............PLOT1

import matplotlib.pyplot as plt

Take input from the user

x = list(map(float, input("Enter x values separated by spaces: ").split()))
y = list(map(float, input("Enter y values separated by spaces: ").split()))

Check if x and y are of the same length

if len(x) != len(y):
print("Error: x and y must have the same number of elements.")
else:
# Create the line plot
plt.plot(x, y)

# Set the title and axis labels
plt.title("Line Plot from User Input")
plt.xlabel("X Values")
plt.ylabel("Y Values")

# Display the plot
plt.show()

.............BAR PLOT

import matplotlib.pyplot as plt

Take input from the user

categories = input("Enter category labels separated by spaces: ").split()
values = list(map(float, input("Enter corresponding values separated by spaces: ").split()))

Optional: Define colors (reuse or generate dynamically)

colors = ['red', 'green', 'blue', 'orange', 'purple', 'cyan', 'magenta', 'yellow']
colors = colors[:len(categories)] # Trim to match number of categories

Check if input lengths match

if len(categories) != len(values):
print("Error: Number of categories and values must match.")
else:
# Create the bar plot
plt.bar(categories, values, color=colors)

# Set the title and axis labels
plt.title("Bar Plot from User Input")
plt.xlabel("Categories")
plt.ylabel("Values")

# Display the plot
plt.show()

....................SCATTER
import matplotlib.pyplot as plt

Get number of points from the user

n = int(input("Enter the number of data points: "))

Initialize empty lists

x = []
y = []
sizes = []
colors = []

Collect data from the user

for i in range(n):
print(f"\nData Point {i+1}:")
x_val = float(input(" Enter x value: "))
y_val = float(input(" Enter y value: "))
size = int(input(" Enter size of the point (e.g., 50): "))
color = input(" Enter color of the point (e.g., red, #00FF00): ")

x.append(x_val)
y.append(y_val)
sizes.append(size)
colors.append(color)

Create the scatter plot

plt.scatter(x, y, s=sizes, c=colors, alpha=0.7, edgecolors='black')

Set the title and axis labels

plt.title("Scatter Plot Example")
plt.xlabel("X Values")
plt.ylabel("Y Values")

Display the plot

plt.show()

.......HISTOGRAM
import matplotlib.pyplot as plt

def plot_histogram():
try:
data = list(map(float, input("Enter numeric values separated by spaces: ").split()))
bins = int(input("Enter number of bins: "))
plt.hist(data, bins=bins, color='skyblue', edgecolor='black')
plt.title("Histogram Example")
plt.xlabel("Values")
plt.ylabel("Frequency")
plt.grid(True)
plt.show()
except ValueError:
print("Invalid input. Please enter numeric values.")

def plot_boxplot():
try:
n = int(input("Enter the number of datasets: "))
datasets = []
for i in range(n):
data = list(map(float, input(f"Enter numeric values for dataset {i+1}, separated by spaces: ").split()))
datasets.append(data)
plt.boxplot(datasets)
plt.title("Boxplot Example")
plt.grid(True)
plt.show()
except ValueError:
print("Invalid input. Please enter numeric values.")

def plot_piechart():
try:
categories = input("Enter category names separated by commas: ").split(',')
values = list(map(float, input("Enter corresponding numeric values separated by spaces: ").split()))

    if len(categories) != len(values):
        print("The number of categories and values must be the same.")
        return

    explode = [0.1 if i == 0 else 0 for i in range(len(categories))]  # highlight the first slice
    plt.pie(values, labels=categories, explode=explode, autopct='%1.1f%%', startangle=140)
    plt.title("Pie Chart Example")
    plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
    plt.show()
except ValueError:
    print("Invalid input. Please ensure numeric values are entered correctly.")

def main():
while True:
print("\n--- Graph Generator Menu ---")
print("1. Histogram")
print("2. Boxplot")
print("3. Pie Chart")
print("4. Exit")
choice = input("Enter your choice (1-4): ")

    if choice == '1':
        plot_histogram()
    elif choice == '2':
        plot_boxplot()
    elif choice == '3':
        plot_piechart()
    elif choice == '4':
        print("Exiting program. Goodbye!")
        break
    else:
        print("Invalid choice. Please enter a number between 1 and 4.")

if name == "main":
main()

...............UNIVARIATE
import statistics
import matplotlib.pyplot as plt
import seaborn as sns

Input: list of numeric values from the user

data_input = input("Enter numbers separated by commas: ")
data = list(map(float, data_input.split(',')))

Measures of Central Tendency

mean = statistics.mean(data)
median = statistics.median(data)

try:
mode = statistics.mode(data)
except statistics.StatisticsError:
mode = "No unique mode"

Measures of Dispersion

variance = statistics.variance(data)
std_dev = statistics.stdev(data)
data_range = max(data) - min(data)

