Line plots with plot() in Pandas

Introduction

Line plots help us see how data changes over time or order. They make trends easy to spot.

Tracking daily temperatures over a week.

Showing sales growth month by month.

Comparing stock prices over several days.

Visualizing how a student's grades change each semester.

Syntax

Pandas

DataFrame.plot(kind='line', x=None, y=None, title=None)

kind='line' tells pandas to make a line plot (default).

x and y specify which columns to use for the horizontal and vertical axes.

Examples

Plot all numeric columns as lines using the DataFrame index for x-axis.

Pandas

df.plot()

Plot 'Sales' values over 'Month' values as a line.

Pandas

df.plot(x='Month', y='Sales')

Plot multiple columns 'Sales' and 'Profit' as separate lines.

Pandas

df.plot(kind='line', y=['Sales', 'Profit'])

Sample Program

This code creates a small table of sales data by month. Then it draws a line plot showing sales for each month. Markers highlight each data point.

Pandas

import pandas as pd
import matplotlib.pyplot as plt

# Create sample data
sales_data = {
    'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
    'Sales': [200, 220, 250, 270, 300],
    'Profit': [50, 55, 60, 65, 70]
}
df = pd.DataFrame(sales_data)

# Plot Sales over Month
ax = df.plot(x='Month', y='Sales', title='Monthly Sales', marker='o')
plt.ylabel('Sales')
plt.show()

Important Notes

If you don't specify x, pandas uses the DataFrame index for the horizontal axis.

You can add markers to points with marker='o' to see exact values.

Use plt.show() to display the plot when running in scripts.

Summary

Line plots show how data changes over an ordered axis like time.

Use df.plot(x=..., y=...) to pick which columns to plot.

Markers and titles make plots easier to understand.

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()