn
Rapidly start using Python for Data Analysis with Pandas
Learn to use SQL with Pandas Dataframe
Learn data operations like merge, sort, append
Learn by seeing workout examples
Learn to develop Histogram, Box Plot, Pie Chart, Bar chart, Line Chart
Learn to interact with SQLlite database from Python
Learn Linear regression, chi square test of independence, Outlier detection etc.

Requirements
The course will train everything from scratch and in a simplified manner for data analysis purpose
The course is less about Python programming and more about using different packages for data analysis purpose.

Description
The course will follow below structureSection 1: Getting started with PythonThis section explains how to install Aanconda distribution and write first codeAdditionally, a walk through of Spyder PlatformSection 2: Working on DataP02 01A running SQL in pythonP02 01 Understand Data n Add Comments in the codeP02 02 Know Contents of the DataP02 03A Missing Value detection n treatment Part1P02 03B Getting Familar with Jupyter IDEP02 03C treating Numeric Missing value with mean n treating date missing valueP02 03D Creating copy of a dataframe n dropping records based on missing value of a particular fieldP02 03E Replacing missing Value with median or modeP02 04 Filtering data n keeping few columns in dataP02 05 use iloc to filter dataP02 06 Numeric Variable Analysis with Group By n Transpose the resultP02 07 Frequency Distribution count n percentage including missing percentageP02 08 Introduction to function n substring stuffSection 3: working on multiple datasetsP03 01 Creating Dataframe on the run Append concatenate dataframeP03 02 Merging DataFramesP03 03 Remove Duplicates Full or column based Sorting Dataframe Keep First Last Max MinP03 04 Getting row for max value of any column easy way n then through idxmaxP03 05 use idxmax iterrows forloop to solve a tricky questionP03 06 Create derived fields using numerical fieldsP03 07 Cross Tab Analysis n putting reult into another dataframe transpose resultP03 08 Derive variable based on character fieldP03 09 Derive variable based on date fieldP03 10 First Day Last Day Same Day of Last n monthSection 4: Data visualization and some frequently used termsP04 01 Histogram n Bar chart in Jupyter and Spyder P04 02 Line Chart Pie Chart Box PlotP04 03 Revisit Some nitty gritty of PythonP04 04 Scope of a variable global scope local scopeP04 05 Range ObjectP04 06 Casting or Variable type conversion n slicing stringsP04 07 Lambda function n dropping columns from pandas dataframeSection 5: Some statistical procedures and other advance stuffsP05 01 Simple Outlier detection n treatmentP05 02 Creating Excel formatted reportP05 03 Creating pivot table on pandas dataframeP05 04 renaming column names of a dataframeP05 05 reading writing appending data into SQLlite databaseP05 06 writing log of code executionP05 07 Linear regression using pythonP05 08 chi square test of independence

Overview
Section 1: Introduction

Lecture 1 Introduction to the course

Lecture 2 P01_01 : Getting started with Python : Installing Anaconda

Lecture 3 P01_02 : Understand Spyder environment and import external data

Section 2: Working with Data

Lecture 4 P02_01 : Understand data and learn to put comments in the code

Lecture 5 P02_01A : Running SQL commands on python pandas dataframe

Lecture 6 P02_02 : Know Contents of the Data

Lecture 7 P02_03A : Missing Value detection n treatment Part1

Lecture 8 P02_03B : Getting Familiar with Jupyter Notebook IDE

Lecture 9 P02_03C : treating Numeric Missing value with mean & treating date missing value

Lecture 10 P02_03D : copy dataframe n dropping records based on value of a particular field

Lecture 11 P02_03E : Replacing missing Value with median or mode

Lecture 12 P02_04 : Filtering data n keeping few columns in data

Lecture 13 P02_05 : use iloc to filter data

Lecture 14 P02_06 : Numeric Variable Analysis with Group By n Transpose the result

Lecture 15 P02_07 : Frequency Distribution count n percentage including missing percentage.

Lecture 16 P02_08 : Introduction to function n substring stuff

Section 3: Working with multiple DataFrames and derive new fields

Lecture 17 P03_01 : Creating Dataframe on the run Append concatenate dataframe

Lecture 18 P03_02 : Merging DataFrames

Lecture 19 P03_03 : Remove Duplicates Full or coulmn based Sorting Dataframe Keep First

Lecture 20 P03_04 : Getting row for max value of any column easy way n then through idxmax

Lecture 21 P03_05 : use idxmax iterrows forloop to solve a tricky question

Lecture 22 P03_06 : Create derived fields using numerical fields

Lecture 23 P03_07 : Cross Tab Analysis n putting result into another dataframe transpose

Lecture 24 P03_08 : Derive variable based on character field

Lecture 25 P0309 : Derive variable based on date field

Lecture 26 P03_10 : First Day Last Day Same Day of Last n month

Section 4: Data Visualization and Some other frequently used syntax

Lecture 27 P04_01 : Histogram n Bar chart in Jupyter Notebook and Spyder

Lecture 28 P04_02 : Line Chart Pie Chart Box Plot

Lecture 29 P04_03 : Revisit Some nitty gritty of Python programming

Lecture 30 P04_04 : Scope of a variable global scope local scope

Lecture 31 P04_05 : Understand Range Object

Lecture 32 P04_06 : Casting or Variable type conversion n slicing strings

Lecture 33 P04_07 : Lambda function n dropping columns from pandas dataframe

Section 5: Some statistical procedures and other advance stuffs

Lecture 34 P05_01 : Simple Outlier detection n treatment

Lecture 35 P05_02 : Creating Excel formatted report

Lecture 36 P05_03 : Creating pivot table on pandas dataframe

Lecture 37 P05_04 : renaming column names of a dataframe

Lecture 38 P05_05 : Reading writing appending data into SQLlite database

Lecture 39 P05_06 : writing log of code execution

Lecture 40 P05_07 : Linear regression using python

Lecture 41 P05_08 : chi square test of independence

Anyone Interested in using Python for Data analysis purpose,Data Analytics professional,People who want to migrate from other platform like SAS to Python,Data Scientist

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