Display results

print("\n--- Univariate Analysis ---")
print(f"Mean: {mean}")
print(f"Median: {median}")
print(f"Mode: {mode}")
print(f"Variance: {variance}")
print(f"Standard Deviation: {std_dev}")
print(f"Range: {data_range} (Max: {max(data)} - Min: {min(data)})")

Histogram

plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)
plt.hist(data, bins=10, color='skyblue', edgecolor='black')
plt.title('Histogram')
plt.xlabel('Value')
plt.ylabel('Frequency')

Boxplot

plt.subplot(1, 2, 2)
sns.boxplot(data, color='lightgreen')
plt.title('Boxplot')

plt.tight_layout()
plt.show()

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

Sample dataset (can be replaced by user input or CSV import)

data = {
'X': ['A', 'A', 'A', 'B', 'B', 'C', 'C', 'C', 'C', 'B', 'A', 'C', 'B', 'A', 'B', 'B'],
'Y': ['Yes', 'No', 'Yes', 'No', 'Yes', 'No', 'Yes', 'Yes', 'No', 'Yes', 'No', 'Yes', 'No', 'Yes', 'No', 'Yes']
}

Convert to DataFrame

df = pd.DataFrame(data)

------------------ Marginal Frequencies ------------------

Marginal frequency of X

marginal_x = df['X'].value_counts().sort_index()

Marginal frequency of Y

marginal_y = df['Y'].value_counts().sort_index()

Plot marginal frequencies

plt.figure(figsize=(12, 5))

plt.subplot(1, 2, 1)
marginal_x.plot(kind='bar', color='skyblue')
plt.title('Marginal Frequency of X')
plt.xlabel('X')
plt.ylabel('Frequency')

plt.subplot(1, 2, 2)
marginal_y.plot(kind='bar', color='salmon')
plt.title('Marginal Frequency of Y')
plt.xlabel('Y')
plt.ylabel('Frequency')

plt.tight_layout()
plt.show()

------------------ Conditional Frequencies ------------------

Conditional frequency of Y given X

conditional_freq = pd.crosstab(df['X'], df['Y'], normalize='index')

Plot grouped bar chart

conditional_freq.plot(kind='bar', stacked=False, figsize=(8, 6), colormap='Set2')
plt.title('Conditional Frequency of Y given X (Grouped Bar)')
plt.ylabel('Proportion')
plt.xlabel('X')
plt.legend(title='Y')
plt.tight_layout()
plt.show()

Plot heatmap of conditional frequencies

plt.figure(figsize=(6, 4))
sns.heatmap(conditional_freq, annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Conditional Frequency Heatmap (Y | X)')
plt.ylabel('X')
plt.xlabel('Y')
plt.tight_layout()
plt.show()

...................CORRELATION
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import linregress, pearsonr
from sklearn.datasets import load_iris

Load Iris dataset

iris = load_iris()
data = iris.data
feature_names = iris.feature_names
target = iris.target

Display feature options

print("Available Features:")
for i, name in enumerate(feature_names):
print(f"{i}: {name}")

Take user input for X and Y axis features

try:
x_index = int(input("Enter the index (0-3) for the X-axis feature: "))
y_index = int(input("Enter the index (0-3) for the Y-axis feature: "))

if x_index == y_index:
    raise ValueError("X and Y features must be different.")

if not (0 <= x_index <= 3) or not (0 <= y_index <= 3):
    raise ValueError("Index must be between 0 and 3.")

except ValueError as e:
print(f"Invalid input: {e}")
exit()

Extract data for selected features

x = data[:, x_index]
y = data[:, y_index]
x_label = feature_names[x_index]
y_label = feature_names[y_index]

Calculate correlation coefficient

corr_coeff, _ = pearsonr(x, y)

Perform linear regression for line of best fit

slope, intercept, r_value, p_value, std_err = linregress(x, y)
line = slope * x + intercept

Create scatter plot

plt.figure(figsize=(10, 6))
scatter = plt.scatter(x, y, c=target, s=80, cmap='plasma', alpha=0.7, edgecolors='k', label='Data Points')
plt.plot(x, line, color='red', linewidth=2, label='Line of Best Fit')

Labels and title

plt.title(f'Scatter Plot: {x_label} vs {y_label}', fontsize=14)
plt.xlabel(f'{x_label} (cm)', fontsize=12)
plt.ylabel(f'{y_label} (cm)', fontsize=12)

Show correlation coefficient

plt.text(min(x)+0.2, max(y)-0.1, f'Correlation Coefficient: {corr_coeff:.2f}', fontsize=12, color='blue')

Legend and display

plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

Interpretation

print("\nINTERPRETATION:")
if corr_coeff > 0:
print("There is a POSITIVE CORRELATION between the selected features.")
print("As the X feature increases, the Y feature tends to increase.")
elif corr_coeff < 0:
print("There is a NEGATIVE CORRELATION between the selected features.")
print("As the X feature increases, the Y feature tends to decrease.")
else:
print("There is NO CORRELATION between the selected features.")