# Import the tools we need
import pandas as pd           # For working with data tables
import numpy as np            # For math and calculations
import matplotlib.pyplot as plt  # For creating charts
import seaborn as sns         # For beautiful visualizations

# Set up nice-looking plots
plt.style.use('seaborn-v0_8-darkgrid')
sns.set_palette("husl")

import pandas as pd
import matplotlib.pyplot as plt

# Create data for favorite fruits
fruits_data = {
    'Fruit': ['Apple', 'Banana', 'Orange', 'Grapes'],
    'Count': [10, 7, 5, 8]
}
df_fruits = pd.DataFrame(fruits_data)

# Display the data
print(df_fruits)
print(f"\nMost popular fruit: {df_fruits.loc[df_fruits['Count'].idxmax(), 'Fruit']}")
print(f"Total students: {df_fruits['Count'].sum()}")

# Create a bar chart
plt.figure(figsize=(10, 6))
plt.bar(df_fruits['Fruit'], df_fruits['Count'], color=['red', 'yellow', 'orange', 'purple'])
plt.xlabel('Fruit Type', fontsize=12)
plt.ylabel('Number of Students', fontsize=12)
plt.title('Favorite Fruits in Class', fontsize=14, fontweight='bold')
plt.grid(axis='y', alpha=0.3)

# Add count labels on top of bars
for i, count in enumerate(df_fruits['Count']):
    plt.text(i, count + 0.2, str(count), ha='center', fontweight='bold')

plt.show()

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Test scores data
scores = [70, 75, 80, 80, 85, 90, 95]

# Calculate summary statistics
mean_score = np.mean(scores)
median_score = np.median(scores)
std_dev = np.std(scores)
variance = np.var(scores)
min_score = np.min(scores)
max_score = np.max(scores)
range_score = max_score - min_score

print("📊 Summary Statistics:")
print(f"Mean (Average): {mean_score:.2f}")
print(f"Median (Middle): {median_score}")
print(f"Standard Deviation: {std_dev:.2f}")
print(f"Variance: {variance:.2f}")
print(f"Minimum: {min_score}")
print(f"Maximum: {max_score}")
print(f"Range: {range_score}")

# Create histogram
plt.figure(figsize=(10, 6))
plt.hist(scores, bins=5, color='skyblue', edgecolor='black', alpha=0.7)
plt.axvline(mean_score, color='red', linestyle='--', linewidth=2, label=f'Mean: {mean_score:.1f}')
plt.axvline(median_score, color='green', linestyle='--', linewidth=2, label=f'Median: {median_score}')
plt.xlabel('Test Scores', fontsize=12)
plt.ylabel('Frequency', fontsize=12)
plt.title('Distribution of Test Scores', fontsize=14, fontweight='bold')
plt.legend()
plt.grid(axis='y', alpha=0.3)
plt.show()

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Batsman's runs in different matches
runs = [23, 45, 67, 12, 89, 34, 56, 78, 45, 23, 67, 90, 12, 45, 56, 
        34, 78, 89, 23, 45, 67, 34, 56, 78, 45, 23, 12, 89, 67, 45]

# Create bins (buckets) for runs
bins = [0, 20, 40, 60, 80, 100]
labels = ['0-20', '21-40', '41-60', '61-80', '81-100']

# Categorize runs into bins
runs_binned = pd.cut(runs, bins=bins, labels=labels, include_lowest=True)

# Count frequency in each bin
frequency = runs_binned.value_counts().sort_index()

print("📊 Runs Distribution:")
print(frequency)
print(f"\n🎯 Most frequent bucket: {frequency.idxmax()} runs (appeared {frequency.max()} times)")

# Create bar chart
plt.figure(figsize=(12, 6))
bars = plt.bar(frequency.index, frequency.values, color='#2ECC71', edgecolor='black', linewidth=1.5)
plt.xlabel('Runs Scored (Buckets)', fontsize=13, fontweight='bold')
plt.ylabel('Frequency (Number of Times)', fontsize=13, fontweight='bold')
plt.title('Cricket Batsman: Runs Distribution Analysis', fontsize=15, fontweight='bold')
plt.grid(axis='y', alpha=0.3, linestyle='--')

