Data_Visualization (Basic).md

1. Box plot for the Revs Per Mile for the Audi, Hyundai, Suzuki, and Toyota car manufacturers.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load data
df = pd.read_csv('Cars93.csv')
df1 = df[df['Manufacturer'].isin(['Audi', 'Hyundai', 'Suzuki', 'Toyota'])]

# Create a boxplot with filled colors
plt.figure(figsize=(12, 6))
box = df1.boxplot(column='Rev.per.mile', by='Manufacturer', patch_artist=True)

plt.title('Boxplot of Rev Per Mile by Manufacturer')
plt.suptitle('')  # Remove the default title to avoid overlap
plt.xlabel('Manufacturer')
plt.ylabel('Rev.per.mile')
plt.show()

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The boxplot indicates that Toyota achieves the highest revolutions per mile.

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2. Histograms of MPG in the city and MPG on the highway.

# Create histograms
plt.figure(figsize=(12, 6))
# Create histograms on the same axis
plt.figure(figsize=(12, 6))

plt.hist(df['MPG.highway'], bins=10, color='blue', alpha=0.7, label='MPG.highway')
plt.hist(df['MPG.city'], bins=10, color='orange', alpha=0.7, label='MPG.city')

plt.title('Histograms of MPG.highway and MPG.city')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.legend()

plt.show()

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The histograms indicate that MPG Highway (blue) typically demonstrates greater fuel efficiency compared to MPG City (orange).

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3. Line plot of Wheelbase vs Turning Circle

# Create a line plot
df = df.sort_values('Wheelbase')
plt.figure(figsize=(10, 6))
plt.plot(df['Wheelbase'], df['Turn.circle'], marker='o', linestyle='-')
plt.title('Turn.circle vs Wheelbase')
plt.xlabel('Wheelbase')
plt.ylabel('Turn.circle')
plt.grid(True)
plt.show()

The line plot illustrates that the turning circle is directly related to the wheelbase.

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4. Bar Chart of average horsepower for different car types.

# Load your data
df = pd.read_csv('Cars93.csv')

# Group by car type and calculate the average horsepower
average_horsepower = df.groupby('Type')['Horsepower'].mean()

# Create a bar chart
plt.figure(figsize=(12, 6))
bars = plt.bar(average_horsepower.index, average_horsepower, color='skyblue')

# Add values on the bars
for bar in bars:
    yval = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2, yval, round(yval, 2), ha='center', va='bottom')

plt.title('Average Horsepower by Car Type')
plt.xlabel('Car Type')
plt.ylabel('Average Horsepower')
plt.xticks(rotation=45)
plt.grid(axis='y', linestyle='--', linewidth=0.7)
plt.show()

The bar chart shows large car type has the highest average horsepower (179.45).