Creating dummy variables in pandas
Creating Dummy Variables

This introduction to pandas is derived from Data School's pandas Q&A with my own notes and code.

Creating dummy variables

In [1]:
import pandas as pd
In [2]:
url = 'http://bit.ly/kaggletrain'
train = pd.read_csv(url)
In [3]:
train.head()
Out[3]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S
In [9]:
# using .map to create dummy variables
# train['category_name'] = train.Category.map({'unique_term':0, 'unique_term2':1})
train['Sex_male'] = train.Sex.map({'female':0, 'male':1})
In [5]:
train.head()
Out[5]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked Sex_male
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S 1
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C 0
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 0
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S 0
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S 1

Alternative method for creating dummy variables

In [10]:
pd.get_dummies(train.Sex)
Out[10]:
female male
0 0.0 1.0
1 1.0 0.0
2 1.0 0.0
3 1.0 0.0
4 0.0 1.0
5 0.0 1.0
6 0.0 1.0
7 0.0 1.0
8 1.0 0.0
9 1.0 0.0
10 1.0 0.0
11 1.0 0.0
12 0.0 1.0
13 0.0 1.0
14 1.0 0.0
15 1.0 0.0
16 0.0 1.0
17 0.0 1.0
18 1.0 0.0
19 1.0 0.0
20 0.0 1.0
21 0.0 1.0
22 1.0 0.0
23 0.0 1.0
24 1.0 0.0
25 1.0 0.0
26 0.0 1.0
27 0.0 1.0
28 1.0 0.0
29 0.0 1.0
... ... ...
861 0.0 1.0
862 1.0 0.0
863 1.0 0.0
864 0.0 1.0
865 1.0 0.0
866 1.0 0.0
867 0.0 1.0
868 0.0 1.0
869 0.0 1.0
870 0.0 1.0
871 1.0 0.0
872 0.0 1.0
873 0.0 1.0
874 1.0 0.0
875 1.0 0.0
876 0.0 1.0
877 0.0 1.0
878 0.0 1.0
879 1.0 0.0
880 1.0 0.0
881 0.0 1.0
882 1.0 0.0
883 0.0 1.0
884 0.0 1.0
885 1.0 0.0
886 0.0 1.0
887 1.0 0.0
888 1.0 0.0
889 0.0 1.0
890 0.0 1.0

891 rows × 2 columns

If you have k unique terms, you use k - 1 dummy variables to represent

In [33]:
# iloc works on positions (integers)
# this iloc code would always work for any number and name of categories
pd.get_dummies(train.Sex, prefix='Sex').iloc[:, 1:]

# alternative using loc that works on labels
# this method has to be modified for each dataset
pd.get_dummies(train.Sex, prefix='Sex').loc[:, 'Sex_male':]
Out[33]:
Sex_male
0 1.0
1 0.0
2 0.0
3 0.0
4 1.0
5 1.0
6 1.0
7 1.0
8 0.0
9 0.0
10 0.0
11 0.0
12 1.0
13 1.0
14 0.0
15 0.0
16 1.0
17 1.0
18 0.0
19 0.0
20 1.0
21 1.0
22 0.0
23 1.0
24 0.0
25 0.0
26 1.0
27 1.0
28 0.0
29 1.0
... ...
861 1.0
862 0.0
863 0.0
864 1.0
865 0.0
866 0.0
867 1.0
868 1.0
869 1.0
870 1.0
871 0.0
872 1.0
873 1.0
874 0.0
875 0.0
876 1.0
877 1.0
878 1.0
879 0.0
880 0.0
881 1.0
882 0.0
883 1.0
884 1.0
885 0.0
886 1.0
887 0.0
888 0.0
889 1.0
890 1.0

891 rows × 1 columns

In [29]:
train.Embarked.value_counts()
Out[29]:
S    644
C    168
Q     77
Name: Embarked, dtype: int64

