X2 = pd.DataFrame(
  {
    "a": range(5),
    "b": [-100, -50, 0, 200, 1000],
  }
)
X2
a b
0 0 -100
1 1 -50
2 2 0
3 3 200
4 4 1000

7.1 표준화 

from sklearn import preprocessing
std = preprocessing.StandardScaler()
std.fit_transform(X2)

array([[-1.41421356, -0.75995002],
       [-0.70710678, -0.63737744],
       [ 0.        , -0.51480485],
       [ 0.70710678, -0.02451452],
       [ 1.41421356,  1.93664683]])
std.scale_

array([  1.41421356, 407.92156109])
std.mean_

array([  2., 210.])
std.var_

array([2.000e+00, 1.664e+05])
X_std = (X2 - X2.mean()) / X2.std()
X_std
a b
0 -1.264911 -0.679720
1 -0.632456 -0.570088
2 0.000000 -0.460455
3 0.632456 -0.021926
4 1.264911 1.732190 
X_std.mean()

a    4.440892e-17
b    0.000000e+00
dtype: float64
X_std.std()

a    1.0
b    1.0
dtype: float64
X3 = X2.copy()
scale_vars(X3, mapper=None) #fastai function locally imported
X3.std()

a    1.118034
b    1.118034
dtype: float64
X3.mean()

a    0.000000e+00
b    4.440892e-17
dtype: float64

7.2 범위 조정 

from sklearn import preprocessing
mms = preprocessing.MinMaxScaler()
mms.fit(X2)
mms.transform(X2)

array([[0.        , 0.        ],
       [0.25      , 0.04545455],
       [0.5       , 0.09090909],
       [0.75      , 0.27272727],
       [1.        , 1.        ]])
(X2 - X2.min()) / (X2.max() - X2.min())
a b
0 0.00 0.000000
1 0.25 0.045455
2 0.50 0.090909
3 0.75 0.272727
4 1.00 1.000000

7.3 더미 변수 

X_cat = pd.DataFrame(
  {
    "name": ["George", "Paul"],
    "inst": ["Bass", "Guitar"],
  }
)

X_cat
name inst
0 George Bass
1 Paul Guitar 
pd.get_dummies(X_cat, drop_first=True)
name_Paul inst_Guitar
0 0 0
1 1 1 
import janitor as jn

X_cat2 = pd.DataFrame(
  {
    "A": [1, None, 3],
    "names": [
        "Fred,George",
        "George",
        "John,Paul",
      ],
  }
)

jn.expand_column(X_cat2, "names", sep=",")
A names Fred George John Paul
0 1.0 Fred,George 1 1 0 0
1 NaN George 0 1 0 0
2 3.0 John,Paul 0 0 1 1

7.4 레이블 인코더 

from sklearn import preprocessing
lab = preprocessing.LabelEncoder()
lab.fit_transform(X_cat['inst'])

array([0, 1])
lab.inverse_transform([1, 1, 0])

array(['Guitar', 'Guitar', 'Bass'], dtype=object)
X_cat.name.astype(
  "category"
).cat.as_ordered().cat.codes + 1

0    1
1    2
dtype: int8

7.5 프리퀀시 인코딩 

mapping = X_cat.name.value_counts()
X_cat.name.map(mapping)

