Pandas for Data Analytics - The Right Way 2024

Mastering Data Analysis with Pandas: Techniques and Best Practices for Data Mastery


What you'll learn
Understanding the basics of Pandas data structures - Series and DataFrame.
Loading data from various sources such as CSV files, Excel files, databases etc
Techniques for cleaning and preprocessing data, including handling missing values, dealing with outliers, and removing duplicates.
Performing operations like filtering, sorting, grouping, and aggregating data to extract insights.
Working with date and time data, including parsing date strings, converting data types, and performing date-based calculations.

Requirements
Fundamentals of Python Programming

Description
Below is the description of the Course
1. Introduction to Pandas
2. Pandas Series
Introduction to Pandas Series, Creating Series , Attributes of a Series, Accessing values within a Series, Deleting a value in a Series, Adding two Series.
3. DataFrame
Introduction to DataFrames, Creating new DataFrames, Attributes of a DataFrame, Selecting Column/Columns from a DataFrame, Creating a new column in a DataFrame, Dropping rows and columns, Inspecting data within a DataFrame, Selecting subset of rows and columns.
4. Handling missing data in DataFrame
Introduction to missing Data, None Datatype, Representing missing values in an Array and a DataFrame, Dropping Rows and Columns with NaN values, Filling missing values, Forward filling and backward filling Row wise and Column wise, Miscellaneous methods.
5. Conditional Selection and Reindexing of a DataFrame
Conditionally Selecting values, Multiple Conditional Selection, Resetting and Setting New Index.
6. Data Input and Data Output
Reading data from a CSV File, Writing DataFrame to a CSV File, Writing DataFrame to an Excel File, Reading data from an HTML File, Reading data from a SAS File.
7. Data Processing
Introduction to Data Processing, Reading first and last rows, Renaming Column names in a DataFrame, Deleting a Column, Dropping Rows and Columns simultaneously, Dropping a range of Rows and Columns, Applying Functions to the columns, Sorting or Ordering a DataFrame, Sorting by a single column, Sorting by multiple columns.
8. Grouping & Aggregation and Pivot Table
Introduction Grouping and aggregation, Grouping by single column, Grouping by multiple Columns, Pivot Tables.
9. Concatenating DataFrames and Inserting new rows
Concatenation, Combining DataFrames along the horizontal axis, Combining DataFrames along the vertical axis, Adding a new row into a DataFrame, Replacing a row at an index, Inserting a new row at an index.
10. Merging and Joining DataFrames
Merging two or more DataFrames, Inner Join, Left Join, Right Join, Outer Join, Merging on columns with different names, Joining DataFrames using Join function.
11. Logic Explanation for Merging and Joining of two DataFrames.
12. Cartesian Product between two DataFrames explanation.
13. Handling Duplicates in a DataFrame
Count of unique values in a Column, Determining duplicate rows in a DataFrame, Extracting duplicate rows, Dropping duplicate rows, considering certain Columns for dropping duplicates, Dropping all duplicate rows.
14. Handling Strings in a DataFrame
Converting Columns to string dtype, String manipulation using different String functions.
In the above syllabus you will find the most lucid explanations covering all the topics related to Pandas module.
Happy Learning!!!
Who this course is for:
Data Analysts: Professionals who work with data to extract insights and make data-driven decisions would benefit from this course. They can learn how to efficiently manipulate and analyze datasets using Pandas, which is a powerful tool in the data analysis toolkit.
Students of Data Science and Machine Learning

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