Exploratory Data Analysis on Titanic Data in Python

class19_25.06_python - EDA Titanic Case Study

Titanic : EDA¶

This is just an example EDA for absolute beginners. For more detailed EDA and proper documentation refer more projects on EDA.

https://www.kaggle.com/c/titanic/data Use this to acquire domain knowledge and for the dataset.

Link to another example EDA: https://www.kaggle.com/code/gunesevitan/titanic-advanced-feature-engineering-tutorial

In [1]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

In [3]:

df = pd.read_csv("titanic.csv", index_col = 'PassengerId')
df

Out[3]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 11 columns

In [ ]:

# df[df.Price == df.Price.max()].Company

In [4]:

df.isnull().sum()

Out[4]:

Survived      0
Pclass        0
Name          0
Sex           0
Age         177
SibSp         0
Parch         0
Ticket        0
Fare          0
Cabin       687
Embarked      2
dtype: int64

In [5]:

df[df.Embarked.isnull()]

Out[5]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
62	1	1	Icard, Miss. Amelie	female	38.0	0	0	113572	80.0	B28	NaN
830	1	1	Stone, Mrs. George Nelson (Martha Evelyn)	female	62.0	0	0	113572	80.0	B28	NaN

Observation:¶

Both of them are travelling together:
- same ticket number and same cabin
Considerable difference in their ages
- Mrs Stone is old
different surnames #### Inference
possibly they are not related but still travelling together #### Assumption
Miss Amelie is at 38 years and still not married. She might be a nurse assisting Mrs. Stone #### Conclusion
Miss Amelie is a caretaker of Mrs. Stone
Both of them embarked from the same location

In [17]:

df[(df.Pclass == 1) & (df.Sex == 'female') & (df.Survived == 1) & (df.SibSp == 0)].Embarked.value_counts()

Out[17]:

S    24
C    22
Name: Embarked, dtype: int64

In [23]:

df['Cabin'].str[0]

Out[23]:

PassengerId
1      NaN
2        C
3      NaN
4        C
5      NaN
      ... 
887    NaN
888      B
889    NaN
890      C
891    NaN
Name: Cabin, Length: 891, dtype: object

In [26]:

df[df.Cabin.notnull()]['Cabin'].str[0]

Out[26]:

PassengerId
2      C
4      C
7      E
11     G
12     C
      ..
872    D
873    B
880    C
888    B
890    C
Name: Cabin, Length: 204, dtype: object

In [7]:

df['Deck'] = df['Cabin'].str[0]

In [8]:

df

Out[8]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Deck
PassengerId
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	NaN
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	NaN
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S	C
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S	NaN
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S	B
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S	NaN
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C	C
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q	NaN

891 rows × 12 columns

In [30]:

df[(df.Pclass == 1) & (df.Sex == 'female') & (df.Survived == 1) & (df.Deck == 'B')].Embarked.mode()

Out[30]:

0    S
dtype: object

In [32]:

df.Embarked.fillna('S', inplace = True)

In [33]:

df

Out[33]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Deck
PassengerId
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	NaN
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	NaN
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S	C
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S	NaN
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S	B
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S	NaN
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C	C
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q	NaN

891 rows × 12 columns

In [9]:

df.isnull().sum()

Out[9]:

Survived      0
Pclass        0
Name          0
Sex           0
Age         177
SibSp         0
Parch         0
Ticket        0
Fare          0
Cabin       687
Embarked      2
Deck        687
dtype: int64

In [35]:

df.loc[[62, 830]]

Out[35]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Deck
PassengerId
62	1	1	Icard, Miss. Amelie	female	38.0	0	0	113572	80.0	B28	S	B
830	1	1	Stone, Mrs. George Nelson (Martha Evelyn)	female	62.0	0	0	113572	80.0	B28	S	B

In [17]:

df['Family'] = df['SibSp'] + df['Parch']
df

Out[17]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Deck	Family
PassengerId
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	NaN	1
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	C	1
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	NaN	0
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S	C	1
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S	NaN	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S	NaN	0
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S	B	0
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S	NaN	3
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C	C	0
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q	NaN	0

891 rows × 13 columns

In [18]:

df.corr(numeric_only = True)   # Correlation Matrix

Out[18]:

	Survived	Pclass	Age	SibSp	Parch	Fare	Family
Survived	1.000000	-0.338481	-0.077221	-0.035322	0.081629	0.257307	0.016639
Pclass	-0.338481	1.000000	-0.369226	0.083081	0.018443	-0.549500	0.065997
Age	-0.077221	-0.369226	1.000000	-0.308247	-0.189119	0.096067	-0.301914
SibSp	-0.035322	0.083081	-0.308247	1.000000	0.414838	0.159651	0.890712
Parch	0.081629	0.018443	-0.189119	0.414838	1.000000	0.216225	0.783111
Fare	0.257307	-0.549500	0.096067	0.159651	0.216225	1.000000	0.217138
Family	0.016639	0.065997	-0.301914	0.890712	0.783111	0.217138	1.000000

In [15]:

df.corr(numeric_only = True).abs()   # Correlation Matrix

Out[15]:

	Survived	Pclass	Age	SibSp	Parch	Fare
Survived	1.000000	0.338481	0.077221	0.035322	0.081629	0.257307
Pclass	0.338481	1.000000	0.369226	0.083081	0.018443	0.549500
Age	0.077221	0.369226	1.000000	0.308247	0.189119	0.096067
SibSp	0.035322	0.083081	0.308247	1.000000	0.414838	0.159651
Parch	0.081629	0.018443	0.189119	0.414838	1.000000	0.216225
Fare	0.257307	0.549500	0.096067	0.159651	0.216225	1.000000

In [16]:

df.corr(numeric_only = True).unstack()

Out[16]:

Survived  Survived    1.000000
          Pclass     -0.338481
          Age        -0.077221
          SibSp      -0.035322
          Parch       0.081629
          Fare        0.257307
Pclass    Survived   -0.338481
          Pclass      1.000000
          Age        -0.369226
          SibSp       0.083081
          Parch       0.018443
          Fare       -0.549500
Age       Survived   -0.077221
          Pclass     -0.369226
          Age         1.000000
          SibSp      -0.308247
          Parch      -0.189119
          Fare        0.096067
SibSp     Survived   -0.035322
          Pclass      0.083081
          Age        -0.308247
          SibSp       1.000000
          Parch       0.414838
          Fare        0.159651
Parch     Survived    0.081629
          Pclass      0.018443
          Age        -0.189119
          SibSp       0.414838
          Parch       1.000000
          Fare        0.216225
Fare      Survived    0.257307
          Pclass     -0.549500
          Age         0.096067
          SibSp       0.159651
          Parch       0.216225
          Fare        1.000000
dtype: float64

In [48]:

df.groupby(['Pclass', 'Family'])[['Age']].median()

Out[48]:

		Age
Pclass	Family
1	0	38.5
	1	37.0
	2	39.5
	3	14.0
	4	19.5
	5	23.5
2	0	31.0
	1	29.0
	2	22.0
	3	24.0
	4	54.0
	5	24.0
3	0	26.0
	1	24.5
	2	24.0
	3	5.0
	4	12.5
	5	8.0
	6	9.0
	7	12.5
	10	NaN

In [23]:

pfa = df.groupby(['Pclass', 'Family'])[['Age']].median().unstack()
pfa

Out[23]:

	Age
Family	0	1	2	3	4	5	6	7	10
Pclass
1	38.5	37.0	39.5	14.0	19.5	23.5	NaN	NaN	NaN
2	31.0	29.0	22.0	24.0	54.0	24.0	NaN	NaN	NaN
3	26.0	24.5	24.0	5.0	12.5	8.0	9.0	12.5	NaN

In [27]:

pfa.plot(kind = 'bar')

Out[27]:

<Axes: xlabel='Pclass'>

g7W1tVhvl4h0RKJSqVT6LoKIiIh+OzjzQURERKJi+CAiIiJRMXwQERGRqBg+iIiISFQMH0RERCQqhg8iIiISFcMHERERiYrhg4iIiETF8EFERESiYvggIiIiUTF8EBERkaj+PzIZQBgOrin3AAAAAElFTkSuQmCC

In [37]:

df.groupby(['Pclass'])[['Age']].median()

Out[37]:

	Age
Pclass
1	37.0
2	29.0
3	24.0

In [38]:

df.groupby(['Pclass'])[['Age']].mean()

Out[38]:

	Age
Pclass
1	38.233441
2	29.877630
3	25.140620

In [39]:

(df.groupby(['Pclass'])[['Age']].mean() + df.groupby(['Pclass'])[['Age']].median())/2

Out[39]:

	Age
Pclass
1	37.616720
2	29.438815
3	24.570310

In [ ]:

Analysis
    - Univariate Analysis
    - Bivariate Analysis
    - Multivariate Analysis

In [21]:

sns.violinplot(y = df['Age'])

Out[21]:

<Axes: ylabel='Age'>

In [22]:

sns.violinplot(y = df['Age'], x = df.Pclass)

Out[22]:

<Axes: xlabel='Pclass', ylabel='Age'>

In [28]:

sns.boxplot(y = df['Age'], x = df.Pclass)

Out[28]:

<Axes: xlabel='Pclass', ylabel='Age'>

In [29]:

df.Age.mean()

Out[29]:

29.69911764705882

In [30]:

df.Age.median()

Out[30]:

28.0

In [31]:

df.Fare.min()

Out[31]:

0.0

In [32]:

df.Fare.mean()

Out[32]:

32.204207968574636

In [33]:

df.Fare.median()

Out[33]:

14.4542

In [34]:

df.Fare.max()

Out[34]:

512.3292

In [ ]:

# Majority of the people paid less money but the people who paid more had to pay really high

In [37]:

sns.displot(x = df.Fare, kde = True)

Out[37]:

<seaborn.axisgrid.FacetGrid at 0x19f507423b0>

In [41]:

df1 = df.copy()
df1

Out[41]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Deck	Family
PassengerId
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	NaN	1
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	C	1
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	NaN	0
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S	C	1
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S	NaN	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S	NaN	0
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S	B	0
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S	NaN	3
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C	C	0
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q	NaN	0

891 rows × 13 columns

In [43]:

df1.Embarked.replace(('S', 'C', 'Q'), (1, 2, 3), inplace = True)
df1

Out[43]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Deck	Family
PassengerId
1	0	3	Braund, Mr. Owen Harris	1	22.0	1	0	A/5 21171	7.2500	NaN	1.0	NaN	1
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	2	38.0	1	0	PC 17599	71.2833	C85	2.0	C	1
3	1	3	Heikkinen, Miss. Laina	2	26.0	0	0	STON/O2. 3101282	7.9250	NaN	1.0	NaN	0
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	2	35.0	1	0	113803	53.1000	C123	1.0	C	1
5	0	3	Allen, Mr. William Henry	1	35.0	0	0	373450	8.0500	NaN	1.0	NaN	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	1	27.0	0	0	211536	13.0000	NaN	1.0	NaN	0
888	1	1	Graham, Miss. Margaret Edith	2	19.0	0	0	112053	30.0000	B42	1.0	B	0
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	2	NaN	1	2	W./C. 6607	23.4500	NaN	1.0	NaN	3
890	1	1	Behr, Mr. Karl Howell	1	26.0	0	0	111369	30.0000	C148	2.0	C	0
891	0	3	Dooley, Mr. Patrick	1	32.0	0	0	370376	7.7500	NaN	3.0	NaN	0

891 rows × 13 columns

In [42]:

df1.Sex.replace(('male', 'female'), (1, 2), inplace = True)
df1

Out[42]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Deck	Family
PassengerId
1	0	3	Braund, Mr. Owen Harris	1	22.0	1	0	A/5 21171	7.2500	NaN	S	NaN	1
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	2	38.0	1	0	PC 17599	71.2833	C85	C	C	1
3	1	3	Heikkinen, Miss. Laina	2	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	NaN	0
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	2	35.0	1	0	113803	53.1000	C123	S	C	1
5	0	3	Allen, Mr. William Henry	1	35.0	0	0	373450	8.0500	NaN	S	NaN	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	1	27.0	0	0	211536	13.0000	NaN	S	NaN	0
888	1	1	Graham, Miss. Margaret Edith	2	19.0	0	0	112053	30.0000	B42	S	B	0
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	2	NaN	1	2	W./C. 6607	23.4500	NaN	S	NaN	3
890	1	1	Behr, Mr. Karl Howell	1	26.0	0	0	111369	30.0000	C148	C	C	0
891	0	3	Dooley, Mr. Patrick	1	32.0	0	0	370376	7.7500	NaN	Q	NaN	0

891 rows × 13 columns

In [44]:

df1 = df1[['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Family', 'Ticket',
       'Fare', 'Cabin', 'Deck', 'Embarked']]
df1

Out[44]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Family	Ticket	Fare	Cabin	Deck	Embarked
PassengerId
1	0	3	Braund, Mr. Owen Harris	1	22.0	1	0	1	A/5 21171	7.2500	NaN	NaN	1.0
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	2	38.0	1	0	1	PC 17599	71.2833	C85	C	2.0
3	1	3	Heikkinen, Miss. Laina	2	26.0	0	0	0	STON/O2. 3101282	7.9250	NaN	NaN	1.0
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	2	35.0	1	0	1	113803	53.1000	C123	C	1.0
5	0	3	Allen, Mr. William Henry	1	35.0	0	0	0	373450	8.0500	NaN	NaN	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	1	27.0	0	0	0	211536	13.0000	NaN	NaN	1.0
888	1	1	Graham, Miss. Margaret Edith	2	19.0	0	0	0	112053	30.0000	B42	B	1.0
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	2	NaN	1	2	3	W./C. 6607	23.4500	NaN	NaN	1.0
890	1	1	Behr, Mr. Karl Howell	1	26.0	0	0	0	111369	30.0000	C148	C	2.0
891	0	3	Dooley, Mr. Patrick	1	32.0	0	0	0	370376	7.7500	NaN	NaN	3.0

891 rows × 13 columns

In [46]:

dfc = df1.corr(numeric_only = True)
dfc

Out[46]:

	Survived	Pclass	Sex	Age	SibSp	Parch	Family	Fare	Embarked
Survived	1.000000	-0.338481	0.543351	-0.077221	-0.035322	0.081629	0.016639	0.257307	0.108669
Pclass	-0.338481	1.000000	-0.131900	-0.369226	0.083081	0.018443	0.065997	-0.549500	0.043835
Sex	0.543351	-0.131900	1.000000	-0.093254	0.114631	0.245489	0.200988	0.182333	0.118593
Age	-0.077221	-0.369226	-0.093254	1.000000	-0.308247	-0.189119	-0.301914	0.096067	0.012186
SibSp	-0.035322	0.083081	0.114631	-0.308247	1.000000	0.414838	0.890712	0.159651	-0.060606
Parch	0.081629	0.018443	0.245489	-0.189119	0.414838	1.000000	0.783111	0.216225	-0.079320
Family	0.016639	0.065997	0.200988	-0.301914	0.890712	0.783111	1.000000	0.217138	-0.081057
Fare	0.257307	-0.549500	0.182333	0.096067	0.159651	0.216225	0.217138	1.000000	0.063462
Embarked	0.108669	0.043835	0.118593	0.012186	-0.060606	-0.079320	-0.081057	0.063462	1.000000

In [50]:

su = df1.corr(numeric_only = True).iloc[[0]].drop(columns = 'Survived')
su

Out[50]:

	Pclass	Sex	Age	SibSp	Parch	Family	Fare	Embarked
Survived	-0.338481	0.543351	-0.077221	-0.035322	0.081629	0.016639	0.257307	0.108669

In [51]:

su.plot(kind = 'bar')

Out[51]:

<Axes: >

In [52]:

su = su.abs()
su

Out[52]:

