Data Analysis with Pandas and Python [Video]

Book Image

Data Analysis with Pandas and Python [Video]

By : Boris Paskhaver

4 (2)

Book Image

Data Analysis with Pandas and Python [Video]

4 (2)

By: Boris Paskhaver

Overview of this book

This course begins with the essentials, introducing you to Anaconda and Jupyter Lab setup for Python and Pandas. You'll gain foundational knowledge in Python before diving into Pandas for data analysis. The focus then shifts to Series and DataFrame structures, providing you with the skills to manage and manipulate data effectively. Further, the course covers handling dates and times, and performing various file input and output operations, essential for real-world data analysis tasks. Advanced sections delve into data visualization using Matplotlib, enabling you to create impactful charts and graphs. You'll also explore advanced Pandas options and settings, enhancing your data manipulation capabilities. By the end of this course, you'll have a comprehensive understanding of data analysis techniques. You'll be equipped to handle complex datasets, perform detailed analysis, and present data visually, opening doors to advanced data analysis and manipulation in professional settings. Resources: https://github.com/PacktPublishing/Data-Analysis-with-Pandas-and-Python

Free Chapter

Installation and Setup

Introduction to the Course

macOS - Download and Install the Anaconda Distribution

Windows - Download and Install the Anaconda Distribution

Use Anaconda Navigator to Create a New Environment

Unpack Course Materials + The Startdown and Shutdown Process

Intro to the Jupyter Lab Interface

Code Cell Execution

Import Libraries into Jupyter Lab

Python Crash Course

Basic Data Types

Built-in Functions

Custom Functions

Index Positions and Slicing

Navigating Libraries using Jupyter Lab

Series

Create a Series Object from a List

Create a Series Object from a Dictionary

Intro to Series Methods

Intro to Attributes

Parameters and Arguments

Import Series with the pd.read_csv Function

The head and tail Methods

Passing Series to Python Built-In Functions

Check for Inclusion with Python's in Keyword

The sort_values Method

The sort_index Method

Extract Series Values by Index Position

Extract Series Values by Index Label

Overwrite a Series Value

The copy Method

Math Methods on Series Objects

The value_counts Method

The apply Method

DataFrames I: Introduction

Methods and Attributes between Series and DataFrames

Differences between Shared Methods

Select One Column from a DataFrame

Select Multiple Columns from a DataFrame

Add New Column to DataFrame

A Review of the value_counts Method

Drop DataFrame Rows with Missing Values

Fill in Missing Values with the fillna Method

The astype Method I

The astype Method II

Sort a DataFrame with the sort_values Method I

Sort a DataFrame with the sort_values Method II

Sort DataFrame with the sort_index Method

Rank Series Values with the rank Method

DataFrames II: Filtering Data

This Module's Dataset + Memory Optimization

Filter a DataFrame Based on a Condition

Filter with More than One Condition (AND - &)

Filter with More than One Condition (OR - |)

The isin Method

The isnull and notnull Methods

The between Method

The duplicated Method

The drop_duplicates Method

The unique and nunique Methods

DataFrames III: Data Extraction

This Module's Dataset

The set_index and reset_index Methods

Retrieve Rows by Index Position with iloc Accessor

Retrieve Rows by Index Label with loc Accessor

Second Arguments to loc and iloc Accessors

Overwrite Value in a DataFrame

Overwrite Multiple Values in a DataFrame

Rename Index Labels or Columns in a DataFrame

Delete Rows or Columns from a DataFrame

Create Random Sample with the sample Method

The nsmallest and nlargest Methods

Filtering with the where Method

The apply Method with DataFrames

Working with Text Data

This Module's Dataset

Common String Methods

Filtering with String Methods

String Methods on Index and Columns

The split Method

More Practice with Splits

The expand and n Parameters of the split Method

MultiIndex

Intro to the MultiIndex Module

Create a MultiIndex

Extract Index Level Values

Rename Index Lebels

The sort_index Method on a MultiIndex DataFrame

Extract Rows from a MultiIndex DataFrame

The transpose Method

The stack Method

The unstack Method

The pivot Method

The melt Method

The pivot_table Method

GroupBy

Intro to the GroupBy Module

The groupby Method

Retrieve A Group with the get_group Method

Methods on the GroupBy Object

Grouping by Multiple Columns

Iterating through Groups

Merging DataFrames

Intro to the Merging DataFrames Module

The pd.concat Function I

The pd.concat Function II

The left_on and right_on Parameters

Full-Outer Joins

Merging by Indexes with the left_index and right_index Parameters

The join Method

Working with Dates and Times

Intro to the Working with Dates and Times Module and Review of Python's datetime

The Timestamp and DatetimeIndex Objects

Create Range of Dates with pd.date_range Function

The dt Attribute

Selecting Rows from a DataFrame with DatetimeIndex

The DateOffset Object

Specialized Date Offsets

Input and Output

Intro to the Input and Output Module

Export DataFrame to CSV File

Install openpyxl Library to Read and Write Excel Files

Import Excel File into pandas

Export Excel File from pandas

Visualization

Install matplotlib Library for Visualization

The plot Method

Modifying Plot Aesthetics with Templates

Options and Settings

Introduction to the Options and Settings Module

Changing Options with Attributes

Changing Options with Functions

The precision Option

Conclusion

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Chapter 14

Options and Settings

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Section 1

Introduction to the Options and Settings Module

This session provides an overview of various options and settings in Pandas, allowing for customization of Pandas' behavior and output.