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Pandas Time Series Analysis

Predict the future with data - Master time series analysis using Pandas!

4.6
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Pandas Time Series Analysis
8 Modules
March 2026
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Course Curriculum

8 Modules · 9 Chapters · 31 Topics · 229 Sub-topics

01
Foundation
2 Chapters · 6 Topics · 27 Sub-topics
Getting Started with Pandas
2 Topics
Introduction to Pandas
5 Sub-topics
What is Pandas and Why Use It
Installing Pandas and Verifying Installation
Understanding Pandas Data Structures Overview
Importing Pandas and Basic Conventions
Your First Pandas Program
Pandas Development Environment
4 Sub-topics
Setting Up Jupyter Notebook for Pandas
Using VS Code with Pandas
Understanding Pandas Documentation
Exploring Pandas Community Resources
DataFrame Fundamentals
4 Topics
Creating DataFrames
5 Sub-topics
Creating DataFrame from Dictionary of Lists
Creating DataFrame from List of Dictionaries
Creating DataFrame from NumPy Arrays
Creating DataFrame from Series
DataFrame Attributes - shape, size, columns, index, dtypes
DataFrame Inspection
3 Sub-topics
Viewing DataFrame - head(), tail(), sample()
DataFrame Info and Memory Usage
Understanding DataFrame Structure
DataFrame Access and Selection
6 Sub-topics
Accessing Columns
Accessing Rows with loc
Accessing Rows with iloc
Selecting Multiple Columns
Selecting Rows and Columns Together
Boolean Indexing in DataFrame
DataFrame Modification
4 Sub-topics
Adding New Columns
Deleting Columns
Renaming Columns and Index
Modifying DataFrame Values
02
DateTime Fundamentals
1 Chapters · 4 Topics · 37 Sub-topics
Working with Dates and Times
4 Topics
DateTime Basics
9 Sub-topics
Creating DateTime Objects
Parsing String to DateTime
DateTime Components - year, month, day
DateTime Components - hour, minute, second
Day of Week and Day Name
Setting DateTime as Index
Datetime Arithmetic
Date Ranges with date_range()
Business Day Frequencies
Time Series Operations
8 Sub-topics
Resampling Time Series Data
Upsampling and Downsampling
Aggregating with Resample
Rolling Windows
Expanding Windows
Shifting Data
Period Objects and Operations
Time Deltas
Timezone Operations
10 Sub-topics
Working with Timezones
Converting Between Timezones
Timezone-Aware DateTimes
Localizing DateTimes
Converting Between Timezones
Handling Daylight Saving Time
UTC Standardization
Timezone Ambiguities
Business Rules with Timezones
Timezone Performance Considerations
Time Series Analysis
10 Sub-topics
Setting Datetime Index
Selecting Date Ranges
Partial String Indexing
Business Day Operations
Holiday Calendars
Time-based Grouping
Lag and Lead Features
Difference Operations
Percentage Change
Autocorrelation
03
Time Series Operations
1 Chapters · 4 Topics · 37 Sub-topics
Working with Dates and Times
4 Topics
DateTime Basics
9 Sub-topics
Creating DateTime Objects
Parsing String to DateTime
DateTime Components - year, month, day
DateTime Components - hour, minute, second
Day of Week and Day Name
Setting DateTime as Index
Datetime Arithmetic
Date Ranges with date_range()
Business Day Frequencies
Time Series Operations
8 Sub-topics
Resampling Time Series Data
Upsampling and Downsampling
Aggregating with Resample
Rolling Windows
Expanding Windows
Shifting Data
Period Objects and Operations
Time Deltas
Timezone Operations
10 Sub-topics
Working with Timezones
Converting Between Timezones
Timezone-Aware DateTimes
Localizing DateTimes
Converting Between Timezones
Handling Daylight Saving Time
UTC Standardization
Timezone Ambiguities
Business Rules with Timezones
Timezone Performance Considerations
Time Series Analysis
10 Sub-topics
Setting Datetime Index
Selecting Date Ranges
Partial String Indexing
Business Day Operations
Holiday Calendars
Time-based Grouping
Lag and Lead Features
Difference Operations
Percentage Change
Autocorrelation
04
Resampling and Frequency Conversion
1 Chapters · 4 Topics · 37 Sub-topics
Working with Dates and Times
4 Topics
DateTime Basics
9 Sub-topics
Creating DateTime Objects
Parsing String to DateTime
DateTime Components - year, month, day
DateTime Components - hour, minute, second
