numpy - одно из фундаментальных расширений языка Python для выполнения научных вычислений. Кроме всего прочего предоставляет:
Мощный объект для работы с данными - N-мерный массив
Высокоуровневые математические функции
Инструменты для интеграции программного кода на C/C++ и Fortran
Реализации функций линейной алгебры, преобразования Фурье, генерации случайных чисел
Основные структуры в pandas:
Series – проиндексированный вектор значений. Имя элемента соответствует индексу, а значение – значению записи.
DataFrame — проиндексированный многомерный массив значений, соответственно каждый столбец DataFrame является структурой Series.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
Немного о Series
pd.Series([2, 3, 6, 9, 'm'])
0 2 1 3 2 6 3 9 4 m dtype: object
s1 = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
print 'Initial\n', s1, '\n'
s1['d'] = 2
print 'Modified\n', s1
Initial a 0.443897 b -1.067706 c -0.881259 d 1.648285 e -2.120162 dtype: float64 Modified a 0.443897 b -1.067706 c -0.881259 d 2.000000 e -2.120162 dtype: float64
pd.Series(0, index=['a', 'b', 'c', 'd', 'e'])
a 0 b 0 c 0 d 0 e 0 dtype: int64
Поведение Series немного похоже на поведение ndarray
s1[0]
-0.36872459490328752
s1[s1 > 0]
c 0.471481 d 2.000000 dtype: float64
s1[[4, 2, 1]]
e -1.024658 c 0.471481 b -0.201952 dtype: float64
np.exp(s1)
a 0.691616 b 0.817134 c 1.602365 d 7.389056 e 0.358919 dtype: float64
Арифметика
s1 + s1
a -0.737449 b -0.403905 c 0.942961 d 4.000000 e -2.049315 dtype: float64
s1[1:] + s1[:-1]
a NaN b -0.403905 c 0.942961 d 4.000000 e NaN dtype: float64
ar1 = np.array([1, 2, 5, 8, 9])
ar1[1:] + ar1[:-1]
array([ 3, 7, 13, 17])
s2 = pd.Series([2, 5, 4, 3, 2], name='grades')
s2.name
'grades'
DataFrame - наиболее часто используемый объект в pandas. Для инициализации можно использовать
Словарь, в котором значениями являются 1D ndarray, списки, словари или Series
2-D numpy.ndarray
Другой DataFrame
И т. д.
df1 = pd.DataFrame({'age': [20, 18, 17, 19, 18],
'city': ['Msk', 'Spb', 'Msk', 'Nov', 'Tmn'],
'name': ['Alexander', 'Maria', 'Daria', 'Nikolay', 'Anatoliy'],
'sex': ['M', 'F', 'F', 'M', 'M']})
df1
age | city | name | sex | |
---|---|---|---|---|
0 | 20 | Msk | Alexander | M |
1 | 18 | Spb | Maria | F |
2 | 17 | Msk | Daria | F |
3 | 19 | Nov | Nikolay | M |
4 | 18 | Tmn | Anatoliy | M |
Индексация
df1['age']
0 20 1 18 2 17 3 19 4 18 Name: age, dtype: int64
df1.age
0 20 1 18 2 17 3 19 4 18 Name: age, dtype: int64
df1[['name', 'city']]
name | city | |
---|---|---|
0 | Alexander | Msk |
1 | Maria | Spb |
2 | Daria | Msk |
3 | Nikolay | Nov |
4 | Anatoliy | Tmn |
df1[0: 2]
age | city | name | sex | |
---|---|---|---|---|
0 | 20 | Msk | Alexander | M |
1 | 18 | Spb | Maria | F |
df1[[0, 2]]
age | name | |
---|---|---|
0 | 20 | Alexander |
1 | 18 | Maria |
2 | 17 | Daria |
3 | 19 | Nikolay |
4 | 18 | Anatoliy |
df1.ix[[0, 2]]
age | city | name | sex | |
---|---|---|---|---|
0 | 20 | Msk | Alexander | M |
2 | 17 | Msk | Daria | F |
df1.loc[[0, 2]]
age | city | name | sex | |
---|---|---|---|---|
0 | 20 | Msk | Alexander | M |
2 | 17 | Msk | Daria | F |
df1.iloc[:2, :3]
age | city | name | |
---|---|---|---|
0 | 20 | Msk | Alexander |
1 | 18 | Spb | Maria |
df1[df1.city == 'Msk']
age | city | name | sex | |
---|---|---|---|---|
0 | 20 | Msk | Alexander | M |
2 | 17 | Msk | Daria | F |
Что можно узнать о данных?
