Udemy

Platform

English

Language

Data Science

Category

The Complete Exploratory Analysis Course With Pandas [2021]

Learn how to use Python and Pandas for data analysis and data manipulation. Transform, clean and merge data with Python.

5.00 (1 reviews)

Students

6 hours

Content

Jul 2021

Last Update
Regular Price


What you will learn

Work with Excel data.

Work with CSV datasets.

Handling missing data.

Reading and Working with JSON format.

Reading and Working with HTML files.

Reading and Working with PICKLE dataset.

Reading and Working with SQL-based database.

Selecting data from the dataset.

Sorting a pandas DataFrame.

Filtering rows of a pandas DataFrame.

Applying multiple filter criteria to a pandas DataFrame.

Using string methods in pandas.

Changing the datatype of a pandas series.

Modifying a pandas DataFrame using the inplace parameter.

Using the Groupby method.

Indexing in pandas DataFrames.

Renaming columns, and Removing columns from a pandas DataFrame.

Working with date and time series data

Applying a function to a pandas series or DataFrame.

Merging and concatenating multiple DataFrames into one.

Controlling plot aesthetics.

Choosing the colours for plots.

Plotting categorical data.

Plotting with Data-Aware Grids.


Description

In the real-world, data is anything but clean, which is why Python libraries like Pandas are so valuable.


If data manipulation is setting your data analysis workflow behind then this course is the key to taking your power back.


Own your data, don’t let your data own you!


When exploratory analysis accounts for up to 80% of your work as a data scientist, learning data munging techniques that take raw data to a final product for analysis as efficiently as possible is essential for success.


Exploratory analysis with Python library Pandas makes it easier for you to achieve better results, increase your productivity, spend more time problem-solving and less time data-wrangling, and communicate your insights more effectively.


This course prepares you to do just that!


With Pandas DataFrame, prepare to learn advanced data manipulation, preparation, and sorting data approaches to turn chaotic bits of data into a final pre-analysis product. This is exactly why Pandas is the most popular Python library in data science and why data scientists at Google, Facebook, JP Morgan, and nearly every other major company that analyzes data use Pandas.


If you want to learn how to efficiently utilize Pandas to manipulate, transform, and merge your data for preparation of visualization, statistical analysis, or machine learning, then this course is for you.


Here’s what you can expect when you enrolled in the course:


  • Learn how to Work with Excel data, CSV datasets.

  • Learn how to Handling missing data.

  • Learn how to read and work with JSON format, HTML files, PICKLE dataset, and  SQL-based database.

  • Learn how to select data from the dataset.

  • Learn how to sort a pandas DataFrame and filtering rows of a pandas DataFrame.

  • Learn how to apply multiple filter criteria to a pandas DataFrame.

  • Learn how to using string methods in pandas.

  • Learn how to change the datatype of a pandas series.

  • Learn how to modifying a pandas DataFrame.

  • Learn how to indexing and renaming columns, and removing columns in and from pandas DataFrame.

  • Learn how to working with date and time series data.

  • Learn how to applying a function to a pandas series or DataFrame.

  • Learn how to merging and concatenating multiple DataFrames into one.

  • Learn how to control plot aesthetics.

  • Learn how to choose the colours for plots.

  • Learn how to plot categorical data.

  • Learn how to plot with Data-Aware Grids.

Performing exploratory analysis with Python’s Pandas library can help you do a lot, but it does have its downsides. And this course helps you beat them head-on:


1. Pandas has a steep learning curve: As you dive deeper into the Pandas library, the learning slope becomes steeper and steeper. This course guides beginners and intermediate users smoothly into every aspect of Pandas.


2. Inadequate documentation: Without proper documentation, it’s difficult to learn a new library. When it comes to advanced functions, Pandas documentation is rarely helpful. This course helps you grasp advanced Pandas techniques easily and saves you time in searching for help.


After this course, you will feel comfortable delving into complex and heterogeneous datasets knowing with absolute confidence that you can produce a useful result for the next stage of Exploratory analysis.


Here’s a closer look at the curriculum:

  • Loading and creating Pandas DataFrames

  • Displaying your data with basic plots, and 1D, 2D and multidimensional visualizations.

  • Working with Different Kinds of Datasets

  • Data Selection

  • Manipulating, Transforming, and Reshaping Data.

  • Visualizing Data Like a Pro

  • Merging Pandas DataFrames

Lastly, this course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice with Pandas too.


Screenshots

The Complete Exploratory Analysis Course With Pandas [2021]
The Complete Exploratory Analysis Course With Pandas [2021]
The Complete Exploratory Analysis Course With Pandas [2021]
The Complete Exploratory Analysis Course With Pandas [2021]

Content

Introduction

Course structure

What is the prerequisite of this course

How To Make The Most Out Of This Course

Important note about tools in this course

Working with Different Kinds of Datasets

Using advanced options while reading data from CSV files

Reading data from Excel files

Reading data from other popular formats

Data Selection

Introduction to datasets

Sorting a pandas DataFrame

Filtering rows of a pandas DataFrame

Applying multiple filter criteria to a pandas DataFrame

Using the axis parameter in pandas

Using string methods in pandas

Changing the datatype of a pandas series

Summary

Manipulating, Transforming, and Reshaping Data

Modifying a pandas DataFrame using the inplace parameter

Using the groupby method

Handling missing values in pandas

Indexing in pandas DataFrames

Renaming columns in a pandas DataFrame

Removing columns from a pandas DataFrame

Working with date and time series data

Applying a function to a pandas series or DataFrame

Merging and concatenating multiple DataFrames into one

Summary

Visualizing Data Like a Pro

Controlling plot aesthetics

Choosing the colors for plots

Plotting categorical data

Plotting with Data-Aware Grids

Summary

Thank you

Thank you


4181198

Udemy ID

7/13/2021

Course created date

7/22/2021

Course Indexed date
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