Multiple time series comparison in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ visualization

intermediate

Identify the correct plot for multiple time series

Given three time series data sets, which plot correctly shows all three series on the same graph with distinct colors and a legend?

Matplotlib

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

dates = pd.date_range('2023-01-01', periods=5)
data1 = np.array([1, 3, 2, 5, 4])
data2 = np.array([2, 2, 3, 4, 5])
data3 = np.array([5, 3, 4, 2, 1])

A

plt.scatter(dates, data1, label='Series 1')
plt.scatter(dates, data2, label='Series 2')
plt.scatter(dates, data3, label='Series 3')
plt.legend()
plt.show()

B

plt.plot(dates, data1)
plt.plot(dates, data2)
plt.plot(dates, data3)
plt.show()

C

plt.plot(data1, label='Series 1')
plt.plot(data2, label='Series 2')
plt.plot(data3, label='Series 3')
plt.legend()
plt.show()

D

plt.plot(dates, data1, label='Series 1')
plt.plot(dates, data2, label='Series 2')
plt.plot(dates, data3, label='Series 3')
plt.legend()
plt.show()

💡 Hint

Look for a line plot that uses dates on the x-axis and includes a legend.

Option D correctly plots all three series with dates on the x-axis, includes labels for each series, and shows a legend. Option D lacks labels and legend. Option D plots against index, not dates. Option D uses scatter plots, not line plots.

❓ Predict Output

intermediate

Output of multiple time series plot code

What will be the output type of the following code snippet?

Matplotlib

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

dates = pd.date_range('2023-01-01', periods=3)
data1 = [1, 2, 3]
data2 = [3, 2, 1]

fig, ax = plt.subplots()
ax.plot(dates, data1, label='A')
ax.plot(dates, data2, label='B')
ax.legend()
plt.show()

A A scatter plot with points for data1 and data2

B A line plot with two lines labeled 'A' and 'B' over the dates

C A bar chart comparing data1 and data2

D A pie chart showing proportions of data1 and data2

💡 Hint

Look at the ax.plot calls and the use of legend().

The code uses ax.plot which creates line plots. The legend labels the two lines. So the output is a line plot with two labeled lines over the date range.

❓ data_output

advanced

Resulting DataFrame after merging multiple time series

Given two time series DataFrames with different date ranges, what is the shape of the merged DataFrame when merged on date with an outer join?

Matplotlib

import pandas as pd

df1 = pd.DataFrame({'date': pd.date_range('2023-01-01', periods=3), 'value1': [10, 20, 30]})
df2 = pd.DataFrame({'date': pd.date_range('2023-01-02', periods=3), 'value2': [15, 25, 35]})
merged = pd.merge(df1, df2, on='date', how='outer')

A (4, 3)

B (6, 3)

C (5, 3)

D (3, 3)

💡 Hint

Count unique dates after combining both date ranges with outer join.

df1 dates: 2023-01-01, 2023-01-02, 2023-01-03
df2 dates: 2023-01-02, 2023-01-03, 2023-01-04
Outer join combines all unique dates: 2023-01-01 to 2023-01-04, total 4 unique dates. Columns are date, value1, value2 = 3 columns. So shape is (4,3).

🔧 Debug

advanced

Identify the error in multiple time series plotting code

What error will this code raise?

Matplotlib

import matplotlib.pyplot as plt
import pandas as pd

dates = pd.date_range('2023-01-01', periods=4)
data1 = [1, 2, 3]
data2 = [4, 5, 6, 7]

plt.plot(dates, data1, label='Series 1')
plt.plot(dates, data2, label='Series 2')
plt.legend()
plt.show()

A ValueError: x and y must have same first dimension

B TypeError: unsupported operand type(s) for +: 'int' and 'str'

C No error, plots successfully

D IndexError: list index out of range

💡 Hint

Check if the x and y data lengths match for each plot call.

data1 has length 3 but dates has length 4, so plt.plot(dates, data1) raises ValueError because x and y lengths differ.

🚀 Application

expert

Calculate correlation between multiple time series

You have three time series of equal length. Which code correctly calculates the pairwise Pearson correlation matrix?

Matplotlib

import pandas as pd
import numpy as np

np.random.seed(0)
data = pd.DataFrame({
    'A': np.random.randn(5),
    'B': np.random.randn(5),
    'C': np.random.randn(5)
})

A data.corrwith(data['A'])

B data.cov()

C data.corr(method='pearson')

D data.corr(method='spearman')

💡 Hint

Look for the method that computes Pearson correlation matrix for all columns.

Option C computes the Pearson correlation matrix between all columns. Option C computes covariance, not correlation. Option C computes Spearman correlation, not Pearson. Option C computes correlation of each column with column 'A' only.