Hands-on Data Analysis and Visualization with Pandas

Preface

Introduction to Data Analysis

• Inspiration for data analysis
• Domain expertise
• Maths and statistics
• Artificial intelligence
• Machine learning
• Data Infrastructure
• Data Analysis Process
• Why Python for Data Analysis?
• Conclusion

JupyterLab

• Introduction to JupyterLab
• Components
• Cell modes
• Menu
• Magic commands
• Keyboard shortcuts
• Conclusion

Python Overview

• Python, Hello World
• Variables and data types
• Functions
• Lambda
• List comprehensions
• Functional programming using (map, filter, and reduce)
• Working with datetime objects
• Conclusion

Introduction to Numpy

• Ndarray
• Difference between List and Numpy arrays
• Storage
• Type check
• Speed
• Copying arrays
• Mathematical operations
• Trigonometric functions
• Statistical operations
• Reshaping
• Vertical and horizontal stacking of Numpy arrays
• Fancy indexing
• Indexing with Boolean arrays
• Broadcasting
• Conclusion

Introduction to Pandas

• Data structures in pandas
• Series
• DataFrames
• Conclusion

Data Analysis

• Handling different file formats
• Handling rows and columns
• Groupby
• Filter
• Concatenate DataFrames
• Merge DataFrames
• Purging duplicate rows
• Data Transformations
• Crosstab
• Cleansing the Data
• Replacing individual values
• Pivot and pivot table
• Grouper
• Handling large datasets
• Modin Pandas
• Conclusion

Time Series Analysis

• Creating time series data
• Converting string-based dates to datetime objects
• Unix / Epoch time
• Time Series Analysis Using a Real-Time Dataset
• Handling Timezones
• Shifting or Lagging
• Handling Holidays
• Conclusion

Introduction to Statistics

• Population
• Sample
• Types of data
• Levels of Measurement
• Inferential Statistics
• Hypothesis Testing
• Conclusion

Matplotlib

• Why data visualization?
• Matplotlib architecture
• Chart properties
• Controlling xticks, y_ticks, and tick_labels
• Scatter plot
• Bar plot
• Histograms
• Pie Chart
• Subplots
• Conclusion

Seaborn

• Why Seaborn?
• Matplotlib versus Seaborn
• About pokemon
• Importing libraries and dataset
• Visualizing Statistical Relationships
• Plotting Categorical Variables
• Visualizing the Distribution of the Data
• Conclusion

Exploratory Data Analysis

• A little story, Titanic
• Importing libraries and dataset
• Handling missing values
• Variable identification
• Categorical nominal
• Univariate analysis
• Bivariate analysis
• HeatMap
• Multivariate Analysis
• Handling Outliers
• Feature Selection
• Conclusion