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.