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...
Let’s say we have the following DataFrame: df1: Column 1 Column 2 Column 3 Column 4 Row 1 1.0 2.0 NaN 4.0 Row 2 5.0 6.0 7.0 8.0 Row 3 9.0 NaN 11.0 12.0 Row 4 NaN 14.0 15.0 16.0 Row 5 NaN 18.0 19.0 NaN The DataFrame has some missing values that are indicated using NaN....
Let’s say we have the following DataFrame: A 0 1 1 2 2 3 3 4 4 5 The DataFrame has a single column named “A”. Let’s say we want to add two columns “B” and “C” in the DataFrame, such that the DataFrame becomes like this: A B C 0 1 6 11 1 2 7 12 2 3 8 13 3 4 9 14 4 5 10...
Please follow the link below to buy The Security Buddy Premium Membership.
