Extracting features for correlation
In this exercise, you'll work with a version of the salaries dataset containing a new column called "date_of_response".
The dataset has been read in as a pandas DataFrame, with "date_of_response" as a datetime data type.
Your task is to extract datetime attributes from this column and then create a heat map to visualize the correlation coefficients between variables.
Seaborn has been imported for you as sns, pandas as pd, and matplotlib.pyplot as plt.
This exercise is part of the course
Exploratory Data Analysis in Python
Exercise instructions
- Extract the month from
"date_of_response", storing it as a column called"month". - Create the
"weekday"column, containing the weekday that the participants completed the survey. - Plot a heat map, including the Pearson correlation coefficient scores.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Get the month of the response
salaries["month"] = ____["____"].____.____
# Extract the weekday of the response
salaries["weekday"] = ____
# Create a heatmap
sns.____(____.____(), annot=____)
plt.show()