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Pandas for Data Analytics - The Right Way 2024 - OneDDL - 06-22-2024
![[Image: 610185062bd718bee77a8a099e8c1b05.jpeg]](https://i123.fastpic.org/big/2024/0622/05/610185062bd718bee77a8a099e8c1b05.jpeg)
Free Download Pandas for Data Analytics - The Right Way 2024
Published 6/2024
Created by Datagai Academy / Ganeshraj Shetty
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 18 Lectures ( 4h 51m ) | Size: 2.8 GB
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 Pandas2. Pandas SeriesIntroduction to Pandas Series, Creating Series , Attributes of a Series, Accessing values within a Series, Deleting a value in a Series, Adding two Series.3. DataFrameIntroduction 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 DataFrameIntroduction 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 DataFrameConditionally Selecting values, Multiple Conditional Selection, Resetting and Setting New Index.6. Data Input and Data OutputReading 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 ProcessingIntroduction 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 TableIntroduction Grouping and aggregation, Grouping by single column, Grouping by multiple Columns, Pivot Tables.9. Concatenating DataFrames and Inserting new rowsConcatenation, 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 DataFramesMerging 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 DataFrameCount 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 DataFrameConverting 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.!
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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