Why built-in plotting matters in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple line plot of the 'sales' column.

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'month': ['Jan', 'Feb', 'Mar'], 'sales': [100, 150, 200]}
df = pd.DataFrame(data)
df['sales'].[1]()
plt.show()

Drag options to blanks, or click blank then click option'

Acount

Bsum

Cmean

Dplot

Attempts:

3 left

💡 Hint

Common Mistakes

Using aggregation methods like sum or mean instead of plot.

Forgetting to call plt.show() to display the plot.

The 'plot' method creates a line plot of the 'sales' column.

2fill in blank

medium

Complete the code to plot a bar chart of the 'sales' column.

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'month': ['Jan', 'Feb', 'Mar'], 'sales': [100, 150, 200]}
df = pd.DataFrame(data)
df['sales'].plot(kind=[1])
plt.show()

Drag options to blanks, or click blank then click option'

A'bar'

B'line'

C'scatter'

D'hist'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'line' which is the default but not a bar chart.

Using 'scatter' which requires x and y parameters.

Setting kind='bar' creates a bar chart of the 'sales' data.

3fill in blank

hard

Fix the error in the code to plot a histogram of the 'sales' column.

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'sales': [100, 150, 200, 150, 100]}
df = pd.DataFrame(data)
df['sales'].plot([1]='hist')
plt.show()

Drag options to blanks, or click blank then click option'

Astyle

Btype

Ckind

Dformat

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'type' or 'style' which are not valid parameters for plot().

Misspelling 'kind'.

The correct parameter to specify plot type is 'kind'.

4fill in blank

hard

Fill both blanks to create a scatter plot with 'month' on x-axis and 'sales' on y-axis.

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'month': ['Jan', 'Feb', 'Mar'], 'sales': [100, 150, 200]}
df = pd.DataFrame(data)
df.plot(x=[1], y=[2], kind='scatter')
plt.show()

Drag options to blanks, or click blank then click option'

A'month'

B'sales'

C'price'

D'quantity'

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping x and y values.

Using column names not in the DataFrame.

The x-axis is 'month' and y-axis is 'sales' for the scatter plot.

5fill in blank

hard

Fill all three blanks to create a line plot with title and grid enabled.

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'day': [1, 2, 3], 'temperature': [22, 21, 23]}
df = pd.DataFrame(data)
ax = df.plot(x=[1], y=[2], kind=[3])
ax.set_title('Daily Temperature')
ax.grid(True)
plt.show()

Drag options to blanks, or click blank then click option'

A'day'

B'temperature'

C'line'

D'scatter'

Attempts:

3 left

💡 Hint

Common Mistakes

Using kind='scatter' which changes the plot type.

Mixing up x and y columns.

Use x='day', y='temperature', and kind='line' to plot the temperature over days.

Practice

1. Why is built-in plotting in pandas useful for beginners?

easy

A. It replaces the need for data cleaning

B. It requires complex coding to create charts

C. It allows quick visualization without extra setup

D. It only works with large datasets

Solution

1. Step 1: Understand built-in plotting purpose

   Built-in plotting in pandas is designed to help users quickly visualize data without needing extra libraries or complex setup.

2. Step 2: Compare options

   It allows quick visualization without extra setup correctly states this benefit. Options A, B, and C are incorrect because built-in plotting is simple, works with any size data, and does not replace data cleaning.

3. Final Answer:

   It allows quick visualization without extra setup -> Option C

4. Quick Check:

   Built-in plotting = Quick visualization [OK]

Hint: Built-in plotting means fast charts with little code [OK]

Common Mistakes:

• Thinking it needs complex code
• Believing it only works on big data
• Confusing plotting with data cleaning

2. Which of the following is the correct syntax to plot a line chart from a pandas DataFrame df?

easy

A. df.plotchart('line')

B. df.plot.line()

C. plot(df, kind='line')

D. df.lineplot()

Solution

1. Step 1: Recall pandas plotting syntax

   The correct method to plot a line chart is using df.plot.line() or df.plot(kind='line').

2. Step 2: Evaluate options

   df.plot.line() matches the correct pandas syntax. Options B, C, and D are not valid pandas plotting methods and will cause errors.

3. Final Answer:

   df.plot.line() -> Option B

4. Quick Check:

   Correct pandas plot method = df.plot.line() [OK]

Hint: Use df.plot.line() for line charts in pandas [OK]

Common Mistakes:

• Using non-existent methods like df.plotchart
• Trying to call plot as a standalone function
• Confusing method names like lineplot

3. Given the code:

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({'A': [1, 3, 2, 4]})
df.plot(kind='bar')
plt.show()

What will this code display?

medium

A. An error because plot kind 'bar' is invalid

B. A line chart showing values 1, 3, 2, 4 for column A

C. A table printed with values 1, 3, 2, 4

D. A bar chart showing values 1, 3, 2, 4 for column A

Solution

1. Step 1: Understand the plot kind parameter

   The code uses df.plot(kind='bar'), which creates a bar chart of the data.

2. Step 2: Check the data and output

   The DataFrame has values 1, 3, 2, 4 in column 'A'. The bar chart will show these values as bars.

3. Final Answer:

   A bar chart showing values 1, 3, 2, 4 for column A -> Option D

4. Quick Check:

   kind='bar' means bar chart [OK]

Hint: kind='bar' creates bar charts in pandas plots [OK]

Common Mistakes:

• Confusing bar chart with line chart
• Expecting printed tables instead of plots
• Thinking 'bar' is an invalid kind

4. What is wrong with this code snippet?

import pandas as pd

df = pd.DataFrame({'X': [5, 2, 7]})
df.plot(kind='scatter', x='X')

medium

A. Missing y parameter for scatter plot

B. DataFrame is empty

C. Scatter plot does not exist in pandas

D. Syntax error in DataFrame creation

Solution

1. Step 1: Check scatter plot requirements

   Scatter plots need both x and y data to plot points.

2. Step 2: Analyze the code

   The code provides only x='X' but no y parameter, so it will raise an error.

3. Final Answer:

   Missing y parameter for scatter plot -> Option A

4. Quick Check:

   Scatter plot needs x and y [OK]

Hint: Scatter plots need both x and y columns [OK]

Common Mistakes:

• Forgetting y parameter in scatter plots
• Thinking scatter plots are unsupported
• Assuming DataFrame is empty

5. You have a DataFrame df with columns ['Month', 'Sales']. You want to quickly check if sales increase over months using built-in plotting. Which code snippet is best?

hard

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

B. df.plot(kind='bar')

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

D. df.plot.pie(y='Month')

Solution

1. Step 1: Identify the goal

   You want to see if sales increase over months, which is best shown by a line chart with Month on x-axis and Sales on y-axis.

2. Step 2: Evaluate options

   df.plot(x='Month', y='Sales', kind='line') uses a line plot with correct x and y, ideal for trends. df.plot(kind='bar') shows bars but no x specified, so less clear. df.plot.scatter(x='Sales', y='Month') swaps axes and uses scatter, less suitable. df.plot.pie(y='Month') uses pie chart on Month, which is not meaningful here.

3. Final Answer:

   df.plot(x='Month', y='Sales', kind='line') -> Option A

4. Quick Check:

   Line plot with x=Month, y=Sales shows trends [OK]

Hint: Use line plot with x and y for trend checks [OK]

Common Mistakes:

• Using pie charts for time series
• Swapping x and y in scatter plots
• Not specifying x and y in plots