Exam DP-100 topic 3 question 136 discussion

Answer is df.shape[0] - df.count() df.shape[0] = total number of rows. df.count() = non-missing values

WRONG. The Correct answer is: shape and 0 The shape attribute of a DataFrame returns the dimensionality of the DataFrame, where shape[0] returns the number of Rows and shape[1] returns the number of Columns. If you have 10,000 rows ( df.shape[0] equals 10,000 ) and you want to find out how many values are missing in each Column, you would use df.shape[0] and subtract the number of non-missing values per column from the total number of rows to get the number of missing values like this: missing_values_per_column = df.shape[0] - df.count() The df.count() method returns the number of non-NA/non-missing values in each column. By subtracting this from the total number of rows (df.shape[0]), you get the count of missing values per column.

A pretty silly approach, but df.shape[0] would be correct to get the #of total rows.

strange examples. I use this approach # Assuming you have loaded the data into a pandas DataFrame named 'df' missing_values_count = df.isnull().sum() # Output the number of missing values in each column print(missing_values_count)