Python으로 for 루프를 사용하여 CSV 파일의 각 행을 반복하는 방법

with open('example.csv', mode='r', encoding='utf-8') as file:
    print(file.read())

import csv

# CSV 파일을 열기
with open('example.csv', mode='r', encoding='utf-8') as file:
    reader = csv.reader(file)  # csv 리더 생성
    for row in reader:  # 각 행을 반복 처리
        print(row)  # 각 행의 리스트 출력

Name,Age,City
Alice,30,New York
Bob,25,Los Angeles

['Name', 'Age', 'City']
['Alice', '30', 'New York']
['Bob', '25', 'Los Angeles']

with open('example.csv', mode='r', encoding='utf-8') as file:
    reader = csv.reader(file)
    next(reader)  # 헤더 행 건너뛰기
    for row in reader:
        print(row)

import csv

with open('example.csv', mode='r', encoding='utf-8') as file:
    reader = csv.reader(file)
    next(reader)  # 헤더 건너뛰기
    for row in reader:
        # 두 번째 열(인덱스 1)의 값 추출
        age = row[1]
        print(f"Age: {age}")

Name,Age,City
Alice,30,New York
Bob,25,Los Angeles

Age: 30  
Age: 25  

with open('example.csv', mode='r', encoding='utf-8') as file:
    reader = csv.reader(file)
    next(reader)  # 헤더 건너뛰기
    for row in reader:
        name = row[0]  # 첫 번째 열
        city = row[2]  # 세 번째 열
        print(f"{name} lives in {city}.")

Alice lives in New York.  
Bob lives in Los Angeles.  

with open('example.csv', mode='r', encoding='utf-8') as file:
    reader = csv.reader(file)
    next(reader)  # 헤더 건너뛰기
    for row in reader:
        try:
            name = row[0]
            age = int(row[1])  # 나이를 숫자로 변환
            print(f"{name} is {age} years old.")
        except (IndexError, ValueError) as e:
            print(f"Error processing row: {row}, Error: {e}")

import csv

with open('example.csv', mode='r', encoding='utf-8') as file:
    reader = csv.DictReader(file)
    for row in reader:
        # 열 이름을 키로 값 추출
        name = row['Name']
        age = row['Age']
        city = row['City']
        print(f"{name} is {age} years old and lives in {city}.")

Name,Age,City
Alice,30,New York
Bob,25,Los Angeles

Alice is 30 years old and lives in New York.  
Bob is 25 years old and lives in Los Angeles.  

with open('example.csv', mode='r', encoding='utf-8') as file:
    reader = csv.DictReader(file, fieldnames=['Name', 'Age', 'City'])  # 사용자 정의 열 이름 지정
    next(reader)  # 첫 번째 행을 건너뛰기
    for row in reader:
        print(row)

with open('example.csv', mode='r', encoding='utf-8') as file:
    reader = csv.DictReader(file)
    for

 row in reader:
        name = row.get('Name', 'Unknown')  # 기본값 설정
        age = row.get('Age', 'N/A')
        print(f"Name: {name}, Age: {age}")

with open('example.csv', mode='r', encoding='utf-8') as file:
    print(file.read())

with open('example.csv', mode='r', encoding='shift_jis') as file:
    print(file.read())

with open('example.csv', mode='r', encoding='utf-8', errors='ignore') as file:
    print(file.read())  # 에러 무시

with open('example.csv', mode='r', encoding='utf-8', errors='replace') as file:
    print(file.read())  # 잘못된 문자를 교체

import chardet

# 인코딩 검출
with open('example.csv', mode='rb') as file:
    result = chardet.detect(file.read())
    print(result['encoding'])

import csv

with open('large_file.csv', mode='r', encoding='utf-8') as file:
    reader = csv.reader(file)
    for row in reader:
        # 각 행을 처리
        print(row)

import csv
from itertools import islice

with open('large_file.csv', mode='r', encoding='utf-8') as file:
    reader = csv.reader(file)
    while True:
        batch = list(islice(reader, 100))  # 100행씩 읽기
        if not batch:
            break
        # 배치 처리 실행
        for row in batch:
            print(row)

