Pandas Basics | Complete Guide to Python Data Analysis

Introduction

“The essential tool for data analysis”

Pandas is Python’s core library for data analysis and manipulation.

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1. Pandas basics

Installation

pip install pandas

Series and DataFrame

import pandas as pd

# Series (1D)
s = pd.Series([1, 2, 3, 4, 5])
print(s)
# 0    1
# 1    2
# 2    3
# 3    4
# 4    5

# DataFrame (2D)
df = pd.DataFrame({
    'name': ['철수', '영희', '민수'],
    'age': [25, 30, 28],
    'city': ['서울', '부산', '대구']
})
print(df)
#   name  age city
# 0  철수   25  서울
# 1  영희   30  부산
# 2  민수   28  대구

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2. Reading and writing data

CSV files

# Read CSV
df = pd.read_csv('data.csv')

# Write CSV
df.to_csv('output.csv', index=False, encoding='utf-8-sig')

# Read selected columns only
df = pd.read_csv('data.csv', usecols=['name', 'age'])

# Custom delimiter
df = pd.read_csv('data.tsv', sep='\t')

Excel files

# Read Excel
df = pd.read_excel('data.xlsx', sheet_name='Sheet1')

# Write Excel
df.to_excel('output.xlsx', index=False)

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3. Exploring data

Basic information

# First 5 rows
print(df.head())

# Last 5 rows
print(df.tail())

# Summary info
print(df.info())

# Descriptive statistics
print(df.describe())

# Shape
print(df.shape)  # (rows, columns)

# Column names
print(df.columns)

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4. Selecting data

Column selection

# Single column
ages = df['age']

# Multiple columns
subset = df[['name', 'age']]

# Boolean filtering
adults = df[df['age'] >= 30]
seoul_users = df[df['city'] == '서울']

# Compound conditions
result = df[(df['age'] >= 25) & (df['city'] == '서울')]

Row selection

# By position (iloc)
first_row = df.iloc[0]
first_three = df.iloc[:3]

# By label (loc)
df_indexed = df.set_index('name')
chul = df_indexed.loc['철수']

# Conditional rows
young = df[df['age'] < 30]

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5. Transforming data

Add and drop columns

# Add columns
df['country'] = '한국'
df['birth_year'] = 2026 - df['age']

# Drop one column
df = df.drop('country', axis=1)

# Drop multiple columns
df = df.drop(['col1', 'col2'], axis=1)

Changing values

# Update specific values
df.loc[df['name'] == '철수', 'age'] = 26

# Apply a function
df['age_group'] = df['age'].apply(
    lambda x: '청년' if x < 30 else '중년'
)

# Apply to multiple columns
df[['age', 'birth_year']] = df[['age', 'birth_year']].applymap(int)

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6. Grouping and aggregation

groupby

# Average age by city
city_avg = df.groupby('city')['age'].mean()
print(city_avg)

# Multiple aggregations
result = df.groupby('city').agg({
    'age': ['mean', 'min', 'max'],
    'name': 'count'
})
print(result)

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7. Combining data

merge (joins)

# Two DataFrames
df1 = pd.DataFrame({
    'id': [1, 2, 3],
    'name': ['철수', '영희', '민수']
})

df2 = pd.DataFrame({
    'id': [1, 2, 4],
    'score': [85, 90, 88]
})

# Inner join
merged = pd.merge(df1, df2, on='id', how='inner')
print(merged)
#    id name  score
# 0   1  철수     85
# 1   2  영희     90

# Left join
merged = pd.merge(df1, df2, on='id', how='left')

concat (stacking)

# Concatenate vertically
df_concat = pd.concat([df1, df2], ignore_index=True)

# Concatenate horizontally
df_concat = pd.concat([df1, df2], axis=1)

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8. Practical example

Sales analysis

import pandas as pd

# Load data
sales = pd.read_csv('sales.csv')

# Basic info
print(f"총 {len(sales)}건의 판매")
print(f"총 매출: {sales['amount'].sum():,}원")

# Monthly revenue
sales['date'] = pd.to_datetime(sales['date'])
sales['month'] = sales['date'].dt.month
monthly_sales = sales.groupby('month')['amount'].sum()
print(monthly_sales)

# Top 10 products by revenue
top_products = sales.groupby('product')['amount'].sum().sort_values(ascending=False).head(10)
print(top_products)

# Save results
monthly_sales.to_csv('monthly_report.csv')

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Practical tips

Pandas tips

# ✅ Memory optimization
df = pd.read_csv('large.csv', dtype={'id': 'int32'})

# ✅ Missing values
df = df.dropna()   # drop rows with NaN
df = df.fillna(0)  # fill with 0

# ✅ Duplicates
df = df.drop_duplicates()

# ✅ Method chaining
result = (df
    .query('age >= 25')
    .groupby('city')['age']
    .mean()
    .sort_values(ascending=False)
)

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Summary

Key takeaways

1. Pandas: the standard library for tabular data analysis
2. DataFrame: 2D labeled table
3. I/O: CSV, Excel, JSON
4. Operations: filtering, grouping, merging
5. Aggregation: sum, mean, count

Next steps

• NumPy
• Matplotlib visualization

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Related posts

• Hands-on data analysis with Pandas