Introduction to Pandas pivot() and how to use it to change a DataFrame from long to wide format — Reshaping is often needed when you work with datasets that contain variables with some kinds of sequences, say, time-series data. Source from University of Virginia, Research Data Service [1] In the previous article, we talked about reshaping the data from a wide to a long format using Pandas melt(). In…

6 Tricks to effectively flatten MultiIndex columns and rows in a Pandas DataFrame — A MultiIndex (also known as a hierarchical index) DataFrame allows you to have multiple columns acting as a row identifier and multiple rows acting as a header identifier.

7 tricks to effectively use the Pandas stack() and unstack() — Reshaping is often needed when you work with datasets that contain variables with some kinds of sequences, say, time-series data. Source from University of Virginia, Research Data Service [1] Pandas provides various built-in methods for reshaping DataFrame. …

A hands-on tutorial to create a multiple Y-axis combo chart with Matplotlib, Seaborn, and Pandas plot() — Visualizing data is vital to analyzing data. If you can’t see your data — and see it in multiple ways you‘ll have a hard time analyzing that data. Source from Python Data [1] There are many different graphs to visualize data in Python and thankfully with Matplotlib, Seaborn, Pandas, Plotly…

9 tricks to master Pandas drop() and speed up your data analysis — Data manipulation refers to the process of adjusting data to make it organised and easier to read. Frequently, there is data that is unusable and can interfere with what matters. Unnecessary or inaccurate data should be cleaned and deleted. Source from solvexia.com [1] Delete one or many rows/columns from a…

A hands-on tutorial to create a dual-axis combo chart with Matplotlib, Seaborn, and Pandas plot() — Visualizing data is vital to analyzing data. If you can’t see your data — and see it in multiple ways you‘ll have a hard time analyzing that data. Source from Python Data [1] There are many different graphs to visualize data in python and thankfully with Matplotlib, Seaborn, Pandas, Plotly…

Hands-on tutorial to effectively add rows at the end of current DataFrame with Pandas append() — Data merging is the process of combining two or more data sets into a single data set. Most often, this process is necessary when you have raw data stored in multiple files, worksheets, or data tables, that you want to analyze all in one go. Source from DisplayR Blog: What…

Hands-on tutorial to change a DataFrame from wide format to long format with Pandas melt() — Reshaping is often needed when you work with datasets that contain variables with some kinds of sequences, say, time-series data. Source from University of Virginia, Research Data Service [1] I published an article a while ago showing a step-by-step tutorial to process COVID-19 time-series data. In the tutorial, one of…

7 hands-on tricks to effectively use Pandas crosstab for data summarization — The most immediate effect of summarizing data is to take data that may be overwhelming to work with, and reduce it to a few key summary values that can be viewed, often in a table or plot. Source from Introduction to Data Science [1] Data summarization is a process to…