0    1
1    1
Name: name, dtype: int64

7.6 문자열에서 범주 가져오기 

df
pclass survived name sex age sibsp parch fare cabin embarked
0 1 1 Allen, Miss. Elisabeth Walton female 29.0000 0 0 211.3375 B5 S
1 1 1 Allison, Master. Hudson Trevor male 0.9167 1 2 151.5500 C22 C26 S
2 1 0 Allison, Miss. Helen Loraine female 2.0000 1 2 151.5500 C22 C26 S
3 1 0 Allison, Mr. Hudson Joshua Creighton male 30.0000 1 2 151.5500 C22 C26 S
4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 1 2 151.5500 C22 C26 S
5 1 1 Anderson, Mr. Harry male 48.0000 0 0 26.5500 E12 S
6 1 1 Andrews, Miss. Kornelia Theodosia female 63.0000 1 0 77.9583 D7 S
7 1 0 Andrews, Mr. Thomas Jr male 39.0000 0 0 0.0000 A36 S
8 1 1 Appleton, Mrs. Edward Dale (Charlotte Lamson) female 53.0000 2 0 51.4792 C101 S
9 1 0 Artagaveytia, Mr. Ramon male 71.0000 0 0 49.5042 NaN C
10 1 0 Astor, Col. John Jacob male 47.0000 1 0 227.5250 C62 C64 C
11 1 1 Astor, Mrs. John Jacob (Madeleine Talmadge Force) female 18.0000 1 0 227.5250 C62 C64 C
12 1 1 Aubart, Mme. Leontine Pauline female 24.0000 0 0 69.3000 B35 C
13 1 1 Barber, Miss. Ellen "Nellie" female 26.0000 0 0 78.8500 NaN S
14 1 1 Barkworth, Mr. Algernon Henry Wilson male 80.0000 0 0 30.0000 A23 S
15 1 0 Baumann, Mr. John D male NaN 0 0 25.9250 NaN S
16 1 0 Baxter, Mr. Quigg Edmond male 24.0000 0 1 247.5208 B58 B60 C
17 1 1 Baxter, Mrs. James (Helene DeLaudeniere Chaput) female 50.0000 0 1 247.5208 B58 B60 C
18 1 1 Bazzani, Miss. Albina female 32.0000 0 0 76.2917 D15 C
19 1 0 Beattie, Mr. Thomson male 36.0000 0 0 75.2417 C6 C
20 1 1 Beckwith, Mr. Richard Leonard male 37.0000 1 1 52.5542 D35 S
21 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0000 1 1 52.5542 D35 S
22 1 1 Behr, Mr. Karl Howell male 26.0000 0 0 30.0000 C148 C
23 1 1 Bidois, Miss. Rosalie female 42.0000 0 0 227.5250 NaN C
24 1 1 Bird, Miss. Ellen female 29.0000 0 0 221.7792 C97 S
25 1 0 Birnbaum, Mr. Jakob male 25.0000 0 0 26.0000 NaN C
26 1 1 Bishop, Mr. Dickinson H male 25.0000 1 0 91.0792 B49 C
27 1 1 Bishop, Mrs. Dickinson H (Helen Walton) female 19.0000 1 0 91.0792 B49 C
28 1 1 Bissette, Miss. Amelia female 35.0000 0 0 135.6333 C99 S
29 1 1 Bjornstrom-Steffansson, Mr. Mauritz Hakan male 28.0000 0 0 26.5500 C52 S
... ... ... ... ... ... ... ... ... ... ...
1279 3 0 Vestrom, Miss. Hulda Amanda Adolfina female 14.0000 0 0 7.8542 NaN S
1280 3 0 Vovk, Mr. Janko male 22.0000 0 0 7.8958 NaN S
1281 3 0 Waelens, Mr. Achille male 22.0000 0 0 9.0000 NaN S
1282 3 0 Ware, Mr. Frederick male NaN 0 0 8.0500 NaN S
1283 3 0 Warren, Mr. Charles William male NaN 0 0 7.5500 NaN S
1284 3 0 Webber, Mr. James male NaN 0 0 8.0500 NaN S
1285 3 0 Wenzel, Mr. Linhart male 32.5000 0 0 9.5000 NaN S
1286 3 1 Whabee, Mrs. George Joseph (Shawneene Abi-Saab) female 38.0000 0 0 7.2292 NaN C
1287 3 0 Widegren, Mr. Carl/Charles Peter male 51.0000 0 0 7.7500 NaN S
1288 3 0 Wiklund, Mr. Jakob Alfred male 18.0000 1 0 6.4958 NaN S
1289 3 0 Wiklund, Mr. Karl Johan male 21.0000 1 0 6.4958 NaN S
1290 3 1 Wilkes, Mrs. James (Ellen Needs) female 47.0000 1 0 7.0000 NaN S
1291 3 0 Willer, Mr. Aaron ("Abi Weller") male NaN 0 0 8.7125 NaN S
1292 3 0 Willey, Mr. Edward male NaN 0 0 7.5500 NaN S
1293 3 0 Williams, Mr. Howard Hugh "Harry" male NaN 0 0 8.0500 NaN S
1294 3 0 Williams, Mr. Leslie male 28.5000 0 0 16.1000 NaN S
1295 3 0 Windelov, Mr. Einar male 21.0000 0 0 7.2500 NaN S
1296 3 0 Wirz, Mr. Albert male 27.0000 0 0 8.6625 NaN S
1297 3 0 Wiseman, Mr. Phillippe male NaN 0 0 7.2500 NaN S
1298 3 0 Wittevrongel, Mr. Camille male 36.0000 0 0 9.5000 NaN S
1299 3 0 Yasbeck, Mr. Antoni male 27.0000 1 0 14.4542 NaN C
1300 3 1 Yasbeck, Mrs. Antoni (Selini Alexander) female 15.0000 1 0 14.4542 NaN C
1301 3 0 Youseff, Mr. Gerious male 45.5000 0 0 7.2250 NaN C
1302 3 0 Yousif, Mr. Wazli male NaN 0 0 7.2250 NaN C
1303 3 0 Yousseff, Mr. Gerious male NaN 0 0 14.4583 NaN C
1304 3 0 Zabour, Miss. Hileni female 14.5000 1 0 14.4542 NaN C
1305 3 0 Zabour, Miss. Thamine female NaN 1 0 14.4542 NaN C
1306 3 0 Zakarian, Mr. Mapriededer male 26.5000 0 0 7.2250 NaN C
1307 3 0 Zakarian, Mr. Ortin male 27.0000 0 0 7.2250 NaN C
1308 3 0 Zimmerman, Mr. Leo male 29.0000 0 0 7.8750 NaN S

