# Setup: imports and sample data creation
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Display settings
pd.options.display.max_columns = 50

# Create a reproducible random dataset for examples
np.random.seed(0)
dates = pd.date_range('2024-01-01', periods=20)

df_time = pd.DataFrame({
    'date': dates,
    'sales_A': np.random.randint(50, 200, size=len(dates)),
    'sales_B': np.random.randint(40, 180, size=len(dates))
})

# Small "tips-like" dataset (for seaborn categorical examples)
tips = pd.DataFrame({
    # Gaussian distribution (uses Mean and Standard Deviation)
    'total_bill': np.random.normal(20, 8, 200).clip(3, 60), # Mean (μ) = 20, Standard Deviation (σ) = 8, 3 = minimum allowed value, 60 = maximum allowed value
    'tip': np.random.normal(3, 1, 200).clip(0, 12),
    'size': np.random.randint(1,6,200),
    'sex': np.random.choice(['Male','Female'], 200),
    'day': np.random.choice(['Thur','Fri','Sat','Sun'],200),
    'time': np.random.choice(['Lunch','Dinner'],200)
})

# Small "iris-like" dataset for pairplots and regression
iris_like = pd.DataFrame({
    'sepal_length': np.random.normal(5.8, 0.8, 150),
    'sepal_width': np.random.normal(3.0, 0.4, 150),
    'petal_length': np.random.normal(3.7, 1.5, 150),
    'petal_width': np.random.normal(1.2, 0.5, 150),
    'species': np.random.choice(['setosa','versicolor','virginica'], 150)
})

print("df_time: " + str(len(df_time)))
print("tips: " + str(len(tips)))
print("iris_like: " + str(len(iris_like)))

print('\nDatasets created: df_time (time-series), tips (categorical), iris_like (pair/regression)')
print(df_time.head())
print()
print(tips.head())
print()
print(iris_like.head())

# Basic line plot with matplotlib: figures and axes
fig, ax = plt.subplots(figsize=(8,4))
ax.plot(df_time['date'], df_time['sales_A'], label='Sales A', marker='o')
ax.plot(df_time['date'], df_time['sales_B'], label='Sales B', marker='s')
ax.set_title('Sales over time (basic)')
ax.set_xlabel('Date')
ax.set_ylabel('Sales')
ax.legend()
plt.tight_layout()
plt.show()

# Subplots: multiple axes
fig, axes = plt.subplots(2, 1, figsize=(8,6), sharex=True) #Makes both subplots share the same X-axis (same scale and ticks).
axes[0].plot(df_time['date'], df_time['sales_A'], label='Sales A', color='tab:blue')
axes[0].set_title('Sales A')
axes[1].plot(df_time['date'], df_time['sales_B'], label='Sales B', color='tab:orange')
axes[1].set_title('Sales B')
for ax in axes:
    ax.grid(True)
plt.tight_layout()
plt.show()

# Customizing style, markers, linewidth, font sizes
plt.style.use('seaborn-v0_8-darkgrid')
fig, ax = plt.subplots(figsize=(8,4))
ax.plot(df_time['date'], df_time['sales_A'], marker='o', linestyle='--', linewidth=2, label='A')
ax.plot(df_time['date'], df_time['sales_B'], marker='d', linestyle='-', linewidth=1.5, label='B')
ax.set_title('Customized sales plot', fontsize=14)
ax.set_xlabel('Date', fontsize=12)
ax.set_ylabel('Sales', fontsize=12)
ax.tick_params(axis='x', rotation=30)
ax.legend(fontsize=10)
plt.tight_layout()
plt.show()

# Histogram of total_bill (tips-like data)
plt.figure(figsize=(7,4))
plt.hist(tips['total_bill'], bins=20, edgecolor='black') #'total_bill', check its distribution
plt.title('Histogram of total_bill')
plt.xlabel('total_bill')
plt.ylabel('Frequency')
plt.show()

# Boxplot to compare distributions
plt.figure(figsize=(6,4))
plt.boxplot([tips['total_bill'], tips['tip']], labels=['total_bill','tip'])
plt.title('Boxplot: total_bill vs tip')
plt.ylabel('Value')
plt.show()

import pandas as pd

# Example data
data = {'Salary': [25, 30, 35, 40, 45, 50, 55, 100, 120, 180]}
df = pd.DataFrame(data)

# Calculate Q1 and Q3
Q1 = df['Salary'].quantile(0.25)
Q3 = df['Salary'].quantile(0.75)
IQR = Q3 - Q1

# Define bounds
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR

print("lower_bound: ", lower_bound)
print("upper_bound: ", upper_bound)

# Identify outliers
outliers = df[(df['Salary'] < lower_bound) | (df['Salary'] > upper_bound)]

print("Q1:", Q1)
print("Q3:", Q3)
print("IQR:", IQR)
print("Lower Bound:", lower_bound)
print("Upper Bound:", upper_bound)
print("\nOutliers:\n", outliers)

# Scatter plot: tip vs total_bill
plt.figure(figsize=(6,4))
plt.scatter(tips['total_bill'], tips['tip'])
plt.title('Scatter: tip vs total_bill')
plt.xlabel('total_bill')
plt.ylabel('tip')
plt.show()

# Heatmap with matplotlib (correlation/confusion matrix)
corr = df_time[['sales_A','sales_B']].corr()
fig, ax = plt.subplots(figsize=(4,3))
cax = ax.imshow(corr, interpolation='nearest', cmap='coolwarm') #'bilinear','nearest'
ax.set_xticks([0,1]); ax.set_yticks([0,1])
ax.set_xticklabels(['sales_A','sales_B']); ax.set_yticklabels(['sales_A','sales_B'])
fig.colorbar(cax)
ax.set_title('Correlation matrix (matplotlib)')
plt.tight_layout()
plt.show()

# Time-series: rolling mean example
ts = df_time.set_index('date').sort_index()
ts['sales_A_7d'] = ts['sales_A'].rolling(window=3, min_periods=1).mean()
fig, ax = plt.subplots(figsize=(8,4))
ax.plot(ts.index, ts['sales_A'], label='Sales A', alpha=.5)
ax.plot(ts.index, ts['sales_A_7d'], label='3-point Rolling Mean', color='red')
ax.set_title('Time-series with rolling mean')
ax.legend()
plt.tight_layout()
plt.show()