Week 08 · Advanced Track

Week 8 — NumPy & Pandas

Accelerate numerical computation with NumPy arrays and explore tabular data with Pandas. Combine them with Matplotlib to visualize results.

Advanced Track2 min readFree lesson

Level: Advanced | Prerequisite: Week 7

Learning Goals

Creating, operating, and indexing NumPy arrays
Basics of Pandas DataFrame
Reading CSV, filtering, aggregation
groupby and pivot tables

pip install numpy pandas matplotlib

1. Basics of NumPy

import numpy as np

a = np.array([1, 2, 3, 4, 5])
print(a.mean(), a.std(), a.sum())

#Vector operations (much faster than Python list)
print(a * 2)        # [2 4 6 8 10]
print(a + a)
print(a > 2)        #Boolean array

# 2D
m = np.arange(12).reshape(3, 4)
print(m)
print(m.shape)
print(m.sum(axis=0))   #Column sum
print(m.sum(axis=1))   #Row sum

2. NumPy Indexing

a = np.arange(10)
print(a[3])
print(a[2:6])
print(a[a > 5])      #Boolean Indexing

m = np.arange(12).reshape(3, 4)
print(m[1, 2])       #Row 1, Column 2
print(m[:, 1])       #All rows, Column 1
print(m[1, :])       #Row 1

3. Pandas DataFrame

import pandas as pd

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Charlie", "Dave"],
    "score": [92, 78, 85, 67],
    "dept": ["A", "B", "A", "B"],
})

print(df)
print(df.describe())
print(df.shape, df.columns.tolist())

4. Filtering, Sorting, Aggregation

#Filter
print(df[df.score >= 80])

#Sort
print(df.sort_values("score", ascending=False))

#Group Aggregate
print(df.groupby("dept")["score"].mean())
print(df.groupby("dept").agg({"score": ["mean", "max", "count"]}))

5. Reading CSV

df = pd.read_csv("sales.csv")
df["date"] = pd.to_datetime(df["date"])
monthly = df.groupby(df["date"].dt.to_period("M"))["amount"].sum()
print(monthly)

6. Visualization (matplotlib)

import matplotlib.pyplot as plt

monthly.plot(kind="bar")
plt.title("Monthly Sales")
plt.tight_layout()
plt.savefig("monthly.png")

Key Examples (examples/)

File	Content
`01_numpy_basic.py`	Arrays, Operations
`02_numpy_indexing.py`	Indexing, Slicing
`03_pandas_basic.py`	Creating and Accessing DataFrame
`04_pandas_groupby.py`	Filtering, groupby

⚠️ Common Mistakes

Processing NumPy arrays with for loop — Vector operations are faster
SettingWithCopyWarning — Use .loc[] instead of df[df.x > 0]["y"] = 5
Confusion between index vs column — df["a"] is column, df.loc[a] is index
Ignoring NaN — Explicit handling with dropna(), fillna()

❓ FAQ

Q1. Difference between NumPy and list? A. NumPy: same type, contiguous memory, vector operations. Over 100x faster for numerical computations.

Q2. Difference between Pandas and SQL? A. Pandas is an in-memory DataFrame. SQL is for persistent storage + large data. Use Pandas for analysis, SQL for operational data.

Q3. I heard polars is faster? A. Correct. It's better for large data. However, the ecosystem is overwhelmingly in favor of pandas.

📝 Exercises (exercises/)

exercise1.md — students.csv statistics by department
exercise2.md — NumPy simulation (BMI for 1000 people)
exercise3.md — Sales pivot table

Next Week

Week 9. Packaging and Distribution

Example files

The runnable files used in this lesson. Copy them into your own project and experiment.

examples/01_numpy_basic.pyPython

import numpy as np

a = np.array([1, 2, 3, 4, 5])
print("array:", a)
print("mean:", a.mean(), "std:", a.std(), "sum:", a.sum())

#Vector Operations
print("a * 2:", a * 2)
print("a + a:", a + a)
print("a > 2:", a > 2)

# 2D
m = np.arange(12).reshape(3, 4)
print(m)
print("shape:", m.shape)
print("Sum along axis=0:", m.sum(axis=0))
print("Sum along axis=1:", m.sum(axis=1))

#Statistics
print("min/max/median:", m.min(), m.max(), np.median(m))

examples/02_numpy_indexing.pyPython

import numpy as np

a = np.arange(10)
print(a[3])
print(a[2:6])

#Boolean Indexing
print("> 5:", a[a > 5])

# 2D
m = np.arange(12).reshape(3, 4)
print(m)
print("(1, 2):", m[1, 2])
print("column 1:", m[:, 1])
print("row 1:", m[1, :])

#fancy indexing
print(m[[0, 2], [1, 3]])   #positions (0,1), (2,3)

