The first thing we must do is gather a basic sense of our data. Remember, except for the transaction amount and time columns, we don't know what the other columns are (due to privacy reasons). The only thing we know is that those columns that are unknown have been scaled already.
Let us get the statistical descriptions for each of the numerical columns in the data. We shall also check if there are any null values in our data.
Note:
describe() is a method used on a data frame to view the statistical description of the numerical columns in the data frame.
isnull() method returns True in the places where there are null values(or missing values) and False if the values are not nulls.
isnull().sum() displays column-wise information about the number of nulls found in each column of the data frame.
Use the describe method on the data frame data to get the statistical description of the data.
data.<< your code comes here >>()
Check for nulls in the data using isnull().sum().
data.<< your code comes here >>()
We observe that the data is having no nulls in any of the columns, so we don't have to work on ways to replace values.
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