df1.describe()
age | |
---|---|
count | 5.000000 |
mean | 18.400000 |
std | 1.140175 |
min | 17.000000 |
25% | 18.000000 |
50% | 18.000000 |
75% | 19.000000 |
max | 20.000000 |
df1.age.mean()
18.399999999999999
df1.sex.value_counts()
M 3 F 2 Name: sex, dtype: int64
df1.city.unique()
array(['Msk', 'Spb', 'Nov', 'Tmn'], dtype=object)
Группировка
df1.groupby('city').age.mean()
city Msk 18.5 Nov 19.0 Spb 18.0 Tmn 18.0 Name: age, dtype: float64
df1.ix[df1.groupby('city').age.idxmin()]
age | city | name | sex | |
---|---|---|---|---|
2 | 17 | Msk | Daria | F |
3 | 19 | Nov | Nikolay | M |
1 | 18 | Spb | Maria | F |
4 | 18 | Tmn | Anatoliy | M |
Операции с таблицами
#Вставка столбцов
country = pd.Series('RU', index=range(5))
df1.insert(2,'country',country)
df1
age | city | country | name | sex | |
---|---|---|---|---|---|
0 | 20 | Msk | RU | Alexander | M |
1 | 18 | Spb | RU | Maria | F |
2 | 17 | Msk | RU | Daria | F |
3 | 19 | Nov | RU | Nikolay | M |
4 | 18 | Tmn | RU | Anatoliy | M |
df2 = pd.DataFrame({'age': [20, 21],
'name': ['Katerina', 'Boris'],
'sex': ['F', 'M']})
df4 = df1.append(df2)
df4
age | city | country | name | sex | |
---|---|---|---|---|---|
0 | 20 | Msk | RU | Alexander | M |
1 | 18 | Spb | RU | Maria | F |
2 | 17 | Msk | RU | Daria | F |
3 | 19 | Nov | RU | Nikolay | M |
4 | 18 | Tmn | RU | Anatoliy | M |
0 | 20 | NaN | NaN | Katerina | F |
1 | 21 | NaN | NaN | Boris | M |
df1
age | city | country | name | sex | |
---|---|---|---|---|---|
0 | 20 | Msk | RU | Alexander | M |
1 | 18 | Spb | RU | Maria | F |
2 | 17 | Msk | RU | Daria | F |
3 | 19 | Nov | RU | Nikolay | M |
4 | 18 | Tmn | RU | Anatoliy | M |
df3 = pd.DataFrame({'city': ['Msk', 'Spb', 'Nov', 'Tmn'],
'population': [15.6, 5.2, 0.22, 0.7]})
df1.merge(df3, on='city')
age | city | country | name | sex | population | |
---|---|---|---|---|---|---|
0 | 20 | Msk | RU | Alexander | M | 15.60 |
1 | 17 | Msk | RU | Daria | F | 15.60 |
2 | 18 | Spb | RU | Maria | F | 5.20 |
3 | 19 | Nov | RU | Nikolay | M | 0.22 |
4 | 18 | Tmn | RU | Anatoliy | M | 0.70 |
Работа с данными
train = pd.read_csv('../Data/titanic.csv')
train.head()
PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35 | 1 | 0 | 113803 | 53.1000 | C123 | S |
4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35 | 0 | 0 | 373450 | 8.0500 | NaN | S |
5 | 6 | 0 | 3 | Moran, Mr. James | male | NaN | 0 | 0 | 330877 | 8.4583 | NaN | Q |