As we can see here, there are 3 unique terms

In [31]:
# we only need k - 1, hence 2 dummy variables here
pd.get_dummies(train.Embarked, prefix='Embarked').iloc[:, 1:]
Out[31]:
Embarked_Q Embarked_S
0 0.0 1.0
1 0.0 0.0
2 0.0 1.0
3 0.0 1.0
4 0.0 1.0
5 1.0 0.0
6 0.0 1.0
7 0.0 1.0
8 0.0 1.0
9 0.0 0.0
10 0.0 1.0
11 0.0 1.0
12 0.0 1.0
13 0.0 1.0
14 0.0 1.0
15 0.0 1.0
16 1.0 0.0
17 0.0 1.0
18 0.0 1.0
19 0.0 0.0
20 0.0 1.0
21 0.0 1.0
22 1.0 0.0
23 0.0 1.0
24 0.0 1.0
25 0.0 1.0
26 0.0 0.0
27 0.0 1.0
28 1.0 0.0
29 0.0 1.0
... ... ...
861 0.0 1.0
862 0.0 1.0
863 0.0 1.0
864 0.0 1.0
865 0.0 1.0
866 0.0 0.0
867 0.0 1.0
868 0.0 1.0
869 0.0 1.0
870 0.0 1.0
871 0.0 1.0
872 0.0 1.0
873 0.0 1.0
874 0.0 0.0
875 0.0 0.0
876 0.0 1.0
877 0.0 1.0
878 0.0 1.0
879 0.0 0.0
880 0.0 1.0
881 0.0 1.0
882 0.0 1.0
883 0.0 1.0
884 0.0 1.0
885 1.0 0.0
886 0.0 1.0
887 0.0 1.0
888 0.0 1.0
889 0.0 0.0
890 1.0 0.0

