Let’s say we have a DataFrame where a column contains integers. Now, we want to change the data type of the column to string or float. Or, let’s say we have multiple columns of a DataFrame and each column contains an integer. Now, we want to change the data type of...

Let’s say we have a column in a DataFrame that contains numerical values, e.g., age. Now, we want to discretize or bin the age to various age groups. For example, those aged 0 to 5 years should be labeled as toddlers. Those aged 5 to 18 years should be labeled as...

Let’s say a dataset contains categorical values in a column and there are some missing categorical values. So, we need to handle missing categorical values. There are two ways of handling missing categorical values using the pandas Python library. We can do mode...

We often see missing values in a dataset. Missing values are those values in a dataset that does not contain any data. These missing values, if not handled properly, can change data patterns. So, it is extremely important to handle missing values in a dataset. A...