6 | 7 | 0 | 1 | McCarthy, Mr. Timothy J | male | 54 | 0 | 0 | 17463 | 51.8625 | E46 | S |
7 | 8 | 0 | 3 | Palsson, Master. Gosta Leonard | male | 2 | 3 | 1 | 349909 | 21.0750 | NaN | S |
8 | 9 | 1 | 3 | Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) | female | 27 | 0 | 2 | 347742 | 11.1333 | NaN | S |
9 | 10 | 1 | 2 | Nasser, Mrs. Nicholas (Adele Achem) | female | 14 | 1 | 0 | 237736 | 30.0708 | NaN | C |
10 | 11 | 1 | 3 | Sandstrom, Miss. Marguerite Rut | female | 4 | 1 | 1 | PP 9549 | 16.7000 | G6 | S |
11 | 12 | 1 | 1 | Bonnell, Miss. Elizabeth | female | 58 | 0 | 0 | 113783 | 26.5500 | C103 | S |
12 | 13 | 0 | 3 | Saundercock, Mr. William Henry | male | 20 | 0 | 0 | A/5. 2151 | 8.0500 | NaN | S |
13 | 14 | 0 | 3 | Andersson, Mr. Anders Johan | male | 39 | 1 | 5 | 347082 | 31.2750 | NaN | S |
14 | 15 | 0 | 3 | Vestrom, Miss. Hulda Amanda Adolfina | female | 14 | 0 | 0 | 350406 | 7.8542 | NaN | S |
15 | 16 | 1 | 2 | Hewlett, Mrs. (Mary D Kingcome) | female | 55 | 0 | 0 | 248706 | 16.0000 | NaN | S |
16 | 17 | 0 | 3 | Rice, Master. Eugene | male | 2 | 4 | 1 | 382652 | 29.1250 | NaN | Q |
17 | 18 | 1 | 2 | Williams, Mr. Charles Eugene | male | NaN | 0 | 0 | 244373 | 13.0000 | NaN | S |
18 | 19 | 0 | 3 | Vander Planke, Mrs. Julius (Emelia Maria Vande... | female | 31 | 1 | 0 | 345763 | 18.0000 | NaN | S |
19 | 20 | 1 | 3 | Masselmani, Mrs. Fatima | female | NaN | 0 | 0 | 2649 | 7.2250 | NaN | C |
20 | 21 | 0 | 2 | Fynney, Mr. Joseph J | male | 35 | 0 | 0 | 239865 | 26.0000 | NaN | S |
21 | 22 | 1 | 2 | Beesley, Mr. Lawrence | male | 34 | 0 | 0 | 248698 | 13.0000 | D56 | S |
22 | 23 | 1 | 3 | McGowan, Miss. Anna "Annie" | female | 15 | 0 | 0 | 330923 | 8.0292 | NaN | Q |
23 | 24 | 1 | 1 | Sloper, Mr. William Thompson | male | 28 | 0 | 0 | 113788 | 35.5000 | A6 | S |
24 | 25 | 0 | 3 | Palsson, Miss. Torborg Danira | female | 8 | 3 | 1 | 349909 | 21.0750 | NaN | S |
25 | 26 | 1 | 3 | Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... | female | 38 | 1 | 5 | 347077 | 31.3875 | NaN | S |
26 | 27 | 0 | 3 | Emir, Mr. Farred Chehab | male | NaN | 0 | 0 | 2631 | 7.2250 | NaN | C |
27 | 28 | 0 | 1 | Fortune, Mr. Charles Alexander | male | 19 | 3 | 2 | 19950 | 263.0000 | C23 C25 C27 | S |
28 | 29 | 1 | 3 | O'Dwyer, Miss. Ellen "Nellie" | female | NaN | 0 | 0 | 330959 | 7.8792 | NaN | Q |
29 | 30 | 0 | 3 | Todoroff, Mr. Lalio | male | NaN | 0 | 0 | 349216 | 7.8958 | NaN | S |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
