Line plots with plot() in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of the plot() function in pandas?

The plot() function in pandas is used to create visual graphs, such as line plots, from data stored in DataFrames or Series. It helps to see trends and patterns easily.

beginner

How do you create a simple line plot from a pandas DataFrame?

Call df.plot() where df is your DataFrame. By default, it creates a line plot using the DataFrame's index for the x-axis and columns for the y-axis.

beginner

What does the kind parameter in plot() do?

The kind parameter tells pandas what type of plot to draw. For example, kind='line' makes a line plot. This is the default for plot().

intermediate

How can you add a title and labels to a line plot in pandas?

After creating the plot, use matplotlib functions like plt.title(), plt.xlabel(), and plt.ylabel() to add a title and axis labels.

beginner

What happens if your DataFrame has multiple columns and you call df.plot()?

Pandas will plot each column as a separate line on the same graph, using the DataFrame index as the x-axis.

What type of plot does df.plot() create by default?

A Scatter plot

B Bar plot

C Line plot

D Histogram

The default plot type for df.plot() is a line plot, which connects data points with lines.

Which library does pandas use under the hood to create plots?

A Matplotlib

B Seaborn

C Plotly

D Bokeh

Pandas uses Matplotlib internally to generate plots.

How do you specify a line plot explicitly using plot()?

A <code>df.plot(kind='line')</code>

B <code>df.plot(type='line')</code>

C <code>df.plot(style='line')</code>

D <code>df.plot(plot='line')</code>

The correct parameter is kind='line' to specify a line plot.

If you want to plot only one column from a DataFrame, what should you do?

A Use <code>df.plot(column='column_name')</code>

B Use <code>df.plot('column_name')</code>

C Use <code>df.plot(columns='column_name')</code>

D Use <code>df['column_name'].plot()</code>

Plotting a single column is done by selecting it first, then calling plot().

What does the x-axis represent in a pandas line plot by default?

A The DataFrame columns

B The DataFrame index

C The first column

D Random values

By default, the x-axis uses the DataFrame's index values.

Explain how to create a basic line plot from a pandas DataFrame and customize its title and axis labels.

Think about combining pandas plotting with matplotlib labeling.

Describe what happens when you call plot() on a DataFrame with multiple columns.

Consider how pandas handles multiple data series in one 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()