	Pclass	Sex	Age	SibSp	Parch	Family	Fare	Embarked
Survived	0.338481	0.543351	0.077221	0.035322	0.081629	0.016639	0.257307	0.108669

In [53]:

su.plot(kind = 'bar')

Out[53]:

<Axes: >

In [54]:

# df1['Deck'] = df1['Cabin'].apply(lambda s: s[0] if pd.notnull(s) else np.nan)

In [55]:

df1

Out[55]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Family	Ticket	Fare	Cabin	Deck	Embarked
PassengerId
1	0	3	Braund, Mr. Owen Harris	1	22.0	1	0	1	A/5 21171	7.2500	NaN	NaN	1.0
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	2	38.0	1	0	1	PC 17599	71.2833	C85	C	2.0
3	1	3	Heikkinen, Miss. Laina	2	26.0	0	0	0	STON/O2. 3101282	7.9250	NaN	NaN	1.0
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	2	35.0	1	0	1	113803	53.1000	C123	C	1.0
5	0	3	Allen, Mr. William Henry	1	35.0	0	0	0	373450	8.0500	NaN	NaN	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	1	27.0	0	0	0	211536	13.0000	NaN	NaN	1.0
888	1	1	Graham, Miss. Margaret Edith	2	19.0	0	0	0	112053	30.0000	B42	B	1.0
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	2	NaN	1	2	3	W./C. 6607	23.4500	NaN	NaN	1.0
890	1	1	Behr, Mr. Karl Howell	1	26.0	0	0	0	111369	30.0000	C148	C	2.0
891	0	3	Dooley, Mr. Patrick	1	32.0	0	0	0	370376	7.7500	NaN	NaN	3.0

891 rows × 13 columns

In [56]:

df1.Deck.unique()

Out[56]:

array([nan, 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], dtype=object)

In [57]:

df1[df1.Deck == 'T']

Out[57]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Family	Ticket	Fare	Cabin	Deck	Embarked
PassengerId
340	0	1	Blackwell, Mr. Stephen Weart	1	45.0	0	0	0	113784	35.5	T	T	1.0

In [58]:

df1.Deck.replace([np.nan, 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], [np.nan, 3, 5, 6, 4, 1, 2, 5, 0], inplace =True)

C:\Users\harsh\AppData\Local\Temp\ipykernel_19292\3993522731.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df1.Deck.replace([np.nan, 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], [np.nan, 3, 5, 6, 4, 1, 2, 5, 0], inplace =True)

In [59]:

df1

Out[59]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Family	Ticket	Fare	Cabin	Deck	Embarked
PassengerId
1	0	3	Braund, Mr. Owen Harris	1	22.0	1	0	1	A/5 21171	7.2500	NaN	NaN	1.0
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	2	38.0	1	0	1	PC 17599	71.2833	C85	3.0	2.0
3	1	3	Heikkinen, Miss. Laina	2	26.0	0	0	0	STON/O2. 3101282	7.9250	NaN	NaN	1.0
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	2	35.0	1	0	1	113803	53.1000	C123	3.0	1.0
5	0	3	Allen, Mr. William Henry	1	35.0	0	0	0	373450	8.0500	NaN	NaN	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	1	27.0	0	0	0	211536	13.0000	NaN	NaN	1.0
888	1	1	Graham, Miss. Margaret Edith	2	19.0	0	0	0	112053	30.0000	B42	2.0	1.0
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	2	NaN	1	2	3	W./C. 6607	23.4500	NaN	NaN	1.0
890	1	1	Behr, Mr. Karl Howell	1	26.0	0	0	0	111369	30.0000	C148	3.0	2.0
891	0	3	Dooley, Mr. Patrick	1	32.0	0	0	0	370376	7.7500	NaN	NaN	3.0

891 rows × 13 columns

In [81]:

dfc = df1.corr(numeric_only = True)
dfc

Out[81]:

	Survived	Pclass	Sex	Age	SibSp	Parch	Family	Fare	Deck	Embarked
Survived	1.000000	-0.338481	0.543351	-0.077221	-0.035322	0.081629	0.016639	0.257307	0.074078	0.108669
Pclass	-0.338481	1.000000	-0.131900	-0.369226	0.083081	0.018443	0.065997	-0.549500	0.522110	0.043835
Sex	0.543351	-0.131900	1.000000	-0.093254	0.114631	0.245489	0.200988	0.182333	0.111241	0.118593
Age	-0.077221	-0.369226	-0.093254	1.000000	-0.308247	-0.189119	-0.301914	0.096067	-0.150319	0.012186
SibSp	-0.035322	0.083081	0.114631	-0.308247	1.000000	0.414838	0.890712	0.159651	0.052095	-0.060606
Parch	0.081629	0.018443	0.245489	-0.189119	0.414838	1.000000	0.783111	0.216225	0.020578	-0.079320
Family	0.016639	0.065997	0.200988	-0.301914	0.890712	0.783111	1.000000	0.217138	0.044137	-0.081057
Fare	0.257307	-0.549500	0.182333	0.096067	0.159651	0.216225	0.217138	1.000000	-0.268925	0.063462
Deck	0.074078	0.522110	0.111241	-0.150319	0.052095	0.020578	0.044137	-0.268925	1.000000	-0.177969
Embarked	0.108669	0.043835	0.118593	0.012186	-0.060606	-0.079320	-0.081057	0.063462	-0.177969	1.000000

In [62]:

dfd = df1.Deck

In [89]:

dfd.plot?

In [70]:

df.Deck.count()/len(df)*100

Out[70]:

22.895622895622896

In [67]:

sns.countplot(x = df.Deck.sort_values())

Out[67]:

<Axes: xlabel='Deck', ylabel='count'>

In [77]:

sns.countplot(x = df1.Deck, hue = df1.Pclass)

Out[77]:

<Axes: xlabel='Deck', ylabel='count'>

In [74]:

df1.Deck.value_counts()

Out[74]:

3.0    59
2.0    47
5.0    45
4.0    33
1.0    15
6.0     4
0.0     1
Name: Deck, dtype: int64

In [78]:

df1[['Deck', 'Pclass']].value_counts().unstack()

Out[78]:

Pclass	1	2	3
Deck
0.0	1.0	NaN	NaN
1.0	15.0	NaN	NaN
2.0	47.0	NaN	NaN
3.0	59.0	NaN	NaN
4.0	29.0	4.0	NaN
5.0	25.0	12.0	8.0
6.0	NaN	NaN	4.0

In [89]:

df[['Sex', 'Survived','Pclass']].value_counts().unstack()

Out[89]:

	Pclass	1	2	3
Sex	Survived
female	0	3	6	72
female	1	91	70	72
male	0	77	91	300
male	1	45	17	47

In [88]:

df[['Sex', 'Survived','Pclass']].value_counts().unstack().plot(kind = 'bar')

Out[88]:

<Axes: xlabel='Sex,Survived'>

In [82]:

sns.heatmap(dfc, cmap = 'coolwarm')

Out[82]:

<Axes: >

In [90]:

len(df[df.Sex == 'male'])

Out[90]:

In [91]:

len(df[df.Sex == 'female'])

Out[91]:

Conclusion: The dataset is biased against females

In [92]:

len(df)

Out[92]:

In [95]:

round((len(df[df['Sex'] == 'male'])/len(df))*100)

Out[95]:

In [96]:

round((len(df[df['Sex'] == 'female'])/len(df))*100)

Out[96]:

In [97]:

df[df['Sex'] == 'female']

Out[97]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Deck	Family
PassengerId
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	C	1
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	NaN	0
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S	C	1
9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S	NaN	2
10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C	NaN	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...
881	1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	0	1	230433	26.0000	NaN	S	NaN	1
883	0	3	Dahlberg, Miss. Gerda Ulrika	female	22.0	0	0	7552	10.5167	NaN	S	NaN	0
886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.1250	NaN	Q	NaN	5
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S	B	0
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S	NaN	3

314 rows × 13 columns

In [7]:

df[(df['Sex'] == 'female')  & (df['Survived'] == 1)]

Out[7]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C
...	...	...	...	...	...	...	...	...	...	...	...
875	1	2	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	0	P/PP 3381	24.0000	NaN	C
876	1	3	Najib, Miss. Adele Kiamie "Jane"	female	15.0	0	0	2667	7.2250	NaN	C
880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C
881	1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	0	1	230433	26.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S

233 rows × 11 columns

In [102]:

len(df[(df['Sex'] == 'female')  & (df['Survived'] == 1)])

Out[102]:

In [98]:

len(df[(df['Sex'] == 'female')  & (df['Survived'] == 1)])/len(df[(df['Sex'] == 'female')])

Out[98]:

0.7420382165605095

In [103]:

len(df[(df['Sex'] == 'male')  & (df['Survived'] == 1)])

Out[103]:

In [99]:

len(df[(df['Sex'] == 'male')  & (df['Survived'] == 1)])/len(df[(df['Sex'] == 'male')])

Out[99]:

0.18890814558058924

Observation: More females survived than males
Inference: The possibility of survival for a female is more than male

In [15]:

df[5:13]

Out[15]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C
11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.0	1	1	PP 9549	16.7000	G6	S
12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.5500	C103	S
13	0	3	Saundercock, Mr. William Henry	male	20.0	0	0	A/5. 2151	8.0500	NaN	S

In [16]:

df.head(10)

Out[16]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C

In [17]:

df.tail(30)

Out[17]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
862	0	2	Giles, Mr. Frederick Edward	male	21.0	1	0	28134	11.5000	NaN	S
863	1	1	Swift, Mrs. Frederick Joel (Margaret Welles Ba...	female	48.0	0	0	17466	25.9292	D17	S
864	0	3	Sage, Miss. Dorothy Edith "Dolly"	female	NaN	8	2	CA. 2343	69.5500	NaN	S
865	0	2	Gill, Mr. John William	male	24.0	0	0	233866	13.0000	NaN	S
866	1	2	Bystrom, Mrs. (Karolina)	female	42.0	0	0	236852	13.0000	NaN	S
867	1	2	Duran y More, Miss. Asuncion	female	27.0	1	0	SC/PARIS 2149	13.8583	NaN	C
868	0	1	Roebling, Mr. Washington Augustus II	male	31.0	0	0	PC 17590	50.4958	A24	S
869	0	3	van Melkebeke, Mr. Philemon	male	NaN	0	0	345777	9.5000	NaN	S
870	1	3	Johnson, Master. Harold Theodor	male	4.0	1	1	347742	11.1333	NaN	S
871	0	3	Balkic, Mr. Cerin	male	26.0	0	0	349248	7.8958	NaN	S
872	1	1	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	female	47.0	1	1	11751	52.5542	D35	S
873	0	1	Carlsson, Mr. Frans Olof	male	33.0	0	0	695	5.0000	B51 B53 B55	S
874	0	3	Vander Cruyssen, Mr. Victor	male	47.0	0	0	345765	9.0000	NaN	S
875	1	2	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	0	P/PP 3381	24.0000	NaN	C
876	1	3	Najib, Miss. Adele Kiamie "Jane"	female	15.0	0	0	2667	7.2250	NaN	C
877	0	3	Gustafsson, Mr. Alfred Ossian	male	20.0	0	0	7534	9.8458	NaN	S
878	0	3	Petroff, Mr. Nedelio	male	19.0	0	0	349212	7.8958	NaN	S
879	0	3	Laleff, Mr. Kristo	male	NaN	0	0	349217	7.8958	NaN	S
880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C
881	1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	0	1	230433	26.0000	NaN	S
882	0	3	Markun, Mr. Johann	male	33.0	0	0	349257	7.8958	NaN	S
883	0	3	Dahlberg, Miss. Gerda Ulrika	female	22.0	0	0	7552	10.5167	NaN	S
884	0	2	Banfield, Mr. Frederick James	male	28.0	0	0	C.A./SOTON 34068	10.5000	NaN	S
885	0	3	Sutehall, Mr. Henry Jr	male	25.0	0	0	SOTON/OQ 392076	7.0500	NaN	S
886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.1250	NaN	Q
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

In [35]:

df.sample(7)

Out[35]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
387	0	3	Goodwin, Master. Sidney Leonard	male	1.0	5	2	CA 2144	46.9000	NaN	S
161	0	3	Cribb, Mr. John Hatfield	male	44.0	0	1	371362	16.1000	NaN	S
870	1	3	Johnson, Master. Harold Theodor	male	4.0	1	1	347742	11.1333	NaN	S
407	0	3	Widegren, Mr. Carl/Charles Peter	male	51.0	0	0	347064	7.7500	NaN	S
530	0	2	Hocking, Mr. Richard George	male	23.0	2	1	29104	11.5000	NaN	S
615	0	3	Brocklebank, Mr. William Alfred	male	35.0	0	0	364512	8.0500	NaN	S
414	0	2	Cunningham, Mr. Alfred Fleming	male	NaN	0	0	239853	0.0000	NaN	S

In [36]:

print(1, ':', len(df[df.Pclass == 1]))
print(2, ':', len(df[df.Pclass == 2]))
print(3, ':', len(df[df.Pclass == 3]))

1 : 216
2 : 184
3 : 491

In [ ]:

# Conclusion: The number of passengers from Pclass 3 are more than Pclass 1 and 2 combined. 
#             There were more lower case passengers on the ship.

In [ ]:

# 100 Questions

# 80 Questions for Training
# 20 Questions for Testing

In [38]:

df.Fare.max()

Out[38]:

512.3292

In [47]:

round((len(df[df.Fare == 0])/len(df))*100, 1)

Out[47]:

1.7

In [ ]:

# Observation: 1.7% of the sample population didn't pay for the ticket
# Inference: Possibilities: Crew Members, Freeloaders passengers, 

In [40]:

df[df.Fare != 0].Fare.min()

Out[40]:

4.0125

In [37]:

df.Fare.mean()

Out[37]:

32.204207968574636

In [41]:

df.Fare.median()

Out[41]:

14.4542

In [ ]:

# Observation: Majority of the passengers paid less Fare.
#              The mean value is more than the median
# Inference: Very less number of people paid high fare but the ticket was priced really high for them.
# Domain Inference: There is a lot of gap between upper class and lower class people in the society. 
# Conclusion: There is a lot of variation in the ticket prices for the ship.