861 | 862 | 0 | 2 | Giles, Mr. Frederick Edward | male | 21 | 1 | 0 | 28134 | 11.5000 | NaN | S |
862 | 863 | 1 | 1 | Swift, Mrs. Frederick Joel (Margaret Welles Ba... | female | 48 | 0 | 0 | 17466 | 25.9292 | D17 | S |
863 | 864 | 0 | 3 | Sage, Miss. Dorothy Edith "Dolly" | female | NaN | 8 | 2 | CA. 2343 | 69.5500 | NaN | S |
864 | 865 | 0 | 2 | Gill, Mr. John William | male | 24 | 0 | 0 | 233866 | 13.0000 | NaN | S |
865 | 866 | 1 | 2 | Bystrom, Mrs. (Karolina) | female | 42 | 0 | 0 | 236852 | 13.0000 | NaN | S |
866 | 867 | 1 | 2 | Duran y More, Miss. Asuncion | female | 27 | 1 | 0 | SC/PARIS 2149 | 13.8583 | NaN | C |
867 | 868 | 0 | 1 | Roebling, Mr. Washington Augustus II | male | 31 | 0 | 0 | PC 17590 | 50.4958 | A24 | S |
868 | 869 | 0 | 3 | van Melkebeke, Mr. Philemon | male | NaN | 0 | 0 | 345777 | 9.5000 | NaN | S |
869 | 870 | 1 | 3 | Johnson, Master. Harold Theodor | male | 4 | 1 | 1 | 347742 | 11.1333 | NaN | S |
870 | 871 | 0 | 3 | Balkic, Mr. Cerin | male | 26 | 0 | 0 | 349248 | 7.8958 | NaN | S |
871 | 872 | 1 | 1 | Beckwith, Mrs. Richard Leonard (Sallie Monypeny) | female | 47 | 1 | 1 | 11751 | 52.5542 | D35 | S |
872 | 873 | 0 | 1 | Carlsson, Mr. Frans Olof | male | 33 | 0 | 0 | 695 | 5.0000 | B51 B53 B55 | S |
873 | 874 | 0 | 3 | Vander Cruyssen, Mr. Victor | male | 47 | 0 | 0 | 345765 | 9.0000 | NaN | S |
874 | 875 | 1 | 2 | Abelson, Mrs. Samuel (Hannah Wizosky) | female | 28 | 1 | 0 | P/PP 3381 | 24.0000 | NaN | C |
875 | 876 | 1 | 3 | Najib, Miss. Adele Kiamie "Jane" | female | 15 | 0 | 0 | 2667 | 7.2250 | NaN | C |
876 | 877 | 0 | 3 | Gustafsson, Mr. Alfred Ossian | male | 20 | 0 | 0 | 7534 | 9.8458 | NaN | S |
877 | 878 | 0 | 3 | Petroff, Mr. Nedelio | male | 19 | 0 | 0 | 349212 | 7.8958 | NaN | S |
878 | 879 | 0 | 3 | Laleff, Mr. Kristo | male | NaN | 0 | 0 | 349217 | 7.8958 | NaN | S |
879 | 880 | 1 | 1 | Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) | female | 56 | 0 | 1 | 11767 | 83.1583 | C50 | C |
880 | 881 | 1 | 2 | Shelley, Mrs. William (Imanita Parrish Hall) | female | 25 | 0 | 1 | 230433 | 26.0000 | NaN | S |
881 | 882 | 0 | 3 | Markun, Mr. Johann | male | 33 | 0 | 0 | 349257 | 7.8958 | NaN | S |
882 | 883 | 0 | 3 | Dahlberg, Miss. Gerda Ulrika | female | 22 | 0 | 0 | 7552 | 10.5167 | NaN | S |
883 | 884 | 0 | 2 | Banfield, Mr. Frederick James | male | 28 | 0 | 0 | C.A./SOTON 34068 | 10.5000 | NaN | S |
884 | 885 | 0 | 3 | Sutehall, Mr. Henry Jr | male | 25 | 0 | 0 | SOTON/OQ 392076 | 7.0500 | NaN | S |
