Line plots with plot() in Pandas - Step-by-Step Execution

Concept Flow - Line plots with plot()

Start with DataFrame

↓

Call df.plot()

↓

Plot function reads data

↓

Draw line plot

↓

Show plot on screen

↓

End

The flow starts with a DataFrame, calls the plot() method, which reads the data and draws a line plot, then shows it on screen.

Execution Sample

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.show()

This code creates a DataFrame with sales data over years and plots a line graph of Sales vs Year.

Execution Table

Step	Action	Data Read	Plotting Action	Output
1	Create DataFrame	{'Year':[2018,2019,2020,2021],'Sales':[250,300,400,350]}	None	DataFrame with 4 rows created
2	Call df.plot(x='Year', y='Sales')	Columns 'Year' and 'Sales'	Prepare line plot axes and lines	Plot object created but not shown
3	Call plt.show()	None	Render plot window	Line plot displayed with Year on x-axis and Sales on y-axis
4	End	None	None	Plot window remains until closed

💡 Plot is shown and program waits until user closes the plot window

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
data	{'Year':[2018,2019,2020,2021],'Sales':[250,300,400,350]}	Same	Same	Same	Same
df	None	DataFrame with 4 rows	Same	Same	Same
plot_object	None	Line plot object created	Same	Same	Same

Key Moments - 2 Insights

Why does the plot not show immediately after calling df.plot()?

Calling df.plot() creates the plot object but does not display it. The plot appears only after plt.show() is called, as shown in execution_table step 3.

What does the x='Year' and y='Sales' mean in df.plot()?

They tell pandas which columns to use for the x-axis and y-axis. This is shown in execution_table step 2 where these columns are read for plotting.

Visual Quiz - 3 Questions

Look at the execution table, what is the output after step 2?

A Line plot displayed

B DataFrame created

C Plot object created but not shown

D Plot window closed

💡 Hint

Check the 'Output' column in execution_table row with Step 2

At which step is the plot actually displayed on screen?

A Step 1

B Step 3

C Step 2

D Step 4

💡 Hint

Look at the 'Plotting Action' and 'Output' columns in execution_table

If we omit plt.show(), what happens according to the execution flow?

A Plot is created but not displayed

B Plot is created and displayed automatically

C DataFrame is not created

D Error occurs

💡 Hint

Refer to key_moments about when the plot shows and execution_table step 3

Concept Snapshot

Use df.plot(x='col1', y='col2') to create a line plot.
The x and y specify columns for axes.
Call plt.show() to display the plot.
Plotting reads data, draws lines, then shows.
Without plt.show(), plot is not visible.

Full Transcript

We start with a DataFrame containing data. Calling df.plot() reads the specified columns and prepares a line plot object. However, the plot does not appear until plt.show() is called, which renders the plot window on screen. Variables like the DataFrame and plot object remain unchanged after creation. Beginners often wonder why the plot is not visible immediately after df.plot(); this is because plt.show() triggers the display. The x and y parameters tell pandas which columns to use for the axes. If plt.show() is omitted, the plot is created but not shown. This step-by-step flow helps understand how line plots are made and displayed using pandas and matplotlib.

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()