After completing this lesson, you'll be able to confidently do the following 3 things.

• ▸✅ Explain why Python became the standard language for AI and data
• ▸✅ Set up venv + requirements.txt for Python 3.x
• ▸✅ Use the 4 built-in functions: print / input / type / dir

Keep the learning goals as a checklist — close the lesson once you can answer all of them.

Pandas = the standard for working with tables. DataFrame = Excel sheet. Read and write CSV, SQL, and JSON in one line.

1. Installation + Creating a DataFrame

$ pip install pandas

import pandas as pd

df = pd.DataFrame({
    "Name": ["Hong Gil-dong", "Lee Mong-ryong", "Seong Chun-hyang"],
    "Age": [28, 30, 25],
    "Score": [85, 92, 78],
})
print(df)
#    Name  Age  Score
# 0  Hong Gil-dong  28   85
# 1  Lee Mong-ryong  30   92
# 2  Seong Chun-hyang  25   78

2. Reading and Writing CSV / Excel

# Reading
df = pd.read_csv("students.csv")
df = pd.read_excel("data.xlsx")
df = pd.read_json("data.json")

# Writing
df.to_csv("out.csv", index=False, encoding="utf-8-sig")    # Korean characters won't break
df.to_excel("out.xlsx", index=False)

3. Preview and Statistics

df.head(3)           # First 3 rows
df.tail(3)           # Last 3 rows
df.shape             # (3, 3)
df.columns           # Index(['Name', 'Age', 'Score'])
df.describe()        # Mean·std·min·max automatically
df.info()            # Column types·null counts

4. Selecting Columns and Rows

df["Name"]                   # Series (1 column)
df[["Name", "Score"]]         # DataFrame (multiple columns)

df.iloc[0]                   # Row 0 (position-based)
df.iloc[0:2]                 # Rows 0-1
df.loc[df["Age"] >= 28]     # Condition (age 28 or older)

5. Adding and Modifying Data

# New column
df["Grade"] = df["Score"].apply(lambda x: "A" if x >= 90 else "B")

# Bulk column update
df["Age"] = df["Age"] + 1     # All +1

# Add row
df.loc[len(df)] = ["Byeon Hak-do", 35, 60, "F"]
print(df)

6. Group Aggregation

df = pd.DataFrame({
    "Region": ["Seoul", "Busan", "Seoul", "Busan"],
    "Sales": [100, 50, 150, 80],
})

# Sum by region
print(df.groupby("Region")["Sales"].sum())
# Region
# Busan   130
# Seoul   250

# Multiple statistics
print(df.groupby("Region")["Sales"].agg(["sum", "mean", "count"]))

7. Sorting and Missing Values

df.sort_values("Score", ascending=False)        # Score descending
df.drop_duplicates()                            # Remove duplicates
df.dropna()                                     # Remove null rows
df.fillna(0)                                    # Fill nulls → 0

One-line Summary

read_csv + groupby.sum/mean + loc[condition] + to_excel — these 4 cover 90% of analysis.

Enabling Copy-on-Write in Pandas 2.0:

pd.options.mode.copy_on_write = True

Modifying a view will no longer affect the original. SettingWithCopyWarning disappears!

Python is used across a wide range of fields thanks to its concise, readable syntax. As an interpreted language, it can be executed immediately in a REPL environment. It follows the PEP 8 coding style guide, with automatic formatting via Black/autopep8. Type hints improve code readability and IDE support. Packages are managed with pip, and virtual environments are set up with venv/conda.

Knowing the concepts in this lesson lets you give AI specific instructions. Instead of a vague 'fix this,' a request with precise vocabulary — that's where token savings begin.

• ▸'Convert this for loop to a numpy vector operation'
• ▸'Refactor this data cleaning step using pandas method chaining'

Why this reduces tokens

Without knowing the concepts, even after receiving an AI response you'll need to ask 'What does that mean?' again. That follow-up question is what eats up tokens. Learn the concept once, and the conversation ends in a single round.