Udemy

Platform

English

Language

Data Science

Category

Learn Data Analysis with Python Pandas

Real world examples of Python Pandas to analyse large data files. Create visual representations of your data.

3.75 (58 reviews)

Students

2.5 hours

Content

Nov 2018

Last Update
Regular Price


What you will learn

How-to install Python and Anaconda - the worlds largest Data Science platform.

How-to create a virtual environment using Conda.

How-to setup the Atom Text Editor.

How to clone a GitHub Repository in Atom Text Editor.

How-to create a new branch in Atom Text Editor.

Use Python Pandas to read in large data-sets such as stock price information, customer information, purchase information and more.

Use Pandas DataFrames to work with tabular data.

Inspect datasets to gain quick valuable insights.

Use conditional filtering to select relevant information from datasets.

Using NumPy and Pandas together.

Create Pandas DataFrames from scratch.

Create DataFrames from Python dictionaries.

Using Broadcasting with DataFrames.

Correctly labeling data and columns.

Data cleansing techniques.

Using Python Pandas to create graphical plots such as bar, line, area, scatter etc.

How-to analysis datasets using statistical methods such as min, mas, mean, std.

Create filters in your code to extract targeted data from large datasets.

How-to manage time data in Python with Pandas.

Correctly index time data and create DateTime indexes.

Partial String Indexing and slicing.

Resampling Pandas Time Data.

Method Chaining.

Separating and Resampling.


Description

Python Pandas are one of the most used libraries in Python when it comes to data analysis and manipulation. Whether in finance, scientific fields, or data science, a familiarity with Pandas is a must have. This course teaches you how to work with real-world data sets for analyzing data in Python using Pandas. Not only will you learn how to manipulate and analyse data you will also learn powerful and easy to use visualization techniques for representing your data. 

This course kicks off by showing you how to get up and running using GitHub, an essential skill in your coding career. Ideally, to get the best from this course you should have some Python programming experience.

Every piece of code and dataset used in this course is available to download for free from GitHub.

Without doubt this course will teach you the necessary skills to apply basic data science techniques which are use the world over by experienced data scientists and those who spend their working day in spreadsheets.


Screenshots

Learn Data Analysis with Python Pandas
Learn Data Analysis with Python Pandas
Learn Data Analysis with Python Pandas
Learn Data Analysis with Python Pandas

Content

Course Introduction

Course Overview

How to access course source code

Setting up Python, Anaconda, Atom and GitHub

Setting up Python with Anaconda

Setting up Atom Text Editor

Creating Virtual Environments

How to clone a GitHub Repository

Introduction to Python Pandas

Introduction to Pandas

Introduction to DataFrames

Inspecting Data

Conditional Filtering

Using NumPy and Pandas Together

Creating DataFrames from NumPy

Creating DataFrames from Dictionaries

Using Broadcasting in Pandas

Labeling data in a DataFrame

Building DataFrames with Broadcasting

Cleansing, Importing and Exporting Data

Creating Plots with Pandas

Visual Data Analysis

Creating Graphs with Pandas Plot Lines

Creating Graphs with Pandas Scatter Plots

Creating Graphs with Pandas Bar Plots

Statistical Exploratory Data Analysis

Filtering Data

Managing Dates and Times with Python Pandas

Introduction to Pandas DateTime

Indexing Pandas Time Series

Creating and using a DateTimeIndex

Resampling Pandas Time Data

Method Chaining

Separating and Resampling

Additional Filtering Methods

Visualizing Pandas Time Data

APPENDIX

APPENDIX A: How to use the Atom Text Editor to push code to GitHub


Reviews

K
Keshav8 February 2020

The course is descriptive enough to learn the basics of Pandas, which the course covers as expected. Thank you Tony.


1980038

Udemy ID

10/21/2018

Course created date

7/24/2020

Course Indexed date
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