1309 rows × 10 columns

from collections import Counter
c = Counter()

def triples(val):
  for i in range(len(val)):
    c[val[i : i + 3]] += 1

df.name.apply(triples)
c.most_common(10)

[(', M', 1282),
 (' Mr', 954),
 ('r. ', 830),
 ('Mr.', 757),
 ('s. ', 460),
 ('n, ', 320),
 (' Mi', 283),
 ('iss', 261),
 ('ss.', 261),
 ('Mis', 260)]
df.name.str.extract(
  "([A-Za-z]+)\.", expand=False
).head()

0      Miss
1    Master
2      Miss
3        Mr
4       Mrs
Name: name, dtype: object
df.name.str.extract(
  "([A-Za-z]+)\.", expand=False
).value_counts()

Mr          757
Miss        260
Mrs         197
Master       61
Dr            8
Rev           8
Col           4
Ms            2
Mlle          2
Major         2
Don           1
Jonkheer      1
Sir           1
Capt          1
Countess      1
Mme           1
Dona          1
Lady          1
Name: name, dtype: int64

7.7 그 밖의 범주형 인코딩 

import category_encoders as ce
he = ce.HashingEncoder(verbose=1)
he.fit_transform(X_cat)
col_0 col_1 col_2 col_3 col_4 col_5 col_6 col_7
0 0 0 0 1 0 1 0 0
1 0 2 0 0 0 0 0 0 
size_df = pd.DataFrame(
  {
    "name": ["Fred", "John", "Matt"],
    "size": ["small", "med", "xxl"],
  }
)

ore = ce.OrdinalEncoder(
  mapping=[
    {
      "col": "size",
      "mapping": {
        "small": 1,
        "med": 2,
        "lg": 3
      }
    }
  ]
)

ore.fit_transform(size_df)
name size
0 Fred 1.0
1 John 2.0
2 Matt -1.0 
def get_title(df):
  return df.name.str.extract(
    "([A-Za-z]+)\.", expand=False
  )

te = ce.TargetEncoder(cols="Title")
te.fit_transform(

df.assign(Title=get_title), df.survived)["Title"].head()

0    0.676923
1    0.508197
2    0.676923
3    0.162483
4    0.786802
Name: Title, dtype: float64

7.8 날짜형 데이터의 특징 공학 

import numpy as np

dates = pd.DataFrame(
  {
    "A": pd.to_datetime(
      ["9/17/2001", "Jan 1, 2002"]
    )
  }
)

add_datepart(dates, "A")
dates.T
0 1
AYear 2001 2002
AMonth 9 1
AWeek 38 1
ADay 17 1
ADayofweek 0 1
ADayofyear 260 1
AIs_month_end False False
AIs_month_start False True
AIs_quarter_end False False
AIs_quarter_start False True
AIs_year_end False False
AIs_year_start False True
AElapsed 1000684800 1009843200