In [14]:

n = df[df['Sex'] == 'male']
n

Out[14]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
884	0	2	Banfield, Mr. Frederick James	male	28.0	0	0	C.A./SOTON 34068	10.5000	NaN	S
885	0	3	Sutehall, Mr. Henry Jr	male	25.0	0	0	SOTON/OQ 392076	7.0500	NaN	S
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

577 rows × 11 columns

In [27]:

len(df[df['Sex'] == 'male'])/len(df)*100

Out[27]:

64.75869809203144

[1] Observation: The dataset is biased for male passengers

In [51]:

((df.isnull().sum())/len(df))*100

Out[51]:

Survived     0.000000
Pclass       0.000000
Name         0.000000
Sex          0.000000
Age         19.865320
SibSp        0.000000
Parch        0.000000
Ticket       0.000000
Fare         0.000000
Cabin       77.104377
Embarked     0.224467
dtype: float64

In [52]:

df.corr()

Out[52]:

	Survived	Pclass	Age	SibSp	Parch	Fare
Survived	1.000000	-0.338481	-0.077221	-0.035322	0.081629	0.257307
Pclass	-0.338481	1.000000	-0.369226	0.083081	0.018443	-0.549500
Age	-0.077221	-0.369226	1.000000	-0.308247	-0.189119	0.096067
SibSp	-0.035322	0.083081	-0.308247	1.000000	0.414838	0.159651
Parch	0.081629	0.018443	-0.189119	0.414838	1.000000	0.216225
Fare	0.257307	-0.549500	0.096067	0.159651	0.216225	1.000000

In [ ]:

# Observation: Age is negatively correlated to Pclass with a value of -0.369.
# Inference: There are more older people in Upper class

In [56]:

print('Upper: ', len(df[(df.Age > 45) & (df.Pclass == 1)]))
print('Middle: ', len(df[(df.Age > 45) & (df.Pclass == 2)]))
print('Lower: ', len(df[(df.Age > 45) & (df.Pclass == 3)]))

Upper:  62
Middle:  23
Lower:  18

In [ ]:

# Conclusion: There are more older people in Upper class

In [57]:

df.corr()

Out[57]:

	Survived	Pclass	Age	SibSp	Parch	Fare
Survived	1.000000	-0.338481	-0.077221	-0.035322	0.081629	0.257307
Pclass	-0.338481	1.000000	-0.369226	0.083081	0.018443	-0.549500
Age	-0.077221	-0.369226	1.000000	-0.308247	-0.189119	0.096067
SibSp	-0.035322	0.083081	-0.308247	1.000000	0.414838	0.159651
Parch	0.081629	0.018443	-0.189119	0.414838	1.000000	0.216225
Fare	0.257307	-0.549500	0.096067	0.159651	0.216225	1.000000

In [58]:

df.corr().abs()

Out[58]:

	Survived	Pclass	Age	SibSp	Parch	Fare
Survived	1.000000	0.338481	0.077221	0.035322	0.081629	0.257307
Pclass	0.338481	1.000000	0.369226	0.083081	0.018443	0.549500
Age	0.077221	0.369226	1.000000	0.308247	0.189119	0.096067
SibSp	0.035322	0.083081	0.308247	1.000000	0.414838	0.159651
Parch	0.081629	0.018443	0.189119	0.414838	1.000000	0.216225
Fare	0.257307	0.549500	0.096067	0.159651	0.216225	1.000000

In [62]:

df.corr().unstack()

Out[62]:

Survived  Survived    1.000000
          Pclass     -0.338481
          Age        -0.077221
          SibSp      -0.035322
          Parch       0.081629
          Fare        0.257307
Pclass    Survived   -0.338481
          Pclass      1.000000
          Age        -0.369226
          SibSp       0.083081
          Parch       0.018443
          Fare       -0.549500
Age       Survived   -0.077221
          Pclass     -0.369226
          Age         1.000000
          SibSp      -0.308247
          Parch      -0.189119
          Fare        0.096067
SibSp     Survived   -0.035322
          Pclass      0.083081
          Age        -0.308247
          SibSp       1.000000
          Parch       0.414838
          Fare        0.159651
Parch     Survived    0.081629
          Pclass      0.018443
          Age        -0.189119
          SibSp       0.414838
          Parch       1.000000
          Fare        0.216225
Fare      Survived    0.257307
          Pclass     -0.549500
          Age         0.096067
          SibSp       0.159651
          Parch       0.216225
          Fare        1.000000
dtype: float64

In [69]:

df.corr().unstack().sort_values(ascending=False).reset_index().loc[6:]

Out[69]:

	level_0	level_1	0
6	SibSp	Parch	0.414838
7	Parch	SibSp	0.414838
8	Survived	Fare	0.257307
9	Fare	Survived	0.257307
10	Fare	Parch	0.216225
11	Parch	Fare	0.216225
12	Fare	SibSp	0.159651
13	SibSp	Fare	0.159651
14	Fare	Age	0.096067
15	Age	Fare	0.096067
16	SibSp	Pclass	0.083081
17	Pclass	SibSp	0.083081
18	Parch	Survived	0.081629
19	Survived	Parch	0.081629
20	Parch	Pclass	0.018443
21	Pclass	Parch	0.018443
22	Survived	SibSp	-0.035322
23	SibSp	Survived	-0.035322
24	Survived	Age	-0.077221
25	Age	Survived	-0.077221
26	Parch	Age	-0.189119
27	Age	Parch	-0.189119
28	SibSp	Age	-0.308247
29	Age	SibSp	-0.308247
30	Survived	Pclass	-0.338481
31	Pclass	Survived	-0.338481
32	Age	Pclass	-0.369226
33	Pclass	Age	-0.369226
34	Fare	Pclass	-0.549500
35	Pclass	Fare	-0.549500

In [70]:

df.corr().abs().unstack().sort_values(ascending=False).reset_index().loc[6:]

Out[70]:

	level_0	level_1	0
6	Fare	Pclass	0.549500
7	Pclass	Fare	0.549500
8	Parch	SibSp	0.414838
9	SibSp	Parch	0.414838
10	Age	Pclass	0.369226
11	Pclass	Age	0.369226
12	Survived	Pclass	0.338481
13	Pclass	Survived	0.338481
14	SibSp	Age	0.308247
15	Age	SibSp	0.308247
16	Fare	Survived	0.257307
17	Survived	Fare	0.257307
18	Fare	Parch	0.216225
19	Parch	Fare	0.216225
20	Age	Parch	0.189119
21	Parch	Age	0.189119
22	SibSp	Fare	0.159651
23	Fare	SibSp	0.159651
24	Age	Fare	0.096067
25	Fare	Age	0.096067
26	Pclass	SibSp	0.083081
27	SibSp	Pclass	0.083081
28	Parch	Survived	0.081629
29	Survived	Parch	0.081629
30	Survived	Age	0.077221
31	Age	Survived	0.077221
32	Survived	SibSp	0.035322
33	SibSp	Survived	0.035322
34	Parch	Pclass	0.018443
35	Pclass	Parch	0.018443

In [72]:

df[df.Embarked.isnull()]

Out[72]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
62	1	1	Icard, Miss. Amelie	female	38.0	0	0	113572	80.0	B28	NaN
830	1	1	Stone, Mrs. George Nelson (Martha Evelyn)	female	62.0	0	0	113572	80.0	B28	NaN

In [28]:

df[df['Sex'] == 'male'].count()

Out[28]:

Survived    577
Pclass      577
Name        577
Sex         577
Age         453
SibSp       577
Parch       577
Ticket      577
Fare        577
Cabin       107
Embarked    577
dtype: int64

In [29]:

df[df['Sex'] == 'male'].count()['Age']

Out[29]:

In [31]:

df.Name

Out[31]:

PassengerId
1                                Braund, Mr. Owen Harris
2      Cumings, Mrs. John Bradley (Florence Briggs Th...
3                                 Heikkinen, Miss. Laina
4           Futrelle, Mrs. Jacques Heath (Lily May Peel)
5                               Allen, Mr. William Henry
                             ...                        
887                                Montvila, Rev. Juozas
888                         Graham, Miss. Margaret Edith
889             Johnston, Miss. Catherine Helen "Carrie"
890                                Behr, Mr. Karl Howell
891                                  Dooley, Mr. Patrick
Name: Name, Length: 891, dtype: object

In [32]:

df[(df['Sex'] == 'male') & (df['Survived'] == 1)]

Out[32]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
18	1	2	Williams, Mr. Charles Eugene	male	NaN	0	0	244373	13.0000	NaN	S
22	1	2	Beesley, Mr. Lawrence	male	34.0	0	0	248698	13.0000	D56	S
24	1	1	Sloper, Mr. William Thompson	male	28.0	0	0	113788	35.5000	A6	S
37	1	3	Mamee, Mr. Hanna	male	NaN	0	0	2677	7.2292	NaN	C
56	1	1	Woolner, Mr. Hugh	male	NaN	0	0	19947	35.5000	C52	S
...	...	...	...	...	...	...	...	...	...	...	...
839	1	3	Chip, Mr. Chang	male	32.0	0	0	1601	56.4958	NaN	S
840	1	1	Marechal, Mr. Pierre	male	NaN	0	0	11774	29.7000	C47	C
858	1	1	Daly, Mr. Peter Denis	male	51.0	0	0	113055	26.5500	E17	S
870	1	3	Johnson, Master. Harold Theodor	male	4.0	1	1	347742	11.1333	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C

109 rows × 11 columns

In [ ]:

In [27]:

len(df[(df['Sex'] == 'male') & (df['Survived'] == 1)])/len(df[df['Sex'] == 'male'])

Out[27]:

0.18890814558058924

In [28]:

len(df[(df['Sex'] == 'female') & (df['Survived'] == 1)])/len(df[df['Sex'] == 'female'])

Out[28]:

0.7420382165605095

In [16]:

df.Embarked.unique()

Out[16]:

array(['S', 'C', 'Q', nan], dtype=object)

In [20]:

len(df[df['Sex'] == 'male'])/len(df)

Out[20]:

0.6475869809203143

In [21]:

len(df[df['Sex'] == 'female'])/len(df)

Out[21]:

0.35241301907968575

In [22]:

len(df[df['Pclass']==3])/len(df)*100

Out[22]:

55.106621773288445

In [23]:

len(df[df['Pclass']==2])/len(df)*100

Out[23]:

20.65095398428732

In [24]:

len(df[df['Pclass']==1])/len(df)*100

Out[24]:

24.242424242424242

In [34]:

df.index

Out[34]:

Int64Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,
            ...
            882, 883, 884, 885, 886, 887, 888, 889, 890, 891],
           dtype='int64', name='PassengerId', length=891)

In [35]:

tuple(df.columns)

Out[35]:

('Survived',
 'Pclass',
 'Name',
 'Sex',
 'Age',
 'SibSp',
 'Parch',
 'Ticket',
 'Fare',
 'Cabin',
 'Embarked')

In [37]:

# access column Age
df["Age"].head(15)

Out[37]:

PassengerId
1     22.0
2     38.0
3     26.0
4     35.0
5     35.0
6      NaN
7     54.0
8      2.0
9     27.0
10    14.0
11     4.0
12    58.0
13    20.0
14    39.0
15    14.0
Name: Age, dtype: float64

In [15]:

# access column Age, Fare, SibSp
df[["Age", "Fare", 'SibSp']].head()

Out[15]:

	Age	Fare	SibSp
PassengerId
1	22.0	7.2500	1
2	38.0	71.2833	1
3	26.0	7.9250	0
4	35.0	53.1000	1
5	35.0	8.0500	0

In [39]:

df.Embarked.unique()      # to see the unique values in a particular column

Out[39]:

array(['S', 'C', 'Q', nan], dtype=object)

In [76]:

df

Out[76]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 11 columns

In [81]:

t = df['Embarked'].isnull()
t

Out[81]:

PassengerId
1      False
2      False
3      False
4      False
5      False
       ...  
887    False
888    False
889    False
890    False
891    False
Name: Embarked, Length: 891, dtype: bool

In [17]:

df[df['Embarked'].isnull()]

Out[17]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
62	1	1	Icard, Miss. Amelie	female	38.0	0	0	113572	80.0	B28	NaN
830	1	1	Stone, Mrs. George Nelson (Martha Evelyn)	female	62.0	0	0	113572	80.0	B28	NaN

In [78]:

df.Embarked

Out[78]:

PassengerId
1      S
2      C
3      S
4      S
5      S
      ..
887    S
888    S
889    S
890    C
891    Q
Name: Embarked, Length: 891, dtype: object

In [52]:

df.Parch.unique()

Out[52]:

array([0, 1, 2, 5, 3, 4, 6], dtype=int64)

In [47]:

df['SibSp'].unique()    #df.SibSp.unique()

Out[47]:

array([1, 0, 3, 4, 2, 5, 8], dtype=int64)

In [21]:

df.head()

Out[21]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

In [69]:

df[3:17]

Out[69]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C
11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.0	1	1	PP 9549	16.7000	G6	S
12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.5500	C103	S
13	0	3	Saundercock, Mr. William Henry	male	20.0	0	0	A/5. 2151	8.0500	NaN	S
14	0	3	Andersson, Mr. Anders Johan	male	39.0	1	5	347082	31.2750	NaN	S
15	0	3	Vestrom, Miss. Hulda Amanda Adolfina	female	14.0	0	0	350406	7.8542	NaN	S
16	1	2	Hewlett, Mrs. (Mary D Kingcome)	female	55.0	0	0	248706	16.0000	NaN	S
17	0	3	Rice, Master. Eugene	male	2.0	4	1	382652	29.1250	NaN	Q

In [46]:

df.loc[4:32, ["Fare"]].head(10)      # df[['Fare']].loc[4:32].head(10)    # df.loc[4:32][['Fare']].head(10)

Out[46]:

	Fare
PassengerId
4	53.1000
5	8.0500
6	8.4583
7	51.8625
8	21.0750
9	11.1333
10	30.0708
11	16.7000
12	26.5500
13	8.0500

In [67]:

df.loc[: ,"Fare"].head(2)         # df['Fare'].head(2)

Out[67]:

PassengerId
1     7.2500
2    71.2833
Name: Fare, dtype: float64

In [71]:

df.loc[10:20][["Fare", "Name"]]        # df[["Fare", "Name"]].loc[10:20]

Out[71]:

	Fare	Name
PassengerId
10	30.0708	Nasser, Mrs. Nicholas (Adele Achem)
11	16.7000	Sandstrom, Miss. Marguerite Rut
12	26.5500	Bonnell, Miss. Elizabeth
13	8.0500	Saundercock, Mr. William Henry
14	31.2750	Andersson, Mr. Anders Johan
15	7.8542	Vestrom, Miss. Hulda Amanda Adolfina
16	16.0000	Hewlett, Mrs. (Mary D Kingcome)
17	29.1250	Rice, Master. Eugene
18	13.0000	Williams, Mr. Charles Eugene
19	18.0000	Vander Planke, Mrs. Julius (Emelia Maria Vande...
20	7.2250	Masselmani, Mrs. Fatima

In [29]:

df.loc[3 : 5 ,["Fare", "Name"]].head(2)

Out[29]:

	Fare	Name
PassengerId
3	7.925	Heikkinen, Miss. Laina
4	53.100	Futrelle, Mrs. Jacques Heath (Lily May Peel)

In [84]:

df

Out[84]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 11 columns

In [43]:

r = df[(df['Embarked'] == 'Q') & (df['Pclass'] == 3)]
r

Out[43]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
17	0	3	Rice, Master. Eugene	male	2.0	4	1	382652	29.1250	NaN	Q
23	1	3	McGowan, Miss. Anna "Annie"	female	15.0	0	0	330923	8.0292	NaN	Q
29	1	3	O'Dwyer, Miss. Ellen "Nellie"	female	NaN	0	0	330959	7.8792	NaN	Q
33	1	3	Glynn, Miss. Mary Agatha	female	NaN	0	0	335677	7.7500	NaN	Q
...	...	...	...	...	...	...	...	...	...	...	...
791	0	3	Keane, Mr. Andrew "Andy"	male	NaN	0	0	12460	7.7500	NaN	Q
826	0	3	Flynn, Mr. John	male	NaN	0	0	368323	6.9500	NaN	Q
829	1	3	McCormack, Mr. Thomas Joseph	male	NaN	0	0	367228	7.7500	NaN	Q
886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.1250	NaN	Q
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

72 rows × 11 columns

In [47]:

df[(df['Embarked'] == 'Q') & (df['Pclass'] == 2)]

Out[47]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
304	1	2	Keane, Miss. Nora A	female	NaN	0	0	226593	12.35	E101	Q
323	1	2	Slayter, Miss. Hilda Mary	female	30.0	0	0	234818	12.35	NaN	Q
627	0	2	Kirkland, Rev. Charles Leonard	male	57.0	0	0	219533	12.35	NaN	Q

In [48]:

df[(df['Embarked'] == 'Q') & (df['Pclass'] == 1)]

Out[48]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
246	0	1	Minahan, Dr. William Edward	male	44.0	2	0	19928	90.0	C78	Q
413	1	1	Minahan, Miss. Daisy E	female	33.0	1	0	19928	90.0	C78	Q

In [ ]:

In [46]:

df[(df['Embarked'] == 'C') & (df['Pclass'] == 1)]

