Session 17 - Pandas DataFrame | Data Science Mentorship Program (DSMP) 2022-23 | Free Session

About this video

• Video Title: Session 17 - Pandas DataFrame | Data Science Mentorship Program (DSMP) 2022-23 | Free Session
• Channel: CampusX
• Speakers: (The transcript does not explicitly name the speaker, but the content suggests a single instructor.)
• Duration: 02:08:27

Overview

This video serves as a free session from the Data Science Mentorship Program (DSMP) 2022-23, focusing on Pandas DataFrames. The instructor explains what DataFrames are, how to create them using various methods (lists, dictionaries, CSV files), explores essential DataFrame attributes and methods, and demonstrates how to select, filter, and add columns. The session also covers common doubts and mathematical operations related to DataFrames.

Key takeaways

• DataFrame Introduction: A DataFrame is a tabular data structure consisting of rows and columns, analogous to a table. Each column and each row within a DataFrame can also be considered a Series.
• DataFrame Creation Methods: DataFrames can be created using 2D lists, dictionaries, or by reading data from CSV files using pd.read_csv().
• Essential Attributes & Methods: Key attributes and methods for exploring DataFrames include .shape (dimensions), .dtypes (data types of columns), .index (row labels), .columns (column labels), .values (data as a NumPy array), .head(), .tail(), and .sample() (for viewing data subsets), and .info() and .describe() (for summary statistics and data overview).
• Data Manipulation: The session covers techniques for selecting single or multiple columns, selecting rows using .iloc (index position) and .loc (index label), combining row and column selection, filtering DataFrames based on conditions, and adding new columns either from scratch or derived from existing ones.
• Data Cleaning & Transformation: Methods like .isnull().sum() for counting missing values, .duplicated().sum() for identifying duplicate rows, and .rename() for changing column names are introduced. The .astype() method is shown for changing data types to optimize memory usage.