Python Data Wizardry with Pandas-Pandas Data Analysis Guide

Example

Reading data from various sources like CSV, Excel, or SQL databases, and performing operations such as handling missing values, removing duplicates, and data type conversions.

Scenario

A business analyst imports a dataset containing sales transactions from an Excel file, cleans the data by filling in missing values with the average sales figures, and converts string dates to datetime objects for time series analysis.

Example

Pivoting data tables, merging datasets, creating new calculated columns, and aggregating data for summary statistics.

Scenario

A market researcher combines customer survey data with purchase history, categorizes responses based on purchase behavior, and calculates aggregate metrics like average spend per category to identify market segments.

Example

Creating plots and graphs such as line plots, bar charts, and scatter plots to visually represent data trends and relationships.

Scenario

A data scientist visualizes the trend of user engagement on a social media platform over time using a line plot and identifies peak periods of activity to inform content strategy.

Example

Applying statistical models, performing time-series analysis, and utilizing machine learning algorithms for predictive modeling.

Scenario

An economist uses pandas to preprocess a dataset for analyzing economic indicators over time, then applies ARIMA (AutoRegressive Integrated Moving Average) models to forecast future economic conditions.