11월 2024 - 8 중 7 번째 페이지

[PYTHON/PANDAS] DataFrame 클래스 : loc 속성을 사용해 특정 조건에 해당하는 컬럼 값 일괄 변경하기

■ DataFrame 클래스의 loc 속성을 사용해 특정 조건에 해당하는 컬럼의 값을 일괄 변경하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame = pd.DataFrame(
     {
          "name"  : ["김철수", "이영희", "박민수", "정지은"],
          "age"   : [25, 30, 35, 28],
          "city"  : ["서울", "부산", "서울", "대전"],
          "score" : [85, 92, 78, 95]
     }
)

print(dataFrame)

"""
  name  age city  score
0  김철수   25   서울     85
1  이영희   30   부산     92
2  박민수   35   서울     78
3  정지은   28   대전     95
"""

print()

dataFrame.loc[dataFrame["city"] == "서울", "score"] = 100

print(dataFrame)

"""
  name  age city  score
0  김철수   25   서울    100
1  이영희   30   부산     92
2  박민수   35   서울    100
3  정지은   28   대전     95
"""

import pandas as pd

dataFrame = pd.DataFrame(

{

"name" : ["김철수", "이영희", "박민수", "정지은"],

"age" : [25, 30, 35, 28],

"city" : ["서울", "부산", "서울", "대전"],

"score" : [85, 92, 78, 95]

}

)

print(dataFrame)

"""

name age city score

0 김철수 25 서울 85

1 이영희 30 부산 92

2 박민수 35 서울 78

3 정지은 28 대전 95

"""

print()

dataFrame.loc[dataFrame["city"] == "서울", "score"] = 100

print(dataFrame)

"""

name age city score

0 김철수 25 서울 100

1 이영희 30 부산 92

2 박민수 35 서울 100

3 정지은 28 대전 95

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※

[PYTHON/PANDAS] DataFrame 클래스 : [] 연산자를 사용해 특정 컬럼 값 일괄 변경하기

■ DataFrame 클래스에서 [] 연산자를 사용해 특정 컬럼의 값을 일괄 변경하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame = pd.DataFrame(
     {
          "name"  : ["김철수", "이영희", "박민수", "정지은"],
          "age"   : [25, 30, 35, 28],
          "city"  : ["서울", "부산", "서울", "대전"],
          "score" : [85, 92, 78, 95]
     }
)

print(dataFrame)

"""
  name  age city  score
0  김철수   25   서울     85
1  이영희   30   부산     92
2  박민수   35   서울     78
3  정지은   28   대전     95
"""

print()

dataFrame["age"] = dataFrame["age"] + 1

print(dataFrame)

"""
  name  age city  score
0  김철수   26   서울     85
1  이영희   31   부산     92
2  박민수   36   서울     78
3  정지은   29   대전     95
"""

import pandas as pd

dataFrame = pd.DataFrame(

{

"name" : ["김철수", "이영희", "박민수", "정지은"],

"age" : [25, 30, 35, 28],

"city" : ["서울", "부산", "서울", "대전"],

"score" : [85, 92, 78, 95]

}

)

print(dataFrame)

"""

name age city score

0 김철수 25 서울 85

1 이영희 30 부산 92

2 박민수 35 서울 78

3 정지은 28 대전 95

"""

print()

dataFrame["age"] = dataFrame["age"] + 1

print(dataFrame)

"""

name age city score

0 김철수 26 서울 85

1 이영희 31 부산 92

2 박민수 36 서울 78

3 정지은 29 대전 95

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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

[PYTHON/PANDAS] DataFrameGroupBy 클래스 : cumcount 메소드를 사용해 그룹 내 항목 순서 구하기 2

■ DataFrameGroupBy 클래스의 cumcount 메소드를 사용해 그룹 내 항목 순서를 구하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame = pd.DataFrame(
    {
     "A" : ["a", "a", "a", "b", "b", "b"],
     "B" : [1, 2, 3, 4, 5, 6]
     }
)

dataFrameGroupBy = dataFrame.groupby("A")

series = dataFrameGroupBy.cumcount()

print(series)

"""
0    0
1    1
2    2
3    0
4    1
5    2
dtype: int64
"""

import pandas as pd

dataFrame = pd.DataFrame(

{

"A" : ["a", "a", "a", "b", "b", "b"],

"B" : [1, 2, 3, 4, 5, 6]

}

)

dataFrameGroupBy = dataFrame.groupby("A")

series = dataFrameGroupBy.cumcount()

print(series)

"""

0 0

1 1

2 2

3 0

4 1

5 2

dtype: int64

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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

[PYTHON/PANDAS] Series 클래스 : rank 메소드에서 method 인자를 사용하기

■ Series 클래스의 rank 메소드에서 method 인자를 사용하는 방법을 보여준다. ▶ main.py


import pandas as pd

series1 = pd.Series([10, 20, 20, 30])

series2 = series1.rank(method = "min")

print(series2)

"""
0    1.0
1    2.0
2    2.0
3    4.0
dtype: float64
"""

print()

series3 = series1.rank(method = "max")

print(series3)

"""
0    1.0
1    3.0
2    3.0
3    4.0
dtype: float64
"""

print()

series3 = series1.rank(method = "first")

print(series3)

"""
dtype: float64
0    1.0
1    2.0
2    3.0
3    4.0
dtype: float64
"""

print()

series4 = series1.rank(method = "dense")

print(series4)

"""
0    1.0
1    2.0
2    2.0
3    3.0
dtype: float64
"""

print()

series5 = series1.rank(method = "average")

print(series5)

"""
0    1.0
1    2.5
2    2.5
3    4.0
dtype: float64
"""

import pandas as pd

series1 = pd.Series([10, 20, 20, 30])

series2 = series1.rank(method = "min")

print(series2)

"""

0 1.0

1 2.0

2 2.0

3 4.0

dtype: float64

"""

print()

series3 = series1.rank(method = "max")

print(series3)

"""

0 1.0

1 3.0

2 3.0

3 4.0

dtype: float64

"""

print()

series3 = series1.rank(method = "first")

print(series3)

"""

dtype: float64

0 1.0

1 2.0

2 3.0

3 4.0

dtype: float64

"""

print()

series4 = series1.rank(method = "dense")

print(series4)

"""

0 1.0

1 2.0

2 2.0

3 3.0

dtype: float64

"""

print()

series5 = series1.rank(method = "average")

print(series5)

"""

0 1.0

1 2.5

2 2.5

3 4.0

dtype: float64

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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.

[PYTHON/PANDAS] DataFrame 클래스 : 그룹당 상위 N개 행 데이터 구하기 3

■ DataFrame 클래스에서 그룹당 상위 N개 행 데이터를 구하는 방법을 보여준다. ▶ main.py


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()

dataFrameGroupBy = dataFrame1.groupby(["sex"])

seriesGroupBy = dataFrameGroupBy["tip"]

series = seriesGroupBy.rank(method = "min")

print(series)

"""
0        4.0
1       20.0
2      109.0
3      103.0
4       70.0
       ...
239    150.0
240     18.0
241     29.0
242     23.0
243     49.0
Name: tip, Length: 244, dtype: float64
"""

print()

dataFrame2 = dataFrame1[dataFrame1["tip"] < 2]

print(dataFrame2)

