The API Response and Pandas

In this exercise you will load data from an API response object into a pandas DataFrame. You will assign user-friendly column names and convert the values from strings to appropriate data types.

After creating the DataFrame, run the sample code to create a scatterplot to visualize the relationship between average family size and median age in the United States.

requests and pandas (as pd) have already been imported. A response object r is loaded.

This exercise is part of the course

Analyzing US Census Data in Python

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Exercise instructions

Build a list col_names of 4 new column names: name, median_age, avg_family_size, and state
Use the DataFrame constructor to create the DataFrame states. The data parameter should be set to r.json(), but use slicing to skip the first item, which contains the old column names
Use the astype method on each column to assign the correct data type.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import seaborn
import seaborn as sns
sns.set()

# Construct the DataFrame
col_names = ____
states = pd.DataFrame(columns = col_names, data = ____)

# Convert each column with numeric data to an appropriate type
states["median_age"] = states["median_age"].____
states["avg_family_size"] = ____

# Scatterplot with regression line
sns.lmplot(x = "avg_family_size", y = "median_age", data = states)
plt.show()

Edit and Run Code