Use Pandas and Plotly to create cloud-hosted data visualizations on-demand in Python.
Making high-quality data visualizations is a critical skill for Data Scientists. Learn how to get better at it using Plotly's Python library.
Split columns containing multiple values in your Pandas DataFrame into multiple columns, each containing a single value.
Find data that is not common between two Pandas DataFrames; effectively the opposite of finding an intersection of data.
Downcast strings in Pandas to their proper data-types using HDF5.
Dealing with duplicate column names in your Pandas DataFrame.
Use Panda's multi-index to create smarter datasets. Speed up your workflow by easily selecting and aggregating related data.
A guide to DataFrame manipulation using groupby, melt, pivot tables, pivot, transpose, and stack.
Brush up on SQL fundamentals such as creating tables, schemas, and views.
Use the Mapbox Python SDK to transform a collection of addresses into lat/long coordinates.