Python Fast Track Coding for Tech Families in Pangyo, Seongnam

1. What a Histogram Shows

A histogram bins one continuous numeric series and counts how many values fall in each bin. Use raw counts for frequency, or density=True so the area under the bars acts like a probability density.

2. Synthetic Normals with NumPy

import numpy as np
import matplotlib.pyplot as plt

group_a = np.random.normal(loc=70, scale=10, size=1000)
group_b = np.random.normal(loc=80, scale=12, size=1000)

plt.hist(group_a, bins=30, density=True, alpha=0.6, label="Group A")
plt.hist(group_b, bins=30, density=True, alpha=0.6, label="Group B")
plt.xlabel("Value")
plt.ylabel("Probability Density")
plt.title("Comparative Normal Distributions")
plt.legend()
plt.show()

alpha keeps overlapping curves readable.

3. Revenue Frequency on Sampled Orders

import pandas as pd

df = pd.read_csv("ecommerce_orders.csv")
sample_df = df.sample(n=1000)

fig, ax = plt.subplots(figsize=(12, 4))
ax.hist(sample_df["Revenue"], bins=30, edgecolor="black")
ax.set_title("Revenue Distribution (Raw Frequency)")
ax.set_xlabel("Revenue ($)")
ax.set_ylabel("Frequency Count")
plt.show()

Lower revenue bins often dominate; high-ticket bins stay sparse.

4. Density + Original vs Discounted

fig, ax = plt.subplots(figsize=(12, 4))
ax.hist(sample_df["Revenue"], bins=30, density=True, alpha=0.6, edgecolor="black")
ax.set_ylabel("Density")
plt.show()

discounted = sample_df["Revenue"] * (1 - sample_df["Discount"])
fig, ax = plt.subplots(figsize=(12, 5))
ax.hist(sample_df["Revenue"], bins=30, alpha=0.6, color="orange", label="Original")
ax.hist(discounted, bins=30, alpha=0.6, color="blue", label="Discounted")
ax.legend()
plt.show()

Discounts typically shift mass toward lower order values.