885 | 886 | 0 | 3 | Rice, Mrs. William (Margaret Norton) | female | 39 | 0 | 5 | 382652 | 29.1250 | NaN | Q |
886 | 887 | 0 | 2 | Montvila, Rev. Juozas | male | 27 | 0 | 0 | 211536 | 13.0000 | NaN | S |
887 | 888 | 1 | 1 | Graham, Miss. Margaret Edith | female | 19 | 0 | 0 | 112053 | 30.0000 | B42 | S |
888 | 889 | 0 | 3 | Johnston, Miss. Catherine Helen "Carrie" | female | NaN | 1 | 2 | W./C. 6607 | 23.4500 | NaN | S |
889 | 890 | 1 | 1 | Behr, Mr. Karl Howell | male | 26 | 0 | 0 | 111369 | 30.0000 | C148 | C |
890 | 891 | 0 | 3 | Dooley, Mr. Patrick | male | 32 | 0 | 0 | 370376 | 7.7500 | NaN | Q |
891 rows × 12 columns
train[(train.Sex == 'female') & (train.Survived==1)& (train.Age<40)]
PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
8 | 9 | 1 | 3 | Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) | female | 27.0 | 0 | 2 | 347742 | 11.1333 | NaN | S |
9 | 10 | 1 | 2 | Nasser, Mrs. Nicholas (Adele Achem) | female | 14.0 | 1 | 0 | 237736 | 30.0708 | NaN | C |
10 | 11 | 1 | 3 | Sandstrom, Miss. Marguerite Rut | female | 4.0 | 1 | 1 | PP 9549 | 16.7000 | G6 | S |
22 | 23 | 1 | 3 | McGowan, Miss. Anna "Annie" | female | 15.0 | 0 | 0 | 330923 | 8.0292 | NaN | Q |
25 | 26 | 1 | 3 | Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... | female | 38.0 | 1 | 5 | 347077 | 31.3875 | NaN | S |
39 | 40 | 1 | 3 | Nicola-Yarred, Miss. Jamila | female | 14.0 | 1 | 0 | 2651 | 11.2417 | NaN | C |
43 | 44 | 1 | 2 | Laroche, Miss. Simonne Marie Anne Andree | female | 3.0 | 1 | 2 | SC/Paris 2123 | 41.5792 | NaN | C |
44 | 45 | 1 | 3 | Devaney, Miss. Margaret Delia | female | 19.0 | 0 | 0 | 330958 | 7.8792 | NaN | Q |
53 | 54 | 1 | 2 | Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin... | female | 29.0 | 1 | 0 | 2926 | 26.0000 | NaN | S |
56 | 57 | 1 | 2 | Rugg, Miss. Emily | female | 21.0 | 0 | 0 | C.A. 31026 | 10.5000 | NaN | S |
58 | 59 | 1 | 2 | West, Miss. Constance Mirium | female | 5.0 | 1 | 2 | C.A. 34651 | 27.7500 | NaN | S |
61 | 62 | 1 | 1 | Icard, Miss. Amelie | female | 38.0 | 0 | 0 | 113572 | 80.0000 | B28 | NaN |
66 | 67 | 1 | 2 | Nye, Mrs. (Elizabeth Ramell) | female | 29.0 | 0 | 0 | C.A. 29395 | 10.5000 | F33 | S |
68 | 69 | 1 | 3 | Andersson, Miss. Erna Alexandra | female | 17.0 | 4 | 2 | 3101281 | 7.9250 | NaN | S |
79 | 80 | 1 | 3 | Dowdell, Miss. Elizabeth | female | 30.0 | 0 | 0 | 364516 | 12.4750 | NaN | S |
