Analyze and manipulate data with Pandas: the iconic Python library for tabular data. Learn how to load, transform, and prepare data to be used for visualizations, or analysis via Python data science libraries. Explore advanced topics such as hierarchical indexing and data shaping to pay dividends down the line.
What You'll Learn
- Loading data from a myriad of sources into Python
- Sanitize data for analysis
- Perform complex joins & table operations
- Create beautiful data visualizations from DataFrames
- Read & write directly from SQL databases
- Powerful hierarchical indexing
For those who...
- Have a basic understanding of Python
- Are entering the Data Science & Analysis field
- Find interest in discovering new Pandas features
1
Another 'Intro to Data Analysis in Python Using Pandas' Post
An introduction to Python's quintessential data analysis library.
7 min read
2
Merge Sets of Data in Python Using Pandas
Perform SQL-like merges of data using Python's Pandas.
5 min read
3
Dropping Rows of Data Using Pandas
Square one of cleaning your Pandas Dataframes: dropping empty or problematic data.
5 min read
4
Connecting Pandas to a Database with SQLAlchemy
Save Pandas DataFrames into SQL database tables, or create DataFrames from SQL using Pandas' built-in SQLAlchemy integration.
16 min read
5
Automagically Turn JSON into Pandas DataFrames
Let Pandas do the heavy lifting for you when turning JSON into a DataFrame.
13 min read
6
Lazy Pandas and Dask
Speed up data analysis by parallelizing your DataFrames.
4 min read
7
Parse Data from PDFs with Tabula and Pandas
Parse data from PDFs into Pandas DataFrames by using Python's Tabula library.
11 min read
8
Data Visualization With Seaborn and Pandas
Create beautiful data visualizations out-of-the-box with Python’s Seaborn.
13 min read
9
Reshaping Pandas DataFrames
A guide to DataFrame manipulation using groupby, melt, pivot tables, pivot, transpose, and stack.
12 min read
10
Using Hierarchical Indexes With Pandas
Use Panda's multi-index to create smarter datasets. Speed up your workflow by easily selecting and aggregating related data.
12 min read
11
Comparing Rows Between Two Pandas DataFrames
Find data that is not common between two Pandas DataFrames; effectively the opposite of finding an intersection of data.
5 min read
