Heatmap of Travel Times By Commute Mode
In this exercise, you will create a heatmap of national data comparing commute times (in minutes) and travel modes. You begin with data_row, a list of values from the single data row of a JSON API response object. Lists of travel modes(5) and commute times (9) have been created, and are printed to the console. You must reshape the single data row into a list of lists, construct a DataFrame suitable to pass to sns.heatmap, and create the heatmap.
The data row contains data on the 5 travel modes in groups of 9 commute times. An iteration is a complete set of commute times.
pandas and seaborn are loaded using the usual aliases.
Este exercício faz parte do curso
Analyzing US Census Data in Python
Instruções do exercício
- Set
iter_lento the length of thetimeslist - In the list comprehension, construct a range with a start of 0, stop of the length of the
data_row, and step ofiter_len - Construct a heatmap with the
commutingDataFrame as the first parameter; annotate the heatmap with the commute count in thousands (use integer division to divide by1000)
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# Set iter_len to the number of commute times
iter_len = ____
# Break row into list of lists by travel mode
data = [data_row[i:i+iter_len] for i in range(____)]
# Create DataFrame, set data type to int
commuting = pd.DataFrame(data=data, index=modes, columns=times)
commuting = commuting.astype(int)
# Create heatmap of commuters by mode by income
sns.heatmap(____, annot=____, fmt = "d", cmap="YlGnBu")
plt.xticks(rotation = 50)
plt.yticks(rotation = 50)
plt.show()