Line plots with plot() in Pandas - Mini Project: Build & Apply

Line plots with plot()

📖 Scenario: You work as a data analyst for a small bakery. You have daily sales data for different types of bread over a week. You want to visualize how sales changed each day to understand trends.

🎯 Goal: Create a line plot using pandas plot() to show daily sales of different bread types over a week.

📋 What You'll Learn

Create a pandas DataFrame with daily sales data for three bread types.

Set a variable for the days of the week.

Use the DataFrame's plot() method to create a line plot.

Display the plot using plt.show().

💡 Why This Matters

🌍 Real World

Line plots help visualize trends over time, such as daily sales, temperature changes, or stock prices.

💼 Career

Data analysts and scientists use line plots to communicate patterns and insights clearly to teams and decision makers.

1

Create the sales data DataFrame

Import pandas as pd and create a DataFrame called sales_data with these exact columns and values: 'Sourdough' with [20, 22, 19, 24, 30, 28, 25], 'Baguette' with [15, 18, 14, 20, 22, 21, 19], and 'Rye' with [10, 12, 11, 13, 15, 14, 13].

Pandas

# Import pandas and create the sales_data DataFrame
# Your code here

Hint

Use pd.DataFrame() with a dictionary where keys are bread types and values are lists of sales numbers.

2

Add days of the week as index

Create a list called days with these exact values: ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']. Then set this list as the index of the sales_data DataFrame.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Sourdough': [20, 22, 19, 24, 30, 28, 25],
    'Baguette': [15, 18, 14, 20, 22, 21, 19],
    'Rye': [10, 12, 11, 13, 15, 14, 13]
})

# Create the days list and set it as index of sales_data
# Your code here

Hint

Assign the list days to sales_data.index to label rows by day.

3

Create the line plot

Import matplotlib.pyplot as plt. Use the plot() method on sales_data to create a line plot showing sales trends for each bread type.

Pandas

import pandas as pd
import matplotlib.pyplot as plt

sales_data = pd.DataFrame({
    'Sourdough': [20, 22, 19, 24, 30, 28, 25],
    'Baguette': [15, 18, 14, 20, 22, 21, 19],
    'Rye': [10, 12, 11, 13, 15, 14, 13]
})

days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
sales_data.index = days

# Use sales_data.plot() to create the line plot
# Your code here

Hint

Call sales_data.plot() to create the line plot. Make sure to import matplotlib.pyplot as plt.

4

Display the plot

Use plt.show() to display the line plot you created.

Pandas

import pandas as pd
import matplotlib.pyplot as plt

sales_data = pd.DataFrame({
    'Sourdough': [20, 22, 19, 24, 30, 28, 25],
    'Baguette': [15, 18, 14, 20, 22, 21, 19],
    'Rye': [10, 12, 11, 13, 15, 14, 13]
})

days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
sales_data.index = days

sales_data.plot()

# Display the plot
# Your code here

Hint

Call plt.show() after plotting to see the graph window.

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()