891 rows × 2 columns

In [34]:
embarked_dummies = pd.get_dummies(train.Embarked, prefix='Embarked').iloc[:, 1:]
In [35]:
# concatenate columns
# axis=1
pd.concat([train, embarked_dummies], axis=1)
Out[35]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked Sex_male Embarked_Q Embarked_S
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S 1 0.0 1.0
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C 0 0.0 0.0
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 0 0.0 1.0
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S 0 0.0 1.0
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S 1 0.0 1.0
5 6 0 3 Moran, Mr. James male NaN 0 0 330877 8.4583 NaN Q 1 1.0 0.0
6 7 0 1 McCarthy, Mr. Timothy J male 54.0 0 0 17463 51.8625 E46 S 1 0.0 1.0
7 8 0 3 Palsson, Master. Gosta Leonard male 2.0 3 1 349909 21.0750 NaN S 1 0.0 1.0
8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 2 347742 11.1333 NaN S 0 0.0 1.0
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 0 237736 30.0708 NaN C 0 0.0 0.0
10 11 1 3 Sandstrom, Miss. Marguerite Rut female 4.0 1 1 PP 9549 16.7000 G6 S 0 0.0 1.0
11 12 1 1 Bonnell, Miss. Elizabeth female 58.0 0 0 113783 26.5500 C103 S 0 0.0 1.0
12 13 0 3 Saundercock, Mr. William Henry male 20.0 0 0 A/5. 2151 8.0500 NaN S 1 0.0 1.0
13 14 0 3 Andersson, Mr. Anders Johan male 39.0 1 5 347082 31.2750 NaN S 1 0.0 1.0
14 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0 0 350406 7.8542 NaN S 0 0.0 1.0
15 16 1 2 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0 0 248706 16.0000 NaN S 0 0.0 1.0
16 17 0 3 Rice, Master. Eugene male 2.0 4 1 382652 29.1250 NaN Q 1 1.0 0.0
17 18 1 2 Williams, Mr. Charles Eugene male NaN 0 0 244373 13.0000 NaN S 1 0.0 1.0
18 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.0 1 0 345763 18.0000 NaN S 0 0.0 1.0
19 20 1 3 Masselmani, Mrs. Fatima female NaN 0 0 2649 7.2250 NaN C 0 0.0 0.0
20 21 0 2 Fynney, Mr. Joseph J male 35.0 0 0 239865 26.0000 NaN S 1 0.0 1.0
21 22 1 2 Beesley, Mr. Lawrence male 34.0 0 0 248698 13.0000 D56 S 1 0.0 1.0
22 23 1 3 McGowan, Miss. Anna "Annie" female 15.0 0 0 330923 8.0292 NaN Q 0 1.0 0.0
23 24 1 1 Sloper, Mr. William Thompson male 28.0 0 0 113788 35.5000 A6 S 1 0.0 1.0
24 25 0 3 Palsson, Miss. Torborg Danira female 8.0 3 1 349909 21.0750 NaN S 0 0.0 1.0
25 26 1 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.0 1 5 347077 31.3875 NaN S 0 0.0 1.0
26 27 0 3 Emir, Mr. Farred Chehab male NaN 0 0 2631 7.2250 NaN C 1 0.0 0.0
27 28 0 1 Fortune, Mr. Charles Alexander male 19.0 3 2 19950 263.0000 C23 C25 C27 S 1 0.0 1.0
28 29 1 3 O'Dwyer, Miss. Ellen "Nellie" female NaN 0 0 330959 7.8792 NaN Q 0 1.0 0.0
29 30 0 3 Todoroff, Mr. Lalio male NaN 0 0 349216 7.8958 NaN S 1 0.0 1.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
861 862 0 2 Giles, Mr. Frederick Edward male 21.0 1 0 28134 11.5000 NaN S 1 0.0 1.0
862 863 1 1 Swift, Mrs. Frederick Joel (Margaret Welles Ba... female 48.0 0 0 17466 25.9292 D17 S 0 0.0 1.0
863 864 0 3 Sage, Miss. Dorothy Edith "Dolly" female NaN 8 2 CA. 2343 69.5500 NaN S 0 0.0 1.0
864 865 0 2 Gill, Mr. John William male 24.0 0 0 233866 13.0000 NaN S 1 0.0 1.0
865 866 1 2 Bystrom, Mrs. (Karolina) female 42.0 0 0 236852 13.0000 NaN S 0 0.0 1.0
866 867 1 2 Duran y More, Miss. Asuncion female 27.0 1 0 SC/PARIS 2149 13.8583 NaN C 0 0.0 0.0
867 868 0 1 Roebling, Mr. Washington Augustus II male 31.0 0 0 PC 17590 50.4958 A24 S 1 0.0 1.0
868 869 0 3 van Melkebeke, Mr. Philemon male NaN 0 0 345777 9.5000 NaN S 1 0.0 1.0
869 870 1 3 Johnson, Master. Harold Theodor male 4.0 1 1 347742 11.1333 NaN S 1 0.0 1.0
870 871 0 3 Balkic, Mr. Cerin male 26.0 0 0 349248 7.8958 NaN S 1 0.0 1.0
871 872 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1 1 11751 52.5542 D35 S 0 0.0 1.0
872 873 0 1 Carlsson, Mr. Frans Olof male 33.0 0 0 695 5.0000 B51 B53 B55 S 1 0.0 1.0
873 874 0 3 Vander Cruyssen, Mr. Victor male 47.0 0 0 345765 9.0000 NaN S 1 0.0 1.0
874 875 1 2 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0 1 0 P/PP 3381 24.0000 NaN C 0 0.0 0.0
875 876 1 3 Najib, Miss. Adele Kiamie "Jane" female 15.0 0 0 2667 7.2250 NaN C 0 0.0 0.0
876 877 0 3 Gustafsson, Mr. Alfred Ossian male 20.0 0 0 7534 9.8458 NaN S 1 0.0 1.0
877 878 0 3 Petroff, Mr. Nedelio male 19.0 0 0 349212 7.8958 NaN S 1 0.0 1.0
878 879 0 3 Laleff, Mr. Kristo male NaN 0 0 349217 7.8958 NaN S 1 0.0 1.0
879 880 1 1 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0 1 11767 83.1583 C50 C 0 0.0 0.0
880 881 1 2 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0 1 230433 26.0000 NaN S 0 0.0 1.0
881 882 0 3 Markun, Mr. Johann male 33.0 0 0 349257 7.8958 NaN S 1 0.0 1.0
882 883 0 3 Dahlberg, Miss. Gerda Ulrika female 22.0 0 0 7552 10.5167 NaN S 0 0.0 1.0
883 884 0 2 Banfield, Mr. Frederick James male 28.0 0 0 C.A./SOTON 34068 10.5000 NaN S 1 0.0 1.0
884 885 0 3 Sutehall, Mr. Henry Jr male 25.0 0 0 SOTON/OQ 392076 7.0500 NaN S 1 0.0 1.0
885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 NaN Q 0 1.0 0.0
886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S 1 0.0 1.0
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.0000 B42 S 0 0.0 1.0
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S 0 0.0 1.0
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C 1 0.0 0.0
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q 1 1.0 0.0

891 rows × 15 columns

In [38]:
# create dummy variables for multiple categories
# drop_first=True handles k - 1 
pd.get_dummies(train, columns=['Sex', 'Embarked'], drop_first=True)