"""
     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
8         15.04  1.96    Male     No   Sun  Dinner     2
10        10.27  1.71    Male     No   Sun  Dinner     2
12        15.42  1.57    Male     No   Sun  Dinner     2
16        10.33  1.67  Female     No   Sun  Dinner     3
30         9.55  1.45    Male     No   Sat  Dinner     2
43         9.68  1.32    Male     No   Sun  Dinner     2
53         9.94  1.56    Male     No   Sun  Dinner     2
57        26.41  1.50  Female     No   Sat  Dinner     2
58        11.24  1.76    Male    Yes   Sat  Dinner     2
62        11.02  1.98    Male    Yes   Sat  Dinner     2
67         3.07  1.00  Female    Yes   Sat  Dinner     1
70        12.02  1.97    Male     No   Sat  Dinner     2
75        10.51  1.25    Male     No   Sat  Dinner     2
82        10.07  1.83  Female     No  Thur   Lunch     1
92         5.75  1.00  Female    Yes   Fri  Dinner     2
97        12.03  1.50    Male    Yes   Fri  Dinner     2
99        12.46  1.50    Male     No   Fri  Dinner     2
105       15.36  1.64    Male    Yes   Sat  Dinner     2
111        7.25  1.00  Female     No   Sat  Dinner     1
117       10.65  1.50  Female     No  Thur   Lunch     2
118       12.43  1.80  Female     No  Thur   Lunch     2
121       13.42  1.68  Female     No  Thur   Lunch     2
126        8.52  1.48    Male     No  Thur   Lunch     2
130       19.08  1.50    Male     No  Thur   Lunch     2
132       11.17  1.50  Female     No  Thur   Lunch     2
135        8.51  1.25  Female     No  Thur   Lunch     2
145        8.35  1.50  Female     No  Thur   Lunch     2
146       18.64  1.36  Female     No  Thur   Lunch     3
147       11.87  1.63  Female     No  Thur   Lunch     2
148        9.78  1.73    Male     No  Thur   Lunch     2
168       10.59  1.61  Female    Yes   Sat  Dinner     2
190       15.69  1.50    Male    Yes   Sun  Dinner     2
195        7.56  1.44    Male     No  Thur   Lunch     2
215       12.90  1.10  Female    Yes   Sat  Dinner     2
217       11.59  1.50    Male    Yes   Sat  Dinner     2
218        7.74  1.44    Male    Yes   Sat  Dinner     2
222        8.58  1.92    Male    Yes   Fri   Lunch     1
224       13.42  1.58    Male    Yes   Fri   Lunch     2
233       10.77  1.47    Male     No   Sat  Dinner     2
235       10.07  1.25    Male     No   Sat  Dinner     2
236       12.60  1.00    Male    Yes   Sat  Dinner     2
237       32.83  1.17    Male    Yes   Sat  Dinner     2
242       17.82  1.75    Male     No   Sat  Dinner     2
"""

print()

dataFrame3 = dataFrame2.assign(rnk_min = series)

print(dataFrame3)

"""
     total_bill   tip     sex smoker   day    time  size  rnk_min
0         16.99  1.01  Female     No   Sun  Dinner     2      4.0
1         10.34  1.66    Male     No   Sun  Dinner     3     20.0
8         15.04  1.96    Male     No   Sun  Dinner     2     26.0
10        10.27  1.71    Male     No   Sun  Dinner     2     21.0
12        15.42  1.57    Male     No   Sun  Dinner     2     17.0
16        10.33  1.67  Female     No   Sun  Dinner     3     14.0
30         9.55  1.45    Male     No   Sat  Dinner     2      8.0
43         9.68  1.32    Male     No   Sun  Dinner     2      5.0
53         9.94  1.56    Male     No   Sun  Dinner     2     16.0
57        26.41  1.50  Female     No   Sat  Dinner     2      8.0
58        11.24  1.76    Male    Yes   Sat  Dinner     2     24.0
62        11.02  1.98    Male    Yes   Sat  Dinner     2     28.0
67         3.07  1.00  Female    Yes   Sat  Dinner     1      1.0
70        12.02  1.97    Male     No   Sat  Dinner     2     27.0
75        10.51  1.25    Male     No   Sat  Dinner     2      3.0
82        10.07  1.83  Female     No  Thur   Lunch     1     17.0
92         5.75  1.00  Female    Yes   Fri  Dinner     2      1.0
97        12.03  1.50    Male    Yes   Fri  Dinner     2     11.0
99        12.46  1.50    Male     No   Fri  Dinner     2     11.0
105       15.36  1.64    Male    Yes   Sat  Dinner     2     19.0
111        7.25  1.00  Female     No   Sat  Dinner     1      1.0
117       10.65  1.50  Female     No  Thur   Lunch     2      8.0
118       12.43  1.80  Female     No  Thur   Lunch     2     16.0
121       13.42  1.68  Female     No  Thur   Lunch     2     15.0
126        8.52  1.48    Male     No  Thur   Lunch     2     10.0
130       19.08  1.50    Male     No  Thur   Lunch     2     11.0
132       11.17  1.50  Female     No  Thur   Lunch     2      8.0
135        8.51  1.25  Female     No  Thur   Lunch     2      6.0
145        8.35  1.50  Female     No  Thur   Lunch     2      8.0
146       18.64  1.36  Female     No  Thur   Lunch     3      7.0
147       11.87  1.63  Female     No  Thur   Lunch     2     13.0
148        9.78  1.73    Male     No  Thur   Lunch     2     22.0
168       10.59  1.61  Female    Yes   Sat  Dinner     2     12.0
190       15.69  1.50    Male    Yes   Sun  Dinner     2     11.0
195        7.56  1.44    Male     No  Thur   Lunch     2      6.0
215       12.90  1.10  Female    Yes   Sat  Dinner     2      5.0
217       11.59  1.50    Male    Yes   Sat  Dinner     2     11.0
218        7.74  1.44    Male    Yes   Sat  Dinner     2      6.0
222        8.58  1.92    Male    Yes   Fri   Lunch     1     25.0
224       13.42  1.58    Male    Yes   Fri   Lunch     2     18.0
233       10.77  1.47    Male     No   Sat  Dinner     2      9.0
235       10.07  1.25    Male     No   Sat  Dinner     2      3.0
236       12.60  1.00    Male    Yes   Sat  Dinner     2      1.0
237       32.83  1.17    Male    Yes   Sat  Dinner     2      2.0
242       17.82  1.75    Male     No   Sat  Dinner     2     23.0
"""

print()

dataFrame4 = dataFrame3.query("rnk_min < 3")

print(dataFrame4)

"""
     total_bill   tip     sex smoker  day    time  size  rnk_min
67         3.07  1.00  Female    Yes  Sat  Dinner     1      1.0
92         5.75  1.00  Female    Yes  Fri  Dinner     2      1.0
111        7.25  1.00  Female     No  Sat  Dinner     1      1.0
236       12.60  1.00    Male    Yes  Sat  Dinner     2      1.0
237       32.83  1.17    Male    Yes  Sat  Dinner     2      2.0
"""

print()

dataFrame5 = dataFrame4.sort_values(["sex", "rnk_min"])

print(dataFrame5)

"""
     total_bill   tip     sex smoker  day    time  size  rnk_min
67         3.07  1.00  Female    Yes  Sat  Dinner     1      1.0
92         5.75  1.00  Female    Yes  Fri  Dinner     2      1.0
111        7.25  1.00  Female     No  Sat  Dinner     1      1.0
236       12.60  1.00    Male    Yes  Sat  Dinner     2      1.0
237       32.83  1.17    Male    Yes  Sat  Dinner     2      2.0
"""

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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()

dataFrameGroupBy = dataFrame1.groupby(["sex"])

seriesGroupBy = dataFrameGroupBy["tip"]

series = seriesGroupBy.rank(method = "min")

print(series)

"""

0 4.0

1 20.0

2 109.0

3 103.0

4 70.0

...

