التحليل الفئوي أحادي المتغير

import pandas as pd

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

# Absolute frequencies
print("Gender counts:")
print(df["gender"].value_counts())

# Relative frequencies (proportions)
print("\nGender proportions:")
print(df["gender"].value_counts(normalize=True).round(3))

# Parental education — ordinal, so sort logically
edu_order = [
    "some high school",
    "high school",
    "some college",
    "associate's degree",
    "master's degree",
    "bachelor's degree",
]
print("\nParental education:")
print(df["parental level of education"].value_counts().reindex(edu_order))

import seaborn as sns
import matplotlib.pyplot as plt

# Without ordering — misleading
fig, axes = plt.subplots(1, 2, figsize=(14, 5))

sns.countplot(data=df, x="parental level of education", ax=axes[0])
axes[0].set_title("Without explicit order")
axes[0].tick_params(axis="x", rotation=45)

# With ordering — correct
sns.countplot(
    data=df,
    x="parental level of education",
    order=edu_order,
    ax=axes[1],
    palette="viridis"
)
axes[1].set_title("With explicit order")
axes[1].tick_params(axis="x", rotation=45)

plt.tight_layout()
plt.show()

fig, axes = plt.subplots(2, 2, figsize=(12, 10))

# Binary variable
sns.countplot(data=df, x="gender", ax=axes[0, 0], palette="Set2")
axes[0, 0].set_title("Gender Distribution")

# Nominal variable
sns.countplot(data=df, x="race/ethnicity", ax=axes[0, 1], palette="Set3")
axes[0, 1].set_title("Ethnicity Distribution")

# Ordinal variable
sns.countplot(
    data=df,
    x="parental level of education",
    order=edu_order,
    ax=axes[1, 0],
    palette="viridis"
)
axes[1, 0].set_title("Parental Education Level")
axes[1, 0].tick_params(axis="x", rotation=45)

# Binary with hue
sns.countplot(data=df, x="test preparation course", hue="gender", ax=axes[1, 1], palette="Set1")
axes[1, 1].set_title("Test Prep by Gender")

plt.tight_layout()
plt.show()

# Ethnicity with horizontal bars
ethnicity_counts = df["race/ethnicity"].value_counts()

fig, ax = plt.subplots(figsize=(8, 5))
ethnicity_counts.plot(kind="barh", ax=ax, color="steelblue", edgecolor="black")
ax.set_title("Ethnicity Distribution")
ax.set_xlabel("Count")
ax.set_ylabel("Ethnicity Group")
plt.show()

# Proportion by gender
gender_prop = df["gender"].value_counts(normalize=True)

fig, axes = plt.subplots(1, 2, figsize=(12, 5))

# Bar chart of proportions
gender_prop.plot(kind="bar", ax=axes[0], color=["#4ECDC4", "#FF6B6B"], edgecolor="black")
axes[0].set_title("Gender Proportions")
axes[0].set_ylabel("Proportion")
axes[0].set_ylim(0, 1)

# Pie chart (use sparingly — bar charts are almost always better)
axes[1].pie(gender_prop, labels=gender_prop.index, autopct="%1.1f%%", colors=["#4ECDC4", "#FF6B6B"])
axes[1].set_title("Gender Split")

plt.tight_layout()
plt.show()

def top_n_with_other(series, n=5):
    """Keep top n categories, merge the rest into 'Other'."""
    top = series.value_counts().head(n).index
    return series.where(series.isin(top), other="Other")

# Example with education level
df["education_grouped"] = top_n_with_other(df["parental level of education"], n=4)

print(df["education_grouped"].value_counts())

fig, ax = plt.subplots(figsize=(8, 5))
sns.countplot(data=df, x="education_grouped", palette="pastel", ax=ax)
ax.set_title("Parental Education (Top 4 + Other)")
plt.show()

fig, ax = plt.subplots(figsize=(8, 5))
counts = df["race/ethnicity"].value_counts()
bars = ax.bar(counts.index, counts.values, color=sns.color_palette("Set3", len(counts)), edgecolor="black")

for bar in bars:
    height = bar.get_height()
    ax.text(
        bar.get_x() + bar.get_width() / 2.,
        height + 0.5,
        f"{int(height)}",
        ha="center",
        va="bottom",
        fontweight="bold"
    )

ax.set_title("Ethnicity Distribution with Counts")
ax.set_ylabel("Count")
plt.show()

import pandas as pd
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")

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

# Frequency tables
for col in cat_cols:
    print(f"\n{col}:")
    print(df[col].value_counts())

# Visualize all categorical variables
fig, axes = plt.subplots(2, 3, figsize=(15, 10))
axes = axes.flatten()

for i, col in enumerate(cat_cols):
    sns.countplot(data=df, x=col, ax=axes[i], palette="Set2")
    axes[i].set_title(col.title())
    axes[i].tick_params(axis="x", rotation=45)

# Hide unused subplot
axes[5].set_visible(False)

plt.tight_layout()
plt.show()