Python Pandas Online Test

What will the following Python code output?

The function high_sess should compute the highest number of events per session of each user in the database by reading a comma-separated value input file of session data. The result should be returned from the function as a dictionary. The first column of each line in the input file is expected to contain the user’s name represented as a string. The second column is expected to contain an integer representing the events in a session. Here is an example input file:

Tony,10
Stark,12
Black,25
Your program should ignore a non-conforming line like this one.
Stark,3
Widow,6
Widow,14

The resulting return value for this file should be the following dictionary: { 'Stark':12, 'Black':25, 'Tony':10, 'Widow':14 }

What should replace the CODE TO FILL line to complete the function?

Below are code lines to create a Python function. Ignoring indentation, what lines should be used and in what order for the following function to be complete:

Consider the following Python code:

In the above code, recursive_search is a function that takes a dictionary (data) and a target key (target) as arguments. It searches for the target key within the dictionary, which could potentially have nested dictionaries and lists as values, and returns the value associated with the target key. If the target key is not found, it returns None.

nested_dict is a dictionary that contains multiple levels of nested dictionaries and lists. The recursive_search function is then called with nested_dict as the data and 'target_key' as the target.

What will the output be after executing the above code?

What does the below function ‘fun’ does?

A: Sum of digits of the number passed to fun.
B: Number of digits of the number passed to fun.
C: 0 if the number passed to fun is divisible by 10. 1 otherwise.
D: Sum of all digits number passed to fun except for the last digit.

You are working with a dataset, `df`, that contains columns 'A', 'B', and 'C'. You need to perform the following tasks:

1. Group the DataFrame `df` by column 'A'.
2. Compute the sum of column 'B' for each group.
3. Append this sum as a new column 'D' to the original DataFrame `df`.

You wrote the following code to perform these tasks:

However, you notice that the new column 'D' contains many missing values. What is the cause of this issue?

A: The groupby method did not work as expected.
B: The sum method did not work as expected.
C: The new column 'D' should be appended to grouped instead of df.
D: The grouped object should be mapped to df['A'] before assigning to a new column in df
E: The groupby method should be called on df['A'] instead of df.

You are working with a dataset, `df`, that contains several columns with missing values. You want to replace all missing values in the dataset with the mean of the non-missing values of their respective columns.

You wrote the following code to perform this task:

However, you notice that some missing values are still not replaced. What is the cause of this issue?

A: The fillna method does not work with the mean method.
B: The mean method does not work with missing values.
C: The fillna method should be called on df.mean() instead of df.
D: The fillna method does not work inplace by default. You should use df.fillna(df.mean(), inplace=True).
E: The mean method should be called on df.fillna() instead of df.