#conditional change
m_copy = m.copy()
m_copy[m_copy > 5] = -1
print(m_copy)

examples/03_pandas_basic.pyPython

import pandas as pd

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Charlie", "Dave", "Eve"],
    "score": [92, 78, 85, 67, 94],
    "dept": ["A", "B", "A", "B", "A"],
})

print(df)
print()
print("describe():")
print(df.describe())
print()
print("shape:", df.shape)
print("columns:", df.columns.tolist())
print("dtypes:")
print(df.dtypes)

#Select row/column
print("Score column:")
print(df["score"])
print("Row 0:")
print(df.iloc[0])

examples/04_pandas_groupby.pyPython

import pandas as pd

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Charlie", "Dave", "Eve"],
    "score": [92, 78, 85, 67, 94],
    "dept": ["A", "B", "A", "B", "A"],
})

#Filter
print("80 points or more:")
print(df[df.score >= 80])

#Sort
print("Descending order of scores:")
print(df.sort_values("score", ascending=False))

# groupby
print("Average by department:")
print(df.groupby("dept")["score"].mean())

print("Various statistics by department:")
print(df.groupby("dept").agg(
    avg=("score", "mean"),
    high=("score", "max"),
    n=("score", "count"),
))

Assignment

Input

name,dept,score
Alice,A,92
Bob,B,78
Charlie,A,85
Dave,B,67
Eve,A,94
Frank,C,71

Output

[Average by Department]
A    90.33
B    72.50
C    71.00

[Overall Statistics]
Average: 81.17
Highest: Eve (94)

Assignment 2. NumPy simulation (BMI for 1000 people)

Goal

Heights of 1000 people (Normal distribution: mean 170cm, standard deviation 8)
Weights of 1000 people (Normal distribution: mean 65kg, standard deviation 10)
Calculate BMI
Print number of people by category (Underweight/Normal/Overweight/Obese)

Hint

heights = np.random.normal(170, 8, 1000)
weights = np.random.normal(65, 10, 1000)
bmi = weights / (heights / 100) ** 2

Assignment 3. Sales pivot table

Input

date,category,amount
2026-01-15,food,12000
2026-01-20,drink,5000
2026-02-03,food,8500
2026-02-10,daily,3200
...

Output (Pivot)

category    daily  drink   food
date
2026-01         0   5000  12000
2026-02      3200      0   8500

Hint

df["date"] = pd.to_datetime(df["date"]).dt.to_period("M")
pivot = df.pivot_table(index="date", columns="category",
                      values="amount", aggfunc="sum", fill_value=0)

Show the reference solution

Try the assignment yourself first — comparing your attempt with a working solution is where most of the learning happens.

solution1.pyPython

import pandas as pd
from io import StringIO

CSV = """name,dept,score
Alice,A,92
Bob,B,78
Charlie,A,85
Dave,B,67
Eve,A,94
Frank,C,71
"""

df = pd.read_csv(StringIO(CSV))

print("[Average by Department]")
print(df.groupby("dept")["score"].mean())

print(f"[Overall Statistics]")
print(f"Average: {df.score.mean():.2f}")
top_idx = df.score.idxmax()
print(f"Top: {df.loc[top_idx, 'name']} ({df.loc[top_idx, 'score']})")

solution2.pyPython

import numpy as np

np.random.seed(42)

heights = np.random.normal(170, 8, 1000)
weights = np.random.normal(65, 10, 1000)
bmi = weights / (heights / 100) ** 2

print(f"Average BMI: {bmi.mean():.2f}")
print(f"Range: {bmi.min():.1f} ~ {bmi.max():.1f}")
print()

#Category
underweight = (bmi < 18.5).sum()
normal = ((bmi >= 18.5) & (bmi < 23)).sum()
overweight = ((bmi >= 23) & (bmi < 25)).sum()
obese = (bmi >= 25).sum()

print(f"Underweight: {underweight} people ({underweight/10:.1f}%)")
print(f"Normal: {normal} people ({normal/10:.1f}%)")
print(f"Overweight: {overweight} people ({overweight/10:.1f}%)")
print(f"Obese: {obese} people ({obese/10:.1f}%)")

solution3.pyPython

import pandas as pd
from io import StringIO

CSV = """date,category,amount
2026-01-15,food,12000
2026-01-20,drink,5000
2026-02-03,food,8500
2026-02-10,daily,3200
2026-02-15,drink,4800
2026-03-01,food,15000
2026-03-12,daily,2700
"""

df = pd.read_csv(StringIO(CSV))
df["month"] = pd.to_datetime(df["date"]).dt.to_period("M")

pivot = df.pivot_table(
    index="month", columns="category",
    values="amount", aggfunc="sum", fill_value=0
)
print(pivot)
print("Monthly total:")
print(pivot.sum(axis=1))

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