# Add value labels on bars
for bar in bars:
    height = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2., height,
             f'{int(height)}',
             ha='center', va='bottom', fontweight='bold', fontsize=11)

plt.tight_layout()
plt.show()

# Additional statistics
print(f"\n📈 Additional Statistics:")
print(f"Total matches: {len(runs)}")
print(f"Average runs: {np.mean(runs):.2f}")
print(f"Highest score: {np.max(runs)}")
print(f"Lowest score: {np.min(runs)}")
print(f"Median score: {np.median(runs):.2f}")

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

# Create dataset for video game scores by age group
data = {
    'Age_Group': ['8-10']*10 + ['11-13']*10 + ['14-16']*10,
    'Score': [72, 78, 75, 70, 80, 73, 76, 74, 71, 81,  # 8-10 years
              88, 82, 87, 85, 90, 83, 86, 84, 81, 89,  # 11-13 years
              95, 90, 93, 92, 96, 91, 94, 89, 97, 88]  # 14-16 years
}
df_games = pd.DataFrame(data)

# Calculate statistics for each group
group_stats = df_games.groupby('Age_Group')['Score'].agg(['mean', 'median', 'min', 'max', 'std'])
print("📊 Statistics by Age Group:")
print(group_stats)

# Create grouped bar chart
plt.figure(figsize=(12, 6))
age_groups = df_games['Age_Group'].unique()
means = [df_games[df_games['Age_Group']==ag]['Score'].mean() for ag in age_groups]

bars = plt.bar(age_groups, means, color=['#9B59B6', '#3498DB', '#2ECC71'], 
               edgecolor='black', linewidth=2)
plt.xlabel('Age Group', fontsize=13, fontweight='bold')
plt.ylabel('Average Score', fontsize=13, fontweight='bold')
plt.title('Video Game Scores by Age Group', fontsize=15, fontweight='bold')
plt.ylim(0, 100)
plt.grid(axis='y', alpha=0.3)

# Add value labels
for bar, mean in zip(bars, means):
    plt.text(bar.get_x() + bar.get_width()/2., bar.get_height() + 1,
             f'{mean:.1f}', ha='center', fontweight='bold', fontsize=12)

plt.tight_layout()
plt.show()

# Create box plot for detailed comparison
plt.figure(figsize=(12, 6))
sns.boxplot(data=df_games, x='Age_Group', y='Score', palette='Set2')
plt.xlabel('Age Group', fontsize=13, fontweight='bold')
plt.ylabel('Score', fontsize=13, fontweight='bold')
plt.title('Score Distribution by Age Group (Box Plot)', fontsize=15, fontweight='bold')
plt.grid(axis='y', alpha=0.3)
plt.tight_layout()
plt.show()

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

# Example 1: Positive Correlation - Study Hours vs Test Scores
study_hours = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
test_scores = [45, 52, 58, 65, 70, 75, 82, 88, 92, 95]

# Calculate correlation
correlation = np.corrcoef(study_hours, test_scores)[0, 1]
print(f"📊 Correlation between Study Hours and Test Scores: {correlation:.3f}")

# Create scatter plot
fig, axes = plt.subplots(1, 3, figsize=(18, 5))

# Plot 1: Positive Correlation
axes[0].scatter(study_hours, test_scores, color='#2ECC71', s=100, edgecolor='black', linewidth=1.5)
axes[0].plot(np.unique(study_hours), 
             np.poly1d(np.polyfit(study_hours, test_scores, 1))(np.unique(study_hours)),
             color='red', linewidth=2, linestyle='--', label='Trend Line')
axes[0].set_xlabel('Study Hours', fontsize=12, fontweight='bold')
axes[0].set_ylabel('Test Scores', fontsize=12, fontweight='bold')
axes[0].set_title(f'Positive Correlation\nr = {correlation:.3f}', fontsize=13, fontweight='bold')
axes[0].grid(alpha=0.3)
axes[0].legend()

