To customise a pie plot in matplotlib, you use the pie() function and pass specific arguments for properties like explode, autopct, and colors.

Here's how to modify the pie plot for the given forest cover data:

1. Prepare the data: Extract the 'ForestCover' values for the sizes of the wedges and 'State' names for the labels.
2. Define explode: Create a tuple or list of the same length as your data, where each element specifies the fraction of the radius by which to offset the corresponding wedge. For the first wedge (Arunachal Pradesh) it's 0.1, for the fifth (Mizoram) it's 0.2, and 0 for the others.
3. Define colors: Create a list of colour strings, one for each wedge, in the order they appear in your data.
4. Define autopct: Use a format string like '%1.1f%%' to display the percentage on each wedge, formatted to one decimal place.

import matplotlib.pyplot as plt
import pandas as pd

# Data from Table 4.10
data = {
    'State': ['Arunachal Pradesh', 'Assam', 'Manipur', 'Meghalaya', 'Mizoram', 'Nagaland', 'Tripura'],
    'GeoArea': [83743, 78438, 22327, 22429, 21081, 16579, 10486],
    'ForestCover': [67353, 27692, 17280, 17321, 19240, 13464, 8073]
}
df = pd.DataFrame(data)

# Data for the pie chart: ForestCover values and State names as labels
sizes = df['ForestCover']
labels = df['State']

# Explode the first wedge (Arunachal Pradesh) by 0.1 and the fifth wedge (Mizoram) by 0.2
explode_values = (0.1, 0, 0, 0, 0.2, 0, 0)

# Set custom wedge colours
wedge_colors = ['r', 'g', 'm', 'c', 'brown', 'pink', 'purple']

# Create the pie chart
plt.figure(figsize=(10, 7)) # Set figure size for better readability
plt.pie(sizes,
        explode=explode_values,
        labels=labels,
        colors=wedge_colors,
        autopct='%1.1f%%', # Display percentage with one decimal place
        shadow=True, # Add a shadow for visual effect
        startangle=90) # Start the first wedge at 90 degrees

plt.axis('equal') # Ensures the pie chart is drawn as a perfect circle
plt.title('Forest Cover Distribution in North-Eastern States')
plt.show()

Explanation of Key Lines:

• explode_values = (0.1, 0, 0, 0, 0.2, 0, 0): This tuple is passed to the explode parameter. It specifies that the first wedge (Arunachal Pradesh) will be offset from the center by 10% of the radius, and the fifth wedge (Mizoram) by 20%. Other wedges remain unexploded (offset by 0).
• wedge_colors = ['r', 'g', 'm', 'c', 'brown', 'pink', 'purple']: This list is passed to the colors parameter. It assigns a specific colour to each wedge in the order they appear in the sizes data. …

Pie charts are a common way to visualize how different parts contribute to a whole. Each slice represents a category's proportion of the total. While intuitive for showing "parts of a whole," they are often misused. Comparing the sizes of different slices, especially when there are many categories or when slice sizes are similar, can be difficult for the human eye. For precise comparisons, bar charts are generally more effective. However, for a clear, high-level overview of a few distinct proportions, a well-designed pie chart can be quite informative.

In this problem, we are asked to customize a pie chart to enhance its readability and highlight specific data points. We will use matplotlib to achieve this, focusing on three key properties:

1. Explode: This property allows you to "pull out" one or more slices from the center of the pie, drawing attention to them. The value for explode is a tuple or list, where each element corresponds to a slice and specifies the fraction of the radius by which that slice should be offset. A value of 0.0 means no explosion.
2. Autopct: This feature automatically displays the percentage value for each slice directly on the chart. It takes a Python format string (e.g., '%1.1f%%') or a function as an argument, which dictates how the percentage text will be formatted.
3. Colors: This allows you to assign specific colors to each wedge, improving visual distinction and aligning with any desired color scheme.

Here's the Python code to create the customized pie chart:

import matplotlib.pyplot as plt

# Data from Table 4.10 (ForestCover values)
states = [
    'Arunachal Pradesh', 'Assam', 'Manipur', 'Meghalaya',
    'Mizoram', 'Nagaland', 'Tripura'
]
forest_cover = [67353, 27692, 17280, 17321, 19240, 13464, 8073]

# Customization parameters
# Explode the first wedge (Arunachal Pradesh) by 0.1 and the fifth (Mizoram) by 0.2
explode_values = (0.1, 0.0, 0.0, 0.0, 0.2, 0.0, 0.0)

# Set custom colors for the wedges
wedge_colors = ['r', 'g', 'm', 'c', 'brown', 'pink', 'purple']

# Create the pie chart
plt.figure(figsize=(10, 8)) # Optional: Adjust figure size for better readability
plt.pie(
    forest_cover,
    explode=explode_values,
    labels=states,
    colors=wedge_colors,
    autopct='%1.1f%%', # Display percentage with one decimal place
    shadow=True,       # Optional: Add a shadow for better visual effect
    startangle=90      # Optional: Start the first wedge at 90 degrees (top)
)

# Add a title to the chart
plt.title('Forest Cover Distribution in Northeastern States')

# Ensure the pie chart is drawn as a circle (equal aspect ratio)
plt.axis('equal')

# Display the chart
plt.show()

Explanation of Key Lines

1. import matplotlib.pyplot as plt: This line imports the pyplot module from the matplotlib library, which provides a MATLAB-like interface for plotting. It's conventionally aliased as plt.
2. states = [...] and forest_cover = [...]: These lines define the labels (state names) and the numerical data (forest cover values) that will be used to create the pie chart. The forest_cover list represents the sizes of the wedges.
3. explode_values = (0.1, 0.0, 0.0, 0.0, 0.2, 0.0, 0.0): This tuple specifies the "explosion" for each wedge.
   • The first value 0.1 means the first wedge (Arunachal Pradesh) will be pulled out by 10% of the radius.
   • The fifth value 0.2 means the fifth wedge (Mizoram) will be pulled out by 20% of the radius.
   • All other 0.0 values indicate that those wedges will remain in their normal position.
4. wedge_colors = ['r', 'g', 'm', 'c', 'brown', 'pink', 'purple']: This list assigns specific colors to each wedge in the order they appear in the forest_cover data. r is red, g is green, m is magenta, c is cyan, and the others are named colors.
5. plt.pie(...): This is the core function call that generates the pie chart.
   • forest_cover: The data array whose values are used to determine the size of each wedge.
   • explode=explode_values: Applies the defined explosion effect to the wedges.
   • labels=states: Assigns the state names as labels to each wedge. These labels typically appear in a legend.
   • colors=wedge_colors: Sets the custom colors for the wedges. …