Line plots with plot() in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a simple line plot code

What will be the output of this code snippet that uses pandas to plot a line graph?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'Year': [2018, 2019, 2020, 2021], 'Sales': [250, 300, 400, 350]}
df = pd.DataFrame(data)
df.plot(x='Year', y='Sales')
plt.close()  # Prevent plot from displaying in test environment
print(type(df.plot(x='Year', y='Sales')))

A <class 'list'>

B <class 'matplotlib.axes._subplots.AxesSubplot'>

C <class 'pandas.core.frame.DataFrame'>

D <class 'matplotlib.figure.Figure'>

💡 Hint

The plot() method returns the axes object where the plot is drawn.

The pandas DataFrame plot() method returns a matplotlib AxesSubplot object representing the plot area.

❓ data_output

intermediate

Number of lines plotted with multiple columns

Given this DataFrame and plot code, how many lines will appear in the plot?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'Month': ['Jan', 'Feb', 'Mar'], 'Temp1': [30, 35, 40], 'Temp2': [25, 30, 35], 'Temp3': [20, 25, 30]}
df = pd.DataFrame(data)
df.plot(x='Month', y=['Temp1', 'Temp2', 'Temp3'])
plt.close()

A 1

B 0

C 4

D 3

💡 Hint

Each column in the y list creates one line.

Specifying multiple columns in y creates one line per column, so 3 lines appear.

❓ visualization

advanced

Effect of setting grid=true in line plots

What visual change occurs when you add grid=true to the plot() method in pandas?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'Day': [1, 2, 3, 4], 'Value': [10, 15, 7, 12]}
df = pd.DataFrame(data)
df.plot(x='Day', y='Value', grid=True)
plt.close()

A The plot lines become dashed instead of solid.

B The plot background color changes to gray.

C Grid lines appear behind the plot lines to help read values.

D The plot title is automatically added.

💡 Hint

Grid lines help to visually align points with axis ticks.

Setting grid=true adds horizontal and vertical grid lines behind the plot lines.

🔧 Debug

advanced

Identify the error in this line plot code

What error will this code raise when trying to plot?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'Date': ['2023-01-01', '2023-01-02'], 'Value': [100, 200]}
df = pd.DataFrame(data)
df.plot(x='Date', y='Value')
plt.close()

A No error, plot will display correctly.

B TypeError because 'Date' column is string, not datetime.

C ValueError because x and y columns have different lengths.

D KeyError because 'Value' column is missing.

💡 Hint

Pandas can plot string dates on x-axis without error.

Pandas can plot string dates on x-axis without error by treating them as categories.

🚀 Application

expert

Customize line plot with multiple styles

Which option correctly produces a line plot with two lines: one dashed red and one dotted blue?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'X': [1, 2, 3, 4], 'A': [10, 20, 25, 30], 'B': [15, 18, 22, 28]}
df = pd.DataFrame(data)

# Choose the correct plotting code below:

A ax = df.plot(x='X', y='A', style='r--')\ndf.plot(x='X', y='B', style='b:', ax=ax)\nplt.close() # Correct: both lines on same axes with styles

B df.plot(x='X', y='A', style='r--')\ndf.plot(x='X', y='B', style='b:')\nplt.close() # Incorrect because second plot overwrites first

C df.plot(x='X', y=['A', 'B'], style=['r--', 'b:'])\nplt.close() # Incorrect: style list must match columns exactly

D df.plot(x='X', y='A', linestyle='--', color='red')\ndf.plot(x='X', y='B', linestyle=':', color='blue')\nplt.close() # Incorrect: second plot overwrites first

💡 Hint

Use the same axes object to plot multiple lines with different styles.

Option A plots both lines on the same axes with specified styles, producing the desired plot.

Practice

1. What does the plot() function in pandas primarily create?

easy

A. A list of column names

B. A table of data values

C. A summary statistics report

D. A line plot to show data trends over an axis

Solution

1. Step 1: Understand the purpose of plot() in pandas

   The plot() function is used to create visual graphs from data, most commonly line plots.

2. Step 2: Identify the type of plot created

   By default, DataFrame.plot() creates a line plot to show how data changes over an ordered axis like time.

