There are some easy coding questions.

Part 1: Code + Module (11 pts)

In this section, you’ll write the functions needed to carry out the analysis.

Q1: function: read_data (3 pts)

Define a function read_data with a single parameter file that accomplishes the following, using pandas functions/methods:

1. uses pandas to read the file in
2. extracts only two of the columns: 'Gender' and '11'
3. Renames these two columns to have the column names 'gender' and 'last_minute'
4. returns the resulting DataFrame from the function.

Notes:

• column ’11’ from the original dataset are respondent’s response on the questionnaire to the statement ‘You tend to leave things to the last minute’
• to test out your function here, you’ll need to import pandas as pd first (outside your function).

Suggested smoke test: Executing the function as follows: read_data(file = 'testfile.csv') should return a pandas DataFrame with two columns and 10 rows.

In [ ]: YOUR CODE HERE

Q2: function: calculate_stats (3 pts)

Now, define a function calculate_stats that takes in two parameters df (the DataFrame it will operate on) and label (the value in the column that we want to extract – which will be either ‘M’ or ‘F’ upon execution).

This function should:

1. Filter to only include those values from the 'gender' column in df that are exactly equal to label
2. Calculate value_counts() on the 'last_minute' column of the dataframe generated in step 1 above, using the normalize=True parameter in the value_counts() method
3. returns the results from step 2 from the function

Suggested smoke tests: Executing the function as follows (where df is the output after having run read_data() on ‘testfile.csv’): calculate_stats(df, 'M') should return:

Neither    0.6
Agree      0.4
Name: last_minute, dtype: float64

and calculate_stats(df, 'F') should return:

Strong Agree    0.4
Disagree        0.4
Agree           0.2
Name: last_minute, dtype: float64