239 150.0

240 18.0

241 29.0

242 23.0

243 49.0

Name: tip, Length: 244, dtype: float64

"""

print()

dataFrame2 = dataFrame1[dataFrame1["tip"] < 2]

print(dataFrame2)

"""

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

8 15.04 1.96 Male No Sun Dinner 2

10 10.27 1.71 Male No Sun Dinner 2

12 15.42 1.57 Male No Sun Dinner 2

16 10.33 1.67 Female No Sun Dinner 3

30 9.55 1.45 Male No Sat Dinner 2

43 9.68 1.32 Male No Sun Dinner 2

53 9.94 1.56 Male No Sun Dinner 2

57 26.41 1.50 Female No Sat Dinner 2

58 11.24 1.76 Male Yes Sat Dinner 2

62 11.02 1.98 Male Yes Sat Dinner 2

67 3.07 1.00 Female Yes Sat Dinner 1

70 12.02 1.97 Male No Sat Dinner 2

75 10.51 1.25 Male No Sat Dinner 2

82 10.07 1.83 Female No Thur Lunch 1

92 5.75 1.00 Female Yes Fri Dinner 2

97 12.03 1.50 Male Yes Fri Dinner 2

99 12.46 1.50 Male No Fri Dinner 2

105 15.36 1.64 Male Yes Sat Dinner 2

111 7.25 1.00 Female No Sat Dinner 1

117 10.65 1.50 Female No Thur Lunch 2

118 12.43 1.80 Female No Thur Lunch 2

121 13.42 1.68 Female No Thur Lunch 2

126 8.52 1.48 Male No Thur Lunch 2

130 19.08 1.50 Male No Thur Lunch 2

132 11.17 1.50 Female No Thur Lunch 2

135 8.51 1.25 Female No Thur Lunch 2

145 8.35 1.50 Female No Thur Lunch 2

146 18.64 1.36 Female No Thur Lunch 3

147 11.87 1.63 Female No Thur Lunch 2

148 9.78 1.73 Male No Thur Lunch 2

168 10.59 1.61 Female Yes Sat Dinner 2

190 15.69 1.50 Male Yes Sun Dinner 2

195 7.56 1.44 Male No Thur Lunch 2

215 12.90 1.10 Female Yes Sat Dinner 2

217 11.59 1.50 Male Yes Sat Dinner 2

218 7.74 1.44 Male Yes Sat Dinner 2

222 8.58 1.92 Male Yes Fri Lunch 1

224 13.42 1.58 Male Yes Fri Lunch 2

233 10.77 1.47 Male No Sat Dinner 2

235 10.07 1.25 Male No Sat Dinner 2

236 12.60 1.00 Male Yes Sat Dinner 2

237 32.83 1.17 Male Yes Sat Dinner 2

242 17.82 1.75 Male No Sat Dinner 2

"""

print()

dataFrame3 = dataFrame2.assign(rnk_min = series)

print(dataFrame3)

"""

total_bill tip sex smoker day time size rnk_min

0 16.99 1.01 Female No Sun Dinner 2 4.0

1 10.34 1.66 Male No Sun Dinner 3 20.0

8 15.04 1.96 Male No Sun Dinner 2 26.0

10 10.27 1.71 Male No Sun Dinner 2 21.0

12 15.42 1.57 Male No Sun Dinner 2 17.0

16 10.33 1.67 Female No Sun Dinner 3 14.0

30 9.55 1.45 Male No Sat Dinner 2 8.0

43 9.68 1.32 Male No Sun Dinner 2 5.0

53 9.94 1.56 Male No Sun Dinner 2 16.0

57 26.41 1.50 Female No Sat Dinner 2 8.0

58 11.24 1.76 Male Yes Sat Dinner 2 24.0

62 11.02 1.98 Male Yes Sat Dinner 2 28.0

67 3.07 1.00 Female Yes Sat Dinner 1 1.0

70 12.02 1.97 Male No Sat Dinner 2 27.0

75 10.51 1.25 Male No Sat Dinner 2 3.0

82 10.07 1.83 Female No Thur Lunch 1 17.0

92 5.75 1.00 Female Yes Fri Dinner 2 1.0

97 12.03 1.50 Male Yes Fri Dinner 2 11.0

99 12.46 1.50 Male No Fri Dinner 2 11.0

105 15.36 1.64 Male Yes Sat Dinner 2 19.0

111 7.25 1.00 Female No Sat Dinner 1 1.0

117 10.65 1.50 Female No Thur Lunch 2 8.0

118 12.43 1.80 Female No Thur Lunch 2 16.0

121 13.42 1.68 Female No Thur Lunch 2 15.0

126 8.52 1.48 Male No Thur Lunch 2 10.0

130 19.08 1.50 Male No Thur Lunch 2 11.0

132 11.17 1.50 Female No Thur Lunch 2 8.0

135 8.51 1.25 Female No Thur Lunch 2 6.0

145 8.35 1.50 Female No Thur Lunch 2 8.0

146 18.64 1.36 Female No Thur Lunch 3 7.0

147 11.87 1.63 Female No Thur Lunch 2 13.0

148 9.78 1.73 Male No Thur Lunch 2 22.0

168 10.59 1.61 Female Yes Sat Dinner 2 12.0

190 15.69 1.50 Male Yes Sun Dinner 2 11.0

195 7.56 1.44 Male No Thur Lunch 2 6.0

215 12.90 1.10 Female Yes Sat Dinner 2 5.0

217 11.59 1.50 Male Yes Sat Dinner 2 11.0

218 7.74 1.44 Male Yes Sat Dinner 2 6.0

222 8.58 1.92 Male Yes Fri Lunch 1 25.0

224 13.42 1.58 Male Yes Fri Lunch 2 18.0

233 10.77 1.47 Male No Sat Dinner 2 9.0

235 10.07 1.25 Male No Sat Dinner 2 3.0

236 12.60 1.00 Male Yes Sat Dinner 2 1.0

237 32.83 1.17 Male Yes Sat Dinner 2 2.0

242 17.82 1.75 Male No Sat Dinner 2 23.0

"""

print()

dataFrame4 = dataFrame3.query("rnk_min < 3")

print(dataFrame4)

"""

total_bill tip sex smoker day time size rnk_min

67 3.07 1.00 Female Yes Sat Dinner 1 1.0

92 5.75 1.00 Female Yes Fri Dinner 2 1.0

111 7.25 1.00 Female No Sat Dinner 1 1.0

236 12.60 1.00 Male Yes Sat Dinner 2 1.0

237 32.83 1.17 Male Yes Sat Dinner 2 2.0

"""

print()

dataFrame5 = dataFrame4.sort_values(["sex", "rnk_min"])

print(dataFrame5)

"""

total_bill tip sex smoker day time size rnk_min

67 3.07 1.00 Female Yes Sat Dinner 1 1.0

92 5.75 1.00 Female Yes Fri Dinner 2 1.0

111 7.25 1.00 Female No Sat Dinner 1 1.0

236 12.60 1.00 Male Yes Sat Dinner 2 1.0

237 32.83 1.17 Male Yes Sat Dinner 2 2.0

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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

[PYTHON/PANDAS] SeriesGroupBy 클래스 : rank 메소드에서 method/ascending 인자를 사용해 그룹 내 항목 순서 구하기

■ SeriesGroupBy 클래스의 rank 메소드에서 method/ascending 인자를 사용해 그룹 내 항목 순서를 구하는 방법을 보여준다. ▶ main.py


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()

dataFrameGroupBy = dataFrame1.groupby(["day"])

seriesGroupBy = dataFrameGroupBy["total_bill"]

series = seriesGroupBy.rank(method = "first", ascending = False)

print(series)

"""
0      49.0
1      68.0
2      34.0
3      27.0
4      23.0
       ...
239    13.0
240    16.0
241    26.0
242    47.0
243    20.0
Name: total_bill, Length: 244, dtype: float64
"""

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()

dataFrameGroupBy = dataFrame1.groupby(["day"])

seriesGroupBy = dataFrameGroupBy["total_bill"]

series = seriesGroupBy.rank(method = "first", ascending = False)

print(series)

"""

0 49.0

1 68.0

2 34.0

3 27.0

4 23.0

...

