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You're reading from Data Analysis with Pandas and Python Master Pandas: Easy Data Analysis in Python for Beginners!

Product type Video

Published in Oct 2017

Last Updated in Feb 2024

Publisher Packt

ISBN-13 9781788622394

Length 19hrs 26mins

Edition 1st Edition

Languages

Python

Tools

Pandas

Concepts

Data Analysis

Author (1):

Boris Paskhaver

View More author details

Table of Contents (15) Chapters

1. Installation and Setup FREE CHAPTER

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

2. Python Crash Course

Comments
Basic Data Types
Operators
Variables
Built-in Functions
Custom Functions
String Methods
Lists
Index Positions and Slicing
Dictionaries
Classes
Navigating Libraries using Jupyter Lab

3. 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
The get Method
Overwrite a Series Value
The copy Method
Math Methods on Series Objects
Broadcasting
The value_counts Method
The apply Method
The map Method

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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
The agg Method
Iterating through Groups

10. Merging DataFrames

Intro to the Merging DataFrames Module
The pd.concat Function I
The pd.concat Function II
Left Joins
The left_on and right_on Parameters
Inner Joins I
Inner Joins II
Full-Outer Joins
Merging by Indexes with the left_index and right_index Parameters
The join Method

11. 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
Timedeltas

12. 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

13. Visualization

Install matplotlib Library for Visualization
The plot Method
Modifying Plot Aesthetics with Templates
Bar Charts
Pie Charts

14. Options and Settings

Introduction to the Options and Settings Module
Changing Options with Attributes
Changing Options with Functions
The precision Option

15. Conclusion

Conclusion

You're reading from Data Analysis with Pandas and Python Master Pandas: Easy Data Analysis in Python for Beginners!

Table of Contents (15) Chapters

Authors (1)

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