7.9 col_na 특징의 추가 

from pandas.api.types import is_numeric_dtype
def fix_missing(df, col, name, na_dict):
  if is_numeric_dtype(col):
    if pd.isnull(col).sum() or (name in na_dict):
      df[name + "_na"] = pd.isnull(col)

      filler = (
          na_dict[name]
          if name in na_dict
          else col.median()
      )

      df[name] = col.fillna(filler)
      na_dict[name] = filler
      return na_dict

data = pd.DataFrame({"A": [0, None, 5, 100]})
fix_missing(data, data.A, "A", {})

{'A': 5.0}
from pandas.api.types import is_numeric_dtype

def fix_missing(df, col, name, na_dict):
  if is_numeric_dtype(col):
    if pd.isnull(col).sum() or (name in na_dict):
      df[name + "_na"] = pd.isnull(col)
      
      filler = (
        na_dict[name]
        if name in na_dict
        else col.median()
      )

      df[name] = col.fillna(filler)
      na_dict[name] = filler
      return na_dict
      
data = pd.DataFrame({"A": [0, None, 5, 100]})
fix_missing(data, data.A, "A", {})

{'A': 5.0}
data
A A_na
0 0.0 False
1 5.0 True
2 5.0 False
3 100.0 False 
data = pd.DataFrame({"A": [0, None, 5, 100]})
data["A_na"] = data.A.isnull()
data["A"] = data.A.fillna(data.A.median())

7.10 수동적 특징 공학 

agg = (
  df.groupby("cabin")
  .agg("min,max,mean,sum".split(","))
  .reset_index()
)

agg.columns = [
  "_".join(c).strip("_")
  for c in agg.columns.values
]