# Example 2: Negative Correlation - Gaming Hours vs Sleep Hours
gaming_hours = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
sleep_hours = [9, 8.5, 8, 7.5, 7, 6.5, 6, 5.5, 5, 4.5]
correlation_neg = np.corrcoef(gaming_hours, sleep_hours)[0, 1]

axes[1].scatter(gaming_hours, sleep_hours, color='#E67E22', s=100, edgecolor='black', linewidth=1.5)
axes[1].plot(np.unique(gaming_hours),
             np.poly1d(np.polyfit(gaming_hours, sleep_hours, 1))(np.unique(gaming_hours)),
             color='red', linewidth=2, linestyle='--', label='Trend Line')
axes[1].set_xlabel('Gaming Hours', fontsize=12, fontweight='bold')
axes[1].set_ylabel('Sleep Hours', fontsize=12, fontweight='bold')
axes[1].set_title(f'Negative Correlation\nr = {correlation_neg:.3f}', fontsize=13, fontweight='bold')
axes[1].grid(alpha=0.3)
axes[1].legend()

# Example 3: No Correlation - Shoe Size vs Math Score
shoe_size = [6, 7, 8, 9, 10, 6.5, 7.5, 8.5, 9.5, 10.5]
math_score = [75, 82, 68, 90, 73, 88, 65, 78, 85, 70]
correlation_zero = np.corrcoef(shoe_size, math_score)[0, 1]

axes[2].scatter(shoe_size, math_score, color='#3498DB', s=100, edgecolor='black', linewidth=1.5)
axes[2].set_xlabel('Shoe Size', fontsize=12, fontweight='bold')
axes[2].set_ylabel('Math Score', fontsize=12, fontweight='bold')
axes[2].set_title(f'No Correlation\nr = {correlation_zero:.3f}', fontsize=13, fontweight='bold')
axes[2].grid(alpha=0.3)

plt.tight_layout()
plt.show()

# Create correlation heatmap for multiple variables
data_multi = {
    'Study_Hours': [2, 4, 6, 8, 3, 5, 7, 9, 1, 10],
    'Test_Score': [55, 68, 78, 88, 62, 72, 82, 92, 48, 95],
    'Sleep_Hours': [7, 7.5, 8, 8.5, 7, 7.5, 8, 8.5, 6.5, 9],
    'Gaming_Hours': [5, 3, 2, 1, 4, 3, 2, 1, 6, 0.5]
}
df_multi = pd.DataFrame(data_multi)

# Calculate correlation matrix
corr_matrix = df_multi.corr()
print("\n📊 Correlation Matrix:")
print(corr_matrix)

# Plot heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', center=0, 
            square=True, linewidths=2, cbar_kws={"shrink": 0.8},
            fmt='.3f', vmin=-1, vmax=1)
plt.title('Correlation Heatmap', fontsize=15, fontweight='bold', pad=20)
plt.tight_layout()
plt.show()

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

# Create gaming habits data
data = {
    'Gender': ['Boys']*28 + ['Girls']*32,
    'Frequency': ['Every Day']*15 + ['Once a Week']*8 + ['Once a Month']*3 + ['Never']*2 +
                 ['Every Day']*8 + ['Once a Week']*12 + ['Once a Month']*5 + ['Never']*7
}
df_gaming = pd.DataFrame(data)

# Create cross-tabulation (contingency table)
cross_tab = pd.crosstab(df_gaming['Gender'], df_gaming['Frequency'])
print("📊 Cross-Tabulation Table:")
print(cross_tab)
print()

# Calculate percentages
cross_tab_pct = pd.crosstab(df_gaming['Gender'], df_gaming['Frequency'], normalize='index') * 100
print("📊 Percentage Distribution:")
print(cross_tab_pct.round(2))