Out[46]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
31	0	1	Uruchurtu, Don. Manuel E	male	40.0	0	0	PC 17601	27.7208	NaN	C
32	1	1	Spencer, Mrs. William Augustus (Marie Eugenie)	female	NaN	1	0	PC 17569	146.5208	B78	C
35	0	1	Meyer, Mr. Edgar Joseph	male	28.0	1	0	PC 17604	82.1708	NaN	C
53	1	1	Harper, Mrs. Henry Sleeper (Myna Haxtun)	female	49.0	1	0	PC 17572	76.7292	D33	C
...	...	...	...	...	...	...	...	...	...	...	...
840	1	1	Marechal, Mr. Pierre	male	NaN	0	0	11774	29.7000	C47	C
843	1	1	Serepeca, Miss. Augusta	female	30.0	0	0	113798	31.0000	NaN	C
850	1	1	Goldenberg, Mrs. Samuel L (Edwiga Grabowska)	female	NaN	1	0	17453	89.1042	C92	C
880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C

85 rows × 11 columns

In [49]:

df[(df['Embarked'] == 'S') & (df['Pclass'] == 1)]

Out[49]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.5500	C103	S
24	1	1	Sloper, Mr. William Thompson	male	28.0	0	0	113788	35.5000	A6	S
28	0	1	Fortune, Mr. Charles Alexander	male	19.0	3	2	19950	263.0000	C23 C25 C27	S
...	...	...	...	...	...	...	...	...	...	...	...
863	1	1	Swift, Mrs. Frederick Joel (Margaret Welles Ba...	female	48.0	0	0	17466	25.9292	D17	S
868	0	1	Roebling, Mr. Washington Augustus II	male	31.0	0	0	PC 17590	50.4958	A24	S
872	1	1	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	female	47.0	1	1	11751	52.5542	D35	S
873	0	1	Carlsson, Mr. Frans Olof	male	33.0	0	0	695	5.0000	B51 B53 B55	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S

127 rows × 11 columns

In [ ]:

C1 C2 C2, S1 S2 S3, Q1 Q2 Q3

In [85]:

df1 = df[(df['Survived'] == 1) & (df['Parch'] == 1) & (df['Pclass'] == 1)]
df1

Out[85]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
98	1	1	Greenfield, Mr. William Bertram	male	23.0	0	1	PC 17759	63.3583	D10 D12	C
167	1	1	Chibnall, Mrs. (Edith Martha Bowerman)	female	NaN	0	1	113505	55.0000	E33	S
249	1	1	Beckwith, Mr. Richard Leonard	male	37.0	1	1	11751	52.5542	D35	S
269	1	1	Graham, Mrs. William Thompson (Edith Junkins)	female	58.0	0	1	PC 17582	153.4625	C125	S
300	1	1	Baxter, Mrs. James (Helene DeLaudeniere Chaput)	female	50.0	0	1	PC 17558	247.5208	B58 B60	C
320	1	1	Spedden, Mrs. Frederic Oakley (Margaretta Corn...	female	40.0	1	1	16966	134.5000	E34	C
330	1	1	Hippach, Miss. Jean Gertrude	female	16.0	0	1	111361	57.9792	B18	C
357	1	1	Bowerman, Miss. Elsie Edith	female	22.0	0	1	113505	55.0000	E33	S
524	1	1	Hippach, Mrs. Louis Albert (Ida Sophia Fischer)	female	44.0	0	1	111361	57.9792	B18	C
559	1	1	Taussig, Mrs. Emil (Tillie Mandelbaum)	female	39.0	1	1	110413	79.6500	E67	S
582	1	1	Thayer, Mrs. John Borland (Marian Longstreth M...	female	39.0	1	1	17421	110.8833	C68	C
588	1	1	Frolicher-Stehli, Mr. Maxmillian	male	60.0	1	1	13567	79.2000	B41	C
680	1	1	Cardeza, Mr. Thomas Drake Martinez	male	36.0	0	1	PC 17755	512.3292	B51 B53 B55	C
690	1	1	Madill, Miss. Georgette Alexandra	female	15.0	0	1	24160	211.3375	B5	S
780	1	1	Robert, Mrs. Edward Scott (Elisabeth Walton Mc...	female	43.0	0	1	24160	211.3375	B3	S
821	1	1	Hays, Mrs. Charles Melville (Clara Jennings Gr...	female	52.0	1	1	12749	93.5000	B69	S
836	1	1	Compton, Miss. Sara Rebecca	female	39.0	1	1	PC 17756	83.1583	E49	C
854	1	1	Lines, Miss. Mary Conover	female	16.0	0	1	PC 17592	39.4000	D28	S
857	1	1	Wick, Mrs. George Dennick (Mary Hitchcock)	female	45.0	1	1	36928	164.8667	NaN	S
872	1	1	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	female	47.0	1	1	11751	52.5542	D35	S
880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C

In [108]:

pd.set_option('display.max_rows', 10)

In [132]:

df['SibSp'].unique()

Out[132]:

array([1, 0, 3, 4, 2, 5, 8], dtype=int64)

In [136]:

len(df.columns)

Out[136]:

In [20]:

df['Fare'].median()

Out[20]:

14.4542

In [19]:

df['Fare'].mean()

Out[19]:

32.204207968574636

In [55]:

len(df[df['Fare'] > df['Fare'].median()])/len(df)

Out[55]:

0.3950617283950617

In [95]:

df.describe()

Out[95]:

	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

In [ ]:

Age:
    A: 0 - 10  Children
    B: 11- 19  Teenagers
    C: 20- 30  Youngsters
    D: 31- 45  Adults
    E: 45- 60  Middle Age
    F: >60     Old

In [56]:

# query for people of age 30 - 40
df[(df.Age > 18) & (df.Age < 28)]    # Apply sample() to analyse random data records

Out[56]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
13	0	3	Saundercock, Mr. William Henry	male	20.0	0	0	A/5. 2151	8.0500	NaN	S
28	0	1	Fortune, Mr. Charles Alexander	male	19.0	3	2	19950	263.0000	C23 C25 C27	S
...	...	...	...	...	...	...	...	...	...	...	...
883	0	3	Dahlberg, Miss. Gerda Ulrika	female	22.0	0	0	7552	10.5167	NaN	S
885	0	3	Sutehall, Mr. Henry Jr	male	25.0	0	0	SOTON/OQ 392076	7.0500	NaN	S
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C

198 rows × 11 columns

In [22]:

df[~(df.Age > 30)]      # Age <= 30

Out[22]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
885	0	3	Sutehall, Mr. Henry Jr	male	25.0	0	0	SOTON/OQ 392076	7.0500	NaN	S
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C

586 rows × 11 columns

In [103]:

df[(df.Sex == 'male') & (df.Survived == 1)]

Out[103]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
18	1	2	Williams, Mr. Charles Eugene	male	NaN	0	0	244373	13.0000	NaN	S
22	1	2	Beesley, Mr. Lawrence	male	34.0	0	0	248698	13.0000	D56	S
24	1	1	Sloper, Mr. William Thompson	male	28.0	0	0	113788	35.5000	A6	S
37	1	3	Mamee, Mr. Hanna	male	NaN	0	0	2677	7.2292	NaN	C
56	1	1	Woolner, Mr. Hugh	male	NaN	0	0	19947	35.5000	C52	S
...	...	...	...	...	...	...	...	...	...	...	...
839	1	3	Chip, Mr. Chang	male	32.0	0	0	1601	56.4958	NaN	S
840	1	1	Marechal, Mr. Pierre	male	NaN	0	0	11774	29.7000	C47	C
858	1	1	Daly, Mr. Peter Denis	male	51.0	0	0	113055	26.5500	E17	S
870	1	3	Johnson, Master. Harold Theodor	male	4.0	1	1	347742	11.1333	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C

109 rows × 11 columns

In [11]:

df[(df.Sex == 'female') & (df.Survived == 1)]

Out[11]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C
...	...	...	...	...	...	...	...	...	...	...	...
875	1	2	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	0	P/PP 3381	24.0000	NaN	C
876	1	3	Najib, Miss. Adele Kiamie "Jane"	female	15.0	0	0	2667	7.2250	NaN	C
880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C
881	1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	0	1	230433	26.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S

233 rows × 11 columns

In [161]:

df[(df.Sex == 'male')].count()

Out[161]:

Survived    577
Pclass      577
Name        577
Sex         577
Age         453
SibSp       577
Parch       577
Ticket      577
Fare        577
Cabin       107
Embarked    577
dtype: int64

In [162]:

df[(df.Sex == 'female')].count()

Out[162]:

Survived    314
Pclass      314
Name        314
Sex         314
Age         261
SibSp       314
Parch       314
Ticket      314
Fare        314
Cabin        97
Embarked    312
dtype: int64

In [104]:

df[df['Sex'] == 'male'].count()

Out[104]:

Survived    577
Pclass      577
Name        577
Sex         577
Age         453
SibSp       577
Parch       577
Ticket      577
Fare        577
Cabin       107
Embarked    577
dtype: int64

In [164]:

df[(df.Sex == 'male') & (df.Survived == 1)].count()

Out[164]:

Survived    109
Pclass      109
Name        109
Sex         109
Age          93
SibSp       109
Parch       109
Ticket      109
Fare        109
Cabin        45
Embarked    109
dtype: int64

In [163]:

df[(df.Sex == 'female') & (df.Survived == 1)].count()

Out[163]:

Survived    233
Pclass      233
Name        233
Sex         233
Age         197
SibSp       233
Parch       233
Ticket      233
Fare        233
Cabin        91
Embarked    231
dtype: int64

In [12]:

len(df[(df.Sex == 'male') & (df.Survived == 1)])/len(df[(df.Sex == 'male')])

Out[12]:

0.18890814558058924

In [13]:

len(df[(df.Sex == 'female') & (df.Survived == 1)])/len(df[(df.Sex == 'female')])

Out[13]:

0.7420382165605095

In [14]:

# dim of df ??
df.shape

Out[14]:

(891, 11)

In [5]:

df.Fare.max()

Out[5]:

512.3292

In [6]:

df.Fare.mode()

Out[6]:

0    8.05
dtype: float64

In [7]:

df.Fare.median()

Out[7]:

14.4542

In [8]:

df.Fare.mean()

Out[8]:

32.204207968574636

In [16]:

df[(df.Fare < df.Fare.mean())]              # df[df.Fare == df.Fare.median()]

Out[16]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

680 rows × 11 columns

In [66]:

df[(df.Fare < df.Fare.mean()) & (df.Survived == 1)]              # df[df.Fare == df.Fare.median()]

Out[66]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C
11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.0	1	1	PP 9549	16.7000	G6	S
12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.5500	C103	S
...	...	...	...	...	...	...	...	...	...	...	...
875	1	2	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	0	P/PP 3381	24.0000	NaN	C
876	1	3	Najib, Miss. Adele Kiamie "Jane"	female	15.0	0	0	2667	7.2250	NaN	C
881	1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	0	1	230433	26.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C

216 rows × 11 columns

In [ ]:

#Observation/Assumption

In [73]:

len(df[(df.Fare < df.Fare.mean()) & (df.Survived == 1)])/len(df[(df.Fare < df.Fare.mean())])

Out[73]:

0.3176470588235294

In [80]:

len(df[(df.Fare < df.Fare.mean()) & (df.Survived == 0)])/len(df[(df.Fare < df.Fare.mean())])

Out[80]:

0.6823529411764706

In [74]:

len(df[(df.Fare >= df.Fare.mean()) & (df.Survived == 1)])/len(df[(df.Fare >= df.Fare.mean())])

Out[74]:

0.5971563981042654

In [81]:

len(df[(df.Fare >= df.Fare.mean()) & (df.Survived == 0)])/len(df[(df.Fare >= df.Fare.mean())])

Out[81]:

0.4028436018957346

In [77]:

len(df[(df.Fare < df.Fare.mean())])

Out[77]:

In [78]:

len(df[(df.Fare >= df.Fare.mean())])

Out[78]:

In [ ]:

#Conclusion/Inference

In [92]:

r[r['Survived'] == 1]

Out[92]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C
11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.0	1	1	PP 9549	16.7000	G6	S
12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.5500	C103	S
...	...	...	...	...	...	...	...	...	...	...	...
875	1	2	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	0	P/PP 3381	24.0000	NaN	C
876	1	3	Najib, Miss. Adele Kiamie "Jane"	female	15.0	0	0	2667	7.2250	NaN	C
881	1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	0	1	230433	26.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C

216 rows × 11 columns

In [76]:

df.Fare

Out[76]:

PassengerId
1       7.2500
2      71.2833
3       7.9250
4      53.1000
5       8.0500
        ...   
887    13.0000
888    30.0000
889    23.4500
890    30.0000
891     7.7500
Name: Fare, Length: 891, dtype: float64

In [32]:

plt.hist(df.Fare)

Out[32]:

(array([732., 106.,  31.,   2.,  11.,   6.,   0.,   0.,   0.,   3.]),
 array([  0.     ,  51.23292, 102.46584, 153.69876, 204.93168, 256.1646 ,
        307.39752, 358.63044, 409.86336, 461.09628, 512.3292 ]),
 <BarContainer object of 10 artists>)

In [1]:

sns.pairplot?

Object `sns.pairplot` not found.

In [22]:

sns.pairplot(df)

Out[22]:

<seaborn.axisgrid.PairGrid at 0x20c463b9a90>

In [ ]:

In [38]:

df.columns

Out[38]:

Index(['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket',
       'Fare', 'Cabin', 'Embarked'],
      dtype='object')

In [39]:

df.Embarked.unique()

Out[39]:

array(['S', 'C', 'Q', nan], dtype=object)

In [45]:

df['Sex'].unique()

Out[45]:

array(['male', 'female'], dtype=object)

In [41]:

df.Pclass.unique()

Out[41]:

array([3, 1, 2], dtype=int64)

In [43]:

for i in df.columns : 
    try:
        print(i, " = \n", df[i].max())
    except:
        pass

Survived  = 
 1
Pclass  = 
 3
Name  = 
 van Melkebeke, Mr. Philemon
Sex  = 
 male
Age  = 
 80.0
SibSp  = 
 8
Parch  = 
 6
Ticket  = 
 WE/P 5735
Fare  = 
 512.3292

In [44]:

# only datatypes of each col

df.dtypes

Out[44]:

Survived      int64
Pclass        int64
Name         object
Sex          object
Age         float64
SibSp         int64
Parch         int64
Ticket       object
Fare        float64
Cabin        object
Embarked     object
dtype: object

In [40]:

# datatypes, rows, cols, size

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 891 entries, 1 to 891
Data columns (total 11 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Survived  891 non-null    int64  
 1   Pclass    891 non-null    int64  
 2   Name      891 non-null    object 
 3   Sex       891 non-null    object 
 4   Age       714 non-null    float64
 5   SibSp     891 non-null    int64  
 6   Parch     891 non-null    int64  
 7   Ticket    891 non-null    object 
 8   Fare      891 non-null    float64
 9   Cabin     204 non-null    object 
 10  Embarked  889 non-null    object 
dtypes: float64(2), int64(4), object(5)
memory usage: 115.8+ KB

In [109]:

df.isnull().sum()

Out[109]:

Survived      0
Pclass        0
Name          0
Sex           0
Age         177
SibSp         0
Parch         0
Ticket        0
Fare          0
Cabin       687
Embarked      2
dtype: int64

In [50]:

(df.isnull().sum() / len(df)) * 100

Out[50]:

Survived     0.000000
Pclass       0.000000
Name         0.000000
Sex          0.000000
Age         19.865320
SibSp        0.000000
Parch        0.000000
Ticket       0.000000
Fare         0.000000
Cabin       77.104377
Embarked     0.224467
dtype: float64

In [49]:

df.count()

Out[49]:

Survived    891
Pclass      891
Name        891
Sex         891
Age         714
SibSp       891
Parch       891
Ticket      891
Fare        891
Cabin       204
Embarked    889
dtype: int64

In [51]:

df.dtypes

Out[51]:

Survived      int64
Pclass        int64
Name         object
Sex          object
Age         float64
SibSp         int64
Parch         int64
Ticket       object
Fare        float64
Cabin        object
Embarked     object
dtype: object