239 13.0

240 16.0

241 26.0

242 47.0

243 20.0

Name: total_bill, Length: 244, dtype: float64

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※

[PYTHON/PANDAS] DataFrameGroupBy 클래스 : cumcount 메소드를 사용해 그룹 내 항목 순서 구하기 1

■ DataFrameGroupBy 클래스의 cumcount 메소드를 사용해 그룹 내 항목 순서를 구하는 방법을 보여준다. ▶ main.py


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
"""

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


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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

[PYTHON/PANDAS] DataFrame 클래스 : 그룹당 상위 N개 행 데이터 구하기 2

■ DataFrame 클래스에서 그룹당 상위 N개 행 데이터를 구하는 방법을 보여준다. ▶ main.py


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()

dataFrameGroupBy = dataFrame1.groupby(["day"])

seriesGroupBy = dataFrameGroupBy["total_bill"]

series = seriesGroupBy.rank(method = "first", ascending = False)

print(series)

"""
0      49.0
1      68.0
2      34.0
3      27.0
4      23.0
       ...
239    13.0
240    16.0
241    26.0
242    47.0
243    20.0
Name: total_bill, Length: 244, dtype: float64
"""

print()

dataFrame2 = dataFrame1.assign(rank = series)

print(dataFrame2)

"""
     total_bill   tip     sex smoker   day    time  size  rank
0         16.99  1.01  Female     No   Sun  Dinner     2  49.0
1         10.34  1.66    Male     No   Sun  Dinner     3  68.0
2         21.01  3.50    Male     No   Sun  Dinner     3  34.0
3         23.68  3.31    Male     No   Sun  Dinner     2  27.0
4         24.59  3.61  Female     No   Sun  Dinner     4  23.0
..          ...   ...     ...    ...   ...     ...   ...   ...
239       29.03  5.92    Male     No   Sat  Dinner     3  13.0
240       27.18  2.00  Female    Yes   Sat  Dinner     2  16.0
241       22.67  2.00    Male    Yes   Sat  Dinner     2  26.0
242       17.82  1.75    Male     No   Sat  Dinner     2  47.0
243       18.78  3.00  Female     No  Thur  Dinner     2  20.0

[244 rows x 8 columns]
"""

print()

dataFrame3 = dataFrame2.query("rank < 3")

print(dataFrame3)

"""
     total_bill    tip     sex smoker   day    time  size  rank
90        28.97   3.00    Male    Yes   Fri  Dinner     2   2.0
95        40.17   4.73    Male    Yes   Fri  Dinner     4   1.0
142       41.19   5.00    Male     No  Thur   Lunch     5   2.0
156       48.17   5.00    Male     No   Sun  Dinner     6   1.0
170       50.81  10.00    Male    Yes   Sat  Dinner     3   1.0
182       45.35   3.50    Male    Yes   Sun  Dinner     3   2.0
197       43.11   5.00  Female    Yes  Thur   Lunch     4   1.0
212       48.33   9.00    Male     No   Sat  Dinner     4   2.0
"""

print()

dataFrame4 = dataFrame3.sort_values(["day", "rank"])

print(dataFrame4)

"""
     total_bill    tip     sex smoker   day    time  size  rnk
95        40.17   4.73    Male    Yes   Fri  Dinner     4  1.0
90        28.97   3.00    Male    Yes   Fri  Dinner     2  2.0
170       50.81  10.00    Male    Yes   Sat  Dinner     3  1.0
212       48.33   9.00    Male     No   Sat  Dinner     4  2.0
156       48.17   5.00    Male     No   Sun  Dinner     6  1.0
182       45.35   3.50    Male    Yes   Sun  Dinner     3  2.0
197       43.11   5.00  Female    Yes  Thur   Lunch     4  1.0
142       41.19   5.00    Male     No  Thur   Lunch     5  2.0
"""

100

101

102

103

104

105

106

107

108

109

110

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()

dataFrameGroupBy = dataFrame1.groupby(["day"])

seriesGroupBy = dataFrameGroupBy["total_bill"]

series = seriesGroupBy.rank(method = "first", ascending = False)

print(series)

"""

0 49.0

1 68.0

2 34.0

3 27.0

4 23.0

...

239 13.0

240 16.0

241 26.0

242 47.0

243 20.0

Name: total_bill, Length: 244, dtype: float64

"""

print()

dataFrame2 = dataFrame1.assign(rank = series)

print(dataFrame2)

"""

total_bill tip sex smoker day time size rank

0 16.99 1.01 Female No Sun Dinner 2 49.0

1 10.34 1.66 Male No Sun Dinner 3 68.0

2 21.01 3.50 Male No Sun Dinner 3 34.0

3 23.68 3.31 Male No Sun Dinner 2 27.0

4 24.59 3.61 Female No Sun Dinner 4 23.0

.. ... ... ... ... ... ... ... ...

239 29.03 5.92 Male No Sat Dinner 3 13.0

240 27.18 2.00 Female Yes Sat Dinner 2 16.0

241 22.67 2.00 Male Yes Sat Dinner 2 26.0

242 17.82 1.75 Male No Sat Dinner 2 47.0

243 18.78 3.00 Female No Thur Dinner 2 20.0

[244 rows x 8 columns]

"""

print()

dataFrame3 = dataFrame2.query("rank < 3")

print(dataFrame3)

"""

total_bill tip sex smoker day time size rank

90 28.97 3.00 Male Yes Fri Dinner 2 2.0

95 40.17 4.73 Male Yes Fri Dinner 4 1.0

142 41.19 5.00 Male No Thur Lunch 5 2.0

156 48.17 5.00 Male No Sun Dinner 6 1.0

170 50.81 10.00 Male Yes Sat Dinner 3 1.0

182 45.35 3.50 Male Yes Sun Dinner 3 2.0

197 43.11 5.00 Female Yes Thur Lunch 4 1.0

212 48.33 9.00 Male No Sat Dinner 4 2.0

"""

print()

dataFrame4 = dataFrame3.sort_values(["day", "rank"])

print(dataFrame4)

"""

total_bill tip sex smoker day time size rnk

95 40.17 4.73 Male Yes Fri Dinner 4 1.0

90 28.97 3.00 Male Yes Fri Dinner 2 2.0

170 50.81 10.00 Male Yes Sat Dinner 3 1.0

212 48.33 9.00 Male No Sat Dinner 4 2.0

156 48.17 5.00 Male No Sun Dinner 6 1.0

182 45.35 3.50 Male Yes Sun Dinner 3 2.0

197 43.11 5.00 Female Yes Thur Lunch 4 1.0

142 41.19 5.00 Male No Thur Lunch 5 2.0

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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

[PYTHON/PANDAS] DataFrame 클래스 : 그룹당 상위 N개 행 데이터 구하기 1

■ DataFrame 클래스에서 그룹당 상위 N개 행 데이터를 구하는 방법을 보여준다. ▶ main.py


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
"""

dataFrame3 = dataFrame1.assign(rank = series)

print(dataFrame3)

