Programming in Python for Data Science Module 1

Introduction to data frames in Python using pandas library. Discusses the structure of data frames with rows as observations and columns as variables. Explains importing data from CSV files and basic data frame manipulation techniques like viewing dimensions and using tools like .head() to display rows.

Explains slicing and indexing data frames in pandas using .loc and .iloc methods. Demonstrates how to select specific rows and columns using labels or positions. Shows examples of slicing based on row and column indices and how to retrieve specific data frame values.

Covers sorting data frames using sort_values() function and obtaining summary statistics using describe() method. Describes the output of describe() function and the statistics it provides such as count, mean, standard deviation, min, 25th percentile, median, 75th percentile, and max.

Explains how to create frequency tables for data frames using value_counts() method. Shows the process of writing data frames to CSV files using to_csv() function. Demonstrates how to save data for future use and discusses the importance of data visualization.

Introduces data visualization using Altair in Python. Demonstrates how to create bar charts and scatter plots using Altair with data frames. Explains the steps involved in plotting data, specifying variables, and customizing plot properties like width, height, colors, opacity, and size of data points.