In [44]:

df.dtypes == "object"

Out[44]:

Survived    False
Pclass      False
Name         True
Sex          True
Age         False
SibSp       False
Parch       False
Ticket       True
Fare        False
Cabin        True
Embarked     True
dtype: bool

In [111]:

df.dtypes[df.dtypes == 'object']

Out[111]:

Name        object
Sex         object
Ticket      object
Cabin       object
Embarked    object
dtype: object

In [48]:

df.dtypes[df.dtypes == 'object'].index

Out[48]:

Index(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], dtype='object')

In [49]:

df_cat = df[df.dtypes[df.dtypes == 'object'].index]
df_cat.head()

Out[49]:

	Name	Sex	Ticket	Cabin	Embarked
PassengerId
1	Braund, Mr. Owen Harris	male	A/5 21171	NaN	S
2	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	PC 17599	C85	C
3	Heikkinen, Miss. Laina	female	STON/O2. 3101282	NaN	S
4	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	113803	C123	S
5	Allen, Mr. William Henry	male	373450	NaN	S

In [50]:

for i in df_cat.columns : 
    print(i, " = \n", df[i].unique())

Name  = 
 ['Braund, Mr. Owen Harris'
 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)'
 'Heikkinen, Miss. Laina' 'Futrelle, Mrs. Jacques Heath (Lily May Peel)'
 'Allen, Mr. William Henry' 'Moran, Mr. James' 'McCarthy, Mr. Timothy J'
 'Palsson, Master. Gosta Leonard'
 'Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)'
 'Nasser, Mrs. Nicholas (Adele Achem)' 'Sandstrom, Miss. Marguerite Rut'
 'Bonnell, Miss. Elizabeth' 'Saundercock, Mr. William Henry'
 'Andersson, Mr. Anders Johan' 'Vestrom, Miss. Hulda Amanda Adolfina'
 'Hewlett, Mrs. (Mary D Kingcome) ' 'Rice, Master. Eugene'
 'Williams, Mr. Charles Eugene'
 'Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)'
 'Masselmani, Mrs. Fatima' 'Fynney, Mr. Joseph J' 'Beesley, Mr. Lawrence'
 'McGowan, Miss. Anna "Annie"' 'Sloper, Mr. William Thompson'
 'Palsson, Miss. Torborg Danira'
 'Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)'
 'Emir, Mr. Farred Chehab' 'Fortune, Mr. Charles Alexander'
 'O\'Dwyer, Miss. Ellen "Nellie"' 'Todoroff, Mr. Lalio'
 'Uruchurtu, Don. Manuel E'
 'Spencer, Mrs. William Augustus (Marie Eugenie)'
 'Glynn, Miss. Mary Agatha' 'Wheadon, Mr. Edward H'
 'Meyer, Mr. Edgar Joseph' 'Holverson, Mr. Alexander Oskar'
 'Mamee, Mr. Hanna' 'Cann, Mr. Ernest Charles'
 'Vander Planke, Miss. Augusta Maria' 'Nicola-Yarred, Miss. Jamila'
 'Ahlin, Mrs. Johan (Johanna Persdotter Larsson)'
 'Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)'
 'Kraeff, Mr. Theodor' 'Laroche, Miss. Simonne Marie Anne Andree'
 'Devaney, Miss. Margaret Delia' 'Rogers, Mr. William John'
 'Lennon, Mr. Denis' "O'Driscoll, Miss. Bridget" 'Samaan, Mr. Youssef'
 'Arnold-Franchi, Mrs. Josef (Josefine Franchi)'
 'Panula, Master. Juha Niilo' 'Nosworthy, Mr. Richard Cater'
 'Harper, Mrs. Henry Sleeper (Myna Haxtun)'
 'Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)'
 'Ostby, Mr. Engelhart Cornelius' 'Woolner, Mr. Hugh' 'Rugg, Miss. Emily'
 'Novel, Mr. Mansouer' 'West, Miss. Constance Mirium'
 'Goodwin, Master. William Frederick' 'Sirayanian, Mr. Orsen'
 'Icard, Miss. Amelie' 'Harris, Mr. Henry Birkhardt'
 'Skoog, Master. Harald' 'Stewart, Mr. Albert A'
 'Moubarek, Master. Gerios' 'Nye, Mrs. (Elizabeth Ramell)'
 'Crease, Mr. Ernest James' 'Andersson, Miss. Erna Alexandra'
 'Kink, Mr. Vincenz' 'Jenkin, Mr. Stephen Curnow'
 'Goodwin, Miss. Lillian Amy' 'Hood, Mr. Ambrose Jr'
 'Chronopoulos, Mr. Apostolos' 'Bing, Mr. Lee' 'Moen, Mr. Sigurd Hansen'
 'Staneff, Mr. Ivan' 'Moutal, Mr. Rahamin Haim'
 'Caldwell, Master. Alden Gates' 'Dowdell, Miss. Elizabeth'
 'Waelens, Mr. Achille' 'Sheerlinck, Mr. Jan Baptist'
 'McDermott, Miss. Brigdet Delia' 'Carrau, Mr. Francisco M'
 'Ilett, Miss. Bertha'
 'Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)'
 'Ford, Mr. William Neal' 'Slocovski, Mr. Selman Francis'
 'Fortune, Miss. Mabel Helen' 'Celotti, Mr. Francesco'
 'Christmann, Mr. Emil' 'Andreasson, Mr. Paul Edvin'
 'Chaffee, Mr. Herbert Fuller' 'Dean, Mr. Bertram Frank'
 'Coxon, Mr. Daniel' 'Shorney, Mr. Charles Joseph'
 'Goldschmidt, Mr. George B' 'Greenfield, Mr. William Bertram'
 'Doling, Mrs. John T (Ada Julia Bone)' 'Kantor, Mr. Sinai'
 'Petranec, Miss. Matilda' 'Petroff, Mr. Pastcho ("Pentcho")'
 'White, Mr. Richard Frasar' 'Johansson, Mr. Gustaf Joel'
 'Gustafsson, Mr. Anders Vilhelm' 'Mionoff, Mr. Stoytcho'
 'Salkjelsvik, Miss. Anna Kristine' 'Moss, Mr. Albert Johan'
 'Rekic, Mr. Tido' 'Moran, Miss. Bertha' 'Porter, Mr. Walter Chamberlain'
 'Zabour, Miss. Hileni' 'Barton, Mr. David John' 'Jussila, Miss. Katriina'
 'Attalah, Miss. Malake' 'Pekoniemi, Mr. Edvard' 'Connors, Mr. Patrick'
 'Turpin, Mr. William John Robert' 'Baxter, Mr. Quigg Edmond'
 'Andersson, Miss. Ellis Anna Maria' 'Hickman, Mr. Stanley George'
 'Moore, Mr. Leonard Charles' 'Nasser, Mr. Nicholas' 'Webber, Miss. Susan'
 'White, Mr. Percival Wayland' 'Nicola-Yarred, Master. Elias'
 'McMahon, Mr. Martin' 'Madsen, Mr. Fridtjof Arne' 'Peter, Miss. Anna'
 'Ekstrom, Mr. Johan' 'Drazenoic, Mr. Jozef'
 'Coelho, Mr. Domingos Fernandeo'
 'Robins, Mrs. Alexander A (Grace Charity Laury)'
 'Weisz, Mrs. Leopold (Mathilde Francoise Pede)'
 'Sobey, Mr. Samuel James Hayden' 'Richard, Mr. Emile'
 'Newsom, Miss. Helen Monypeny' 'Futrelle, Mr. Jacques Heath'
 'Osen, Mr. Olaf Elon' 'Giglio, Mr. Victor'
 'Boulos, Mrs. Joseph (Sultana)' 'Nysten, Miss. Anna Sofia'
 'Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)'
 'Burke, Mr. Jeremiah' 'Andrew, Mr. Edgardo Samuel'
 'Nicholls, Mr. Joseph Charles'
 'Andersson, Mr. August Edvard ("Wennerstrom")'
 'Ford, Miss. Robina Maggie "Ruby"'
 'Navratil, Mr. Michel ("Louis M Hoffman")'
 'Byles, Rev. Thomas Roussel Davids' 'Bateman, Rev. Robert James'
 'Pears, Mrs. Thomas (Edith Wearne)' 'Meo, Mr. Alfonzo'
 'van Billiard, Mr. Austin Blyler' 'Olsen, Mr. Ole Martin'
 'Williams, Mr. Charles Duane' 'Gilnagh, Miss. Katherine "Katie"'
 'Corn, Mr. Harry' 'Smiljanic, Mr. Mile' 'Sage, Master. Thomas Henry'
 'Cribb, Mr. John Hatfield'
 'Watt, Mrs. James (Elizabeth "Bessie" Inglis Milne)'
 'Bengtsson, Mr. John Viktor' 'Calic, Mr. Jovo'
 'Panula, Master. Eino Viljami'
 'Goldsmith, Master. Frank John William "Frankie"'
 'Chibnall, Mrs. (Edith Martha Bowerman)'
 'Skoog, Mrs. William (Anna Bernhardina Karlsson)' 'Baumann, Mr. John D'
 'Ling, Mr. Lee' 'Van der hoef, Mr. Wyckoff' 'Rice, Master. Arthur'
 'Johnson, Miss. Eleanor Ileen' 'Sivola, Mr. Antti Wilhelm'
 'Smith, Mr. James Clinch' 'Klasen, Mr. Klas Albin'
 'Lefebre, Master. Henry Forbes' 'Isham, Miss. Ann Elizabeth'
 'Hale, Mr. Reginald' 'Leonard, Mr. Lionel' 'Sage, Miss. Constance Gladys'
 'Pernot, Mr. Rene' 'Asplund, Master. Clarence Gustaf Hugo'
 'Becker, Master. Richard F' 'Kink-Heilmann, Miss. Luise Gretchen'
 'Rood, Mr. Hugh Roscoe'
 'O\'Brien, Mrs. Thomas (Johanna "Hannah" Godfrey)'
 'Romaine, Mr. Charles Hallace ("Mr C Rolmane")' 'Bourke, Mr. John'
 'Turcin, Mr. Stjepan' 'Pinsky, Mrs. (Rosa)' 'Carbines, Mr. William'
 'Andersen-Jensen, Miss. Carla Christine Nielsine'
 'Navratil, Master. Michel M' 'Brown, Mrs. James Joseph (Margaret Tobin)'
 'Lurette, Miss. Elise' 'Mernagh, Mr. Robert'
 'Olsen, Mr. Karl Siegwart Andreas' 'Madigan, Miss. Margaret "Maggie"'
 'Yrois, Miss. Henriette ("Mrs Harbeck")' 'Vande Walle, Mr. Nestor Cyriel'
 'Sage, Mr. Frederick' 'Johanson, Mr. Jakob Alfred' 'Youseff, Mr. Gerious'
 'Cohen, Mr. Gurshon "Gus"' 'Strom, Miss. Telma Matilda'
 'Backstrom, Mr. Karl Alfred' 'Albimona, Mr. Nassef Cassem'
 'Carr, Miss. Helen "Ellen"' 'Blank, Mr. Henry' 'Ali, Mr. Ahmed'
 'Cameron, Miss. Clear Annie' 'Perkin, Mr. John Henry'
 'Givard, Mr. Hans Kristensen' 'Kiernan, Mr. Philip'
 'Newell, Miss. Madeleine' 'Honkanen, Miss. Eliina'
 'Jacobsohn, Mr. Sidney Samuel' 'Bazzani, Miss. Albina'
 'Harris, Mr. Walter' 'Sunderland, Mr. Victor Francis'
 'Bracken, Mr. James H' 'Green, Mr. George Henry' 'Nenkoff, Mr. Christo'
 'Hoyt, Mr. Frederick Maxfield' 'Berglund, Mr. Karl Ivar Sven'
 'Mellors, Mr. William John' 'Lovell, Mr. John Hall ("Henry")'
 'Fahlstrom, Mr. Arne Jonas' 'Lefebre, Miss. Mathilde'
 'Harris, Mrs. Henry Birkhardt (Irene Wallach)' 'Larsson, Mr. Bengt Edvin'
 'Sjostedt, Mr. Ernst Adolf' 'Asplund, Miss. Lillian Gertrud'
 'Leyson, Mr. Robert William Norman' 'Harknett, Miss. Alice Phoebe'
 'Hold, Mr. Stephen' 'Collyer, Miss. Marjorie "Lottie"'
 'Pengelly, Mr. Frederick William' 'Hunt, Mr. George Henry'
 'Zabour, Miss. Thamine' 'Murphy, Miss. Katherine "Kate"'
 'Coleridge, Mr. Reginald Charles' 'Maenpaa, Mr. Matti Alexanteri'
 'Attalah, Mr. Sleiman' 'Minahan, Dr. William Edward'
 'Lindahl, Miss. Agda Thorilda Viktoria' 'Hamalainen, Mrs. William (Anna)'
 'Beckwith, Mr. Richard Leonard' 'Carter, Rev. Ernest Courtenay'
 'Reed, Mr. James George' 'Strom, Mrs. Wilhelm (Elna Matilda Persson)'
 'Stead, Mr. William Thomas' 'Lobb, Mr. William Arthur'
 'Rosblom, Mrs. Viktor (Helena Wilhelmina)'
 'Touma, Mrs. Darwis (Hanne Youssef Razi)'
 'Thorne, Mrs. Gertrude Maybelle' 'Cherry, Miss. Gladys'
 'Ward, Miss. Anna' 'Parrish, Mrs. (Lutie Davis)' 'Smith, Mr. Thomas'
 'Asplund, Master. Edvin Rojj Felix' 'Taussig, Mr. Emil'
 'Harrison, Mr. William' 'Henry, Miss. Delia' 'Reeves, Mr. David'
 'Panula, Mr. Ernesti Arvid' 'Persson, Mr. Ernst Ulrik'
 'Graham, Mrs. William Thompson (Edith Junkins)' 'Bissette, Miss. Amelia'
 'Cairns, Mr. Alexander' 'Tornquist, Mr. William Henry'
 'Mellinger, Mrs. (Elizabeth Anne Maidment)' 'Natsch, Mr. Charles H'
 'Healy, Miss. Hanora "Nora"' 'Andrews, Miss. Kornelia Theodosia'
 'Lindblom, Miss. Augusta Charlotta' 'Parkes, Mr. Francis "Frank"'
 'Rice, Master. Eric' 'Abbott, Mrs. Stanton (Rosa Hunt)'
 'Duane, Mr. Frank' 'Olsson, Mr. Nils Johan Goransson'
 'de Pelsmaeker, Mr. Alfons' 'Dorking, Mr. Edward Arthur'
 'Smith, Mr. Richard William' 'Stankovic, Mr. Ivan'
 'de Mulder, Mr. Theodore' 'Naidenoff, Mr. Penko' 'Hosono, Mr. Masabumi'
 'Connolly, Miss. Kate' 'Barber, Miss. Ellen "Nellie"'
 'Bishop, Mrs. Dickinson H (Helen Walton)' 'Levy, Mr. Rene Jacques'
 'Haas, Miss. Aloisia' 'Mineff, Mr. Ivan' 'Lewy, Mr. Ervin G'
 'Hanna, Mr. Mansour' 'Allison, Miss. Helen Loraine'
 'Saalfeld, Mr. Adolphe' 'Baxter, Mrs. James (Helene DeLaudeniere Chaput)'
 'Kelly, Miss. Anna Katherine "Annie Kate"' 'McCoy, Mr. Bernard'
 'Johnson, Mr. William Cahoone Jr' 'Keane, Miss. Nora A'
 'Williams, Mr. Howard Hugh "Harry"' 'Allison, Master. Hudson Trevor'
 'Fleming, Miss. Margaret'
 'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)'
 'Abelson, Mr. Samuel' 'Francatelli, Miss. Laura Mabel'
 'Hays, Miss. Margaret Bechstein' 'Ryerson, Miss. Emily Borie'
 'Lahtinen, Mrs. William (Anna Sylfven)' 'Hendekovic, Mr. Ignjac'
 'Hart, Mr. Benjamin' 'Nilsson, Miss. Helmina Josefina'
 'Kantor, Mrs. Sinai (Miriam Sternin)' 'Moraweck, Dr. Ernest'
 'Wick, Miss. Mary Natalie'
 'Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)'
 'Dennis, Mr. Samuel' 'Danoff, Mr. Yoto' 'Slayter, Miss. Hilda Mary'
 'Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)'
 'Sage, Mr. George John Jr' 'Young, Miss. Marie Grice'
 'Nysveen, Mr. Johan Hansen' 'Ball, Mrs. (Ada E Hall)'
 'Goldsmith, Mrs. Frank John (Emily Alice Brown)'
 'Hippach, Miss. Jean Gertrude' 'McCoy, Miss. Agnes' 'Partner, Mr. Austen'
 'Graham, Mr. George Edward' 'Vander Planke, Mr. Leo Edmondus'
 'Frauenthal, Mrs. Henry William (Clara Heinsheimer)' 'Denkoff, Mr. Mitto'
 'Pears, Mr. Thomas Clinton' 'Burns, Miss. Elizabeth Margaret'
 'Dahl, Mr. Karl Edwart' 'Blackwell, Mr. Stephen Weart'
 'Navratil, Master. Edmond Roger' 'Fortune, Miss. Alice Elizabeth'
 'Collander, Mr. Erik Gustaf' 'Sedgwick, Mr. Charles Frederick Waddington'
 'Fox, Mr. Stanley Hubert' 'Brown, Miss. Amelia "Mildred"'
 'Smith, Miss. Marion Elsie' 'Davison, Mrs. Thomas Henry (Mary E Finck)'
 'Coutts, Master. William Loch "William"' 'Dimic, Mr. Jovan'
 'Odahl, Mr. Nils Martin' 'Williams-Lambert, Mr. Fletcher Fellows'
 'Elias, Mr. Tannous' 'Arnold-Franchi, Mr. Josef' 'Yousif, Mr. Wazli'
 'Vanden Steen, Mr. Leo Peter' 'Bowerman, Miss. Elsie Edith'
 'Funk, Miss. Annie Clemmer' 'McGovern, Miss. Mary'
