# First, let us import libraries we will use
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
 
 

# Let us make some example data for the lesson
data = pd.DataFrame({"gender": ["female", "male", "female", "other", "male", "male", "female", "female"],
                     "grade":   ["A", "C", "B", "B", "A", "D", "C", "B"],
                     "score":  [90, 75, 82, 89, 93, 60, 72, 85]})
data

# Show the count of each gender
sns.countplot(x="gender", data=data)
plt.title("Count of Each Gender in Class")
plt.show()

# Count grades with countplot
sns.countplot(x="grade", data=data)
plt.title("Grade Counts in Class")
plt.show()

# Use countplot with a different color palette
sns.countplot(x="grade", data=data, palette="Set2")
plt.title("Grades With Different Colors")
plt.show()

# Show score distribution with a boxplot
sns.boxplot(y="score", data=data)
plt.title("Distribution of Scores")
plt.show()

# Compare scores by gender with a boxplot
sns.boxplot(x="gender", y="score", data=data, palette="pastel")
plt.title("Scores by Gender")
plt.show()

# See pairwise correlations with heatmap
corr = data.corr(numeric_only=True)
sns.heatmap(corr, annot=True, cmap="YlGnBu")
plt.title("Correlation Heatmap")
plt.show()

# Create a fake grid of temperatures to plot as a heatmap
temps = pd.DataFrame([[20, 22, 21], [19, 23, 20], [21, 22, 24]],
                     columns=["Morning", "Noon", "Evening"],
                     index=["Monday", "Tuesday", "Wednesday"])
sns.heatmap(temps, annot=True, fmt="d", cmap="coolwarm")
plt.title("Weekly Temperatures")
plt.show()

# Handle missing data safely with heatmap
temps.loc["Thursday"] = [22, None, 19]
sns.heatmap(temps, annot=True, fmt="g", cmap="YlOrRd")
plt.title("Temperatures Including Missing Day")
plt.show()

# Collect a few student grades from user
students = []
grades = []
scores = []
for i in range(3):
    name = input("Student name: ")
    grade = input("Grade (A-D): ")
    score = int(input("Score (0-100): "))
    students.append(name)
    grades.append(grade)
    scores.append(score)
grade_df = pd.DataFrame({"student": students, "grade": grades, "score": scores})
grade_df

# Use countplot, boxplot, and heatmap on your mini data
sns.countplot(x="grade", data=grade_df)
plt.title("Mini Grade Count")
plt.show()
sns.boxplot(y="score", data=grade_df)
plt.title("Mini Scores Spread")
plt.show()
sns.heatmap(grade_df[["score"]].T, annot=True, cmap="YlGn")
plt.title("Scores Heatmap (Rows: Students)")
plt.yticks([0], ["score"]); plt.show()

# TIP: Fast data check with .info() and .describe()
print(grade_df.info())
print(grade_df.describe())

# Common error: Misspelled column or typo
# print(grade_df["scorre"])  # This line has a typo
 

# Bonus: Switch Seaborn style for better looks
sns.set_style("whitegrid")
sns.countplot(x="gender", data=data)
plt.title("Gender Count with Whitegrid Style")
plt.show()

# Advanced: Heatmap with bigger numbers and annotations
import numpy as np
np.random.seed(1)
big_data = pd.DataFrame(np.random.randint(10, 90, size=(5, 5)),
                       columns=["A", "B", "C", "D", "E"])
sns.heatmap(big_data, annot=True, linewidths=1, fmt="d", cmap="viridis")
plt.title("Big Heatmap Example")
plt.show()