# Create grouped bar chart
cross_tab.plot(kind='bar', figsize=(12, 6), color=['#E74C3C', '#3498DB', '#2ECC71', '#F39C12'],
               edgecolor='black', linewidth=1.5)
plt.xlabel('Gender', fontsize=13, fontweight='bold')
plt.ylabel('Count', fontsize=13, fontweight='bold')
plt.title('Gaming Frequency by Gender', fontsize=15, fontweight='bold')
plt.legend(title='Frequency', title_fontsize=12, fontsize=11)
plt.xticks(rotation=0)
plt.grid(axis='y', alpha=0.3)
plt.tight_layout()
plt.show()

# Create stacked bar chart
cross_tab_pct.plot(kind='bar', stacked=True, figsize=(12, 6),
                   color=['#E74C3C', '#3498DB', '#2ECC71', '#F39C12'],
                   edgecolor='black', linewidth=1.5)
plt.xlabel('Gender', fontsize=13, fontweight='bold')
plt.ylabel('Percentage (%)', fontsize=13, fontweight='bold')
plt.title('Gaming Frequency Distribution by Gender (%)', fontsize=15, fontweight='bold')
plt.legend(title='Frequency', title_fontsize=12, fontsize=11, bbox_to_anchor=(1.05, 1))
plt.xticks(rotation=0)
plt.tight_layout()
plt.show()

# Create heatmap
plt.figure(figsize=(10, 6))
sns.heatmap(cross_tab, annot=True, fmt='d', cmap='YlOrRd', cbar_kws={'label': 'Count'},
            linewidths=2, linecolor='black')
plt.xlabel('Gaming Frequency', fontsize=13, fontweight='bold')
plt.ylabel('Gender', fontsize=13, fontweight='bold')
plt.title('Gaming Habits Heatmap', fontsize=15, fontweight='bold')
plt.tight_layout()
plt.show()

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Create sample data
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve', 'Frank', 'Grace', 'Henry'],
    'Age': [10, 12, 15, 13, 11, 14, 16, 9],
    'Height_cm': [140, 155, 170, 160, 145, 165, 175, 135],
    'Weight_kg': [35, 45, 60, 50, 38, 55, 65, 30],
    'Test1': [85, 78, 92, 88, 75, 90, 95, 70],
    'Test2': [88, 82, 90, 85, 78, 88, 92, 75],
    'Test3': [90, 80, 95, 90, 80, 92, 98, 72]
}
df = pd.DataFrame(data)

# 1. Type-Driven: Create Age Groups
df['Age_Group'] = pd.cut(df['Age'], bins=[0, 10, 13, 18], 
                         labels=['Children (≤10)', 'Tweens (11-13)', 'Teens (14+)'])

# 2. Data-Driven: Calculate BMI
df['Height_m'] = df['Height_cm'] / 100  # Convert cm to meters
df['BMI'] = df['Weight_kg'] / (df['Height_m'] ** 2)
df['BMI_Category'] = pd.cut(df['BMI'], 
                             bins=[0, 18.5, 25, 30, 100],
                             labels=['Underweight', 'Normal', 'Overweight', 'Obese'])

# 3. Data-Driven: Calculate Average Test Score
df['Average_Score'] = (df['Test1'] + df['Test2'] + df['Test3']) / 3

# 4. Business-Driven: Performance Category
df['Performance'] = pd.cut(df['Average_Score'],
                            bins=[0, 70, 80, 90, 100],
                            labels=['Needs Improvement', 'Good', 'Very Good', 'Excellent'])

print("📊 Original Data with Derived Metrics:")
print(df[['Name', 'Age', 'Age_Group', 'BMI', 'BMI_Category', 'Average_Score', 'Performance']])