import pandas as pd

for chunk in pd.read_csv('large_file.csv', chunksize=1000, encoding='utf-8'):
    # 각 청크 처리
    print(chunk)

import csv
from multiprocessing import Pool

def process_chunk(chunk):
    for row in chunk:
        print(row)

def split_file(filename, chunk_size=1000):
    with open(filename, mode='r', encoding='utf-8') as file:
        reader = csv.reader(file)
        batch = []
        for i, row in enumerate(reader):
            batch.append(row)
            if (i + 1) % chunk_size == 0:
                yield batch
                batch = []
        if batch:
            yield batch

if __name__ == "__main__":
    filename = 'large_file.csv'
    with Pool(4) as pool:  # 4 프로세스로 병렬 처리
        pool.map(process_chunk, split_file(filename))

import csv

with open('large_file.csv', mode='r', encoding='utf-8') as infile, \
     open('filtered_file.csv', mode='w', encoding='utf-8', newline='') as outfile:
    reader = csv.reader(infile)
    writer = csv.writer(outfile)
    for row in reader:
        if int(row[1]) > 50:  # 조건: 2번째 열의 값이 50을 초과
            writer.writerow(row)

import csv

with open('example.csv', mode='r', encoding='utf-8') as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)

import csv

data = [
    ['Name', 'Age', 'City'],
    ['Alice', '30', 'New York'],
    ['Bob', '25', 'Los Angeles']
]

with open('output.csv', mode='w', encoding='utf-8', newline='') as file:
    writer = csv.writer(file)
    writer.writerows(data)

import csv

with open('input.csv', mode='r', encoding='utf-8') as infile, \
     open('output.csv', mode='w', encoding='utf-8', newline='') as outfile:
    reader = csv.reader(infile)
    writer = csv.writer(outfile)
    for row in reader:
        writer.writerow(row)

file = open('example.csv', mode='r', encoding='utf-8')
reader = csv.reader(file)
for row in reader:
    print(row)
file.close()

import csv

with open('input.csv', mode='r', encoding='utf-8') as infile, \
     open('filtered_output.csv', mode='w', encoding='utf-8', newline='') as outfile:
    reader = csv.reader(infile)
    writer = csv.writer(outfile)
    for row in reader:
        if row[1] == '30':  # 조건: 2번째 열의 값이 '30'
            writer.writerow(row)

import csv

# 입력 파일과 출력 파일 경로
input_file = 'input.csv'
output_file = 'output.csv'

with open(input_file, mode='r', encoding='utf-8') as infile, \
     open(output_file, mode='w', encoding='utf-8', newline='') as outfile:
    reader = csv.reader(infile)
    writer = csv.writer(outfile)

    # 헤더 처리 (특정 열만 추출)
    header = next(reader)
    selected_columns = [0, 1]  # 추출할 열(예: 1열과 2열)
    writer.writerow([header[i] for i in selected_columns])

    # 각 행에서 특정 열을 추출하여 작성
    for row in reader:
        writer.writerow([row[i] for i in selected_columns])

Name,Age,City,Country
Alice,30,New York,USA
Bob,25,Los Angeles,USA
Charlie,35,London,UK

Name,Age
Alice,30
Bob,25
Charlie,35

with open(input_file, mode='r', encoding='utf-8') as infile, \
     open(output_file, mode='w', encoding='utf-8', newline='') as outfile:
    reader = csv.DictReader(infile)
    writer = csv.writer(outfile)

    # 추출할 열 이름
    columns_to_extract = ['Name', 'City']
    writer.writerow(columns_to_extract)

    # 각 행에서 지정된 열을 추출하여 작성
    for row in reader:
        writer.writerow([row[col] for col in columns_to_extract])

with open(input_file, mode='r', encoding='utf-8') as infile, \
     open(output_file, mode='w', encoding='utf-8', newline='') as outfile:
    reader = csv.DictReader(infile)
    writer = csv.DictWriter(outfile, fieldnames=['Name', 'City'])
    writer.writeheader()

    for row in reader:
        if int(row['Age']) > 30:  # 나이가 30 이상인 데이터만 추출
            writer.writerow({'Name': row['Name'], 'City': row['City']})

Name,City
Charlie,London