Data Cleansing

The video tutorial covers the importance of data cleansing in data engineering, using examples from wind farms to illustrate the impact of bad data. It explains how to identify and remove bad data using Numpy and Pandas, including handling not-a-number values and outliers. The tutorial also demonstrates advanced filtering techniques and concludes with an overview of the next steps in data engineering, focusing on feature engineering.

Why is data cleansing crucial in data engineering?

• It reduces the cost of data collection.

• It helps in optimizing data storage.

• It ensures accurate decision-making by removing bad data.

• It speeds up data processing.

What is the first step in the data cleansing process?

• Data splitting

• Data scaling

• Data gathering

• Data visualization

Which library is used to handle 'not a number' values in the example?

• Matplotlib

• Pandas

• Scikit-learn

• Numpy

How does Numpy identify bad data in the example?

• By using histograms

• By using Boolean arrays

• By using scatter plots

• By using data frames

What is a key advantage of using Pandas over Numpy for data cleansing?

• Pandas is faster than Numpy

• Pandas has more visualization options

• Pandas can handle larger datasets

• Pandas provides data frames for easier manipulation

Which method in Pandas is used to remove rows with missing values?

• fillna()

• dropna()

• replace()

• remove()

What does the fillna() method do in Pandas?

• Removes missing values

• Fills missing values with a specified value

• Splits the data frame

• Sorts the data frame

How can you reset the index in a Pandas data frame?

• By using reset_index()

• By using set_index()

• By using drop_index()

• By using index_reset()

What is the purpose of using Boolean conditions in Pandas filtering?

• To select specific rows based on conditions

• To create new columns

• To visualize data

• To merge data frames

What is the next topic after data cleansing in the course?

• Data visualization

• Feature engineering

• Data scaling

• Data splitting