import pandas as pd
df1 = pd.read_csv("./sungjuk.csv")
df1
번호 | 이름 | 성별 | 국어 | 영어 | 수학 | |
---|---|---|---|---|---|---|
0 | 1 | 강민경 | 여 | 98 | 96 | 76 |
1 | 2 | 강순애 | 여 | 94 | 79 | 60 |
2 | 3 | 강영하 | 남 | 55 | 47 | 93 |
3 | 4 | 강혜정 | 여 | 99 | 76 | 78 |
4 | 5 | 권명숙 | 여 | 98 | 73 | 61 |
... | ... | ... | ... | ... | ... | ... |
95 | 96 | 하혜연 | 여 | 96 | 96 | 71 |
96 | 97 | 한경규 | 남 | 96 | 94 | 95 |
97 | 98 | 한수정 | 여 | 93 | 97 | 77 |
98 | 99 | 한의병 | 남 | 93 | 59 | 63 |
99 | 100 | 한정희 | 여 | 93 | 78 | 52 |
100 rows × 6 columns
import pandas as pd
import seaborn as sns
df2 = sns.load_dataset('titanic')
df2
survived | pclass | sex | age | sibsp | parch | fare | embarked | class | who | adult_male | deck | embark_town | alive | alone | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 3 | male | 22.0 | 1 | 0 | 7.2500 | S | Third | man | True | NaN | Southampton | no | False |
1 | 1 | 1 | female | 38.0 | 1 | 0 | 71.2833 | C | First | woman | False | C | Cherbourg | yes | False |
2 | 1 | 3 | female | 26.0 | 0 | 0 | 7.9250 | S | Third | woman | False | NaN | Southampton | yes | True |
3 | 1 | 1 | female | 35.0 | 1 | 0 | 53.1000 | S | First | woman | False | C | Southampton | yes | False |
4 | 0 | 3 | male | 35.0 | 0 | 0 | 8.0500 | S | Third | man | True | NaN | Southampton | no | True |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
886 | 0 | 2 | male | 27.0 | 0 | 0 | 13.0000 | S | Second | man | True | NaN | Southampton | no | True |
887 | 1 | 1 | female | 19.0 | 0 | 0 | 30.0000 | S | First | woman | False | B | Southampton | yes | True |
888 | 0 | 3 | female | NaN | 1 | 2 | 23.4500 | S | Third | woman | False | NaN | Southampton | no | False |
889 | 1 | 1 | male | 26.0 | 0 | 0 | 30.0000 | C | First | man | True | C | Cherbourg | yes | True |
890 | 0 | 3 | male | 32.0 | 0 | 0 | 7.7500 | Q | Third | man | True | NaN | Queenstown | no | True |
891 rows × 15 columns
df1['국어']
df1[['국어','영어']]
df1.국어
0 98 1 94 2 55 3 99 4 98 .. 95 96 96 96 97 93 98 93 99 93 Name: 국어, Length: 100, dtype: int64
df1.loc[1]
df1.iloc[1]
번호 2 이름 강순애 성별 여 국어 94 영어 79 수학 60 Name: 1, dtype: object
df1.loc[1,'이름']
#df1.loc[[1,2], '이름']
#df1.loc[[1,2], ['이름', '국어']]
#df1.loc[1:3]
#df1.loc[1:3, '이름':'수학']
#df1.iloc[1,1]
#df1.iloc[[1,2],1]
#df1.iloc[[1,2],[1,2]]
#df1.iloc[1:3]
#df1.iloc[1:3, 1:3]
'강순애'
import pandas as pd
df = pd.DataFrame(
[['hong',90, 88, 78], ['gil',95, 89,98], ['dong',85, 98, 67] ]
, columns=['name','kor', 'eng', 'mat']
, index=['2', '4', '6']
)
df
#df.loc[2]
#df.iloc[2]
#df.loc['2']
name | kor | eng | mat | |
---|---|---|---|---|
2 | hong | 90 | 88 | 78 |
4 | gil | 95 | 89 | 98 |
6 | dong | 85 | 98 | 67 |
import pandas as pd
df = pd.DataFrame(
{ 'name': ['hong', 'gil', 'dong'],'kor' :[90, 95, 85],
'eng' : [88, 89, 98], 'mat' : [78, 76, 67]}
)
df.columns = ['name','kor', 'eng', 'mat']
df.index = [202100001,202100002,202100003]
df
#df.loc[2]
#df.loc[202100001]
#df.loc['202100001']
#df.iloc[2]
#df.iloc['202100001']
name | kor | eng | mat | |
---|---|---|---|---|
202100001 | hong | 90 | 88 | 78 |
202100002 | gil | 95 | 89 | 76 |
202100003 | dong | 85 | 98 | 67 |
df3 = df.copy()
#df3['kor'] = 100
#df3
#df3.loc[202100002] = ['길', 100, 100, 100]
#df3
#df3.loc[202100002, 'kor'] = 100
#df3
#df3.loc[202100002, ['kor', 'eng']] = 100
#df3
#df3.loc[202100002, ['kor', 'eng']] = [5, 6]
#df3
df3 = df.copy()
#df3['sci'] = 100
#df3
#df3['sci'] = [10, 20, 30]
#df3
#df3['sci'] = df3['kor'] * 2
#df3
#df3.loc[202100004] = 100
#df3
#df3.loc[202100004] = ['sir', 100, 100, 100]
#df3
#df3.drop('name', axis=1)
#df3
#df3.drop('name', axis=1, inplace=True)
#df3
#df3 = df3.drop('name', axis=1)
#df3
#df3.drop(202100002, axis=0)
#df3
#df3.drop(202100002, axis=0, inplace=True)
#df3
#df3 = df3.drop(202100002, axis=0)
#df3
df3 = df.copy()
#df3.transpose()
#df3
#df3.transpose(inplace=True)
#df3
#df3 = df3.transpose()
#df3
#df3.T
#df3
#df3 = df3.T
#df3
df3 = df.copy()
df3
#df3.set_index('name')
#df3
#df3 = df3.set_index('name')
#df3
df3.set_index('name', inplace=True)
df3
df3.loc['hong':'gil']
kor | eng | mat | |
---|---|---|---|
name | |||
hong | 90 | 88 | 78 |
gil | 95 | 89 | 76 |
df3 = df.copy()
df3
df3 = df3.sort_index(ascending=False)
df3
name | kor | eng | mat | |
---|---|---|---|---|
202100003 | dong | 85 | 98 | 67 |
202100002 | gil | 95 | 89 | 76 |
202100001 | hong | 90 | 88 | 78 |
df3 = df.copy()
df3
df3 = df3.reindex(['10', '20', '30'])
df3
name | kor | eng | mat | |
---|---|---|---|---|
10 | NaN | NaN | NaN | NaN |
20 | NaN | NaN | NaN | NaN |
30 | NaN | NaN | NaN | NaN |
df4 = df1.copy()
df4
#df4.set_index('이름')
#df4
df4 = df4.set_index('이름')
df4
#df4.set_index('이름', inplace=True)
#df4
df4.loc['강순애':'권명숙']
번호 | 성별 | 국어 | 영어 | 수학 | |
---|---|---|---|---|---|
이름 | |||||
강순애 | 2 | 여 | 94 | 79 | 60 |
강영하 | 3 | 남 | 55 | 47 | 93 |
강혜정 | 4 | 여 | 99 | 76 | 78 |
권명숙 | 5 | 여 | 98 | 73 | 61 |