# this drops original Sex and Embarked columns
# and creates dummy variables
Out[38]:
PassengerId Survived Pclass Name Age SibSp Parch Ticket Fare Cabin Sex_male Sex_male Embarked_Q Embarked_S
0 1 0 3 Braund, Mr. Owen Harris 22.0 1 0 A/5 21171 7.2500 NaN 1 1.0 0.0 1.0
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... 38.0 1 0 PC 17599 71.2833 C85 0 0.0 0.0 0.0
2 3 1 3 Heikkinen, Miss. Laina 26.0 0 0 STON/O2. 3101282 7.9250 NaN 0 0.0 0.0 1.0
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) 35.0 1 0 113803 53.1000 C123 0 0.0 0.0 1.0
4 5 0 3 Allen, Mr. William Henry 35.0 0 0 373450 8.0500 NaN 1 1.0 0.0 1.0
5 6 0 3 Moran, Mr. James NaN 0 0 330877 8.4583 NaN 1 1.0 1.0 0.0
6 7 0 1 McCarthy, Mr. Timothy J 54.0 0 0 17463 51.8625 E46 1 1.0 0.0 1.0
7 8 0 3 Palsson, Master. Gosta Leonard 2.0 3 1 349909 21.0750 NaN 1 1.0 0.0 1.0
8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 27.0 0 2 347742 11.1333 NaN 0 0.0 0.0 1.0
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) 14.0 1 0 237736 30.0708 NaN 0 0.0 0.0 0.0
10 11 1 3 Sandstrom, Miss. Marguerite Rut 4.0 1 1 PP 9549 16.7000 G6 0 0.0 0.0 1.0
11 12 1 1 Bonnell, Miss. Elizabeth 58.0 0 0 113783 26.5500 C103 0 0.0 0.0 1.0
12 13 0 3 Saundercock, Mr. William Henry 20.0 0 0 A/5. 2151 8.0500 NaN 1 1.0 0.0 1.0
13 14 0 3 Andersson, Mr. Anders Johan 39.0 1 5 347082 31.2750 NaN 1 1.0 0.0 1.0
14 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina 14.0 0 0 350406 7.8542 NaN 0 0.0 0.0 1.0
15 16 1 2 Hewlett, Mrs. (Mary D Kingcome) 55.0 0 0 248706 16.0000 NaN 0 0.0 0.0 1.0
16 17 0 3 Rice, Master. Eugene 2.0 4 1 382652 29.1250 NaN 1 1.0 1.0 0.0
17 18 1 2 Williams, Mr. Charles Eugene NaN 0 0 244373 13.0000 NaN 1 1.0 0.0 1.0
18 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vande... 31.0 1 0 345763 18.0000 NaN 0 0.0 0.0 1.0
19 20 1 3 Masselmani, Mrs. Fatima NaN 0 0 2649 7.2250 NaN 0 0.0 0.0 0.0
20 21 0 2 Fynney, Mr. Joseph J 35.0 0 0 239865 26.0000 NaN 1 1.0 0.0 1.0
21 22 1 2 Beesley, Mr. Lawrence 34.0 0 0 248698 13.0000 D56 1 1.0 0.0 1.0
22 23 1 3 McGowan, Miss. Anna "Annie" 15.0 0 0 330923 8.0292 NaN 0 0.0 1.0 0.0
23 24 1 1 Sloper, Mr. William Thompson 28.0 0 0 113788 35.5000 A6 1 1.0 0.0 1.0
24 25 0 3 Palsson, Miss. Torborg Danira 8.0 3 1 349909 21.0750 NaN 0 0.0 0.0 1.0
25 26 1 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... 38.0 1 5 347077 31.3875 NaN 0 0.0 0.0 1.0
26 27 0 3 Emir, Mr. Farred Chehab NaN 0 0 2631 7.2250 NaN 1 1.0 0.0 0.0
27 28 0 1 Fortune, Mr. Charles Alexander 19.0 3 2 19950 263.0000 C23 C25 C27 1 1.0 0.0 1.0
28 29 1 3 O'Dwyer, Miss. Ellen "Nellie" NaN 0 0 330959 7.8792 NaN 0 0.0 1.0 0.0
