Python Bootcamp for Data Analysis #5: Pandas

Name: Python Bootcamp for Data Analysis #5: Pandas
Rating: 4.6 (33 reviews)

From Zero to Hero: The Fifth Module of Miuul's Python Bootcamp

Created byMiuul Data Science & Deep Learning

Last updated 1/2025

English

What you'll learn

Understand the core functionalities of the Pandas library and its importance in data analysis
Create and manipulate Pandas Series and DataFrames to efficiently handle data
Perform data selection, conditional filtering, and various operations on variables within Pandas
Utilize advanced data aggregation, grouping, and pivot table techniques for comprehensive data analysis

Course content

1 section • 13 lectures • 1h 36m total length

Course Materials0:03
What is Pandas?1:07
Explore Pandas, the Python library for data manipulation and analysis, from econometric roots to reading data, mastering series and data frames, and operations like selection, aggregation, grouping, apply, lambda, merging.
Pandas Series6:28
Explore the Pandas Series, a one-dimensional data structure with an index, and use pd.Series to access its values and basic methods like head and tail.
Reading Data4:47
Learn to read data with pandas using read_csv and other formats, locate files with PyCharm, preview with head, and explore multiple data sources and formats for analysis.
A Quick Look at Data9:12
Master quick data exploration in pandas with head, tail, shape, info, describe, and isnull, using Seaborn Titanic datasets and value counts for categorical variables.
Selecting Data in Pandas8:56
Master data selection in pandas by manipulating a DataFrame's indices, selecting variables, and using drop and inplace options. Learn converting indices to columns and resetting the index for analysis.
Operations on Variables8:57
Explore Pandas column index operations, including selecting variables as data frames or series, adding and deleting variables, and programmatic filtering with contains and lock selections.
Loc and Iloc5:10
Demonstrate i-loc and loc selections in dataframes, comparing integer-based versus label-based indexing, and perform row and column picks by index or by name.
Conditional Selection9:10
Learn conditional selection in pandas data frames by applying multi-criteria queries using age over 50, gender, class, and embarked. Use loc, iloc, and parentheses to combine conditions with and/or logic.
Aggregation and Grouping11:42
Explore aggregation and grouping with pandas, using groupby to compute mean ages, sums, and survival rates by gender, embarkation, and class on the Titanic dataset.
Pivot Table12:00
Explore pivot tables in pandas to analyze Titanic survival by gender, embarkation port, class, and age groups; age conversion uses cut or q-cut, with the default mean aggregation.
Apply and Lambda11:48
Master apply and lambda in pandas to apply functions across rows or columns, use lambda for one-time operations, and demonstrate dividing age-related variables and standardization.
Join6:41
Learn how to merge data using pandas with concat and merge, including vertical and horizontal merges, ignore index, and on argument to combine employees, departments, start dates, and managers.

Requirements

No programming experience needed. You will learn everything you need to know.

Description

Welcome to the fifth module of Miuul's Python Bootcamp for Data Analysis!

This module is a crucial step in your journey as it introduces you to Pandas, an essential library for data manipulation and analysis in Python. We are excited to guide you through the foundational and advanced skills needed to effectively use Pandas for your data tasks.

In this module, you'll start by understanding what Pandas is and the importance of this powerful library. You'll learn about Pandas Series, how to read data, and quickly inspect it to gain insights. We will cover how to select data within Pandas, perform operations on variables, and use loc and iloc for precise data manipulation. You'll also explore conditional selection to filter data efficiently.

As you progress, you'll dive into aggregation and grouping techniques to summarize data, and learn how to create pivot tables for multidimensional data analysis. Finally, we'll cover how to apply functions using apply and lambda, and how to join datasets to merge information effectively.

This comprehensive exploration of Pandas will prepare you for more advanced topics in future courses and enhance your ability to tackle data analysis challenges with confidence.

Join us at Miuul's Python Bootcamp for Data Analysis, where learning to code becomes an adventure, empowering you to write, analyze, and innovate. Each line of code you write brings you one step closer to mastering the art of Python programming.

Who this course is for:

Beginner Python developers curious about data science and data analysis.
Professionals seeking to enhance their data manipulation skills using Pandas
Students and researchers who need to handle and analyze large datasets efficiently