"""
     total_bill   tip     sex smoker   day    time  size  rank
0         16.99  1.01  Female     No   Sun  Dinner     2    49
1         10.34  1.66    Male     No   Sun  Dinner     3    68
2         21.01  3.50    Male     No   Sun  Dinner     3    34
3         23.68  3.31    Male     No   Sun  Dinner     2    27
4         24.59  3.61  Female     No   Sun  Dinner     4    23
..          ...   ...     ...    ...   ...     ...   ...   ...
239       29.03  5.92    Male     No   Sat  Dinner     3    13
240       27.18  2.00  Female    Yes   Sat  Dinner     2    16
241       22.67  2.00    Male    Yes   Sat  Dinner     2    26
242       17.82  1.75    Male     No   Sat  Dinner     2    47
243       18.78  3.00  Female     No  Thur  Dinner     2    20

[244 rows x 8 columns]
"""

print()

dataFrame4 = dataFrame3.query("rank < 3")

print(dataFrame4)

"""
     total_bill    tip     sex smoker   day    time  size  rank
90        28.97   3.00    Male    Yes   Fri  Dinner     2     2
95        40.17   4.73    Male    Yes   Fri  Dinner     4     1
142       41.19   5.00    Male     No  Thur   Lunch     5     2
156       48.17   5.00    Male     No   Sun  Dinner     6     1
170       50.81  10.00    Male    Yes   Sat  Dinner     3     1
182       45.35   3.50    Male    Yes   Sun  Dinner     3     2
197       43.11   5.00  Female    Yes  Thur   Lunch     4     1
212       48.33   9.00    Male     No   Sat  Dinner     4     2
"""

print()

dataFrame5 = dataFrame4.sort_values(["day", "rank"])

print(dataFrame5)

"""
     total_bill    tip     sex smoker   day    time  size  rank
95        40.17   4.73    Male    Yes   Fri  Dinner     4     1
90        28.97   3.00    Male    Yes   Fri  Dinner     2     2
170       50.81  10.00    Male    Yes   Sat  Dinner     3     1
212       48.33   9.00    Male     No   Sat  Dinner     4     2
156       48.17   5.00    Male     No   Sun  Dinner     6     1
182       45.35   3.50    Male    Yes   Sun  Dinner     3     2
197       43.11   5.00  Female    Yes  Thur   Lunch     4     1
142       41.19   5.00    Male     No  Thur   Lunch     5     2
"""

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

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125

126

127

128

129

130

131

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

"""

dataFrame3 = dataFrame1.assign(rank = series)

print(dataFrame3)

"""

total_bill tip sex smoker day time size rank

0 16.99 1.01 Female No Sun Dinner 2 49

1 10.34 1.66 Male No Sun Dinner 3 68

2 21.01 3.50 Male No Sun Dinner 3 34

3 23.68 3.31 Male No Sun Dinner 2 27

4 24.59 3.61 Female No Sun Dinner 4 23

.. ... ... ... ... ... ... ... ...

239 29.03 5.92 Male No Sat Dinner 3 13

240 27.18 2.00 Female Yes Sat Dinner 2 16

241 22.67 2.00 Male Yes Sat Dinner 2 26

242 17.82 1.75 Male No Sat Dinner 2 47

243 18.78 3.00 Female No Thur Dinner 2 20

[244 rows x 8 columns]

"""

print()

dataFrame4 = dataFrame3.query("rank < 3")

print(dataFrame4)

"""

total_bill tip sex smoker day time size rank

90 28.97 3.00 Male Yes Fri Dinner 2 2

95 40.17 4.73 Male Yes Fri Dinner 4 1

142 41.19 5.00 Male No Thur Lunch 5 2

156 48.17 5.00 Male No Sun Dinner 6 1

170 50.81 10.00 Male Yes Sat Dinner 3 1

182 45.35 3.50 Male Yes Sun Dinner 3 2

197 43.11 5.00 Female Yes Thur Lunch 4 1

212 48.33 9.00 Male No Sat Dinner 4 2

"""

print()

dataFrame5 = dataFrame4.sort_values(["day", "rank"])

print(dataFrame5)

"""

total_bill tip sex smoker day time size rank

95 40.17 4.73 Male Yes Fri Dinner 4 1

90 28.97 3.00 Male Yes Fri Dinner 2 2

170 50.81 10.00 Male Yes Sat Dinner 3 1

212 48.33 9.00 Male No Sat Dinner 4 2

156 48.17 5.00 Male No Sun Dinner 6 1

182 45.35 3.50 Male Yes Sun Dinner 3 2

197 43.11 5.00 Female Yes Thur Lunch 4 1

142 41.19 5.00 Male No Thur Lunch 5 2

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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

[PYTHON/PANDAS] DataFrame 클래스 : nlargest/tail 메소드를 사용해 특정 컬럼의 값이 큰 순서로 N개 데이터 구하기 (오프셋 설정)

■ DataFrame 클래스의 nlargest/tail 메소드를 사용해 특정 컬럼의 값이 큰 순서로 N개 데이터를 구하는 방법을 보여준다. (오프셋 설정) ▶ main.py


import pandas as pd

dataFrame1 = pd.DataFrame(
    {
        "value" : [x for x in range(10)]
    }
)

print(dataFrame1)

"""
    value
0       0
1       1
2       2
3       3
4       4
5       5
6       6
7       7
8       8
9       9
"""

print()

dataOffset = 5
dataCount  = 3

dataFrame2 = dataFrame1.nlargest(dataOffset + dataCount, columns = "value")

print(dataFrame2)

"""
   value
9      9
8      8
7      7
6      6
5      5
4      4
3      3
2      2
"""

print()

dataFrame3 = dataFrame2.tail(dataCount)

print(dataFrame3)

"""
   value
4      4
3      3
2      2
"""

import pandas as pd

dataFrame1 = pd.DataFrame(

{

"value" : [x for x in range(10)]

}

)

print(dataFrame1)

"""

value

0 0

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

"""

print()

dataOffset = 5

dataCount = 3

dataFrame2 = dataFrame1.nlargest(dataOffset + dataCount, columns = "value")

print(dataFrame2)

"""

value

9 9

8 8

7 7

6 6

5 5

4 4

3 3

2 2

"""

print()

dataFrame3 = dataFrame2.tail(dataCount)

print(dataFrame3)

"""

value

4 4

3 3

2 2

"""

▶

[PYTHON/PANDAS] DataFrame 클래스 : nlargest 메소드에서 columns 인자를 사용해 특정 컬럼의 값이 큰 순서로 N개 데이터 구하기

■ DataFrame 클래스의 nlargest 메소드에서 columns 인자를 사용해 특정 컬럼의 값이 큰 순서로 N개 데이터를 구하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame1 = pd.DataFrame(
    {
        "value" : [x for x in range(10)]
    }
)

print(dataFrame1)

"""
    value
0       0
1       1
2       2
3       3
4       4
5       5
6       6
7       7
8       8
9       9
"""

print()

dataFrame2 = dataFrame1.nlargest(5, columns = "value")

print(dataFrame2)

"""
   value
9      9
8      8
7      7
6      6
5      5
"""

import pandas as pd

dataFrame1 = pd.DataFrame(

{

"value" : [x for x in range(10)]

}

)

print(dataFrame1)

"""

value

0 0

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

"""

print()

dataFrame2 = dataFrame1.nlargest(5, columns = "value")

print(dataFrame2)

"""

value

9 9

8 8

7 7

6 6

5 5

"""

▶

[PYTHON/PANDAS] DataFrame 클래스 : drop_duplicates 메소드를 사용해 UNION 데이터 만들기

■ DataFrame 클래스의 drop_duplicates 메소드를 사용해 UNION 데이터를 만드는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame1 = pd.DataFrame(
    {
        "city" : ["Chicago", "San Francisco", "New York City"],
        "rank" : range(1, 4)
    }
)

print(dataFrame1)