29 30 0 3 Todoroff, Mr. Lalio NaN 0 0 349216 7.8958 NaN 1 1.0 0.0 1.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
861 862 0 2 Giles, Mr. Frederick Edward 21.0 1 0 28134 11.5000 NaN 1 1.0 0.0 1.0
862 863 1 1 Swift, Mrs. Frederick Joel (Margaret Welles Ba... 48.0 0 0 17466 25.9292 D17 0 0.0 0.0 1.0
863 864 0 3 Sage, Miss. Dorothy Edith "Dolly" NaN 8 2 CA. 2343 69.5500 NaN 0 0.0 0.0 1.0
864 865 0 2 Gill, Mr. John William 24.0 0 0 233866 13.0000 NaN 1 1.0 0.0 1.0
865 866 1 2 Bystrom, Mrs. (Karolina) 42.0 0 0 236852 13.0000 NaN 0 0.0 0.0 1.0
866 867 1 2 Duran y More, Miss. Asuncion 27.0 1 0 SC/PARIS 2149 13.8583 NaN 0 0.0 0.0 0.0
867 868 0 1 Roebling, Mr. Washington Augustus II 31.0 0 0 PC 17590 50.4958 A24 1 1.0 0.0 1.0
868 869 0 3 van Melkebeke, Mr. Philemon NaN 0 0 345777 9.5000 NaN 1 1.0 0.0 1.0
869 870 1 3 Johnson, Master. Harold Theodor 4.0 1 1 347742 11.1333 NaN 1 1.0 0.0 1.0
870 871 0 3 Balkic, Mr. Cerin 26.0 0 0 349248 7.8958 NaN 1 1.0 0.0 1.0
871 872 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) 47.0 1 1 11751 52.5542 D35 0 0.0 0.0 1.0
872 873 0 1 Carlsson, Mr. Frans Olof 33.0 0 0 695 5.0000 B51 B53 B55 1 1.0 0.0 1.0
873 874 0 3 Vander Cruyssen, Mr. Victor 47.0 0 0 345765 9.0000 NaN 1 1.0 0.0 1.0
874 875 1 2 Abelson, Mrs. Samuel (Hannah Wizosky) 28.0 1 0 P/PP 3381 24.0000 NaN 0 0.0 0.0 0.0
875 876 1 3 Najib, Miss. Adele Kiamie "Jane" 15.0 0 0 2667 7.2250 NaN 0 0.0 0.0 0.0
876 877 0 3 Gustafsson, Mr. Alfred Ossian 20.0 0 0 7534 9.8458 NaN 1 1.0 0.0 1.0
877 878 0 3 Petroff, Mr. Nedelio 19.0 0 0 349212 7.8958 NaN 1 1.0 0.0 1.0
878 879 0 3 Laleff, Mr. Kristo NaN 0 0 349217 7.8958 NaN 1 1.0 0.0 1.0
879 880 1 1 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) 56.0 0 1 11767 83.1583 C50 0 0.0 0.0 0.0
880 881 1 2 Shelley, Mrs. William (Imanita Parrish Hall) 25.0 0 1 230433 26.0000 NaN 0 0.0 0.0 1.0
881 882 0 3 Markun, Mr. Johann 33.0 0 0 349257 7.8958 NaN 1 1.0 0.0 1.0
882 883 0 3 Dahlberg, Miss. Gerda Ulrika 22.0 0 0 7552 10.5167 NaN 0 0.0 0.0 1.0
883 884 0 2 Banfield, Mr. Frederick James 28.0 0 0 C.A./SOTON 34068 10.5000 NaN 1 1.0 0.0 1.0
884 885 0 3 Sutehall, Mr. Henry Jr 25.0 0 0 SOTON/OQ 392076 7.0500 NaN 1 1.0 0.0 1.0
885 886 0 3 Rice, Mrs. William (Margaret Norton) 39.0 0 5 382652 29.1250 NaN 0 0.0 1.0 0.0
886 887 0 2 Montvila, Rev. Juozas 27.0 0 0 211536 13.0000 NaN 1 1.0 0.0 1.0
887 888 1 1 Graham, Miss. Margaret Edith 19.0 0 0 112053 30.0000 B42 0 0.0 0.0 1.0
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" NaN 1 2 W./C. 6607 23.4500 NaN 0 0.0 0.0 1.0
889 890 1 1 Behr, Mr. Karl Howell 26.0 0 0 111369 30.0000 C148 1 1.0 0.0 0.0
890 891 0 3 Dooley, Mr. Patrick 32.0 0 0 370376 7.7500 NaN 1 1.0 1.0 0.0

891 rows × 14 columns

Tags: pandas