84 | 85 | 1 | 2 | Ilett, Miss. Bertha | female | 17.0 | 0 | 0 | SO/C 14885 | 10.5000 | NaN | S |
85 | 86 | 1 | 3 | Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu... | female | 33.0 | 3 | 0 | 3101278 | 15.8500 | NaN | S |
88 | 89 | 1 | 1 | Fortune, Miss. Mabel Helen | female | 23.0 | 3 | 2 | 19950 | 263.0000 | C23 C25 C27 | S |
98 | 99 | 1 | 2 | Doling, Mrs. John T (Ada Julia Bone) | female | 34.0 | 0 | 1 | 231919 | 23.0000 | NaN | S |
106 | 107 | 1 | 3 | Salkjelsvik, Miss. Anna Kristine | female | 21.0 | 0 | 0 | 343120 | 7.6500 | NaN | S |
123 | 124 | 1 | 2 | Webber, Miss. Susan | female | 32.5 | 0 | 0 | 27267 | 13.0000 | E101 | S |
133 | 134 | 1 | 2 | Weisz, Mrs. Leopold (Mathilde Francoise Pede) | female | 29.0 | 1 | 0 | 228414 | 26.0000 | NaN | S |
136 | 137 | 1 | 1 | Newsom, Miss. Helen Monypeny | female | 19.0 | 0 | 2 | 11752 | 26.2833 | D47 | S |
141 | 142 | 1 | 3 | Nysten, Miss. Anna Sofia | female | 22.0 | 0 | 0 | 347081 | 7.7500 | NaN | S |
142 | 143 | 1 | 3 | Hakkarainen, Mrs. Pekka Pietari (Elin Matilda ... | female | 24.0 | 1 | 0 | STON/O2. 3101279 | 15.8500 | NaN | S |
151 | 152 | 1 | 1 | Pears, Mrs. Thomas (Edith Wearne) | female | 22.0 | 1 | 0 | 113776 | 66.6000 | C2 | S |
156 | 157 | 1 | 3 | Gilnagh, Miss. Katherine "Katie" | female | 16.0 | 0 | 0 | 35851 | 7.7333 | NaN | Q |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
710 | 711 | 1 | 1 | Mayne, Mlle. Berthe Antonine ("Mrs de Villiers") | female | 24.0 | 0 | 0 | PC 17482 | 49.5042 | C90 | C |
716 | 717 | 1 | 1 | Endres, Miss. Caroline Louise | female | 38.0 | 0 | 0 | PC 17757 | 227.5250 | C45 | C |
717 | 718 | 1 | 2 | Troutt, Miss. Edwina Celia "Winnie" | female | 27.0 | 0 | 0 | 34218 | 10.5000 | E101 | S |
720 | 721 | 1 | 2 | Harper, Miss. Annie Jessie "Nina" | female | 6.0 | 0 | 1 | 248727 | 33.0000 | NaN | S |
726 | 727 | 1 | 2 | Renouf, Mrs. Peter Henry (Lillian Jefferys) | female | 30.0 | 3 | 0 | 31027 | 21.0000 | NaN | S |
730 | 731 | 1 | 1 | Allen, Miss. Elisabeth Walton | female | 29.0 | 0 | 0 | 24160 | 211.3375 | B5 | S |
742 | 743 | 1 | 1 | Ryerson, Miss. Susan Parker "Suzette" | female | 21.0 | 2 | 2 | PC 17608 | 262.3750 | B57 B59 B63 B66 | C |
747 | 748 | 1 | 2 | Sinkkonen, Miss. Anna | female | 30.0 | 0 | 0 | 250648 | 13.0000 | NaN | S |
750 | 751 | 1 | 2 | Wells, Miss. Joan | female | 4.0 | 1 | 1 | 29103 | 23.0000 | NaN | S |
759 | 760 | 1 | 1 | Rothes, the Countess. of (Lucy Noel Martha Dye... | female | 33.0 | 0 | 0 | 110152 | 86.5000 | B77 | S |