3. Final Answer:

   A line plot to show data trends over an axis -> Option D

4. Quick Check:

   plot() = line plot [OK]

Hint: plot() makes line plots by default to show trends [OK]

Common Mistakes:

• Thinking plot() creates tables
• Confusing plot() with summary functions
• Assuming plot() lists columns

2. Which of the following is the correct syntax to plot the column 'Sales' from a DataFrame df using the y parameter?

easy

A. df.plot('Sales')

B. df.plot(y='Sales')

C. df.plot(column='Sales')

D. df.plot(x='Sales')

Solution

1. Step 1: Recall the parameter to select columns in plot()

   The y parameter specifies which column(s) to plot on the y-axis.

2. Step 2: Match the correct syntax

   Using df.plot(y='Sales') correctly tells pandas to plot the 'Sales' column as the y-axis data.

3. Final Answer:

   df.plot(y='Sales') -> Option B

4. Quick Check:

   Use y='column' to plot that column [OK]

Hint: Use y='column' to plot that column in plot() [OK]

Common Mistakes:

• Using 'column' instead of 'y' parameter
• Passing column name without parameter
• Using x='Sales' to plot y-axis

3. Given the code:

import pandas as pd
import matplotlib.pyplot as plt

data = {'Month': ['Jan', 'Feb', 'Mar'], 'Sales': [100, 150, 120]}
df = pd.DataFrame(data)
df.plot(x='Month', y='Sales')
plt.show()

What will the plot show?

medium

A. A scatter plot of sales vs months

B. A bar plot with sales on x-axis and months on y-axis

C. A line plot with months on x-axis and sales on y-axis

D. An error because 'Month' is not numeric

Solution

1. Step 1: Understand the plot parameters

   The code uses x='Month' and y='Sales' to plot sales against months.

2. Step 2: Recognize default plot type

   By default, plot() creates a line plot, even if x-axis is categorical like months.

3. Final Answer:

   A line plot with months on x-axis and sales on y-axis -> Option C

4. Quick Check:

   plot(x=cat, y=num) = line plot [OK]

Hint: plot() defaults to line plot even with categorical x-axis [OK]

Common Mistakes:

• Expecting bar or scatter plot by default
• Thinking non-numeric x causes error
• Confusing axis labels

4. What is wrong with this code snippet?

df = pd.DataFrame({'A': [1, 2, 3]})
df.plot('A')

medium

A. It will plot column 'A' on y-axis but x-axis will be default index

B. It will cause a syntax error because 'y' parameter is missing

C. It will raise a runtime error because 'A' is not specified as y

D. It will plot column 'A' on x-axis instead of y-axis

Solution

1. Step 1: Analyze the plot() call with one positional string argument

   When a string is passed positionally to plot(), it specifies the column for the y-axis.

2. Step 2: Understand default x-axis behavior

   Since no x parameter is specified, the x-axis uses the DataFrame's index.

3. Final Answer:

   It will plot column 'A' on y-axis but x-axis will be default index -> Option A

4. Quick Check:

   plot('A') = y='A' vs index [OK]

Hint: plot('col') sets y='col' vs index; specify x/y to control [OK]

Common Mistakes:

• Assuming 'A' is x-axis without specifying
• Expecting syntax or runtime error
• Confusing x and y parameters

5. You have a DataFrame df with columns 'Date', 'Temperature', and 'Humidity'. You want to plot both 'Temperature' and 'Humidity' over 'Date' on the same line plot with a title 'Weather Data'. Which code correctly does this?

hard

A. df.plot(x='Date', y=['Temperature', 'Humidity'], title='Weather Data')

B. df.plot(y=['Temperature', 'Humidity'], x='Date', label='Weather Data')

C. df.plot(x='Date', y='Temperature', 'Humidity', title='Weather Data')

D. df.plot(x='Date', y=['Temperature', 'Humidity']).title('Weather Data')

Solution

1. Step 1: Select multiple columns for y-axis correctly

   Use a list for the y parameter: y=['Temperature', 'Humidity'].

2. Step 2: Add title properly in plot()

   The title parameter inside plot() sets the plot title directly.

3. Step 3: Verify syntax correctness

   df.plot(x='Date', y=['Temperature', 'Humidity'], title='Weather Data') uses correct syntax with x, y as list, and title parameter all in one call.

4. Final Answer:

   df.plot(x='Date', y=['Temperature', 'Humidity'], title='Weather Data') -> Option A

5. Quick Check:

   Use y=[cols], x=col, title='text' in plot() [OK]

Hint: Use y=[cols], x=col, title='text' inside plot() [OK]

Common Mistakes:

• Passing multiple y columns without list
• Using label instead of title for plot title
• Using .title() instead of .set_title()