 'Mockler, Miss. Helen Mary "Ellie"' 'Skoog, Mr. Wilhelm'
 'del Carlo, Mr. Sebastiano' 'Barbara, Mrs. (Catherine David)'
 'Asim, Mr. Adola' "O'Brien, Mr. Thomas" 'Adahl, Mr. Mauritz Nils Martin'
 'Warren, Mrs. Frank Manley (Anna Sophia Atkinson)'
 'Moussa, Mrs. (Mantoura Boulos)' 'Jermyn, Miss. Annie'
 'Aubart, Mme. Leontine Pauline' 'Harder, Mr. George Achilles'
 'Wiklund, Mr. Jakob Alfred' 'Beavan, Mr. William Thomas'
 'Ringhini, Mr. Sante' 'Palsson, Miss. Stina Viola'
 'Meyer, Mrs. Edgar Joseph (Leila Saks)' 'Landergren, Miss. Aurora Adelia'
 'Widener, Mr. Harry Elkins' 'Betros, Mr. Tannous'
 'Gustafsson, Mr. Karl Gideon' 'Bidois, Miss. Rosalie'
 'Nakid, Miss. Maria ("Mary")' 'Tikkanen, Mr. Juho'
 'Holverson, Mrs. Alexander Oskar (Mary Aline Towner)'
 'Plotcharsky, Mr. Vasil' 'Davies, Mr. Charles Henry'
 'Goodwin, Master. Sidney Leonard' 'Buss, Miss. Kate'
 'Sadlier, Mr. Matthew' 'Lehmann, Miss. Bertha'
 'Carter, Mr. William Ernest' 'Jansson, Mr. Carl Olof'
 'Gustafsson, Mr. Johan Birger' 'Newell, Miss. Marjorie'
 'Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)'
 'Johansson, Mr. Erik' 'Olsson, Miss. Elina' 'McKane, Mr. Peter David'
 'Pain, Dr. Alfred' 'Trout, Mrs. William H (Jessie L)'
 'Niskanen, Mr. Juha' 'Adams, Mr. John' 'Jussila, Miss. Mari Aina'
 'Hakkarainen, Mr. Pekka Pietari' 'Oreskovic, Miss. Marija'
 'Gale, Mr. Shadrach' 'Widegren, Mr. Carl/Charles Peter'
 'Richards, Master. William Rowe' 'Birkeland, Mr. Hans Martin Monsen'
 'Lefebre, Miss. Ida' 'Sdycoff, Mr. Todor' 'Hart, Mr. Henry'
 'Minahan, Miss. Daisy E' 'Cunningham, Mr. Alfred Fleming'
 'Sundman, Mr. Johan Julian' 'Meek, Mrs. Thomas (Annie Louise Rowley)'
 'Drew, Mrs. James Vivian (Lulu Thorne Christian)'
 'Silven, Miss. Lyyli Karoliina' 'Matthews, Mr. William John'
 'Van Impe, Miss. Catharina' 'Gheorgheff, Mr. Stanio'
 'Charters, Mr. David' 'Zimmerman, Mr. Leo'
 'Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)'
 'Rosblom, Mr. Viktor Richard' 'Wiseman, Mr. Phillippe'
 'Clarke, Mrs. Charles V (Ada Maria Winfield)'
 'Phillips, Miss. Kate Florence ("Mrs Kate Louise Phillips Marshall")'
 'Flynn, Mr. James' 'Pickard, Mr. Berk (Berk Trembisky)'
 'Bjornstrom-Steffansson, Mr. Mauritz Hakan'
 'Thorneycroft, Mrs. Percival (Florence Kate White)'
 'Louch, Mrs. Charles Alexander (Alice Adelaide Slow)'
 'Kallio, Mr. Nikolai Erland' 'Silvey, Mr. William Baird'
 'Carter, Miss. Lucile Polk' 'Ford, Miss. Doolina Margaret "Daisy"'
 'Richards, Mrs. Sidney (Emily Hocking)' 'Fortune, Mr. Mark'
 'Kvillner, Mr. Johan Henrik Johannesson'
 'Hart, Mrs. Benjamin (Esther Ada Bloomfield)' 'Hampe, Mr. Leon'
 'Petterson, Mr. Johan Emil' 'Reynaldo, Ms. Encarnacion'
 'Johannesen-Bratthammer, Mr. Bernt' 'Dodge, Master. Washington'
 'Mellinger, Miss. Madeleine Violet' 'Seward, Mr. Frederic Kimber'
 'Baclini, Miss. Marie Catherine' 'Peuchen, Major. Arthur Godfrey'
 'West, Mr. Edwy Arthur' 'Hagland, Mr. Ingvald Olai Olsen'
 'Foreman, Mr. Benjamin Laventall' 'Goldenberg, Mr. Samuel L'
 'Peduzzi, Mr. Joseph' 'Jalsevac, Mr. Ivan' 'Millet, Mr. Francis Davis'
 'Kenyon, Mrs. Frederick R (Marion)' 'Toomey, Miss. Ellen'
 "O'Connor, Mr. Maurice" 'Anderson, Mr. Harry' 'Morley, Mr. William'
 'Gee, Mr. Arthur H' 'Milling, Mr. Jacob Christian' 'Maisner, Mr. Simon'
 'Goncalves, Mr. Manuel Estanslas' 'Campbell, Mr. William'
 'Smart, Mr. John Montgomery' 'Scanlan, Mr. James'
 'Baclini, Miss. Helene Barbara' 'Keefe, Mr. Arthur' 'Cacic, Mr. Luka'
 'West, Mrs. Edwy Arthur (Ada Mary Worth)'
 'Jerwan, Mrs. Amin S (Marie Marthe Thuillard)'
 'Strandberg, Miss. Ida Sofia' 'Clifford, Mr. George Quincy'
 'Renouf, Mr. Peter Henry' 'Braund, Mr. Lewis Richard'
 'Karlsson, Mr. Nils August' 'Hirvonen, Miss. Hildur E'
 'Goodwin, Master. Harold Victor' 'Frost, Mr. Anthony Wood "Archie"'
 'Rouse, Mr. Richard Henry' 'Turkula, Mrs. (Hedwig)'
 'Bishop, Mr. Dickinson H' 'Lefebre, Miss. Jeannie'
 'Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)'
 'Kent, Mr. Edward Austin' 'Somerton, Mr. Francis William'
 'Coutts, Master. Eden Leslie "Neville"'
 'Hagland, Mr. Konrad Mathias Reiersen' 'Windelov, Mr. Einar'
 'Molson, Mr. Harry Markland' 'Artagaveytia, Mr. Ramon'
 'Stanley, Mr. Edward Roland' 'Yousseff, Mr. Gerious'
 'Eustis, Miss. Elizabeth Mussey' 'Shellard, Mr. Frederick William'
 'Allison, Mrs. Hudson J C (Bessie Waldo Daniels)' 'Svensson, Mr. Olof'
 'Calic, Mr. Petar' 'Canavan, Miss. Mary' "O'Sullivan, Miss. Bridget Mary"
 'Laitinen, Miss. Kristina Sofia' 'Maioni, Miss. Roberta'
 'Penasco y Castellana, Mr. Victor de Satode'
 'Quick, Mrs. Frederick Charles (Jane Richards)'
 'Bradley, Mr. George ("George Arthur Brayton")'
 'Olsen, Mr. Henry Margido' 'Lang, Mr. Fang' 'Daly, Mr. Eugene Patrick'
 'Webber, Mr. James' 'McGough, Mr. James Robert'
 'Rothschild, Mrs. Martin (Elizabeth L. Barrett)' 'Coleff, Mr. Satio'
 'Walker, Mr. William Anderson' 'Lemore, Mrs. (Amelia Milley)'
 'Ryan, Mr. Patrick'
 'Angle, Mrs. William A (Florence "Mary" Agnes Hughes)'
 'Pavlovic, Mr. Stefo' 'Perreault, Miss. Anne' 'Vovk, Mr. Janko'
 'Lahoud, Mr. Sarkis' 'Hippach, Mrs. Louis Albert (Ida Sophia Fischer)'
 'Kassem, Mr. Fared' 'Farrell, Mr. James' 'Ridsdale, Miss. Lucy'
 'Farthing, Mr. John' 'Salonen, Mr. Johan Werner'
 'Hocking, Mr. Richard George' 'Quick, Miss. Phyllis May'
 'Toufik, Mr. Nakli' 'Elias, Mr. Joseph Jr'
 'Peter, Mrs. Catherine (Catherine Rizk)' 'Cacic, Miss. Marija'
 'Hart, Miss. Eva Miriam' 'Butt, Major. Archibald Willingham'
 'LeRoy, Miss. Bertha' 'Risien, Mr. Samuel Beard'
 'Frolicher, Miss. Hedwig Margaritha' 'Crosby, Miss. Harriet R'
 'Andersson, Miss. Ingeborg Constanzia'
 'Andersson, Miss. Sigrid Elisabeth' 'Beane, Mr. Edward'
 'Douglas, Mr. Walter Donald' 'Nicholson, Mr. Arthur Ernest'
 'Beane, Mrs. Edward (Ethel Clarke)' 'Padro y Manent, Mr. Julian'
 'Goldsmith, Mr. Frank John' 'Davies, Master. John Morgan Jr'
 'Thayer, Mr. John Borland Jr' 'Sharp, Mr. Percival James R'
 "O'Brien, Mr. Timothy" 'Leeni, Mr. Fahim ("Philip Zenni")'
 'Ohman, Miss. Velin' 'Wright, Mr. George'
 'Duff Gordon, Lady. (Lucille Christiana Sutherland) ("Mrs Morgan")'
 'Robbins, Mr. Victor' 'Taussig, Mrs. Emil (Tillie Mandelbaum)'
 'de Messemaeker, Mrs. Guillaume Joseph (Emma)' 'Morrow, Mr. Thomas Rowan'
 'Sivic, Mr. Husein' 'Norman, Mr. Robert Douglas' 'Simmons, Mr. John'
 'Meanwell, Miss. (Marion Ogden)' 'Davies, Mr. Alfred J'
 'Stoytcheff, Mr. Ilia' 'Palsson, Mrs. Nils (Alma Cornelia Berglund)'
 'Doharr, Mr. Tannous' 'Jonsson, Mr. Carl' 'Harris, Mr. George'
 'Appleton, Mrs. Edward Dale (Charlotte Lamson)'
 'Flynn, Mr. John Irwin ("Irving")' 'Kelly, Miss. Mary'
 'Rush, Mr. Alfred George John' 'Patchett, Mr. George'
 'Garside, Miss. Ethel' 'Silvey, Mrs. William Baird (Alice Munger)'
 'Caram, Mrs. Joseph (Maria Elias)' 'Jussila, Mr. Eiriik'
 'Christy, Miss. Julie Rachel'
 'Thayer, Mrs. John Borland (Marian Longstreth Morris)'
 'Downton, Mr. William James' 'Ross, Mr. John Hugo' 'Paulner, Mr. Uscher'
 'Taussig, Miss. Ruth' 'Jarvis, Mr. John Denzil'
 'Frolicher-Stehli, Mr. Maxmillian' 'Gilinski, Mr. Eliezer'
 'Murdlin, Mr. Joseph' 'Rintamaki, Mr. Matti'
 'Stephenson, Mrs. Walter Bertram (Martha Eustis)'
 'Elsbury, Mr. William James' 'Bourke, Miss. Mary'
 'Chapman, Mr. John Henry' 'Van Impe, Mr. Jean Baptiste'
 'Leitch, Miss. Jessie Wills' 'Johnson, Mr. Alfred' 'Boulos, Mr. Hanna'
 'Duff Gordon, Sir. Cosmo Edmund ("Mr Morgan")'
 'Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)'
 'Slabenoff, Mr. Petco' 'Harrington, Mr. Charles H'
 'Torber, Mr. Ernst William' 'Homer, Mr. Harry ("Mr E Haven")'
 'Lindell, Mr. Edvard Bengtsson' 'Karaic, Mr. Milan'
 'Daniel, Mr. Robert Williams'
 'Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)'
 'Shutes, Miss. Elizabeth W'
 'Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)'
 'Jardin, Mr. Jose Neto' 'Murphy, Miss. Margaret Jane' 'Horgan, Mr. John'
 'Brocklebank, Mr. William Alfred' 'Herman, Miss. Alice'
 'Danbom, Mr. Ernst Gilbert'
 'Lobb, Mrs. William Arthur (Cordelia K Stanlick)'
 'Becker, Miss. Marion Louise' 'Gavey, Mr. Lawrence' 'Yasbeck, Mr. Antoni'
 'Kimball, Mr. Edwin Nelson Jr' 'Nakid, Mr. Sahid'
 'Hansen, Mr. Henry Damsgaard' 'Bowen, Mr. David John "Dai"'
 'Sutton, Mr. Frederick' 'Kirkland, Rev. Charles Leonard'
 'Longley, Miss. Gretchen Fiske' 'Bostandyeff, Mr. Guentcho'
 "O'Connell, Mr. Patrick D" 'Barkworth, Mr. Algernon Henry Wilson'
 'Lundahl, Mr. Johan Svensson' 'Stahelin-Maeglin, Dr. Max'
 'Parr, Mr. William Henry Marsh' 'Skoog, Miss. Mabel' 'Davis, Miss. Mary'
 'Leinonen, Mr. Antti Gustaf' 'Collyer, Mr. Harvey'
 'Panula, Mrs. Juha (Maria Emilia Ojala)' 'Thorneycroft, Mr. Percival'
 'Jensen, Mr. Hans Peder' 'Sagesser, Mlle. Emma'
 'Skoog, Miss. Margit Elizabeth' 'Foo, Mr. Choong'
 'Baclini, Miss. Eugenie' 'Harper, Mr. Henry Sleeper' 'Cor, Mr. Liudevit'
 'Simonius-Blumer, Col. Oberst Alfons' 'Willey, Mr. Edward'
 'Stanley, Miss. Amy Zillah Elsie' 'Mitkoff, Mr. Mito'
 'Doling, Miss. Elsie' 'Kalvik, Mr. Johannes Halvorsen'
 'O\'Leary, Miss. Hanora "Norah"' 'Hegarty, Miss. Hanora "Nora"'
 'Hickman, Mr. Leonard Mark' 'Radeff, Mr. Alexander'
 'Bourke, Mrs. John (Catherine)' 'Eitemiller, Mr. George Floyd'
 'Newell, Mr. Arthur Webster' 'Frauenthal, Dr. Henry William'
 'Badt, Mr. Mohamed' 'Colley, Mr. Edward Pomeroy' 'Coleff, Mr. Peju'
 'Lindqvist, Mr. Eino William' 'Hickman, Mr. Lewis'
 'Butler, Mr. Reginald Fenton' 'Rommetvedt, Mr. Knud Paust'
 'Cook, Mr. Jacob' 'Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)'
 'Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)'
 'Davidson, Mr. Thornton' 'Mitchell, Mr. Henry Michael'
 'Wilhelms, Mr. Charles' 'Watson, Mr. Ennis Hastings'
 'Edvardsson, Mr. Gustaf Hjalmar' 'Sawyer, Mr. Frederick Charles'
 'Turja, Miss. Anna Sofia' 'Goodwin, Mrs. Frederick (Augusta Tyler)'
 'Cardeza, Mr. Thomas Drake Martinez' 'Peters, Miss. Katie'
 'Hassab, Mr. Hammad' 'Olsvigen, Mr. Thor Anderson'
 'Goodwin, Mr. Charles Edward' 'Brown, Mr. Thomas William Solomon'
 'Laroche, Mr. Joseph Philippe Lemercier' 'Panula, Mr. Jaako Arnold'
 'Dakic, Mr. Branko' 'Fischer, Mr. Eberhard Thelander'
 'Madill, Miss. Georgette Alexandra' 'Dick, Mr. Albert Adrian'
 'Karun, Miss. Manca' 'Lam, Mr. Ali' 'Saad, Mr. Khalil' 'Weir, Col. John'
 'Chapman, Mr. Charles Henry' 'Kelly, Mr. James'
 'Mullens, Miss. Katherine "Katie"' 'Thayer, Mr. John Borland'
 'Humblen, Mr. Adolf Mathias Nicolai Olsen'
 'Astor, Mrs. John Jacob (Madeleine Talmadge Force)'
 'Silverthorne, Mr. Spencer Victor' 'Barbara, Miss. Saiide'
 'Gallagher, Mr. Martin' 'Hansen, Mr. Henrik Juul'
 'Morley, Mr. Henry Samuel ("Mr Henry Marshall")'
 'Kelly, Mrs. Florence "Fannie"' 'Calderhead, Mr. Edward Pennington'
 'Cleaver, Miss. Alice'
 'Moubarek, Master. Halim Gonios ("William George")'
 'Mayne, Mlle. Berthe Antonine ("Mrs de Villiers")' 'Klaber, Mr. Herman'
 'Taylor, Mr. Elmer Zebley' 'Larsson, Mr. August Viktor'
 'Greenberg, Mr. Samuel' 'Soholt, Mr. Peter Andreas Lauritz Andersen'
 'Endres, Miss. Caroline Louise' 'Troutt, Miss. Edwina Celia "Winnie"'
 'McEvoy, Mr. Michael' 'Johnson, Mr. Malkolm Joackim'
 'Harper, Miss. Annie Jessie "Nina"' 'Jensen, Mr. Svend Lauritz'
 'Gillespie, Mr. William Henry' 'Hodges, Mr. Henry Price'
 'Chambers, Mr. Norman Campbell' 'Oreskovic, Mr. Luka'
 'Renouf, Mrs. Peter Henry (Lillian Jefferys)' 'Mannion, Miss. Margareth'
 'Bryhl, Mr. Kurt Arnold Gottfrid' 'Ilmakangas, Miss. Pieta Sofia'
 'Allen, Miss. Elisabeth Walton' 'Hassan, Mr. Houssein G N'
 'Knight, Mr. Robert J' 'Berriman, Mr. William John'
 'Troupiansky, Mr. Moses Aaron' 'Williams, Mr. Leslie'
 'Ford, Mrs. Edward (Margaret Ann Watson)' 'Lesurer, Mr. Gustave J'
 'Ivanoff, Mr. Kanio' 'Nankoff, Mr. Minko' 'Hawksford, Mr. Walter James'
 'Cavendish, Mr. Tyrell William' 'Ryerson, Miss. Susan Parker "Suzette"'
 'McNamee, Mr. Neal' 'Stranden, Mr. Juho' 'Crosby, Capt. Edward Gifford'
 'Abbott, Mr. Rossmore Edward' 'Sinkkonen, Miss. Anna'
 'Marvin, Mr. Daniel Warner' 'Connaghton, Mr. Michael' 'Wells, Miss. Joan'
 'Moor, Master. Meier' 'Vande Velde, Mr. Johannes Joseph'
