import pandas as pd
import seaborn as sns

df = sns.load_dataset('titanic')
print(df.shape)
df.head()

(891, 15)

	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

mask 로 먼저 확인

mask = (df['sibsp'] >= 3) & (df['sibsp'] <= 5)  # or나 and를 나타낼 때, `||` 나 `&&` 이 아니라, 한개씩
mask.value_counts()  # 39행

False    852
True      39
Name: sibsp, dtype: int64

df_boolean = df.loc[mask, :]
print(df_boolean.shape)  # 39행
df_boolean.head()

(39, 15)

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
7	0	3	male	2.0	3	1	21.0750	S	Third	child	False	NaN	Southampton	no	False
16	0	3	male	2.0	4	1	29.1250	Q	Third	child	False	NaN	Queenstown	no	False
24	0	3	female	8.0	3	1	21.0750	S	Third	child	False	NaN	Southampton	no	False
27	0	1	male	19.0	3	2	263.0000	S	First	man	True	C	Southampton	no	False
50	0	3	male	7.0	4	1	39.6875	S	Third	child	False	NaN	Southampton	no	False

isin() 활용

isin_filter = df['sibsp'].isin([3, 4, 5])  # 3, 4, 5 에 해당하면 True
isin_filter.value_counts()  # 39행

False    852
True      39
Name: sibsp, dtype: int64

df_isin = df.loc[isin_filter, :]
print(df_isin.shape)  # 39행
df_isin.head()  # boolean indexing 한거랑 동일

(39, 15)

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
7	0	3	male	2.0	3	1	21.0750	S	Third	child	False	NaN	Southampton	no	False
16	0	3	male	2.0	4	1	29.1250	Q	Third	child	False	NaN	Queenstown	no	False
24	0	3	female	8.0	3	1	21.0750	S	Third	child	False	NaN	Southampton	no	False
27	0	1	male	19.0	3	2	263.0000	S	First	man	True	C	Southampton	no	False
50	0	3	male	7.0	4	1	39.6875	S	Third	child	False	NaN	Southampton	no	False