"""
            city  rank
0        Chicago     1
1  San Francisco     2
2  New York City     3
"""

print()

dataFrame2 = pd.DataFrame(
    {
        "city" : ["Chicago", "Boston", "Los Angeles"],
        "rank" : [1, 4, 5]
    }
)

print(dataFrame2)

"""
          city  rank
0      Chicago     1
1       Boston     4
2  Los Angeles     5
"""

print()

dataFrame3 = pd.concat([dataFrame1, dataFrame2])

print(dataFrame3)

"""
            city  rank
0        Chicago     1
1  San Francisco     2
2  New York City     3
0        Chicago     1
1         Boston     4
2    Los Angeles     5
"""

print()

dataFrame4 = dataFrame3.drop_duplicates()

print(dataFrame4)

"""
0        Chicago     1
1  San Francisco     2
2  New York City     3
1         Boston     4
2    Los Angeles     5
"""

import pandas as pd

dataFrame1 = pd.DataFrame(

{

"city" : ["Chicago", "San Francisco", "New York City"],

"rank" : range(1, 4)

}

)

print(dataFrame1)

"""

city rank

0 Chicago 1

1 San Francisco 2

2 New York City 3

"""

print()

dataFrame2 = pd.DataFrame(

{

"city" : ["Chicago", "Boston", "Los Angeles"],

"rank" : [1, 4, 5]

}

)

print(dataFrame2)

"""

city rank

0 Chicago 1

1 Boston 4

2 Los Angeles 5

"""

print()

dataFrame3 = pd.concat([dataFrame1, dataFrame2])

print(dataFrame3)

"""

city rank

0 Chicago 1

1 San Francisco 2

2 New York City 3

0 Chicago 1

1 Boston 4

2 Los Angeles 5

"""

print()

dataFrame4 = dataFrame3.drop_duplicates()

print(dataFrame4)

"""

0 Chicago 1

1 San Francisco 2

2 New York City 3

1 Boston 4

2 Los Angeles 5

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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

[PYTHON/PANDAS] concat 함수 : UNION ALL 데이터 만들기

■ concat 함수를 사용해 UNION ALL 데이터를 만드는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame1 = pd.DataFrame(
    {
        "city" : ["Chicago", "San Francisco", "New York City"],
        "rank" : range(1, 4)
    }
)

print(dataFrame1)

"""
            city  rank
0        Chicago     1
1  San Francisco     2
2  New York City     3
"""

print()

dataFrame2 = pd.DataFrame(
    {
        "city" : ["Chicago", "Boston", "Los Angeles"],
        "rank" : [1, 4, 5]
    }
)

print(dataFrame2)

"""
          city  rank
0      Chicago     1
1       Boston     4
2  Los Angeles     5
"""

print()

dataFrame3 = pd.concat([dataFrame1, dataFrame2])

print(dataFrame3)

"""
            city  rank
0        Chicago     1
1  San Francisco     2
2  New York City     3
0        Chicago     1
1         Boston     4
2    Los Angeles     5
"""

import pandas as pd

dataFrame1 = pd.DataFrame(

{

"city" : ["Chicago", "San Francisco", "New York City"],

"rank" : range(1, 4)

}

)

print(dataFrame1)

"""

city rank

0 Chicago 1

1 San Francisco 2

2 New York City 3

"""

print()

dataFrame2 = pd.DataFrame(

{

"city" : ["Chicago", "Boston", "Los Angeles"],

"rank" : [1, 4, 5]

}

)

print(dataFrame2)

"""

city rank

0 Chicago 1

1 Boston 4

2 Los Angeles 5

"""

print()

dataFrame3 = pd.concat([dataFrame1, dataFrame2])

print(dataFrame3)

"""

city rank

0 Chicago 1

1 San Francisco 2

2 New York City 3

0 Chicago 1

1 Boston 4

2 Los Angeles 5

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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.

[PYTHON/PANDAS] merge 함수 : on/how 인자를 사용해 FULL JOIN 데이터 만들기

■ merge 함수에서 on/how 인자를 사용해 FULL JOIN 데이터를 만드는 방법을 보여준다. ▶ main.py


import pandas as pd
import numpy  as np

dataFrame1 = pd.DataFrame({"key" : ["A", "B", "C", "D"], "value" : np.random.randn(4)})
dataFrame2 = pd.DataFrame({"key" : ["B", "D", "D", "E"], "value" : np.random.randn(4)})

print(dataFrame1)

"""
  key     value
0   A -0.050330
1   B  0.754153
2   C  0.693311
3   D -0.543704
"""

print()

print(dataFrame2)

"""
  key     value
0   B -0.291885
1   D  0.871071
2   D  0.270238
3   E  0.509288
"""

print()

dataFrame3 = pd.merge(dataFrame1, dataFrame2, on = "key", how = "outer") # FULL JOIN

print(dataFrame3)

"""
  key   value_x   value_y
0   A -0.050330       NaN
1   B  0.754153 -0.291885
2   C  0.693311       NaN
3   D -0.543704  0.871071
4   D -0.543704  0.270238
5   E       NaN  0.509288
"""

import pandas as pd

import numpy as np

dataFrame1 = pd.DataFrame({"key" : ["A", "B", "C", "D"], "value" : np.random.randn(4)})

dataFrame2 = pd.DataFrame({"key" : ["B", "D", "D", "E"], "value" : np.random.randn(4)})

print(dataFrame1)

"""

key value

0 A -0.050330

1 B 0.754153

2 C 0.693311

3 D -0.543704

"""

print()

print(dataFrame2)

"""

key value

0 B -0.291885

1 D 0.871071

2 D 0.270238

3 E 0.509288

"""

print()

dataFrame3 = pd.merge(dataFrame1, dataFrame2, on = "key", how = "outer") # FULL JOIN

print(dataFrame3)

"""

key value_x value_y

0 A -0.050330 NaN

1 B 0.754153 -0.291885

2 C 0.693311 NaN

3 D -0.543704 0.871071

4 D -0.543704 0.270238

5 E NaN 0.509288

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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

[PYTHON/PANDAS] merge 함수 : on/how 인자를 사용해 RIGHT OUTER JOIN 데이터 만들기

■ merge 함수에사 on/how 인자를 사용해 RIGHT OUTER JOIN 데이터를 만드는 방법을 보여준다. ▶ main.py


import pandas as pd
import numpy  as np

dataFrame1 = pd.DataFrame({"key" : ["A", "B", "C", "D"], "value" : np.random.randn(4)})
dataFrame2 = pd.DataFrame({"key" : ["B", "D", "D", "E"], "value" : np.random.randn(4)})

print(dataFrame1)

"""
  key     value
0   A  1.604546
1   B -1.037901
2   C -0.910113
3   D -0.925161
"""

print()

print(dataFrame2)

"""
  key     value
0   B -0.448881
1   D -0.173234
2   D  0.109069
3   E -0.766205
"""

print()

dataFrame3 = pd.merge(dataFrame1, dataFrame2, on = "key", how = "right") # RIGHT OUTER JOIN

print(dataFrame3)

"""
  key   value_x   value_y
0   B -1.037901 -0.448881
1   D -0.925161 -0.173234
2   D -0.925161  0.109069
3   E       NaN -0.766205
"""

import pandas as pd

import numpy as np

dataFrame1 = pd.DataFrame({"key" : ["A", "B", "C", "D"], "value" : np.random.randn(4)})

dataFrame2 = pd.DataFrame({"key" : ["B", "D", "D", "E"], "value" : np.random.randn(4)})

print(dataFrame1)

"""

key value

0 A 1.604546

1 B -1.037901

2 C -0.910113

3 D -0.925161

"""

print()

print(dataFrame2)

