■ DataFrameGroupBy 클래스의 cumcount 메소드를 사용해 그룹 내 항목 순서를 구하는 방법을 보여준다.
▶ main.py
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import pandas as pd url = "https://raw.githubusercontent.com/pandas-dev/pandas/main/pandas/tests/io/data/csv/tips.csv" dataFrame1 = pd.read_csv(url) print(dataFrame1) """ total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 3 23.68 3.31 Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 .. ... ... ... ... ... ... ... 239 29.03 5.92 Male No Sat Dinner 3 240 27.18 2.00 Female Yes Sat Dinner 2 241 22.67 2.00 Male Yes Sat Dinner 2 242 17.82 1.75 Male No Sat Dinner 2 243 18.78 3.00 Female No Thur Dinner 2 [244 rows x 7 columns] """ print() dataFrame2 = dataFrame1.sort_values(["total_bill"], ascending = False) print(dataFrame2) """ total_bill tip sex smoker day time size 170 50.81 10.00 Male Yes Sat Dinner 3 212 48.33 9.00 Male No Sat Dinner 4 59 48.27 6.73 Male No Sat Dinner 4 156 48.17 5.00 Male No Sun Dinner 6 182 45.35 3.50 Male Yes Sun Dinner 3 .. ... ... ... ... ... ... ... 149 7.51 2.00 Male No Thur Lunch 2 111 7.25 1.00 Female No Sat Dinner 1 172 7.25 5.15 Male Yes Sun Dinner 2 92 5.75 1.00 Female Yes Fri Dinner 2 67 3.07 1.00 Female Yes Sat Dinner 1 [244 rows x 7 columns] """ print() dataFrameGroupBy = dataFrame2.groupby(["day"]) series = dataFrameGroupBy.cumcount() + 1 # 그룹 내에서 각 항목의 누적 카운트(순서)를 반환한다. print(series) print() """ 170 1 212 2 59 3 156 1 182 2 .. 149 62 111 86 172 76 92 19 67 87 Length: 244, dtype: int64 """ |
▶ requirements.txt
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numpy==2.1.2 pandas==2.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 six==1.16.0 tzdata==2024.2 |
※ pip install pandas 명령을 실행했다.