📘 Day 26: Practical Statistics for Business Analysis

"""
Day 26: Solutions to Exercises
"""

from pathlib import Path

import pandas as pd
from scipy.stats import ttest_ind

# --- Exercise 1: Descriptive Statistics of Sales ---
print("--- Solution to Exercise 1 ---")
resource_dir = Path(__file__).resolve().parent
data_path = resource_dir / "sales_data.csv"

try:
    df = pd.read_csv(data_path)
    df.dropna(inplace=True)  # Clean the data first

    revenue = df["Revenue"]

    print(f"Mean Revenue: ${revenue.mean():,.2f}")
    print(f"Median Revenue: ${revenue.median():,.2f}")
    print(f"Standard Deviation of Revenue: ${revenue.std():,.2f}")
    print(f"Minimum Revenue: ${revenue.min():,.2f}")
    print(f"Maximum Revenue: ${revenue.max():,.2f}")

except FileNotFoundError:
    print("Error: sales_data.csv not found. Keep the CSV beside this script.")
    df = pd.DataFrame()
print("-" * 20)


# --- Exercise 2: Correlation Analysis ---
print("--- Solution to Exercise 2 ---")
if not df.empty:
    # Select only the numerical columns
    numerical_cols = df[["Units Sold", "Price", "Revenue"]]

    # Calculate the correlation matrix
    correlation_matrix = numerical_cols.corr()

    print("Correlation Matrix:")
    print(correlation_matrix)
    print(
        "\nAnswer: 'Units Sold' and 'Revenue' have the strongest positive correlation (0.93)."
    )
else:
    print("DataFrame not available for this exercise.")
print("-" * 20)


# --- Exercise 3: A/B Test Analysis (T-Test) ---
print("--- Solution to Exercise 3 ---")
group_a_durations = [10.5, 12.1, 11.8, 13.0, 12.5]
group_b_durations = [12.8, 13.5, 13.2, 14.0, 13.8]

print(f"Group A Durations: {group_a_durations}")
print(f"Group B Durations: {group_b_durations}")

# Perform the t-test
t_stat, p_value = ttest_ind(group_a_durations, group_b_durations)

print(f"\nP-value: {p_value:.4f}")

# Conclusion based on the p-value
if p_value < 0.05:  # pyright: ignore[reportOperatorIssue]
    print(
        "Conclusion: The result is statistically significant. The two headlines likely have different effects on session duration."
    )
else:
    print(
        "Conclusion: The result is not statistically significant. We cannot conclude there is a difference between the headlines."
    )
print("-" * 20)

"""Day 26: Practical Statistics in Python.

This module provides helper functions for loading sales data, generating
summary statistics, computing correlations, and running a simple A/B test.

All side effects (such as printing to the console) are encapsulated in the
``main`` function so that the individual helpers are easy to import and test.
"""

from __future__ import annotations

from pathlib import Path
from typing import Iterable, Mapping, MutableMapping

import pandas as pd
import plotly.graph_objects as go
from pandas import DataFrame, Series
from scipy.stats import ttest_ind


def load_sales_data(csv_path: Path | str | None = None) -> DataFrame:
    """Load and clean the sales CSV file."""

    resource_dir = Path(__file__).resolve().parent
    path = Path(csv_path) if csv_path is not None else resource_dir / "sales_data.csv"

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        return pd.DataFrame()

    return df.dropna(axis=0, how="any")


def summarize_revenue(df: DataFrame) -> Mapping[str, float | Series | DataFrame]:
    """Return descriptive statistics for the ``Revenue`` column."""

    if "Revenue" not in df:
        raise KeyError("DataFrame must contain a 'Revenue' column")

    revenue = df["Revenue"]
    summary: MutableMapping[str, float | Series | DataFrame] = {
        "mean": float(revenue.mean()),
        "median": float(revenue.median()),
        "std": float(revenue.std()),
        "min": float(revenue.min()),
        "max": float(revenue.max()),
        "describe": df.describe(),
    }
    return summary


def compute_correlations(df: DataFrame) -> DataFrame:
    """Return the correlation matrix for the key numeric columns."""

    columns = [col for col in ("Units Sold", "Price", "Revenue") if col in df.columns]
    if len(columns) < 2:
        raise ValueError(
            "At least two of 'Units Sold', 'Price', or 'Revenue' must be present"
        )

    return df[columns].corr()


def build_revenue_distribution_chart(df: DataFrame) -> go.Figure:
    """Create a histogram visualising the distribution of the ``Revenue`` column."""

