Perfilado del Desempeño de los Estudiantes

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

# students-performance.csv ships with the course — load it from the browser file system.
df = pd.read_csv("students-performance.csv")

print(f"Dataset loaded: {df.shape[0]} rows, {df.shape[1]} columns")
print(f"\nColumns: {list(df.columns)}")
print(f"\nFirst 5 rows:")
df.head()

def profile_dataset(df, name="Dataset"):
    """Generate a complete profiling report."""
    print(f"{'='*60}")
    print(f"  PROFILING REPORT: {name}")
    print(f"{'='*60}")

    print(f"\nSTRUCTURE")
    print(f"  Rows: {df.shape[0]:,}")
    print(f"  Columns: {df.shape[1]}")
    print(f"  Memory: {df.memory_usage(deep=True).sum() / 1e6:.2f} MB")

    print(f"\nCOLUMN TYPES")
    print(df.dtypes.value_counts().to_string())

    missing = df.isnull().sum()
    if missing.any():
        print(f"\nMISSING VALUES")
        for col in missing[missing > 0].index:
            pct = missing[col] / len(df) * 100
            print(f"  {col}: {missing[col]:,} ({pct:.1f}%)")
    else:
        print(f"\nMISSING VALUES: None detected")

    n_dupes = df.duplicated().sum()
    print(f"\nDUPLICATES: {n_dupes} rows ({n_dupes/len(df)*100:.1f}%)")

    num_df = df.select_dtypes(include="number")
    if not num_df.empty:
        print(f"\nNUMERICAL SUMMARY")
        print(num_df.describe().round(2).to_string())

    cat_df = df.select_dtypes(include="object")
    if not cat_df.empty:
        print(f"\nCATEGORICAL SUMMARY")
        for col in cat_df.columns:
            n_unique = df[col].nunique()
            print(f"  {col}: {n_unique} unique — {df[col].value_counts().to_dict()}")

    print(f"\n{'='*60}")

profile_dataset(df, "Students Performance in Exams")

cat_cols = ["gender", "race/ethnicity", "parental level of education",
            "lunch", "test preparation course"]

for col in cat_cols:
    print(f"\n{'─'*40}")
    print(f"  {col.upper()}")
    print(f"{'─'*40}")
    vc = df[col].value_counts()
    pct = df[col].value_counts(normalize=True).round(3)
    summary = pd.DataFrame({"count": vc, "proportion": pct})
    print(summary)

num_cols = ["math score", "reading score", "writing score"]

for col in num_cols:
    print(f"\n{'─'*40}")
    print(f"  {col.upper()}")
    print(f"{'─'*40}")
    s = df[col]
    print(f"  Mean:   {s.mean():.2f}")
    print(f"  Median: {s.median():.2f}")
    print(f"  Std:    {s.std():.2f}")
    print(f"  Min:    {s.min()}")
    print(f"  Max:    {s.max()}")
    print(f"  Skew:   {s.skew():.3f}")
    print(f"  Kurt:   {s.kurtosis():.3f}")
    print(f"  IQR:    {s.quantile(0.75) - s.quantile(0.25):.2f}")
    print(f"  Outliers (IQR): {((s < s.quantile(0.25) - 1.5 * (s.quantile(0.75) - s.quantile(0.25))) | (s > s.quantile(0.75) + 1.5 * (s.quantile(0.75) - s.quantile(0.25)))).sum()}")

fig, axes = plt.subplots(2, 3, figsize=(15, 10))

# Numerical distributions
for i, col in enumerate(num_cols):
    sns.histplot(df[col], kde=True, ax=axes[0, i], bins=20, color="steelblue")
    mean_val = df[col].mean()
    axes[0, i].axvline(mean_val, color="red", linestyle="--", label=f"Mean: {mean_val:.1f}")
    axes[0, i].set_title(col.replace(" score", " Scores").title())
    axes[0, i].legend(fontsize=9)

# Categorical counts
cat_to_plot = ["gender", "race/ethnicity", "lunch"]
for i, col in enumerate(cat_to_plot):
    sns.countplot(data=df, x=col, ax=axes[1, i], palette="Set2")
    axes[1, i].set_title(col.title())
    axes[1, i].tick_params(axis="x", rotation=45)

plt.suptitle("Students Performance — Visual Profile", fontsize=14, fontweight="bold")
plt.tight_layout()
plt.show()

eda_questions = {
    "primary": "What factors most strongly predict student performance on exams?",

    "secondary": [
        "How do math, reading, and writing scores distribute across the population?",
        "Do gender and ethnicity interact to produce score differences?",
        "What is the magnitude of the test preparation effect?",
        "Does lunch type (proxy for socioeconomic status) predict scores?",
        "How does parental education level relate to student performance?",
    ],

    "tertiary": [
        "Are the three scores correlated? Is the correlation the same across genders?",
        "Do outliers represent data entry errors or genuine high/low performers?",
        "Is there an interaction between test preparation and lunch type?",
        "Which subgroup shows the largest performance gap?",
    ],

    "analysis_plan": [
        "Step 1: Univariate analysis of all 8 variables",
        "Step 2: Bivariate analysis — scores vs each categorical variable",
        "Step 3: Correlation analysis of the three score variables",
        "Step 4: Multivariate analysis — interactions between factors",
        "Step 5: Advanced visualizations and storytelling",
        "Step 6: Final report with findings and recommendations",
    ]
}

print("PRIMARY QUESTION:")
print(f"  {eda_questions['primary']}\n")

print("SECONDARY QUESTIONS:")
for i, q in enumerate(eda_questions["secondary"], 1):
    print(f"  {i}. {q}")

print("\nTERTIARY QUESTIONS:")
for i, q in enumerate(eda_questions["tertiary"], 1):
    print(f"  {i}. {q}")

print("\nANALYSIS PLAN:")
for step in eda_questions["analysis_plan"]:
    print(f"  {step}")

# Complete the profiling and answer:
questions = {
    "q1": "How many students are in the dataset?",
    "q2": "What are the column names?",
    "q3": "Are there any missing values?",
    "q4": "How many unique values does each categorical column have?",
    "q5": "What is the mean math score?",
    "q6": "What is the standard deviation of reading scores?",
    "q7": "Which categorical column has the most balanced distribution?",
    "q8": "Are there any numerical outliers in the score columns?",
}

# Your answers:
answers = {
    "q1": "1000 students",
    "q2": "gender, race/ethnicity, parental level of education, lunch, test preparation course, math score, reading score, writing score",
    "q3": "No missing values",
    "q4": "gender: 2, race/ethnicity: 5, parental level of education: 6, lunch: 2, test preparation course: 2",
    "q5": "66.1",
    "q6": "14.6",
    "q7": "gender (50/50 split) or lunch (roughly 65/35 split)",
    "q8": "Yes — some students scored below 30 on math (potential outliers)",
}

for q, a in answers.items():
    print(f"{questions[q]}\n  → {a}\n")