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Data Analysis with Pandas and Python

Data Analysis with Pandas and Python

By : Boris Paskhaver
4.3 (3)
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Data Analysis with Pandas and Python

Data Analysis with Pandas and Python

4.3 (3)
By: Boris Paskhaver
Buy this Video

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
Table of Contents (15 chapters)
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1
Installation and Setup
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Introduction to the Course
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macOS - Download and Install the Anaconda Distribution
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Windows - Download and Install the Anaconda Distribution
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Use Anaconda Navigator to Create a New Environment
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Unpack Course Materials + The Startdown and Shutdown Process
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Intro to the Jupyter Lab Interface
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Code Cell Execution
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Import Libraries into Jupyter Lab
2
Python Crash Course
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Comments
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Basic Data Types
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Operators
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Variables
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Built-in Functions
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Custom Functions
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String Methods
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Lists
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Index Positions and Slicing
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Dictionaries
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Classes
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Navigating Libraries using Jupyter Lab
3
Series
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Create a Series Object from a List
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Create a Series Object from a Dictionary
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Intro to Series Methods
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Intro to Attributes
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Parameters and Arguments
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Import Series with the pd.read_csv Function
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The head and tail Methods
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Passing Series to Python Built-In Functions
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Check for Inclusion with Python's in Keyword
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The sort_values Method
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The sort_index Method
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Extract Series Values by Index Position
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Extract Series Values by Index Label
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The get Method
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Overwrite a Series Value
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The copy Method
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Math Methods on Series Objects
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Broadcasting
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The value_counts Method
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The apply Method
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The map Method
4
DataFrames I: Introduction
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Methods and Attributes between Series and DataFrames
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Differences between Shared Methods
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Select One Column from a DataFrame
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Select Multiple Columns from a DataFrame
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Add New Column to DataFrame
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A Review of the value_counts Method
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Drop DataFrame Rows with Missing Values
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Fill in Missing Values with the fillna Method
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The astype Method I
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The astype Method II
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Sort a DataFrame with the sort_values Method I
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Sort a DataFrame with the sort_values Method II
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Sort DataFrame with the sort_index Method
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Rank Series Values with the rank Method
5
DataFrames II: Filtering Data
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This Module's Dataset + Memory Optimization
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Filter a DataFrame Based on a Condition
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Filter with More than One Condition (AND - &)
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Filter with More than One Condition (OR - |)
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The isin Method
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The isnull and notnull Methods
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The between Method
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The duplicated Method
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The drop_duplicates Method
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The unique and nunique Methods
6
DataFrames III: Data Extraction
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This Module's Dataset
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The set_index and reset_index Methods
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Retrieve Rows by Index Position with iloc Accessor
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Retrieve Rows by Index Label with loc Accessor
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Second Arguments to loc and iloc Accessors
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Overwrite Value in a DataFrame
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Overwrite Multiple Values in a DataFrame
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Rename Index Labels or Columns in a DataFrame
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Delete Rows or Columns from a DataFrame
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Create Random Sample with the sample Method
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The nsmallest and nlargest Methods
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Filtering with the where Method
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The apply Method with DataFrames
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Working with Text Data
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This Module's Dataset
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Common String Methods
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Filtering with String Methods
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String Methods on Index and Columns
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The split Method
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More Practice with Splits
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The expand and n Parameters of the split Method
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MultiIndex
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Intro to the MultiIndex Module
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Create a MultiIndex
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Extract Index Level Values
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Rename Index Lebels
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The sort_index Method on a MultiIndex DataFrame
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Extract Rows from a MultiIndex DataFrame
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The transpose Method
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The stack Method
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The unstack Method
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The pivot Method
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The melt Method
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The pivot_table Method
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GroupBy
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Intro to the GroupBy Module
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The groupby Method
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Retrieve A Group with the get_group Method
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Methods on the GroupBy Object
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Grouping by Multiple Columns
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The agg Method
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Iterating through Groups
10
Merging DataFrames
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Intro to the Merging DataFrames Module
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The pd.concat Function I
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The pd.concat Function II
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Left Joins
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The left_on and right_on Parameters
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Inner Joins I
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Inner Joins II
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Full-Outer Joins
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Merging by Indexes with the left_index and right_index Parameters
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The join Method
11
Working with Dates and Times
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Intro to the Working with Dates and Times Module and Review of Python's datetime
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The Timestamp and DatetimeIndex Objects
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Create Range of Dates with pd.date_range Function
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The dt Attribute
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Selecting Rows from a DataFrame with DatetimeIndex
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The DateOffset Object
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Specialized Date Offsets
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Timedeltas
12
Input and Output
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Intro to the Input and Output Module
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Export DataFrame to CSV File
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Install openpyxl Library to Read and Write Excel Files
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Import Excel File into pandas
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Export Excel File from pandas
13
Visualization
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Install matplotlib Library for Visualization
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The plot Method
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Modifying Plot Aesthetics with Templates
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Bar Charts
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Pie Charts
14
Options and Settings
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Introduction to the Options and Settings Module
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Changing Options with Attributes
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Changing Options with Functions
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The precision Option
15
Conclusion
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Conclusion
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Chapter: 13
Visualization
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Section: 2
The plot Method
Introduces the plot method in Pandas for basic line plots, enabling quick and easy visualization of data series.
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Data Analysis with Pandas and Python
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