    if "Revenue" not in df:
        raise KeyError("DataFrame must contain a 'Revenue' column")

    revenue = df["Revenue"].dropna()
    figure = go.Figure(
        data=[
            go.Histogram(
                x=revenue,
                nbinsx=min(30, max(5, revenue.nunique() // 2 or 5)),
                marker_color="#636EFA",
                opacity=0.85,
                hovertemplate="Revenue: %{x:$,.0f}<extra></extra>",
            )
        ]
    )
    figure.update_layout(
        title="Revenue Distribution",
        xaxis_title="Revenue",
        yaxis_title="Frequency",
        template="plotly_white",
        bargap=0.05,
    )
    return figure


def build_correlation_heatmap(df: DataFrame) -> go.Figure:
    """Create a heatmap to visualise correlations between key numeric metrics."""

    correlations = compute_correlations(df)
    heatmap = go.Heatmap(
        z=correlations.values,
        x=list(correlations.columns),
        y=list(correlations.index),
        colorscale="Blues",
        zmin=-1,
        zmax=1,
        hovertemplate="%{y} vs %{x}: %{z:.2f}<extra></extra>",
        colorbar=dict(title="Correlation"),
    )
    figure = go.Figure(data=[heatmap])
    figure.update_layout(
        title="Correlation Heatmap",
        template="plotly_white",
    )
    return figure


def run_ab_test(
    group_a: Iterable[float], group_b: Iterable[float], alpha: float = 0.05
) -> Mapping[str, float | bool]:
    """Run an independent t-test on two groups of durations."""

    t_statistic, p_value = ttest_ind(list(group_a), list(group_b))
    return {
        "t_statistic": float(t_statistic),
        "p_value": float(p_value),
        "alpha": float(alpha),
        "is_significant": bool(p_value < alpha),
    }


def main() -> None:
    """Execute the lesson workflow with helpful console output."""

    print("--- 1. Descriptive Statistics of Sales Data ---")
    df = load_sales_data()
    if df.empty:
        print("Error: sales_data.csv not found. Keep the CSV beside this script.")
    else:
        revenue_summary = summarize_revenue(df)
        print(f"Mean Revenue: ${revenue_summary['mean']:,.2f}")
        print(f"Median Revenue: ${revenue_summary['median']:,.2f}")
        print(f"Standard Deviation of Revenue: ${revenue_summary['std']:,.2f}")
        print(f"Minimum Revenue: ${revenue_summary['min']:,.2f}")
        print(f"Maximum Revenue: ${revenue_summary['max']:,.2f}")
        print("\nFull descriptive statistics (df.describe()):")
        print(revenue_summary["describe"])
    print("-" * 20)

    print("--- 2. Correlation Analysis ---")
    if not df.empty:
        correlation_matrix = compute_correlations(df)
        print("Correlation Matrix:")
        print(correlation_matrix)
        print(
            "\nAnalysis: 'Units Sold' and 'Revenue' have a strong positive correlation (0.93)."
        )
        print("'Price' and 'Revenue' also have a strong positive correlation (0.83).")
        print(
            "'Price' and 'Units Sold' have a weak negative correlation (-0.23), which might be expected (higher price can sometimes mean fewer units)."
        )
    else:
        print("DataFrame not available for this exercise.")
    print("-" * 20)

    print("--- 3. A/B Test Analysis (T-Test) ---")
    group_a_durations = [10.5, 12.1, 11.8, 13.0, 12.5, 11.9, 12.3]
    group_b_durations = [12.8, 13.5, 13.2, 14.0, 13.8, 14.1, 13.6]
    print(f"Group A (Old Headline) Durations: {group_a_durations}")
    print(f"Group B (New Headline) Durations: {group_b_durations}")

    test_results = run_ab_test(group_a_durations, group_b_durations)
    print(f"\nT-statistic: {test_results['t_statistic']:.4f}")
    print(f"P-value: {test_results['p_value']:.4f}")

    if test_results["is_significant"]:
        print("\nConclusion: The difference is statistically significant.")
        print(
            "We can conclude that the new headline (Group B) likely leads to longer session durations."
        )
    else:
        print("\nConclusion: The difference is not statistically significant.")
        print(
            "We cannot conclude that the new headline had a real effect on session duration."
        )
    print("-" * 20)


if __name__ == "__main__":
    main()