pandas - 3

로우 드롭하기

로우 인덱스로 로우를 드롭할 수 있습니다.

friend_dict_list = [{'age': 20, 'job': 'student'},
         {'age': 30, 'job': 'developer'},
         {'age': 30, 'job': 'teacher'}]
df = pd.DataFrame(friend_dict_list, index = ['John', 'Jenny', 'Nate'])

df.head()

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	age	job
John	20	student
Jenny	30	developer
Nate	30	teacher

드롭된 결과는 데이터프레임에 저장되지 않습니다. 저장하고 싶으실 경우, 결과를 데이터프레임에 따로 저장하셔야 합니다.

df.drop(['John', 'Nate'])

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	age	job
Jenny	30	developer

df

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	age	job
John	20	student
Jenny	30	developer
Nate	30	teacher

드롭된 결과를 데이터프레임에 저장하는 예제입니다.

df = df.drop(['John', 'Nate'])

df

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	age	job
Jenny	30	developer

드롭된 결과를 바로 데이터프레임에 저장하는 방법

inplace 키워드를 사용하시면, 따로 저장할 필요없이, 드롭된 결과가 데이터프레임에 반영됩니다.

friend_dict_list = [{'age': 20, 'job': 'student'},
         {'age': 30, 'job': 'developer'},
         {'age': 30, 'job': 'teacher'}]
df = pd.DataFrame(friend_dict_list, index = ['John', 'Jenny', 'Nate'])

df.drop(['John', 'Nate'], inplace = True)

df

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	age	job
Jenny	30	developer

로우 인덱스로 드롭하기

friend_dict_list = [{'name': 'Jone', 'age': 20, 'job': 'student'},
         {'name': 'Jenny', 'age': 30, 'job': 'developer'},
         {'name': 'Nate', 'age': 30, 'job': 'teacher'}]
df = pd.DataFrame(friend_dict_list)

df

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	age	job	name
0	20	student	Jone
1	30	developer	Jenny
2	30	teacher	Nate

로우 인덱스로 드롭하는 예제입니다.

df = df.drop(df.index[[0,2]])

df

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	age	job	name
1	30	developer	Jenny

컬럼값으로 로우 드롭하기

friend_dict_list = [{'name': 'Jone', 'age': 20, 'job': 'student'},
         {'name': 'Jenny', 'age': 30, 'job': 'developer'},
         {'name': 'Nate', 'age': 30, 'job': 'teacher'}]
df = pd.DataFrame(friend_dict_list)
df

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	age	job	name
0	20	student	Jone
1	30	developer	Jenny
2	30	teacher	Nate

df = df[df.age != 30]

df

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	age	job	name
0	20	student	Jone

컬럼 드롭하기

friend_dict_list = [{'name': 'Jone', 'age': 20, 'job': 'student'},
         {'name': 'Jenny', 'age': 30, 'job': 'developer'},
         {'name': 'Nate', 'age': 30, 'job': 'teacher'}]
df = pd.DataFrame(friend_dict_list)
df

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	age	job	name
0	20	student	Jone
1	30	developer	Jenny
2	30	teacher	Nate

df = df.drop('age', axis=1)

df

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	job	name
0	student	Jone
1	developer	Jenny
2	teacher	Nate