Deep Learning - Computer Vision for Beginners Using PyTorch - Working with Null Values

The video tutorial explains how to handle missing values in a pandas dataframe. It covers methods to identify missing values using isnull and sum, and demonstrates how to delete rows or columns with null values using the dropna method. The tutorial also explains the use of the inplace parameter to apply changes directly to the original dataframe. Finally, it previews the next topic on slicing dataframes.

What is the first step in handling missing values in a dataset?

• Load the dataset using pandas

• Delete all rows

• Replace all values with zeros

• Ignore the missing values

Which method is used to identify missing values in a pandas dataframe?

• replace

• fillna

• isnull

• dropna

How can you count the number of missing values in each column?

• Using the count method

• Using the sum method with isnull

• Using the mean method

• Using the median method

What does the dropna method do by default?

• Ignores null values

• Deletes columns with null values

• Deletes rows with at least one null value

• Replaces null values with zeros

How can you ensure that changes made by dropna are saved to the original dataframe?

• By using the inplace parameter

• By using the save method

• By using the commit method

• By using the apply method

What parameter is used to delete columns instead of rows using dropna?

• axis=0

• axis=1

• columns=True

• rows=False

What is the shape of the dataframe after deleting columns with null values?

• 140x50

• 152x6

• 140x15

• 152x50