Exploring Pandas, Numpy, and Dictionaries

What is the primary purpose of the Pandas library in Python?

• Machine learning

• Web development

• Data manipulation and analysis

• Data visualization

How do you create a DataFrame in Pandas?

• Use pd.DataFrame(data) where data can be a dictionary, list, or NumPy array.

• Use pd.createFrame(data) for creating a DataFrame.

• DataFrames can only be created from CSV files.

• You can only create a DataFrame using SQL queries.

What function would you use to read a CSV file into a Pandas DataFrame?

• parse_csv

• read_csv

• load_csv

• import_csv

Explain the difference between a Series and a DataFrame in Pandas.

• A Series is one-dimensional; a DataFrame is two-dimensional.

• A Series can hold multiple data types; a DataFrame can only hold one.

• A Series is used for data visualization; a DataFrame is used for data storage.

• A Series is a table-like structure; a DataFrame is a list of values.

How can you handle missing data in a Pandas DataFrame?

• Use the mean() function to replace missing values.

• Ignore missing data and proceed with analysis.

• Use dropna(), fillna(), isnull(), or interpolation methods.

• Use the concat() function to merge with another DataFrame.

What is Numpy primarily used for in Python?

• Numerical computing and array manipulation.

• Web development and server-side scripting.

• Data visualization and plotting.

• Game development and graphics rendering.

How do you create a Numpy array from a list?

• Use np.array(your_list) to create a Numpy array from a list.

• Use list(np.array(your_list)) to create a Numpy array.

• Convert your_list to a tuple first, then use np.array().

• You cannot create a Numpy array from a list.

What is the purpose of the 'reshape' function in Numpy?

• To increase the size of an array by adding more elements.

• To convert an array into a list format.

• To sort the elements of an array in ascending order.

• To change the shape of an array without altering its data.

Explain the difference between a list and a Numpy array.

• A list can only store integers; a Numpy array can store any data type.

• A list is a fixed size; a Numpy array can change size dynamically.

• A list is faster for numerical operations; a Numpy array is slower.

• A list can contain mixed data types; a Numpy array requires all elements to be of the same type.

How can you perform element-wise operations on Numpy arrays?

• Use arithmetic operators or Numpy functions for element-wise operations.

• Use loops for all array operations.

• Element-wise operations are not possible in Numpy.

• Use only Numpy functions for all operations.