Priyanku-AI

import pandas as pd

# Making a score list
data = {'Subject': ['Math', 'English', 'Math', 'English', 'Math'],
        'Score': [85, 90, 92, 88, 78]}
df = pd.DataFrame(data)

# Grouping by 'Subject' and finding the average score
grouped_data = df.groupby('Subject').mean()

Priyanku-AI

import seaborn as sns
import matplotlib.pyplot as plt

# Example Data (randomly generated)
data = sns.load_dataset("tips")

# Creating the Box Plot
sns.boxplot(x="day", y="total_bill", data=data, palette="Set2")
# Create a box plot of total bill amounts grouped by day, using the 'Set2' color palette

Priyanku-AI

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# Generating a synthetic correlation matrix
np.random.seed(42)
data = np.random.rand(10, 10)

# Creating the Heatmap
sns.heatmap(data, cmap="YlGnBu", annot=True, fmt=".2f", linewidths=.5)

Priyanku-AI

import matplotlib.pyplot as plt
import numpy as np

# Example Data (randomly generated)
data = np.random.randn(1000) * 30 + 50  # Creating a sample dataset with a normal distribution

# Creating the Histogram
plt.hist(data, bins=30, color='skyblue', edgecolor='black')
# Create a histogram with 30 bins, using skyblue color and black edges

Priyanku-AI

import matplotlib.pyplot as plt

# Example Data
categories = ['Category A', 'Category B', 'Category C', 'Category D']
values = [25, 30, 20, 25]

# Creating the Pie Chart
plt.pie(values, labels=categories, autopct='%1.1f%%', startangle=90, colors=['#ff9999','#66b3ff','#99ff99','#ffcc99'])
# Create a pie chart with the given values and categories
# autopct displays the percentage on each slice, startangle rotates the chart, and colors customize each slice

Priyanku-AI

import matplotlib.pyplot as plt

# Example Data
study_hours = [3, 5, 7, 2, 6, 8, 4, 5, 7, 1]
exam_scores = [60, 75, 85, 50, 70, 90, 65, 80, 88, 45]

# Creating the Scatter Plot
plt.scatter(study_hours, exam_scores, color='blue', marker='o', label='Students')
# Scatter the points on the graph with study hours on the x-axis and exam scores on the y-axis
# Customize the appearance with a blue color, circular markers, and label the data as 'Students'

Priyanku-AI

import matplotlib.pyplot as plt

# Example Data
time = [1, 2, 3, 4, 5]  # X-axis values (e.g., time)
values = [10, 12, 8, 15, 11]  # Y-axis values (e.g., some measurement)

# Creating the Line Chart
plt.plot(time, values, marker='o', linestyle='-')  # 'o' for markers, '-' for line style

# Adding Labels

Priyanku-AI

import matplotlib.pyplot as plt

# Example data
categories = ['Category A', 'Category B', 'Category C']
values = [25, 40, 30]

# Bar chart
plt.bar(categories, values, color=['skyblue', 'salmon', 'lightgreen'])
plt.title('Bar Chart for Categorical Data')
plt.xlabel('Categories')

Priyanku-AI

# Import necessary libraries
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression

# Set a seed for reproducibility
np.random.seed(42)

# Simulate data for superheroes
superheroes_data = {

Priyanku-AI

import pandas as pd
from pandas_profiling import ProfileReport
import numpy as np

# Create a toy dataset
data = {
    'Name': ['Paul', 'Pallabi', 'Peter', 'Gwen', 'Emily'],
    'Age': [24, 23, 35, 22, 28],
    'Salary': [50000, 60000, 75000, 45000, 55000],
    'Gender': ['Male', 'Female', 'Male', 'Female', 'Female'],