agg_df = df.merge(agg, on="cabin")
agg_df
pclass survived name sex age sibsp parch fare cabin embarked pclass_min pclass_max pclass_mean pclass_sum survived_min survived_max survived_mean survived_sum age_min age_max age_mean age_sum sibsp_min sibsp_max sibsp_mean sibsp_sum parch_min parch_max parch_mean parch_sum fare_min fare_max fare_mean fare_sum
0 1 1 Allen, Miss. Elisabeth Walton female 29.0000 0 0 211.3375 B5 S 1 1 1 2 1 1 1.000000 2 15.0000 29.0 22.000000 44.0000 0 0 0.000000 0 0 1 0.5 1 211.3375 211.3375 211.337500 422.6750
1 1 1 Madill, Miss. Georgette Alexandra female 15.0000 0 1 211.3375 B5 S 1 1 1 2 1 1 1.000000 2 15.0000 29.0 22.000000 44.0000 0 0 0.000000 0 0 1 0.5 1 211.3375 211.3375 211.337500 422.6750
2 1 1 Allison, Master. Hudson Trevor male 0.9167 1 2 151.5500 C22 C26 S 1 1 1 4 0 1 0.250000 1 0.9167 30.0 14.479175 57.9167 1 1 1.000000 4 2 2 2.0 8 151.5500 151.5500 151.550000 606.2000
3 1 0 Allison, Miss. Helen Loraine female 2.0000 1 2 151.5500 C22 C26 S 1 1 1 4 0 1 0.250000 1 0.9167 30.0 14.479175 57.9167 1 1 1.000000 4 2 2 2.0 8 151.5500 151.5500 151.550000 606.2000
4 1 0 Allison, Mr. Hudson Joshua Creighton male 30.0000 1 2 151.5500 C22 C26 S 1 1 1 4 0 1 0.250000 1 0.9167 30.0 14.479175 57.9167 1 1 1.000000 4 2 2 2.0 8 151.5500 151.5500 151.550000 606.2000
5 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 1 2 151.5500 C22 C26 S 1 1 1 4 0 1 0.250000 1 0.9167 30.0 14.479175 57.9167 1 1 1.000000 4 2 2 2.0 8 151.5500 151.5500 151.550000 606.2000
6 1 1 Anderson, Mr. Harry male 48.0000 0 0 26.5500 E12 S 1 1 1 1 1 1 1.000000 1 48.0000 48.0 48.000000 48.0000 0 0 0.000000 0 0 0 0.0 0 26.5500 26.5500 26.550000 26.5500
7 1 1 Andrews, Miss. Kornelia Theodosia female 63.0000 1 0 77.9583 D7 S 1 1 1 1 1 1 1.000000 1 63.0000 63.0 63.000000 63.0000 1 1 1.000000 1 0 0 0.0 0 77.9583 77.9583 77.958300 77.9583
8 1 0 Andrews, Mr. Thomas Jr male 39.0000 0 0 0.0000 A36 S 1 1 1 1 0 0 0.000000 0 39.0000 39.0 39.000000 39.0000 0 0 0.000000 0 0 0 0.0 0 0.0000 0.0000 0.000000 0.0000
9 1 1 Appleton, Mrs. Edward Dale (Charlotte Lamson) female 53.0000 2 0 51.4792 C101 S 1 1 1 3 1 1 1.000000 3 53.0000 59.0 55.666667 167.0000 2 2 2.000000 6 0 0 0.0 0 25.7000 51.4792 42.886133 128.6584
10 1 1 Brown, Mrs. John Murray (Caroline Lane Lamson) female 59.0000 2 0 51.4792 C101 S 1 1 1 3 1 1 1.000000 3 53.0000 59.0 55.666667 167.0000 2 2 2.000000 6 0 0 0.0 0 25.7000 51.4792 42.886133 128.6584
11 1 1 Cornell, Mrs. Robert Clifford (Malvina Helen L... female 55.0000 2 0 25.7000 C101 S 1 1 1 3 1 1 1.000000 3 53.0000 59.0 55.666667 167.0000 2 2 2.000000 6 0 0 0.0 0 25.7000 51.4792 42.886133 128.6584
12 1 0 Astor, Col. John Jacob male 47.0000 1 0 227.5250 C62 C64 C 1 1 1 2 0 1 0.500000 1 18.0000 47.0 32.500000 65.0000 1 1 1.000000 2 0 0 0.0 0 227.5250 227.5250 227.525000 455.0500