763 | 764 | 1 | 1 | Carter, Mrs. William Ernest (Lucile Polk) | female | 36.0 | 1 | 2 | 113760 | 120.0000 | B96 B98 | S |
777 | 778 | 1 | 3 | Emanuel, Miss. Virginia Ethel | female | 5.0 | 0 | 0 | 364516 | 12.4750 | NaN | S |
780 | 781 | 1 | 3 | Ayoub, Miss. Banoura | female | 13.0 | 0 | 0 | 2687 | 7.2292 | NaN | C |
781 | 782 | 1 | 1 | Dick, Mrs. Albert Adrian (Vera Gillespie) | female | 17.0 | 1 | 0 | 17474 | 57.0000 | B20 | S |
786 | 787 | 1 | 3 | Sjoblom, Miss. Anna Sofia | female | 18.0 | 0 | 0 | 3101265 | 7.4958 | NaN | S |
797 | 798 | 1 | 3 | Osman, Mrs. Mara | female | 31.0 | 0 | 0 | 349244 | 8.6833 | NaN | S |
801 | 802 | 1 | 2 | Collyer, Mrs. Harvey (Charlotte Annie Tate) | female | 31.0 | 1 | 1 | C.A. 31921 | 26.2500 | NaN | S |
809 | 810 | 1 | 1 | Chambers, Mrs. Norman Campbell (Bertha Griggs) | female | 33.0 | 1 | 0 | 113806 | 53.1000 | E8 | S |
823 | 824 | 1 | 3 | Moor, Mrs. (Beila) | female | 27.0 | 0 | 1 | 392096 | 12.4750 | E121 | S |
830 | 831 | 1 | 3 | Yasbeck, Mrs. Antoni (Selini Alexander) | female | 15.0 | 1 | 0 | 2659 | 14.4542 | NaN | C |
835 | 836 | 1 | 1 | Compton, Miss. Sara Rebecca | female | 39.0 | 1 | 1 | PC 17756 | 83.1583 | E49 | C |
842 | 843 | 1 | 1 | Serepeca, Miss. Augusta | female | 30.0 | 0 | 0 | 113798 | 31.0000 | NaN | C |
853 | 854 | 1 | 1 | Lines, Miss. Mary Conover | female | 16.0 | 0 | 1 | PC 17592 | 39.4000 | D28 | S |
855 | 856 | 1 | 3 | Aks, Mrs. Sam (Leah Rosen) | female | 18.0 | 0 | 1 | 392091 | 9.3500 | NaN | S |
858 | 859 | 1 | 3 | Baclini, Mrs. Solomon (Latifa Qurban) | female | 24.0 | 0 | 3 | 2666 | 19.2583 | NaN | C |
866 | 867 | 1 | 2 | Duran y More, Miss. Asuncion | female | 27.0 | 1 | 0 | SC/PARIS 2149 | 13.8583 | NaN | C |
874 | 875 | 1 | 2 | Abelson, Mrs. Samuel (Hannah Wizosky) | female | 28.0 | 1 | 0 | P/PP 3381 | 24.0000 | NaN | C |
875 | 876 | 1 | 3 | Najib, Miss. Adele Kiamie "Jane" | female | 15.0 | 0 | 0 | 2667 | 7.2250 | NaN | C |
880 | 881 | 1 | 2 | Shelley, Mrs. William (Imanita Parrish Hall) | female | 25.0 | 0 | 1 | 230433 | 26.0000 | NaN | S |
887 | 888 | 1 | 1 | Graham, Miss. Margaret Edith | female | 19.0 | 0 | 0 | 112053 | 30.0000 | B42 | S |
155 rows × 12 columns
plt.figure(figsize=(12, 9))
plt.scatter(train.Fare[train.Survived == 1], train.Age[train.Survived == 1], color='r')
plt.scatter(train.Fare[train.Survived == 0], train.Age[train.Survived == 0], color='b')
plt.ylabel('Age', fontsize=20)
plt.xlabel('Fare', fontsize=20)
plt.show()