Pandas Basics | Complete Guide to Python Data Analysis

Pandas Basics | Complete Guide to Python Data Analysis

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Pandas Basics | Complete Guide to Python Data Analysis

이 글의 핵심

Pandas basics for Python: Series and DataFrame, I/O, filtering, groupby, merge, and real-world patterns. Practical tutorial with examples for CSV, Excel, and analytics workflows.

Introduction

“The essential tool for data analysis”

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

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

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)

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)

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]

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)

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)

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)

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')

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)
)

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

Frequently Asked Questions (FAQ)

Q. When would I use this in practice?

A. Pandas basics for Python: Series and DataFrame, I/O, filtering, groupby, merge, and real-world patterns. Practical tutor…

Q. What should I read before this?

A. Follow the previous article or related articles links at the bottom of each post to learn in sequence. See the Python series index for the full picture.

Q. Where can I study this more deeply?

A. Check cppreference and the relevant library’s official documentation. The reference links at the end of the article are also worth using.

Other articles related to this topic.

Keywords Covered in This Article (Related Search Terms)

This article covers Python, Pandas, Data Analysis, DataFrame, CSV, Data Wrangling.