Handle Nulls¶

Load Titanic Dataset¶

import seaborn as sns

titanic = sns.load_dataset('titanic')
titanic.head()

	survived	pclass	sex	age	sibsp	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

Count Nulls For Each Column¶

titanic.isnull().sum()

survived         0
pclass           0
sex              0
age            177
sibsp            0
parch            0
fare             0
embarked         2
class            0
who              0
adult_male       0
deck           688
embark_town      2
alive            0
alone            0
dtype: int64

Visualize Rows With Nulls¶

titanic[titanic.isnull().any(axis=1)]

	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
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True
5	0	3	male	NaN	0	0	8.4583	Q	Third	man	True	NaN	Queenstown	no	True
7	0	3	male	2.0	3	1	21.0750	S	Third	child	False	NaN	Southampton	no	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
884	0	3	male	25.0	0	0	7.0500	S	Third	man	True	NaN	Southampton	no	True
885	0	3	female	39.0	0	5	29.1250	Q	Third	woman	False	NaN	Queenstown	no	False
886	0	2	male	27.0	0	0	13.0000	S	Second	man	True	NaN	Southampton	no	True
888	0	3	female	NaN	1	2	23.4500	S	Third	woman	False	NaN	Southampton	no	False
890	0	3	male	32.0	0	0	7.7500	Q	Third	man	True	NaN	Queenstown	no	True

709 rows × 15 columns

Impute Replacement Values¶

Use mean to replace numeric values.

titanic.age = titanic.age.fillna(titanic.age.mean())
titanic

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.000000	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.000000	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.000000	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.000000	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.000000	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	0	2	male	27.000000	0	0	13.0000	S	Second	man	True	NaN	Southampton	no	True
887	1	1	female	19.000000	0	0	30.0000	S	First	woman	False	B	Southampton	yes	True
888	0	3	female	29.699118	1	2	23.4500	S	Third	woman	False	NaN	Southampton	no	False
889	1	1	male	26.000000	0	0	30.0000	C	First	man	True	C	Cherbourg	yes	True
890	0	3	male	32.000000	0	0	7.7500	Q	Third	man	True	NaN	Queenstown	no	True

891 rows × 15 columns

titanic.isnull().sum()

survived         0
pclass           0
sex              0
age              0
sibsp            0
parch            0
fare             0
embarked         2
class            0
who              0
adult_male       0
deck           688
embark_town      2
alive            0
alone            0
dtype: int64

Drop Rows with Nulls¶

titanic = titanic.dropna(how='any', axis=0)
titanic.isnull().sum()

survived       0
pclass         0
sex            0
age            0
sibsp          0
parch          0
fare           0
embarked       0
class          0
who            0
adult_male     0
deck           0
embark_town    0
alive          0
alone          0
dtype: int64

titanic

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
6	0	1	male	54.0	0	0	51.8625	S	First	man	True	E	Southampton	no	True
10	1	3	female	4.0	1	1	16.7000	S	Third	child	False	G	Southampton	yes	False
11	1	1	female	58.0	0	0	26.5500	S	First	woman	False	C	Southampton	yes	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
871	1	1	female	47.0	1	1	52.5542	S	First	woman	False	D	Southampton	yes	False
872	0	1	male	33.0	0	0	5.0000	S	First	man	True	B	Southampton	no	True
879	1	1	female	56.0	0	1	83.1583	C	First	woman	False	C	Cherbourg	yes	False
887	1	1	female	19.0	0	0	30.0000	S	First	woman	False	B	Southampton	yes	True
889	1	1	male	26.0	0	0	30.0000	C	First	man	True	C	Cherbourg	yes	True

201 rows × 15 columns