Often, we need to share our code explanations and figures written in a Google colab Jupyter notebook (.py or .ipynb files) with our friends/colleagues/bosses. The simple way is to save them as PDF files. However, PDF files are unable to load large figures or the markdown explanations.
| A complete step-by-step exploratory data analysis with simple explanation.
Exploratory Data Analysis (EDA) is a pre-processing step to understand the data. There are numerous methods and steps in performing EDA, however, most of them are specific, focusing on either visualization or distribution, and are incomplete. Therefore, here, I will walk-through step-by-step to understand, explore, and extract the information from…
Automated exploratory data analysis using Pandas Profiling in Jupyter on Google Colab.
Recently, pandas have come up with an amazing open-source library called pandas-profiling. Generally, EDA starts by df.describe(), df.info() and etc which to be done separately. Pandas_profiling extends the general data frame report using a single line of code: df.profile_report() which interactively describes the statistics, you can read it more here.
However, pandas_profiling cannot be straightforwardly used on Colab. The code will result in an error, as below;