"""

key value

0 B -0.448881

1 D -0.173234

2 D 0.109069

3 E -0.766205

"""

print()

dataFrame3 = pd.merge(dataFrame1, dataFrame2, on = "key", how = "right") # RIGHT OUTER JOIN

print(dataFrame3)

"""

key value_x value_y

0 B -1.037901 -0.448881

1 D -0.925161 -0.173234

2 D -0.925161 0.109069

3 E NaN -0.766205

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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

[PYTHON/PANDAS] merge 함수 : on/how 인자를 사용해 LEFT OUTER JOIN 데이터 만들기

■ merge 함수에사 on/how 인자를 사용해 LEFT OUTER JOIN 데이터를 만드는 방법을 보여준다. ▶ main.py


import pandas as pd
import numpy  as np

dataFrame1 = pd.DataFrame({"key" : ["A", "B", "C", "D"], "value" : np.random.randn(4)})
dataFrame2 = pd.DataFrame({"key" : ["B", "D", "D", "E"], "value" : np.random.randn(4)})

print(dataFrame1)

"""
  key     value
0   A -1.241331
1   B -0.767490
2   C  2.178568
3   D  0.576111
"""

print()

print(dataFrame2)

"""
  key     value
0   B -0.947082
1   D -0.126260
2   D  1.114855
3   E  0.106320
"""

print()

dataFrame3 = pd.merge(dataFrame1, dataFrame2, on = "key", how = "left") # LEFT OUTER JOIN

print(dataFrame3)

"""
  key   value_x   value_y
0   A -1.241331       NaN
1   B -0.767490 -0.947082
2   C  2.178568       NaN
3   D  0.576111 -0.126260
4   D  0.576111  1.114855
"""

import pandas as pd

import numpy as np

dataFrame1 = pd.DataFrame({"key" : ["A", "B", "C", "D"], "value" : np.random.randn(4)})

dataFrame2 = pd.DataFrame({"key" : ["B", "D", "D", "E"], "value" : np.random.randn(4)})

print(dataFrame1)

"""

key value

0 A -1.241331

1 B -0.767490

2 C 2.178568

3 D 0.576111

"""

print()

print(dataFrame2)

"""

key value

0 B -0.947082

1 D -0.126260

2 D 1.114855

3 E 0.106320

"""

print()

dataFrame3 = pd.merge(dataFrame1, dataFrame2, on = "key", how = "left") # LEFT OUTER JOIN

print(dataFrame3)

"""

key value_x value_y

0 A -1.241331 NaN

1 B -0.767490 -0.947082

2 C 2.178568 NaN

3 D 0.576111 -0.126260

4 D 0.576111 1.114855

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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

[PYTHON/PANDAS] DataFrame 클래스 : set_index 메소드를 사용해 인덱스 컬럼 설정하기

■ DataFrame 클래스의 set_index 메소드를 사용해 인덱스 컬럼을 설정하는 방법을 보여준다. ▶ main.py


import pandas as pd
import numpy  as np

dataFrame1 = pd.DataFrame({"key" : ["B", "D", "D", "E"], "value" : np.random.randn(4)})

print(dataFrame1)

"""
  key     value
0   B -0.289287
1   D  0.661021
2   D  0.196723
3   E -0.426194
"""

print()

dataFrame2 = dataFrame1.set_index("key")

print(dataFrame2)

"""
        value
key
B   -0.289287
D    0.661021
D    0.196723
E   -0.426194
"""

import pandas as pd

import numpy as np

dataFrame1 = pd.DataFrame({"key" : ["B", "D", "D", "E"], "value" : np.random.randn(4)})

print(dataFrame1)

"""

key value

0 B -0.289287

1 D 0.661021

2 D 0.196723

3 E -0.426194

"""

print()

dataFrame2 = dataFrame1.set_index("key")

print(dataFrame2)

"""

value

key

B -0.289287

D 0.661021

D 0.196723

E -0.426194

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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

[PYTHON/PANDAS] merge 함수 : left_on/right_index 인자를 사용해 INNER JOIN 데이터 만들기

■ merge 함수의 left_on/right_index 인자를 사용해 INNER JOIN 데이터를 만드는 방법을 보여준다. ▶ main.py


import pandas as pd
import numpy  as np

dataFrame1 = pd.DataFrame({"key" : ["A", "B", "C", "D"], "value" : np.random.randn(4)})
dataFrame2 = pd.DataFrame({"key" : ["B", "D", "D", "E"], "value" : np.random.randn(4)})

print(dataFrame1)

"""
  key     value
0   A -0.874219
1   B  1.437653
2   C  0.561296
3   D -0.169027
"""

print()

print(dataFrame2)

"""
  key     value
0   B  0.382614
1   D -0.157619
2   D  0.047836
3   E  1.650731
"""

print()

dataFrame3 = dataFrame2.set_index("key")

print(dataFrame3)

"""
        value
key
B    0.382614
D   -0.157619
D    0.047836
E    1.650731
"""

print()

dataFrame4 = pd.merge(dataFrame1, dataFrame3, left_on = "key", right_index = True) # INNER JOIN

print(dataFrame4)

"""  key   value_x   value_y
1   B  1.437653  0.382614
3   D -0.169027 -0.157619
3   D -0.169027  0.047836
"""

import pandas as pd

import numpy as np

dataFrame1 = pd.DataFrame({"key" : ["A", "B", "C", "D"], "value" : np.random.randn(4)})

dataFrame2 = pd.DataFrame({"key" : ["B", "D", "D", "E"], "value" : np.random.randn(4)})

print(dataFrame1)

"""

key value

0 A -0.874219

1 B 1.437653

2 C 0.561296

3 D -0.169027

"""

print()

print(dataFrame2)

"""

key value

0 B 0.382614

1 D -0.157619

2 D 0.047836

3 E 1.650731

"""

print()

dataFrame3 = dataFrame2.set_index("key")

print(dataFrame3)

"""

value

key

B 0.382614

D -0.157619

D 0.047836

E 1.650731

"""

print()

dataFrame4 = pd.merge(dataFrame1, dataFrame3, left_on = "key", right_index = True) # INNER JOIN

print(dataFrame4)

""" key value_x value_y

1 B 1.437653 0.382614

3 D -0.169027 -0.157619

3 D -0.169027 0.047836

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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

[PYTHON/PANDAS] merge 함수 : on 인자를 사용해 INNER JOIN 데이터 만들기

■ merge 함수에서 on 인자를 사용해 INNER JOIN 데이터를 만드는 방법을 보여준다. ▶ main.py


import pandas as pd
import numpy  as np

dataFrame1 = pd.DataFrame({"key" : ["A", "B", "C", "D"], "value" : np.random.randn(4)})
dataFrame2 = pd.DataFrame({"key" : ["B", "D", "D", "E"], "value" : np.random.randn(4)})

print(dataFrame1)

"""
  key     value
0   A -0.937492
1   B  0.070809
2   C  1.060784
3   D -1.170711
"""

print()

print(dataFrame2)

"""
  key     value
0   B -0.501735
1   D -0.681131
2   D  0.570853
3   E -0.171309
"""

print()

dataFrame3 = pd.merge(dataFrame1, dataFrame2, on = "key") # INNER JOIN

print(dataFrame3)

"""
  key   value_x   value_y
0   B  0.070809 -0.501735
1   D -1.170711 -0.681131
2   D -1.170711  0.570853
"""

import pandas as pd

import numpy as np

dataFrame1 = pd.DataFrame({"key" : ["A", "B", "C", "D"], "value" : np.random.randn(4)})

dataFrame2 = pd.DataFrame({"key" : ["B", "D", "D", "E"], "value" : np.random.randn(4)})

print(dataFrame1)

