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| File Name: | Data Analysis Using Pandas In Python : Learn By Exercise |
| Content Source: | https://www.udemy.com/course/data-analysis-using-pandas-in-python-learn-by-exercise/ |
| Genre / Category: | Other Tutorials |
| File Size : | 3.4GB |
| Publisher: | udemy |
| Updated and Published: | November 15, 2023 |
Learn to analyze data, run SQL commands, create pivot table on pandas dataframe, filter / sort dataframe, derive fields
The course will follow below structure
Section 1: Getting started with Python
- This section explains how to install Aanconda distribution and write first code
- Additionally, a walk through of Spyder Platform
Section 2: Working on Data
- P02 01A running SQL in python
- P02 01 Understand Data n Add Comments in the code
- P02 02 Know Contents of the Data
- P02 03A Missing Value detection n treatment Part1
- P02 03B Getting Familar with Jupyter IDE
- P02 03C treating Numeric Missing value with mean n treating date missing value
- P02 03D Creating copy of a dataframe n dropping records based on missing value of a particular field
- P02 03E Replacing missing Value with median or mode
- P02 04 Filtering data n keeping few columns in data
- P02 05 use iloc to filter data
- P02 06 Numeric Variable Analysis with Group By n Transpose the result
- P02 07 Frequency Distribution count n percentage including missing percentage
- P02 08 Introduction to function n substring stuff
Section 3: working on multiple datasets
- P03 01 Creating Dataframe on the run Append concatenate dataframe
- P03 02 Merging DataFrames
- P03 03 Remove Duplicates Full or column based Sorting Dataframe Keep First Last Max Min
- P03 04 Getting row for max value of any column easy way n then through idxmax
- P03 05 use idxmax iterrows forloop to solve a tricky question
- P03 06 Create derived fields using numerical fields
- P03 07 Cross Tab Analysis n putting reult into another dataframe transpose result
- P03 08 Derive variable based on character field
- P03 09 Derive variable based on date field
- P03 10 First Day Last Day Same Day of Last n month
Section 4: Data visualization and some frequently used terms
- P04 01 Histogram n Bar chart in Jupyter and Spyder
- P04 02 Line Chart Pie Chart Box Plot
- P04 03 Revisit Some nitty gritty of Python
- P04 04 Scope of a variable global scope local scope
- P04 05 Range Object
- P04 06 Casting or Variable type conversion n slicing strings
- P04 07 Lambda function n dropping columns from pandas dataframe
Section 5: Some statistical procedures and other advance stuffs
- P05 01 Simple Outlier detection n treatment
- P05 02 Creating Excel formatted report
- P05 03 Creating pivot table on pandas dataframe
- P05 04 renaming column names of a dataframe
- P05 05 reading writing appending data into SQLlite database
- P05 06 writing log of code execution
- P05 07 Linear regression using python
- P05 08 chi square test of independence
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