 'Jonkoff, Mr. Lalio' 'Herman, Mrs. Samuel (Jane Laver)'
 'Hamalainen, Master. Viljo' 'Carlsson, Mr. August Sigfrid'
 'Bailey, Mr. Percy Andrew' 'Theobald, Mr. Thomas Leonard'
 'Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)'
 'Garfirth, Mr. John' 'Nirva, Mr. Iisakki Antino Aijo'
 'Barah, Mr. Hanna Assi' 'Carter, Mrs. William Ernest (Lucile Polk)'
 'Eklund, Mr. Hans Linus' 'Hogeboom, Mrs. John C (Anna Andrews)'
 'Brewe, Dr. Arthur Jackson' 'Mangan, Miss. Mary' 'Moran, Mr. Daniel J'
 'Gronnestad, Mr. Daniel Danielsen' 'Lievens, Mr. Rene Aime'
 'Jensen, Mr. Niels Peder' 'Mack, Mrs. (Mary)' 'Elias, Mr. Dibo'
 'Hocking, Mrs. Elizabeth (Eliza Needs)'
 'Myhrman, Mr. Pehr Fabian Oliver Malkolm' 'Tobin, Mr. Roger'
 'Emanuel, Miss. Virginia Ethel' 'Kilgannon, Mr. Thomas J'
 'Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)'
 'Ayoub, Miss. Banoura' 'Dick, Mrs. Albert Adrian (Vera Gillespie)'
 'Long, Mr. Milton Clyde' 'Johnston, Mr. Andrew G' 'Ali, Mr. William'
 'Harmer, Mr. Abraham (David Lishin)' 'Sjoblom, Miss. Anna Sofia'
 'Rice, Master. George Hugh' 'Dean, Master. Bertram Vere'
 'Guggenheim, Mr. Benjamin' 'Keane, Mr. Andrew "Andy"'
 'Gaskell, Mr. Alfred' 'Sage, Miss. Stella Anna'
 'Hoyt, Mr. William Fisher' 'Dantcheff, Mr. Ristiu' 'Otter, Mr. Richard'
 'Leader, Dr. Alice (Farnham)' 'Osman, Mrs. Mara'
 'Ibrahim Shawah, Mr. Yousseff'
 'Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)'
 'Ponesell, Mr. Martin' 'Collyer, Mrs. Harvey (Charlotte Annie Tate)'
 'Carter, Master. William Thornton II' 'Thomas, Master. Assad Alexander'
 'Hedman, Mr. Oskar Arvid' 'Johansson, Mr. Karl Johan'
 'Andrews, Mr. Thomas Jr' 'Pettersson, Miss. Ellen Natalia'
 'Meyer, Mr. August' 'Chambers, Mrs. Norman Campbell (Bertha Griggs)'
 'Alexander, Mr. William' 'Lester, Mr. James' 'Slemen, Mr. Richard James'
 'Andersson, Miss. Ebba Iris Alfrida' 'Tomlin, Mr. Ernest Portage'
 'Fry, Mr. Richard' 'Heininen, Miss. Wendla Maria' 'Mallet, Mr. Albert'
 'Holm, Mr. John Fredrik Alexander' 'Skoog, Master. Karl Thorsten'
 'Hays, Mrs. Charles Melville (Clara Jennings Gregg)' 'Lulic, Mr. Nikola'
 'Reuchlin, Jonkheer. John George' 'Moor, Mrs. (Beila)'
 'Panula, Master. Urho Abraham' 'Flynn, Mr. John' 'Lam, Mr. Len'
 'Mallet, Master. Andre' 'McCormack, Mr. Thomas Joseph'
 'Stone, Mrs. George Nelson (Martha Evelyn)'
 'Yasbeck, Mrs. Antoni (Selini Alexander)'
 'Richards, Master. George Sibley' 'Saad, Mr. Amin'
 'Augustsson, Mr. Albert' 'Allum, Mr. Owen George'
 'Compton, Miss. Sara Rebecca' 'Pasic, Mr. Jakob' 'Sirota, Mr. Maurice'
 'Chip, Mr. Chang' 'Marechal, Mr. Pierre' 'Alhomaki, Mr. Ilmari Rudolf'
 'Mudd, Mr. Thomas Charles' 'Serepeca, Miss. Augusta'
 'Lemberopolous, Mr. Peter L' 'Culumovic, Mr. Jeso' 'Abbing, Mr. Anthony'
 'Sage, Mr. Douglas Bullen' 'Markoff, Mr. Marin' 'Harper, Rev. John'
 'Goldenberg, Mrs. Samuel L (Edwiga Grabowska)'
 'Andersson, Master. Sigvard Harald Elias' 'Svensson, Mr. Johan'
 'Boulos, Miss. Nourelain' 'Lines, Miss. Mary Conover'
 'Carter, Mrs. Ernest Courtenay (Lilian Hughes)'
 'Aks, Mrs. Sam (Leah Rosen)' 'Wick, Mrs. George Dennick (Mary Hitchcock)'
 'Daly, Mr. Peter Denis ' 'Baclini, Mrs. Solomon (Latifa Qurban)'
 'Razi, Mr. Raihed' 'Hansen, Mr. Claus Peter'
 'Giles, Mr. Frederick Edward'
 'Swift, Mrs. Frederick Joel (Margaret Welles Barron)'
 'Sage, Miss. Dorothy Edith "Dolly"' 'Gill, Mr. John William'
 'Bystrom, Mrs. (Karolina)' 'Duran y More, Miss. Asuncion'
 'Roebling, Mr. Washington Augustus II' 'van Melkebeke, Mr. Philemon'
 'Johnson, Master. Harold Theodor' 'Balkic, Mr. Cerin'
 'Beckwith, Mrs. Richard Leonard (Sallie Monypeny)'
 'Carlsson, Mr. Frans Olof' 'Vander Cruyssen, Mr. Victor'
 'Abelson, Mrs. Samuel (Hannah Wizosky)'
 'Najib, Miss. Adele Kiamie "Jane"' 'Gustafsson, Mr. Alfred Ossian'
 'Petroff, Mr. Nedelio' 'Laleff, Mr. Kristo'
 'Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)'
 'Shelley, Mrs. William (Imanita Parrish Hall)' 'Markun, Mr. Johann'
 'Dahlberg, Miss. Gerda Ulrika' 'Banfield, Mr. Frederick James'
 'Sutehall, Mr. Henry Jr' 'Rice, Mrs. William (Margaret Norton)'
 'Montvila, Rev. Juozas' 'Graham, Miss. Margaret Edith'
 'Johnston, Miss. Catherine Helen "Carrie"' 'Behr, Mr. Karl Howell'
 'Dooley, Mr. Patrick']
Sex  = 
 ['male' 'female']
Ticket  = 
 ['A/5 21171' 'PC 17599' 'STON/O2. 3101282' '113803' '373450' '330877'
 '17463' '349909' '347742' '237736' 'PP 9549' '113783' 'A/5. 2151'
 '347082' '350406' '248706' '382652' '244373' '345763' '2649' '239865'
 '248698' '330923' '113788' '347077' '2631' '19950' '330959' '349216'
 'PC 17601' 'PC 17569' '335677' 'C.A. 24579' 'PC 17604' '113789' '2677'
 'A./5. 2152' '345764' '2651' '7546' '11668' '349253' 'SC/Paris 2123'
 '330958' 'S.C./A.4. 23567' '370371' '14311' '2662' '349237' '3101295'
 'A/4. 39886' 'PC 17572' '2926' '113509' '19947' 'C.A. 31026' '2697'
 'C.A. 34651' 'CA 2144' '2669' '113572' '36973' '347088' 'PC 17605' '2661'
 'C.A. 29395' 'S.P. 3464' '3101281' '315151' 'C.A. 33111' 'S.O.C. 14879'
 '2680' '1601' '348123' '349208' '374746' '248738' '364516' '345767'
 '345779' '330932' '113059' 'SO/C 14885' '3101278' 'W./C. 6608'
 'SOTON/OQ 392086' '343275' '343276' '347466' 'W.E.P. 5734' 'C.A. 2315'
 '364500' '374910' 'PC 17754' 'PC 17759' '231919' '244367' '349245'
 '349215' '35281' '7540' '3101276' '349207' '343120' '312991' '349249'
 '371110' '110465' '2665' '324669' '4136' '2627' 'STON/O 2. 3101294'
 '370369' 'PC 17558' 'A4. 54510' '27267' '370372' 'C 17369' '2668'
 '347061' '349241' 'SOTON/O.Q. 3101307' 'A/5. 3337' '228414' 'C.A. 29178'
 'SC/PARIS 2133' '11752' '7534' 'PC 17593' '2678' '347081'
 'STON/O2. 3101279' '365222' '231945' 'C.A. 33112' '350043' '230080'
 '244310' 'S.O.P. 1166' '113776' 'A.5. 11206' 'A/5. 851' 'Fa 265302'
 'PC 17597' '35851' 'SOTON/OQ 392090' '315037' 'CA. 2343' '371362'
 'C.A. 33595' '347068' '315093' '363291' '113505' 'PC 17318' '111240'
 'STON/O 2. 3101280' '17764' '350404' '4133' 'PC 17595' '250653' 'LINE'
 'SC/PARIS 2131' '230136' '315153' '113767' '370365' '111428' '364849'
 '349247' '234604' '28424' '350046' 'PC 17610' '368703' '4579' '370370'
 '248747' '345770' '3101264' '2628' 'A/5 3540' '347054' '2699' '367231'
 '112277' 'SOTON/O.Q. 3101311' 'F.C.C. 13528' 'A/5 21174' '250646'
 '367229' '35273' 'STON/O2. 3101283' '243847' '11813' 'W/C 14208'
 'SOTON/OQ 392089' '220367' '21440' '349234' '19943' 'PP 4348' 'SW/PP 751'
 'A/5 21173' '236171' '347067' '237442' 'C.A. 29566' 'W./C. 6609' '26707'
 'C.A. 31921' '28665' 'SCO/W 1585' '367230' 'W./C. 14263'
 'STON/O 2. 3101275' '2694' '19928' '347071' '250649' '11751' '244252'
 '362316' '113514' 'A/5. 3336' '370129' '2650' 'PC 17585' '110152'
 'PC 17755' '230433' '384461' '110413' '112059' '382649' 'C.A. 17248'
 '347083' 'PC 17582' 'PC 17760' '113798' '250644' 'PC 17596' '370375'
 '13502' '347073' '239853' 'C.A. 2673' '336439' '347464' '345778'
 'A/5. 10482' '113056' '349239' '345774' '349206' '237798' '370373'
 '19877' '11967' 'SC/Paris 2163' '349236' '349233' 'PC 17612' '2693'
 '113781' '19988' '9234' '367226' '226593' 'A/5 2466' '17421' 'PC 17758'
 'P/PP 3381' 'PC 17485' '11767' 'PC 17608' '250651' '349243'
 'F.C.C. 13529' '347470' '29011' '36928' '16966' 'A/5 21172' '349219'
 '234818' '345364' '28551' '111361' '113043' 'PC 17611' '349225' '7598'
 '113784' '248740' '244361' '229236' '248733' '31418' '386525'
 'C.A. 37671' '315088' '7267' '113510' '2695' '2647' '345783' '237671'
 '330931' '330980' 'SC/PARIS 2167' '2691' 'SOTON/O.Q. 3101310' 'C 7076'
 '110813' '2626' '14313' 'PC 17477' '11765' '3101267' '323951' 'C 7077'
 '113503' '2648' '347069' 'PC 17757' '2653' 'STON/O 2. 3101293' '349227'
 '27849' '367655' 'SC 1748' '113760' '350034' '3101277' '350052' '350407'
 '28403' '244278' '240929' 'STON/O 2. 3101289' '341826' '4137' '315096'
 '28664' '347064' '29106' '312992' '349222' '394140' 'STON/O 2. 3101269'
 '343095' '28220' '250652' '28228' '345773' '349254' 'A/5. 13032' '315082'
 '347080' 'A/4. 34244' '2003' '250655' '364851' 'SOTON/O.Q. 392078'
 '110564' '376564' 'SC/AH 3085' 'STON/O 2. 3101274' '13507' 'C.A. 18723'
 '345769' '347076' '230434' '65306' '33638' '113794' '2666' '113786'
 '65303' '113051' '17453' 'A/5 2817' '349240' '13509' '17464'
 'F.C.C. 13531' '371060' '19952' '364506' '111320' '234360' 'A/S 2816'
 'SOTON/O.Q. 3101306' '113792' '36209' '323592' '315089' 'SC/AH Basle 541'
 '7553' '31027' '3460' '350060' '3101298' '239854' 'A/5 3594' '4134'
 '11771' 'A.5. 18509' '65304' 'SOTON/OQ 3101317' '113787' 'PC 17609'
 'A/4 45380' '36947' 'C.A. 6212' '350035' '315086' '364846' '330909'
 '4135' '26360' '111427' 'C 4001' '382651' 'SOTON/OQ 3101316' 'PC 17473'
 'PC 17603' '349209' '36967' 'C.A. 34260' '226875' '349242' '12749'
 '349252' '2624' '2700' '367232' 'W./C. 14258' 'PC 17483' '3101296'
 '29104' '2641' '2690' '315084' '113050' 'PC 17761' '364498' '13568'
 'WE/P 5735' '2908' '693' 'SC/PARIS 2146' '244358' '330979' '2620'
 '347085' '113807' '11755' '345572' '372622' '349251' '218629'
 'SOTON/OQ 392082' 'SOTON/O.Q. 392087' 'A/4 48871' '349205' '2686'
 '350417' 'S.W./PP 752' '11769' 'PC 17474' '14312' 'A/4. 20589' '358585'
 '243880' '2689' 'STON/O 2. 3101286' '237789' '13049' '3411' '237565'
 '13567' '14973' 'A./5. 3235' 'STON/O 2. 3101273' 'A/5 3902' '364848'
 'SC/AH 29037' '248727' '2664' '349214' '113796' '364511' '111426'
 '349910' '349246' '113804' 'SOTON/O.Q. 3101305' '370377' '364512'
 '220845' '31028' '2659' '11753' '350029' '54636' '36963' '219533'
 '349224' '334912' '27042' '347743' '13214' '112052' '237668'
 'STON/O 2. 3101292' '350050' '349231' '13213' 'S.O./P.P. 751' 'CA. 2314'
 '349221' '8475' '330919' '365226' '349223' '29751' '2623' '5727' '349210'
 'STON/O 2. 3101285' '234686' '312993' 'A/5 3536' '19996' '29750'
 'F.C. 12750' 'C.A. 24580' '244270' '239856' '349912' '342826' '4138'
 '330935' '6563' '349228' '350036' '24160' '17474' '349256' '2672'
 '113800' '248731' '363592' '35852' '348121' 'PC 17475' '36864' '350025'
 '223596' 'PC 17476' 'PC 17482' '113028' '7545' '250647' '348124' '34218'
 '36568' '347062' '350048' '12233' '250643' '113806' '315094' '36866'
 '236853' 'STON/O2. 3101271' '239855' '28425' '233639' '349201' '349218'
 '16988' '376566' 'STON/O 2. 3101288' '250648' '113773' '335097' '29103'
 '392096' '345780' '349204' '350042' '29108' '363294' 'SOTON/O2 3101272'
 '2663' '347074' '112379' '364850' '8471' '345781' '350047' 'S.O./P.P. 3'
 '2674' '29105' '347078' '383121' '36865' '2687' '113501' 'W./C. 6607'
 'SOTON/O.Q. 3101312' '374887' '3101265' '12460' 'PC 17600' '349203'
 '28213' '17465' '349244' '2685' '2625' '347089' '347063' '112050'
 '347087' '248723' '3474' '28206' '364499' '112058' 'STON/O2. 3101290'
 'S.C./PARIS 2079' 'C 7075' '315098' '19972' '368323' '367228' '2671'
 '347468' '2223' 'PC 17756' '315097' '392092' '11774' 'SOTON/O2 3101287'
 '2683' '315090' 'C.A. 5547' '349213' '347060' 'PC 17592' '392091'
 '113055' '2629' '350026' '28134' '17466' '233866' '236852'
 'SC/PARIS 2149' 'PC 17590' '345777' '349248' '695' '345765' '2667'
 '349212' '349217' '349257' '7552' 'C.A./SOTON 34068' 'SOTON/OQ 392076'
 '211536' '112053' '111369' '370376']
Cabin  = 
 [nan 'C85' 'C123' 'E46' 'G6' 'C103' 'D56' 'A6' 'C23 C25 C27' 'B78' 'D33'
 'B30' 'C52' 'B28' 'C83' 'F33' 'F G73' 'E31' 'A5' 'D10 D12' 'D26' 'C110'
 'B58 B60' 'E101' 'F E69' 'D47' 'B86' 'F2' 'C2' 'E33' 'B19' 'A7' 'C49'
 'F4' 'A32' 'B4' 'B80' 'A31' 'D36' 'D15' 'C93' 'C78' 'D35' 'C87' 'B77'
 'E67' 'B94' 'C125' 'C99' 'C118' 'D7' 'A19' 'B49' 'D' 'C22 C26' 'C106'
 'C65' 'E36' 'C54' 'B57 B59 B63 B66' 'C7' 'E34' 'C32' 'B18' 'C124' 'C91'
 'E40' 'T' 'C128' 'D37' 'B35' 'E50' 'C82' 'B96 B98' 'E10' 'E44' 'A34'
 'C104' 'C111' 'C92' 'E38' 'D21' 'E12' 'E63' 'A14' 'B37' 'C30' 'D20' 'B79'
 'E25' 'D46' 'B73' 'C95' 'B38' 'B39' 'B22' 'C86' 'C70' 'A16' 'C101' 'C68'
 'A10' 'E68' 'B41' 'A20' 'D19' 'D50' 'D9' 'A23' 'B50' 'A26' 'D48' 'E58'
 'C126' 'B71' 'B51 B53 B55' 'D49' 'B5' 'B20' 'F G63' 'C62 C64' 'E24' 'C90'
 'C45' 'E8' 'B101' 'D45' 'C46' 'D30' 'E121' 'D11' 'E77' 'F38' 'B3' 'D6'
 'B82 B84' 'D17' 'A36' 'B102' 'B69' 'E49' 'C47' 'D28' 'E17' 'A24' 'C50'
 'B42' 'C148']
Embarked  = 
 ['S' 'C' 'Q' nan]

