Avoiding these common mistakes when exploring data in Python

People frequently make some basic mistakes while using Pandas to explore data in Python. Here are several examples and some related codes. By avoiding these errors, you will stand out from the crowd.

1. Not checking the data types correctly:

The proper data types for columns may be overlooked by newbies, which might have an impact on later analysis and operations.

In this case, using improper data types could make calculations difficult or result in unforeseen errors.

2. Not handling categorical variables properly:

Categorical variables may not be handled properly by beginners, which may affect how accurate an analysis or model is.

The category variable in this instance is not acknowledged as such, resulting in inaccurate summary statistics.

3. Not handling date-time data appropriately:

Learners may handle date-time information incorrectly, which might hinder useful time-based analysis.

The above case prevents time-based analysis or visualizations because the date-time column is not transformed to the proper data type.