# Visualize BMI distribution
plt.figure(figsize=(14, 6))

# Plot 1: BMI by Person
plt.subplot(1, 2, 1)
colors = ['#E74C3C' if x < 18.5 else '#2ECC71' if x < 25 else '#E67E22' 
          for x in df['BMI']]
bars = plt.bar(df['Name'], df['BMI'], color=colors, edgecolor='black', linewidth=1.5)
plt.axhline(y=18.5, color='red', linestyle='--', label='Underweight threshold')
plt.axhline(y=25, color='orange', linestyle='--', label='Overweight threshold')
plt.xlabel('Name', fontsize=12, fontweight='bold')
plt.ylabel('BMI', fontsize=12, fontweight='bold')
plt.title('BMI by Person', fontsize=14, fontweight='bold')
plt.xticks(rotation=45)
plt.legend()
plt.grid(axis='y', alpha=0.3)

# Plot 2: Average Score by Age Group
plt.subplot(1, 2, 2)
age_group_avg = df.groupby('Age_Group')['Average_Score'].mean()
bars = plt.bar(age_group_avg.index, age_group_avg.values, 
               color=['#9B59B6', '#3498DB', '#2ECC71'], 
               edgecolor='black', linewidth=1.5)
plt.xlabel('Age Group', fontsize=12, fontweight='bold')
plt.ylabel('Average Test Score', fontsize=12, fontweight='bold')
plt.title('Performance by Age Group', fontsize=14, fontweight='bold')
plt.ylim(0, 100)
plt.grid(axis='y', alpha=0.3)

# Add value labels
for bar in bars:
    height = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2., height + 1,
             f'{height:.1f}', ha='center', fontweight='bold')

plt.tight_layout()
plt.show()

# Summary statistics
print("\n📈 Summary by Age Group:")
print(df.groupby('Age_Group')[['BMI', 'Average_Score']].mean().round(2))

print("\n📊 Performance Distribution:")
print(df['Performance'].value_counts())

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Player's runs in 25 matches
runs = [45, 23, 67, 89, 12, 56, 78, 34, 90, 45, 67, 23, 89, 56, 78, 
        34, 45, 67, 89, 23, 56, 78, 45, 67, 90]

# Tasks:
# 1. Calculate mean, median, mode
# 2. Find the range
# 3. Create bins: 0-20, 21-40, 41-60, 61-80, 81-100
# 4. Plot a histogram showing frequency in each bin
# 5. Answer: In which bucket did the player score most often?

# Your code here:

# Student scores in different subjects
data = {
    'Student': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'],
    'Math': [85, 78, 92, 88, 75, 90, 95, 70, 82, 88],
    'Science': [88, 82, 90, 85, 78, 88, 92, 75, 85, 90],
    'English': [90, 80, 95, 90, 80, 92, 98, 72, 88, 92]
}

# Tasks:
# 1. Calculate average score for each subject
# 2. Find which subject has highest average
# 3. Create a grouped bar chart
# 4. Calculate correlation between Math and Science
# 5. Create a scatter plot showing Math vs Science scores

# Sales data
sales_data = {
    'Region': ['North']*6 + ['South']*6 + ['East']*6 + ['West']*6,
    'Product': ['A', 'B', 'C']*8,
    'Sales': [120, 150, 180, 130, 160, 190, 140, 170, 200, 125, 155, 185,
              135, 165, 195, 145, 175, 205, 128, 158, 188, 138, 168, 198]
}

# Tasks:
# 1. Create a cross-tabulation of Region vs Product
# 2. Find which region has highest total sales
# 3. Find which product sells best overall
# 4. Create a heatmap showing sales by region and product
# 5. Calculate average sales by region

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Your data here
my_data = {
    'Variable1': [],  # Add your data
    'Variable2': []   # Add your data if applicable
}

df = pd.DataFrame(my_data)

# Step 1: Basic statistics
print("Summary Statistics:")
print(df.describe())

# Step 2: Visualizations
# Add your plots here

# Step 3: Analysis
# Add your analysis here

# Step 4: Insights
print("\n📊 My Findings:")
# Write what you discovered!