"""

key value

0 A -0.937492

1 B 0.070809

2 C 1.060784

3 D -1.170711

"""

print()

print(dataFrame2)

"""

key value

0 B -0.501735

1 D -0.681131

2 D 0.570853

3 E -0.171309

"""

print()

dataFrame3 = pd.merge(dataFrame1, dataFrame2, on = "key") # INNER JOIN

print(dataFrame3)

"""

key value_x value_y

0 B 0.070809 -0.501735

1 D -1.170711 -0.681131

2 D -1.170711 0.570853

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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

[PYTHON/PANDAS] DataFrameGroupBy 클래스 : agg 메소드를 사용해 그룹 데이터 평균/수 집계하기 2

■ DataFrameGroupBy 클래스의 agg 메소드를 사용해 그룹 데이터의 평균/수를 집계하는 방법을 보여준다. ▶ main.py


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)

dataFrameGroupBy = dataFrame1.groupby(["smoker", "day"])

dataFrame2 = dataFrameGroupBy.agg({"tip" : ["size", "mean"]})

print(dataFrame2)

"""
             tip
            size      mean
smoker day
No     Fri     4  2.812500
       Sat    45  3.102889
       Sun    57  3.167895
       Thur   45  2.673778
Yes    Fri    15  2.714000
       Sat    42  2.875476
       Sun    19  3.516842
       Thur   17  3.030000
"""

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)

dataFrameGroupBy = dataFrame1.groupby(["smoker", "day"])

dataFrame2 = dataFrameGroupBy.agg({"tip" : ["size", "mean"]})

print(dataFrame2)

"""

tip

size mean

smoker day

No Fri 4 2.812500

Sat 45 3.102889

Sun 57 3.167895

Thur 45 2.673778

Yes Fri 15 2.714000

Sat 42 2.875476

Sun 19 3.516842

Thur 17 3.030000

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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

[PYTHON/PANDAS] DataFrameGroupBy 클래스 : agg 메소드를 사용해 그룹 데이터 평균/수 집계하기 1

■ DataFrameGroupBy 클래스의 agg 메소드를 사용해 그룹 데이터의 평균/수를 집계하는 방법을 보여준다. ▶ main.py


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)

dataFrameGroupBy = dataFrame1.groupby("day")

dataFrame2 = dataFrameGroupBy.agg({"tip" : "mean", "day" : "size"})

print(dataFrame2)

"""
           tip  day
day
Fri   2.734737   19
Sat   2.993103   87
Sun   3.255132   76
Thur  2.771452   62
"""

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)

dataFrameGroupBy = dataFrame1.groupby("day")

dataFrame2 = dataFrameGroupBy.agg({"tip" : "mean", "day" : "size"})

print(dataFrame2)

"""

tip day

day

Fri 2.734737 19

Sat 2.993103 87

Sun 3.255132 76

Thur 2.771452 62

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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

[PYTHON/PANDAS] SeriesGroupBy 클래스 : count 메소드를 사용해 그룹 컬럼에 대한 특정 컬럼 값 수 구하기 (NaN 값 제외)

■ SeriesGroupBy 클래스의 count 메소드를 사용해 그룹 컬럼에 대한 특정 컬럼 값 수를 구하는 방법을 보여준다. (NaN 값 제외) ▶ main.py


import pandas as pd

url = "https://raw.githubusercontent.com/pandas-dev/pandas/main/pandas/tests/io/data/csv/tips.csv"

dataFrame = pd.read_csv(url)

dataFrameGroupBy = dataFrame.groupby("sex")

seriesGroupBy = dataFrameGroupBy["total_bill"]

series = seriesGroupBy.count()

print(series)

"""
sex
Female     87
Male      157
Name: total_bill, dtype: int64
"""

import pandas as pd

url = "https://raw.githubusercontent.com/pandas-dev/pandas/main/pandas/tests/io/data/csv/tips.csv"

dataFrame = pd.read_csv(url)

dataFrameGroupBy = dataFrame.groupby("sex")

seriesGroupBy = dataFrameGroupBy["total_bill"]

series = seriesGroupBy.count()

print(series)

"""

sex

Female 87

Male 157

Name: total_bill, dtype: int64

"""

[PYTHON/PANDAS] DataFrameGroupBy 클래스 : count 메소드를 사용해 그룹 컬럼에 대한 모든 컬럼 값 수 구하기 (NaN 값 제외)

■ DataFrameGroupBy 클래스의 count 메소드를 사용해 그룹 컬럼에 대한 모든 컬럼 값의 수를 구하는 방법을 보여준다. (NaN 값 제외) ▶ main.py


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)

dataFrameGroupBy = dataFrame1.groupby("sex")

dataFrame2 = dataFrameGroupBy.count()

print(dataFrame2)

"""
        total_bill  tip  smoker  day  time  size
sex
Female          87   87      87   87    87    87
Male           157  157     157  157   157   157
"""

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)

dataFrameGroupBy = dataFrame1.groupby("sex")

dataFrame2 = dataFrameGroupBy.count()

print(dataFrame2)

"""

total_bill tip smoker day time size

sex

Female 87 87 87 87 87 87

Male 157 157 157 157 157 157

"""

[PYTHON/PANDAS] DataFrameGroupBy 클래스 : size 메소드를 사용해 그룹 컬럼 값 수 구하기

■ DataFrameGroupBy 클래스의 size 메소드를 사용해 그룹 컬럼 값의 수를 구하는 방법을 보여준다. ▶ main.py


import pandas as pd

url = "https://raw.githubusercontent.com/pandas-dev/pandas/main/pandas/tests/io/data/csv/tips.csv"

dataFrame = pd.read_csv(url)

dataFrameGroupBy = dataFrame.groupby("sex")

series = dataFrameGroupBy.size()

print(series)

"""
sex
Female     87
Male      157
dtype: int64
"""

import pandas as pd

url = "https://raw.githubusercontent.com/pandas-dev/pandas/main/pandas/tests/io/data/csv/tips.csv"

dataFrame = pd.read_csv(url)

dataFrameGroupBy = dataFrame.groupby("sex")

series = dataFrameGroupBy.size()

print(series)

"""

sex

Female 87

Male 157

dtype: int64

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

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

[PYTHON/PANDAS] DataFrame 클래스 : notna 메소드를 사용해 특정 컬럼 값이 NaN이 아닌 데이터 구하기

■ DataFrame 클래스의 notna 메소드를 사용해 특정 컬럼 값이 NaN이 아닌 데이터를 구하는 방법을 보여준다. ▶ main.py


import pandas as pd
import numpy  as np

dataFrame1 = pd.DataFrame(
    {
        "col1" : ["A", "B", np.nan, "C", "D"],
        "col2" : ["F", np.nan, "G", "H", "I"]
    }
)

print(dataFrame1)

"""
  col1 col2
0    A    F
1    B  NaN
2  NaN    G
3    C    H
4    D    I
"""

print()

dataFrame2 = dataFrame1[dataFrame1["col2"].notna()]

print(dataFrame2)

"""
  col1 col2
0    A    F
2  NaN    G
3    C    H
4    D    I
"""

import pandas as pd

import numpy as np

dataFrame1 = pd.DataFrame(

{

"col1" : ["A", "B", np.nan, "C", "D"],

"col2" : ["F", np.nan, "G", "H", "I"]

}

)

print(dataFrame1)

"""

col1 col2

0 A F

1 B NaN

2 NaN G

3 C H

4 D I

"""

print()

dataFrame2 = dataFrame1[dataFrame1["col2"].notna()]

print(dataFrame2)

"""

col1 col2

0 A F

2 NaN G

3 C H

4 D I

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※