Before we jump into the need for a data wrangling cheat sheet, first, what is data wrangling? Data wrangling, often referred to as data preparation, is the process of transforming raw data into a refined output. It’s a necessary step for anyone that works with data. Data wrangling remedies missing information, duplicates or errors found in raw datasets and ensures that these datasets are appropriately structured for use in any given machine learning, visualization, or analytics projects.

Pandas Sheet

The process of preparing data is notoriously laborious. Experts still identify data preparation as the biggest bottleneck in any analytics project, with estimates of time spent preparing data as high as 80%. A traditional data wrangling cheat sheet helps accelerate this process. The majority of data wrangling cheat sheets were created as a handy guide for those using technical languages, such as R or Python, to prepare data. A data wrangling cheat sheet compiles all of the most common scripts used to prepare data for easy reference on one page. Data scientists spend less time second-guessing and simply look at their data wrangling cheat sheet to get the job done. You can see an example of a data wrangling cheat sheet here.

Python Data Wrangling Cheat Sheet

Dataframe Cheat Sheet

The Pandas cheat sheet will guide you through some more advanced indexing techniques, DataFrame iteration, handling missing values or duplicate data, grouping and combining data, data functionality, and data visualization. In short, everything that you need to complete your data manipulation with Python! Data Wrangling with dplyr and tidyr Cheat Sheet Data Wrangling with dplyr and tidyr Cheat Sheet Scipy SciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy, and an expanding set of scientific computing libraries.