13 1 1 Astor, Mrs. John Jacob (Madeleine Talmadge Force) female 18.0000 1 0 227.5250 C62 C64 C 1 1 1 2 0 1 0.500000 1 18.0000 47.0 32.500000 65.0000 1 1 1.000000 2 0 0 0.0 0 227.5250 227.5250 227.525000 455.0500
14 1 1 Aubart, Mme. Leontine Pauline female 24.0000 0 0 69.3000 B35 C 1 1 1 2 1 1 1.000000 2 24.0000 24.0 24.000000 48.0000 0 0 0.000000 0 0 0 0.0 0 69.3000 69.3000 69.300000 138.6000
15 1 1 Sagesser, Mlle. Emma female 24.0000 0 0 69.3000 B35 C 1 1 1 2 1 1 1.000000 2 24.0000 24.0 24.000000 48.0000 0 0 0.000000 0 0 0 0.0 0 69.3000 69.3000 69.300000 138.6000
16 1 1 Barkworth, Mr. Algernon Henry Wilson male 80.0000 0 0 30.0000 A23 S 1 1 1 1 1 1 1.000000 1 80.0000 80.0 80.000000 80.0000 0 0 0.000000 0 0 0 0.0 0 30.0000 30.0000 30.000000 30.0000
17 1 0 Baxter, Mr. Quigg Edmond male 24.0000 0 1 247.5208 B58 B60 C 1 1 1 3 0 1 0.666667 2 24.0000 50.0 33.666667 101.0000 0 1 0.333333 1 1 1 1.0 3 247.5208 247.5208 247.520800 742.5624
18 1 1 Baxter, Mrs. James (Helene DeLaudeniere Chaput) female 50.0000 0 1 247.5208 B58 B60 C 1 1 1 3 0 1 0.666667 2 24.0000 50.0 33.666667 101.0000 0 1 0.333333 1 1 1 1.0 3 247.5208 247.5208 247.520800 742.5624
19 1 1 Douglas, Mrs. Frederick Charles (Mary Helene B... female 27.0000 1 1 247.5208 B58 B60 C 1 1 1 3 0 1 0.666667 2 24.0000 50.0 33.666667 101.0000 0 1 0.333333 1 1 1 1.0 3 247.5208 247.5208 247.520800 742.5624
20 1 1 Bazzani, Miss. Albina female 32.0000 0 0 76.2917 D15 C 1 1 1 2 1 1 1.000000 2 32.0000 60.0 46.000000 92.0000 0 0 0.000000 0 0 0 0.0 0 76.2917 76.2917 76.291700 152.5834
21 1 1 Bucknell, Mrs. William Robert (Emma Eliza Ward) female 60.0000 0 0 76.2917 D15 C 1 1 1 2 1 1 1.000000 2 32.0000 60.0 46.000000 92.0000 0 0 0.000000 0 0 0 0.0 0 76.2917 76.2917 76.291700 152.5834
22 1 0 Beattie, Mr. Thomson male 36.0000 0 0 75.2417 C6 C 1 1 1 2 0 0 0.000000 0 36.0000 46.0 41.000000 82.0000 0 0 0.000000 0 0 0 0.0 0 75.2417 75.2417 75.241700 150.4834
23 1 0 McCaffry, Mr. Thomas Francis male 46.0000 0 0 75.2417 C6 C 1 1 1 2 0 0 0.000000 0 36.0000 46.0 41.000000 82.0000 0 0 0.000000 0 0 0 0.0 0 75.2417 75.2417 75.241700 150.4834
24 1 1 Beckwith, Mr. Richard Leonard male 37.0000 1 1 52.5542 D35 S 1 1 1 2 1 1 1.000000 2 37.0000 47.0 42.000000 84.0000 1 1 1.000000 2 1 1 1.0 2 52.5542 52.5542 52.554200 105.1084
25 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0000 1 1 52.5542 D35 S 1 1 1 2 1 1 1.000000 2 37.0000 47.0 42.000000 84.0000 1 1 1.000000 2 1 1 1.0 2 52.5542 52.5542 52.554200 105.1084
26 1 1 Behr, Mr. Karl Howell male 26.0000 0 0 30.0000 C148 C 1 1 1 1 1 1 1.000000 1 26.0000 26.0 26.000000 26.0000 0 0 0.000000 0 0 0 0.0 0 30.0000 30.0000 30.000000 30.0000
27 1 1 Bird, Miss. Ellen female 29.0000 0 0 221.7792 C97 S 1 1 1 1 1 1 1.000000 1 29.0000 29.0 29.000000 29.0000 0 0 0.000000 0 0 0 0.0 0 221.7792 221.7792 221.779200 221.7792
28 1 1 Bishop, Mr. Dickinson H male 25.0000 1 0 91.0792 B49 C 1 1 1 2 1 1 1.000000 2 19.0000 25.0 22.000000 44.0000 1 1 1.000000 2 0 0 0.0 0 91.0792 91.0792 91.079200 182.1584
