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()