Pandas with Python for Data Science

Pandas with Python for Data Science, available at $49.99, has an average rating of 4.55, with 58 lectures, based on 159 reviews, and has 23470 subscribers.

You will learn about You will get to learn about the basics of pandas and python libraries, what it can offer, and what kind of problems could be solved using these libraries. Data analysis and visualization using Pandas. This course is ideal for individuals who are This Pandas and NumPy Tutorial Course is designed for professionals with different backgrounds who are willing to learn data science in simple and easy steps using Python as a programming language. If you are into any kind of data and analytics work on any platform then this course is very useful for you or Managers and seniors should look up to this course as the current market is at its transition stage where you must have a good understanding of data and analysis techniques. It is particularly useful for This Pandas and NumPy Tutorial Course is designed for professionals with different backgrounds who are willing to learn data science in simple and easy steps using Python as a programming language. If you are into any kind of data and analytics work on any platform then this course is very useful for you or Managers and seniors should look up to this course as the current market is at its transition stage where you must have a good understanding of data and analysis techniques.

Enroll now: Pandas with Python for Data Science

Summary

Title: Pandas with Python for Data Science

Price: $49.99

Average Rating: 4.55

Number of Lectures: 58

Number of Published Lectures: 58

Number of Curriculum Items: 58

Number of Published Curriculum Objects: 58

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

You will get to learn about the basics of pandas and python libraries, what it can offer, and what kind of problems could be solved using these libraries.
Data analysis and visualization using Pandas.

Who Should Attend

This Pandas and NumPy Tutorial Course is designed for professionals with different backgrounds who are willing to learn data science in simple and easy steps using Python as a programming language. If you are into any kind of data and analytics work on any platform then this course is very useful for you
Managers and seniors should look up to this course as the current market is at its transition stage where you must have a good understanding of data and analysis techniques.

Target Audiences

This Pandas and NumPy Tutorial Course is designed for professionals with different backgrounds who are willing to learn data science in simple and easy steps using Python as a programming language. If you are into any kind of data and analytics work on any platform then this course is very useful for you
Managers and seniors should look up to this course as the current market is at its transition stage where you must have a good understanding of data and analysis techniques.

The goal of this course is to make the trainees expert on working with Pandas python libraries. This training will be helping folks to achieve proficiency in introducing the concept of data science with the help of libraries that we will be covering here. This course has been focused on training on Pandas. All the concepts that revolve around these libraries will be detailed very precisely through this course. The sole objective of this course is to enrich the trainees with the entire set of skills that are required to work with these python-based libraries. In this unit, you will get to learn about the basics of these libraries, what it can offer, and what kind of problems could be solved using these libraries. The initial hour in this unit has been given to explain the introduction while the rest of the time has been devoted to explaining the main concepts.

Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. This Python course will get you up and running with using Python for data analysis and visualization. The training will include the following:

Installing Jupyter
- Jupyter Environment
- Read data using Pandas
- Series vs Data Frame
- Basic Operations in Pandas
- Analyze the imported data
- Renaming Columns
- Sorting
- Filtering Data
- Filtering Function
- Read Selective Columns & Rows

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Pandas with Python

Chapter 2: Data Set

Lecture 1: Understanding Jupiter Environment

Lecture 2: Reading the Data Set

Lecture 3: Series and Data Frame

Lecture 4: Operations in Data Set

Lecture 5: More on Panda Functions

Lecture 6: Column Names and Operation

Lecture 7: Removing Columns and Rows

Lecture 8: Sorting Data Frame

Chapter 3: Data Analysis

Lecture 1: Filter Multiple Criteria

Lecture 2: Selective Columns and Rows

Lecture 3: Data Frame and Series

Lecture 4: Axis Parameter

Lecture 5: String Methods in Pandas

Lecture 6: Changing the Data Types

Lecture 7: Example of Data Type Change

Lecture 8: Group by Functions

Lecture 9: Functions on Series

Lecture 10: Plotting series in Pandas

Lecture 11: Dealing with Null Values

Lecture 12: Uses of Index

Lecture 13: Column in Index

Lecture 14: Output of Data

Lecture 15: Functions of iX Method

Lecture 16: InPlace Parameter

Lecture 17: Inspecting the Space

Lecture 18: Reducing the Space

Lecture 19: Using in Country Series

Lecture 20: Creating Manual Data Frame

Lecture 21: Random Sampling with Pandas

Lecture 22: Concept of Dummy Coding

Lecture 23: Creating Dummified Values

Lecture 24: Duplicates in Data Frame

Lecture 25: Functions for Date and Time

Lecture 26: Setting with Copy Warning

Lecture 27: Example on Copy Warning

Lecture 28: Changing the Display Option

Lecture 29: Formatting the Data

Lecture 30: Tricks for Display Options

Lecture 31: Data with Rows and Columns

Lecture 32: Converting Data Frame

Chapter 4: Azure Data Lake

Lecture 1: Introduction to Azure Data Lake

Lecture 2: Merging Data Frames

Lecture 3: Shaping a Data Frame

Lecture 4: Filling NA Values

Lecture 5: Importing Time Series Data

Lecture 6: Working with Interpolate Method

Lecture 7: Stacking and Unstacking

Lecture 8: Stacking and Unstacking for 3 Levels

Lecture 9: Concept of Crosstab

Lecture 10: More on Crosstab

Lecture 11: More Options with Crosstab

Lecture 12: Functions of Pivot

Lecture 13: Pivot Table Method

Lecture 14: Example on Pivot Table

Lecture 15: Data Frame to CSV File

Lecture 16: Using Excel Functions

Chapter 5: Summary

Lecture 1: Summary on Pandas

Instructors

Exam Turf
#1 Brand for Competitive Exam Preparation and Test Series

Rating Distribution

1 stars: 2 votes
2 stars: 3 votes
3 stars: 17 votes
4 stars: 57 votes
5 stars: 80 votes

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