29 1 1 Bishop, Mrs. Dickinson H (Helen Walton) female 19.0000 1 0 91.0792 B49 C 1 1 1 2 1 1 1.000000 2 19.0000 25.0 22.000000 44.0000 1 1 1.000000 2 0 0 0.0 0 91.0792 91.0792 91.079200 182.1584
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
265 2 1 Brown, Miss. Amelia "Mildred" female 24.0000 0 0 13.0000 F33 S 2 2 2 8 1 1 1.000000 4 22.0000 34.0 27.250000 109.0000 0 0 0.000000 0 0 0 0.0 0 10.5000 13.0000 11.125000 44.5000
266 2 1 Cook, Mrs. (Selena Rogers) female 22.0000 0 0 10.5000 F33 S 2 2 2 8 1 1 1.000000 4 22.0000 34.0 27.250000 109.0000 0 0 0.000000 0 0 0 0.0 0 10.5000 13.0000 11.125000 44.5000
267 2 1 Lemore, Mrs. (Amelia Milley) female 34.0000 0 0 10.5000 F33 S 2 2 2 8 1 1 1.000000 4 22.0000 34.0 27.250000 109.0000 0 0 0.000000 0 0 0 0.0 0 10.5000 13.0000 11.125000 44.5000
268 2 1 Nye, Mrs. (Elizabeth Ramell) female 29.0000 0 0 10.5000 F33 S 2 2 2 8 1 1 1.000000 4 22.0000 34.0 27.250000 109.0000 0 0 0.000000 0 0 0 0.0 0 10.5000 13.0000 11.125000 44.5000
269 2 1 Keane, Miss. Nora A female NaN 0 0 12.3500 E101 Q 2 2 2 6 1 1 1.000000 3 27.0000 32.5 29.750000 59.5000 0 0 0.000000 0 0 0 0.0 0 10.5000 13.0000 11.950000 35.8500
270 2 1 Troutt, Miss. Edwina Celia "Winnie" female 27.0000 0 0 10.5000 E101 S 2 2 2 6 1 1 1.000000 3 27.0000 32.5 29.750000 59.5000 0 0 0.000000 0 0 0 0.0 0 10.5000 13.0000 11.950000 35.8500
271 2 1 Webber, Miss. Susan female 32.5000 0 0 13.0000 E101 S 2 2 2 6 1 1 1.000000 3 27.0000 32.5 29.750000 59.5000 0 0 0.000000 0 0 0 0.0 0 10.5000 13.0000 11.950000 35.8500
272 2 0 Mack, Mrs. (Mary) female 57.0000 0 0 10.5000 E77 S 2 2 2 2 0 0 0.000000 0 57.0000 57.0 57.000000 57.0000 0 0 0.000000 0 0 0 0.0 0 10.5000 10.5000 10.500000 10.5000
273 2 1 Navratil, Master. Edmond Roger male 2.0000 1 1 26.0000 F2 S 2 2 2 8 0 1 0.500000 2 2.0000 36.5 16.875000 67.5000 0 1 0.500000 2 0 2 1.0 4 13.0000 26.0000 22.750000 91.0000
274 2 1 Navratil, Master. Michel M male 3.0000 1 1 26.0000 F2 S 2 2 2 8 0 1 0.500000 2 2.0000 36.5 16.875000 67.5000 0 1 0.500000 2 0 2 1.0 4 13.0000 26.0000 22.750000 91.0000
275 2 0 Navratil, Mr. Michel ("Louis M Hoffman") male 36.5000 0 2 26.0000 F2 S 2 2 2 8 0 1 0.500000 2 2.0000 36.5 16.875000 67.5000 0 1 0.500000 2 0 2 1.0 4 13.0000 26.0000 22.750000 91.0000
276 2 0 Nesson, Mr. Israel male 26.0000 0 0 13.0000 F2 S 2 2 2 8 0 1 0.500000 2 2.0000 36.5 16.875000 67.5000 0 1 0.500000 2 0 2 1.0 4 13.0000 26.0000 22.750000 91.0000
277 2 1 Nourney, Mr. Alfred ("Baron von Drachstedt") male 20.0000 0 0 13.8625 D38 C 2 2 2 2 1 1 1.000000 1 20.0000 20.0 20.000000 20.0000 0 0 0.000000 0 0 0 0.0 0 13.8625 13.8625 13.862500 13.8625
278 2 0 Swane, Mr. George male 18.5000 0 0 13.0000 F S 2 2 2 2 0 0 0.000000 0 18.5000 18.5 18.500000 18.5000 0 0 0.000000 0 0 0 0.0 0 13.0000 13.0000 13.000000 13.0000
