Data Science and Machine Learning (Theory and Projects) A to Z - Python for Data Science: NumPy Pandas and Matplotlib (P

The video tutorial covers the use of Pandas for data analysis, focusing on conditional selections, loading and exploring the Titanic dataset, computing descriptive statistics, and handling missing data. It provides a practical introduction to using Pandas for data manipulation and analysis in Python.

What is the purpose of using parentheses in conditional selections?

• To format the output

• To increase computational speed

• To separate different data types

• To prioritize operations

Which logical operator is represented by a pipe symbol in conditional selections?

• OR

• NOT

• AND

• XOR

What function is used to load a CSV file into a Pandas DataFrame?

• pd.read_csv()

• pd.open_csv()

• pd.import_csv()

• pd.load_csv()

What does the 'head' function in Pandas do?

• Deletes the first few rows of a DataFrame

• Sorts the DataFrame

• Displays the first few rows of a DataFrame

• Displays the last few rows of a DataFrame

Which function in Pandas provides statistical summaries of numeric attributes?

• summary()

• describe()

• overview()

• stats()

What does the 'describe' function in Pandas NOT provide?

• Median

• Mean

• Standard Deviation

• Mode

How can you filter records in Pandas where age is greater than 50 and survived?

• Using the 'filter' function

• Using conditional selection with '&' operator

• Using the 'query' function

• Using the 'select' function

What function can be used to count the number of non-null entries in a DataFrame?

• length()

• total()

• sum()

• count()

What does the 'isnull' function in Pandas return?

• A list of null values

• A DataFrame with null values removed

• A Boolean array indicating null entries

• A count of null values

What is a common strategy for handling missing data in a DataFrame?

• Filling or dropping missing entries

• Ignoring the missing data

• Converting missing data to zero

• Duplicating the missing data