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Learn Data Wrangling with Python

Perform Data Wrangling with the Python Programming Language. Practice and Solution Notebooks included.

Created byValentine Mwangi

Last updated 10/2023

English

What you'll learn

To load a local dataset from CSV and Excel files.
To import a dataset from CSV and Excel files via a URL.
To determine the size of a dataset.
To explore the first and last records of a dataset.
To explore the datatypes of the features of a dataset.
To check for missing data in a dataset.
To deal with missing data in a dataset.
To filter for records with certain values from a dataset.
To filter records with multiple filters from a dataset.
To filter for records from a dataset through the use of conditions.
To perform sorting in ascending and descending order.
To split a column in a dataset.
To merge data frames to form a dataset.
To concatenate two columns to one column in a dataset.
To export a dataset in CSV and Excel formats.

Course content

18 sections • 18 lectures • 1h 44m total length

Introduction7:17

Requirements

You will need to have basic python programming proficiency.
You will need a modern browser i.e. Google Chrome or Mozilla Firefox.

Description

By the end of this course, you will be able to:

Load a local dataset from CSV and Excel files.
Import a dataset from CSV and Excel files via a URL.
Determine the size of a dataset.
Explore the first and last records of a dataset.
Explore the datatypes of the features of a dataset.
Check for missing data in a dataset.
Deal with missing data in a dataset.
Filter for records with certain values from a dataset.
Filter records with multiple filters from a dataset.
Filter for records from a dataset through the use of conditions.
Perform sorting in ascending and descending order.
Split a column in a dataset.
Merge data frames to form a dataset.
Concatenate two columns to one column in a dataset.
Export a dataset in CSV and Excel formats.

Who this course is for:

This course is designed for professionals with an interest in getting hands-on experience with the respective data science techniques and tools.