Questions

1. How do we plot a histogram of each variable in a pandas DataFrame, and why are histograms useful?

We can plot a histogram by calling the df.hist() function built into a pandas DataFrame class. A histogram provides an accurate representation of the distribution of our numerical data.

2. How do we check for missing values (NaN values) in a pandas DataFrame?

We can call the df.isnull().any() function to easily check whether there are any null values in each column of the dataset.

3. Besides NaN values, what other kinds of missing values could appear in a dataset?

Missing values can also appear in the form of 0 values. Missing values are often recorded as 0 in a dataset due to certain issues during data collection—perhaps the equipment was faulty, or there are other issues hindering data collection.

4. Why is it crucial to remove missing values in a dataset before...