Day of Week and Day Name
Setting DateTime as Index
Datetime Arithmetic
Date Ranges with date_range()
Business Day Frequencies
Time Series Operations
8 Sub-topics
Resampling Time Series Data
Upsampling and Downsampling
Aggregating with Resample
Rolling Windows
Expanding Windows
Shifting Data
Period Objects and Operations
Time Deltas
Timezone Operations
10 Sub-topics
Working with Timezones
Converting Between Timezones
Timezone-Aware DateTimes
Localizing DateTimes
Converting Between Timezones
Handling Daylight Saving Time
UTC Standardization
Timezone Ambiguities
Business Rules with Timezones
Timezone Performance Considerations
Time Series Analysis
10 Sub-topics
Setting Datetime Index
Selecting Date Ranges
Partial String Indexing
Business Day Operations
Holiday Calendars
Time-based Grouping
Lag and Lead Features
Difference Operations
Percentage Change
Autocorrelation
05
Rolling and Expanding Windows
1 Chapters · 3 Topics · 19 Sub-topics
Statistical Operations
3 Topics
Window Functions
7 Sub-topics
Rolling Mean and Sum
Rolling Min and Max
Rolling Standard Deviation
Expanding Windows
Exponentially Weighted Moving Average
Window Aggregations
Custom Window Functions
Statistical Measures
4 Sub-topics
Correlation and Covariance
Quantile Calculations
Cumulative Operations
Statistical Analysis Methods
Outlier Detection
8 Sub-topics
Detecting Outliers with Z-Score
Detecting Outliers with IQR
Detecting Outliers with Percentiles
Visualizing Outliers Concept
Handling Outliers - Removal
Handling Outliers - Capping
Handling Outliers - Transformation
Domain-Specific Outlier Rules
06
Timezone Management
1 Chapters · 4 Topics · 37 Sub-topics
Working with Dates and Times
4 Topics
DateTime Basics
9 Sub-topics
Creating DateTime Objects
Parsing String to DateTime
DateTime Components - year, month, day
DateTime Components - hour, minute, second
Day of Week and Day Name
Setting DateTime as Index
Datetime Arithmetic
Date Ranges with date_range()
Business Day Frequencies
Time Series Operations
8 Sub-topics
Resampling Time Series Data
Upsampling and Downsampling
Aggregating with Resample
Rolling Windows
Expanding Windows
Shifting Data
Period Objects and Operations
Time Deltas
Timezone Operations
10 Sub-topics
Working with Timezones
Converting Between Timezones
Timezone-Aware DateTimes
Localizing DateTimes
Converting Between Timezones
Handling Daylight Saving Time
UTC Standardization
Timezone Ambiguities
Business Rules with Timezones
Timezone Performance Considerations
Time Series Analysis
10 Sub-topics
Setting Datetime Index
Selecting Date Ranges
Partial String Indexing
Business Day Operations
Holiday Calendars
Time-based Grouping
Lag and Lead Features
Difference Operations
Percentage Change
Autocorrelation
07
Time Series Transformations
1 Chapters · 3 Topics · 16 Sub-topics
Applying Functions
3 Topics
Apply Basics
8 Sub-topics
Element-wise Operations
Using apply() on Series
Using apply() on DataFrame Columns
Using apply() on DataFrame Rows
Lambda Functions with apply()
Applying Functions with Arguments
Using applymap() for Element-wise Application
Vectorized Operations vs apply()
Map and Transform
4 Sub-topics
Using map() for Series Transformation
Using replace() with Dictionaries
Using transform() in Groupby
Using pipe() for Method Chaining
Conditional Operations
4 Sub-topics
Conditional Application with where()
Conditional Application with mask()
Using np.where() with Pandas
Creating Calculated Columns
08
Forecasting Preparation
1 Chapters · 3 Topics · 19 Sub-topics
Statistical Operations
3 Topics
Window Functions
7 Sub-topics
Rolling Mean and Sum
Rolling Min and Max
Rolling Standard Deviation
Expanding Windows
Exponentially Weighted Moving Average
Window Aggregations
Custom Window Functions
Statistical Measures
4 Sub-topics
Correlation and Covariance
Quantile Calculations
Cumulative Operations
Statistical Analysis Methods
Outlier Detection
8 Sub-topics
Detecting Outliers with Z-Score
Detecting Outliers with IQR
Detecting Outliers with Percentiles
Visualizing Outliers Concept
Handling Outliers - Removal
Handling Outliers - Capping
Handling Outliers - Transformation
Domain-Specific Outlier Rules

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