279 3 1 Abelseth, Mr. Olaus Jorgensen male 25.0000 0 0 7.6500 F G63 S 3 3 3 6 0 1 0.500000 1 25.0000 42.0 33.500000 67.0000 0 0 0.000000 0 0 0 0.0 0 7.6500 7.6500 7.650000 15.3000
280 3 0 Humblen, Mr. Adolf Mathias Nicolai Olsen male 42.0000 0 0 7.6500 F G63 S 3 3 3 6 0 1 0.500000 1 25.0000 42.0 33.500000 67.0000 0 0 0.000000 0 0 0 0.0 0 7.6500 7.6500 7.650000 15.3000
281 3 1 Krekorian, Mr. Neshan male 25.0000 0 0 7.2292 F E57 C 3 3 3 3 1 1 1.000000 1 25.0000 25.0 25.000000 25.0000 0 0 0.000000 0 0 0 0.0 0 7.2292 7.2292 7.229200 7.2292
282 3 0 Mardirosian, Mr. Sarkis male NaN 0 0 7.2292 F E46 C 3 3 3 3 0 0 0.000000 0 NaN NaN NaN 0.0000 0 0 0.000000 0 0 0 0.0 0 7.2292 7.2292 7.229200 7.2292
283 3 0 Moen, Mr. Sigurd Hansen male 25.0000 0 0 7.6500 F G73 S 3 3 3 6 0 0 0.000000 0 19.0000 25.0 22.000000 44.0000 0 0 0.000000 0 0 0 0.0 0 7.6500 7.6500 7.650000 15.3000
284 3 0 Soholt, Mr. Peter Andreas Lauritz Andersen male 19.0000 0 0 7.6500 F G73 S 3 3 3 6 0 0 0.000000 0 19.0000 25.0 22.000000 44.0000 0 0 0.000000 0 0 0 0.0 0 7.6500 7.6500 7.650000 15.3000
285 3 1 Moor, Master. Meier male 6.0000 0 1 12.4750 E121 S 3 3 3 6 1 1 1.000000 2 6.0000 27.0 16.500000 33.0000 0 0 0.000000 0 1 1 1.0 2 12.4750 12.4750 12.475000 24.9500
286 3 1 Moor, Mrs. (Beila) female 27.0000 0 1 12.4750 E121 S 3 3 3 6 1 1 1.000000 2 6.0000 27.0 16.500000 33.0000 0 0 0.000000 0 1 1 1.0 2 12.4750 12.4750 12.475000 24.9500
287 3 1 Peter, Miss. Anna female NaN 1 1 22.3583 F E69 C 3 3 3 3 1 1 1.000000 1 NaN NaN NaN 0.0000 1 1 1.000000 1 1 1 1.0 1 22.3583 22.3583 22.358300 22.3583
288 3 1 Pickard, Mr. Berk (Berk Trembisky) male 32.0000 0 0 8.0500 E10 S 3 3 3 3 1 1 1.000000 1 32.0000 32.0 32.000000 32.0000 0 0 0.000000 0 0 0 0.0 0 8.0500 8.0500 8.050000 8.0500
289 3 1 Sandstrom, Miss. Beatrice Irene female 1.0000 1 1 16.7000 G6 S 3 3 3 15 0 1 0.600000 3 1.0000 29.0 12.000000 60.0000 0 1 0.600000 3 1 2 1.2 6 10.4625 16.7000 14.205000 71.0250
290 3 1 Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengt... female 24.0000 0 2 16.7000 G6 S 3 3 3 15 0 1 0.600000 3 1.0000 29.0 12.000000 60.0000 0 1 0.600000 3 1 2 1.2 6 10.4625 16.7000 14.205000 71.0250
291 3 1 Sandstrom, Miss. Marguerite Rut female 4.0000 1 1 16.7000 G6 S 3 3 3 15 0 1 0.600000 3 1.0000 29.0 12.000000 60.0000 0 1 0.600000 3 1 2 1.2 6 10.4625 16.7000 14.205000 71.0250
292 3 0 Strom, Miss. Telma Matilda female 2.0000 0 1 10.4625 G6 S 3 3 3 15 0 1 0.600000 3 1.0000 29.0 12.000000 60.0000 0 1 0.600000 3 1 2 1.2 6 10.4625 16.7000 14.205000 71.0250
293 3 0 Strom, Mrs. Wilhelm (Elna Matilda Persson) female 29.0000 1 1 10.4625 G6 S 3 3 3 15 0 1 0.600000 3 1.0000 29.0 12.000000 60.0000 0 1 0.600000 3 1 2 1.2 6 10.4625 16.7000 14.205000 71.0250
294 3 0 Tobin, Mr. Roger male NaN 0 0 7.7500 F38 Q 3 3 3 3 0 0 0.000000 0 NaN NaN NaN 0.0000 0 0 0.000000 0 0 0 0.0 0 7.7500 7.7500 7.750000 7.7500

295 rows × 34 columns