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패턴으로 열 선택하기

filter(like=), filter(regex=), 리스트 컴프리헨션을 사용해 이름 패턴과 일치하는 열을 선택합니다.

패턴으로 열 선택하기은(는) CoddyKit의 무료 Pandas & NumPy Academy 강의입니다. 이것은 4개 중 4번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 Pandas & NumPy Academy 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. Pandas & NumPy Academy 강의에는 총 4개의 강의가 포함되어 있습니다.

이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.

Why Select Columns by Pattern?

DataFrames from real-world sources often have dozens or hundreds of columns, and you rarely need all of them. When columns follow a naming convention — prefixes like sales_, suffixes like _2023, or keyword patterns — selecting them by name pattern is far more maintainable than listing every column explicitly. If the schema changes, pattern-based selection adapts automatically.

Pandas provides two primary tools: the filter() method and list comprehensions on df.columns.

import pandas as pd

df = pd.DataFrame({
    'sales_jan': [100, 200],
    'sales_feb': [150, 250],
    'cost_jan': [50, 80],
    'cost_feb': [60, 90],
    'profit': [140, 280]
})

print(df.columns.tolist())
# ['sales_jan', 'sales_feb', 'cost_jan', 'cost_feb', 'profit']

filter(like=) for Substring Matching

DataFrame.filter(like='substring') selects columns whose names contain the given substring anywhere in the name (case-sensitive). It operates on the column axis by default and returns a new DataFrame with only the matching columns.

This is the simplest pattern-selection tool: no regex knowledge required, just a substring to look for.

import pandas as pd

df = pd.DataFrame({
    'sales_jan': [100, 200],
    'sales_feb': [150, 250],
    'cost_jan': [50, 80],
    'revenue_q1': [110, 210]
})

# Select columns whose name contains 'sales'
sales_cols = df.filter(like='sales')
print(sales_cols)
#    sales_jan  sales_feb
# 0        100        150
# 1        200        250

filter(regex=) for Pattern Matching

DataFrame.filter(regex='pattern') selects columns whose names match the given regular expression. This is more powerful than like= because regex lets you match prefixes, suffixes, digit positions, or complex patterns. The regex is matched anywhere in the column name (it is not anchored to the start).

import pandas as pd

df = pd.DataFrame({
    'q1_revenue': [100],
    'q2_revenue': [200],
    'q1_cost': [40],
    'q2_cost': [60],
    'annual_total': [360]
})

# Select columns ending with '_revenue'
revenue_cols = df.filter(regex='_revenue$')
print(revenue_cols)
#    q1_revenue  q2_revenue
# 0         100         200

# Select columns starting with 'q' (quarter columns)
quarter_cols = df.filter(regex='^q')
print(quarter_cols.columns.tolist())
# ['q1_revenue', 'q2_revenue', 'q1_cost', 'q2_cost']

List Comprehension on df.columns

For maximum flexibility, iterate over df.columns with a list comprehension and apply any Python string method to build your column list. This works for startswith, endswith, contains, string length checks, or any custom logic that regex alone cannot express.

import pandas as pd

df = pd.DataFrame({
    'age': [25, 30],
    'age_group': ['young', 'mid'],
    'income': [50000, 70000],
    'income_tax': [8000, 12000],
    'name': ['Alice', 'Bob']
})

# Select columns that start with 'income'
income_cols = [c for c in df.columns if c.startswith('income')]
print(df[income_cols])
#    income  income_tax
# 0   50000        8000
# 1   70000       12000

Selecting by Column Name Suffix

The str.endswith() method on the column Index is the cleanest way to select columns by suffix. Pandas Index objects support .str accessor methods, so you can apply string operations across all column names at once and use the resulting boolean array to slice the DataFrame.

import pandas as pd

df = pd.DataFrame({
    'revenue_2022': [100],
    'cost_2022': [40],
    'revenue_2023': [150],
    'cost_2023': [55],
    'notes': ['ok']
})

# Use Index.str.endswith for suffix selection
cols_2023 = df.columns[df.columns.str.endswith('_2023')]
print(df[cols_2023])
#    revenue_2023  cost_2023
# 0           150         55

Dropping Columns by Pattern

Sometimes it is easier to exclude a set of columns rather than name the ones to keep. Combine pattern selection with drop() or use the complement: build a list of columns to drop, then call df.drop(columns=cols_to_drop). Alternatively, get the columns you don't want and pass them to drop.

import pandas as pd

df = pd.DataFrame({
    'id': [1, 2],
    'name': ['A', 'B'],
    'temp_col1': [0, 0],
    'temp_col2': [0, 0],
    'score': [90, 80]
})

# Drop columns whose name starts with 'temp_'
temp_cols = [c for c in df.columns if c.startswith('temp_')]
cleaned = df.drop(columns=temp_cols)
print(cleaned)
#    id name  score
# 0   1    A     90
# 1   2    B     80

Selecting Numeric Columns with select_dtypes

When you want columns of a particular data type rather than a name pattern, use df.select_dtypes(include=). Passing include='number' selects all numeric columns (int and float), include='object' selects string columns, and include='datetime' selects datetime columns.

This is especially useful before applying numeric aggregations — you never accidentally call mean() on a text column.

import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bob'],
    'age': [25, 30],
    'salary': [50000.0, 70000.0],
    'dept': ['Eng', 'HR']
})

# Select only numeric columns
numeric_df = df.select_dtypes(include='number')
print(numeric_df)
#    age   salary
# 0   25  50000.0
# 1   30  70000.0

# Exclude numeric columns
text_df = df.select_dtypes(exclude='number')
print(text_df)
#     name dept
# 0  Alice  Eng
# 1    Bob   HR

filter() on Row Index with axis=0

By default, filter() operates on columns (axis=1). But you can also filter rows by index label by passing axis=0. This is useful when your DataFrame has meaningful string index labels and you want to select rows whose index label matches a pattern — a less common but handy trick.

import pandas as pd

df = pd.DataFrame({
    'value': [10, 20, 30, 40]
}, index=['alpha_a', 'beta_b', 'alpha_c', 'gamma_d'])

# Filter rows by index label pattern
alpha_rows = df.filter(like='alpha', axis=0)
print(alpha_rows)
#          value
# alpha_a     10
# alpha_c     30

Combining Column Selection Patterns

Real column selection tasks often combine multiple strategies: regex to find a base set, then further filtering with a list comprehension. Building the column list in steps and inspecting it before slicing prevents silent bugs where you accidentally drop needed columns or include unwanted ones.

import pandas as pd

df = pd.DataFrame(columns=[
    'user_id', 'user_name', 'user_email',
    'product_id', 'product_name', 'product_price',
    'order_total', 'order_date'
])

# Step 1: columns that start with 'user_' or 'order_'
step1 = [c for c in df.columns if c.startswith('user_') or c.startswith('order_')]

# Step 2: exclude email (PII)
final_cols = [c for c in step1 if 'email' not in c]
print(final_cols)
# ['user_id', 'user_name', 'order_total', 'order_date']

Reordering Columns with Pattern

Pattern-based selection is also useful for reordering columns. Select the columns you want first (e.g., ID and metadata), then append the remaining columns in a specific order. Reordering makes DataFrames easier to read and ensures outputs have a consistent structure.

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'metric_b': [1], 'metric_a': [2],
    'id': [10], 'label': ['x'],
    'metric_c': [3]
})

# Put id and label first, then sort metric columns alphabetically
id_cols = ['id', 'label']
metric_cols = sorted([c for c in df.columns if c.startswith('metric')])
ordered = df[id_cols + metric_cols]
print(ordered.columns.tolist())
# ['id', 'label', 'metric_a', 'metric_b', 'metric_c']

Practical Example: Wide Survey Data

A classic use case for pattern selection is a wide survey dataset where each question generates multiple columns like q1_score, q1_text, q2_score, etc. You can extract all score columns with one regex, compute their mean, and keep the analysis columns separate from free-text responses.

import pandas as pd

survey = pd.DataFrame({
    'respondent_id': [1, 2, 3],
    'q1_score': [4, 3, 5],
    'q2_score': [3, 4, 4],
    'q3_score': [5, 5, 3],
    'q1_comment': ['good', 'ok', 'great'],
    'q2_comment': ['fine', 'nice', 'meh']
})

# All score columns
score_cols = survey.filter(regex='_score$').columns
survey['avg_score'] = survey[score_cols].mean(axis=1)
print(survey[['respondent_id', 'avg_score']])
#    respondent_id  avg_score
# 0              1   4.000000
# 1              2   4.000000
# 2              3   4.000000

Quick Check

Test your understanding of selecting columns by pattern.

Lesson Recap

In this lesson you learned: filter(like=) selects columns containing a substring, filter(regex=) uses regular expressions for flexible pattern matching, and list comprehensions on df.columns with Python string methods offer maximum flexibility. Use select_dtypes() to select by data type. Next up we explore detecting missing values (NaN) in DataFrames.

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filter(like=), filter(regex=), 리스트 컴프리헨션을 사용해 이름 패턴과 일치하는 열을 선택합니다. 브라우저에서 직접 실행하는 실습 코드로 Pandas & NumPy Academy을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.

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