In [51]:

df.describe()

Out[51]:

	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

In [52]:

df.describe(include = np.object)    #List of data types to be included while describing dataframe. Default is None

<ipython-input-52-a823d8da510e>:1: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. 
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  df.describe(include = np.object)

Out[52]:

	Name	Sex	Ticket	Cabin	Embarked
count	891	891	891	204	889
unique	891	2	681	147	3
top	Frost, Mr. Anthony Wood "Archie"	male	1601	C23 C25 C27	S
freq	1	577	7	4	644

In [78]:

df.describe(include = 'all')    #List of data types to be included while describing dataframe. Default is None

Out[78]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
count	891.000000	891.000000	891	891	714.000000	891.000000	891.000000	891	891.000000	204	889
unique	NaN	NaN	891	2	NaN	NaN	NaN	681	NaN	147	3
top	NaN	NaN	Wells, Miss. Joan	male	NaN	NaN	NaN	CA. 2343	NaN	B96 B98	S
freq	NaN	NaN	1	577	NaN	NaN	NaN	7	NaN	4	644
mean	0.383838	2.308642	NaN	NaN	29.699118	0.523008	0.381594	NaN	32.204208	NaN	NaN
std	0.486592	0.836071	NaN	NaN	14.526497	1.102743	0.806057	NaN	49.693429	NaN	NaN
min	0.000000	1.000000	NaN	NaN	0.420000	0.000000	0.000000	NaN	0.000000	NaN	NaN
25%	0.000000	2.000000	NaN	NaN	20.125000	0.000000	0.000000	NaN	7.910400	NaN	NaN
50%	0.000000	3.000000	NaN	NaN	28.000000	0.000000	0.000000	NaN	14.454200	NaN	NaN
75%	1.000000	3.000000	NaN	NaN	38.000000	1.000000	0.000000	NaN	31.000000	NaN	NaN
max	1.000000	3.000000	NaN	NaN	80.000000	8.000000	6.000000	NaN	512.329200	NaN	NaN

In [113]:

df.describe(percentiles = [0.1, 0.6, 0.9])

Out[113]:

	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
10%	0.000000	1.000000	14.000000	0.000000	0.000000	7.550000
50%	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
60%	0.000000	3.000000	31.800000	0.000000	0.000000	21.679200
90%	1.000000	3.000000	50.000000	1.000000	2.000000	77.958300
max	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

In [53]:

# list like data type of numbers between 0-1 to return the respective percentile
df.describe(include = 'all', percentiles = [0.1, 0.9])

Out[53]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
count	891.000000	891.000000	891	891	714.000000	891.000000	891.000000	891	891.000000	204	889
unique	NaN	NaN	891	2	NaN	NaN	NaN	681	NaN	147	3
top	NaN	NaN	Frost, Mr. Anthony Wood "Archie"	male	NaN	NaN	NaN	1601	NaN	C23 C25 C27	S
freq	NaN	NaN	1	577	NaN	NaN	NaN	7	NaN	4	644
mean	0.383838	2.308642	NaN	NaN	29.699118	0.523008	0.381594	NaN	32.204208	NaN	NaN
std	0.486592	0.836071	NaN	NaN	14.526497	1.102743	0.806057	NaN	49.693429	NaN	NaN
min	0.000000	1.000000	NaN	NaN	0.420000	0.000000	0.000000	NaN	0.000000	NaN	NaN
10%	0.000000	1.000000	NaN	NaN	14.000000	0.000000	0.000000	NaN	7.550000	NaN	NaN
50%	0.000000	3.000000	NaN	NaN	28.000000	0.000000	0.000000	NaN	14.454200	NaN	NaN
90%	1.000000	3.000000	NaN	NaN	50.000000	1.000000	2.000000	NaN	77.958300	NaN	NaN
max	1.000000	3.000000	NaN	NaN	80.000000	8.000000	6.000000	NaN	512.329200	NaN	NaN

In [53]:

df.drop(['Name', 'Ticket', "Cabin"], axis = 1, inplace = True)

In [54]:

df

Out[54]:

	Survived	Pclass	Sex	Age	SibSp	Parch	Fare	Embarked
PassengerId
1	0	3	male	22.0	1	0	7.2500	S
2	1	1	female	38.0	1	0	71.2833	C
3	1	3	female	26.0	0	0	7.9250	S
4	1	1	female	35.0	1	0	53.1000	S
5	0	3	male	35.0	0	0	8.0500	S
...	...	...	...	...	...	...	...	...
887	0	2	male	27.0	0	0	13.0000	S
888	1	1	female	19.0	0	0	30.0000	S
889	0	3	female	NaN	1	2	23.4500	S
890	1	1	male	26.0	0	0	30.0000	C
891	0	3	male	32.0	0	0	7.7500	Q

891 rows × 8 columns

In [55]:

df.head()

Out[55]:

	Survived	Pclass	Sex	Age	SibSp	Parch	Fare	Embarked
PassengerId
1	0	3	male	22.0	1	0	7.2500	S
2	1	1	female	38.0	1	0	71.2833	C
3	1	3	female	26.0	0	0	7.9250	S
4	1	1	female	35.0	1	0	53.1000	S
5	0	3	male	35.0	0	0	8.0500	S

In [57]:

df.fillna(value = 100, axis = 1).info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 891 entries, 1 to 891
Data columns (total 8 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Survived  891 non-null    int64  
 1   Pclass    891 non-null    int64  
 2   Sex       891 non-null    object 
 3   Age       891 non-null    float64
 4   SibSp     891 non-null    int64  
 5   Parch     891 non-null    int64  
 6   Fare      891 non-null    float64
 7   Embarked  891 non-null    object 
dtypes: float64(2), int64(4), object(2)
memory usage: 94.9+ KB

In [61]:

plt.hist(df.Age)   #This parameter is an optional parameter and it contains the integer or sequence or string.
plt.show()

In [62]:

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 891 entries, 1 to 891
Data columns (total 8 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Survived  891 non-null    int64  
 1   Pclass    891 non-null    int64  
 2   Sex       891 non-null    object 
 3   Age       714 non-null    float64
 4   SibSp     891 non-null    int64  
 5   Parch     891 non-null    int64  
 6   Fare      891 non-null    float64
 7   Embarked  889 non-null    object 
dtypes: float64(2), int64(4), object(2)
memory usage: 94.9+ KB

In [56]:

df1 = df.copy()

# DataFrame.copy(deep=True)

# When deep=True (default), a new object will be created with a copy of the calling object’s data and indices. 
# Modifications to the data or indices of the copy will not be reflected in the original object (see notes below).

# When deep=False, a new object will be created without copying the calling object’s data or 
# index (only references to the data and index are copied). Any changes to the data of the original will 
# be reflected in the shallow copy (and vice versa).

In [57]:

df1

Out[57]:

	Survived	Pclass	Sex	Age	SibSp	Parch	Fare	Embarked
PassengerId
1	0	3	male	22.0	1	0	7.2500	S
2	1	1	female	38.0	1	0	71.2833	C
3	1	3	female	26.0	0	0	7.9250	S
4	1	1	female	35.0	1	0	53.1000	S
5	0	3	male	35.0	0	0	8.0500	S
...	...	...	...	...	...	...	...	...
887	0	2	male	27.0	0	0	13.0000	S
888	1	1	female	19.0	0	0	30.0000	S
889	0	3	female	NaN	1	2	23.4500	S
890	1	1	male	26.0	0	0	30.0000	C
891	0	3	male	32.0	0	0	7.7500	Q

891 rows × 8 columns

In [59]:

df1['Age'].median()

Out[59]:

28.0

In [58]:

df1.fillna({"Age" : df1['Age'].median(), "Embarked" : "S"}, inplace = True)
# df1['Age'].fillna(df1['Age'].median(), inplace = True)
# df1['Embarked'].fillna('S', inplace= True)

Out[58]:

	Survived	Pclass	Sex	Age	SibSp	Parch	Fare	Embarked
PassengerId
1	0	3	male	22.0	1	0	7.2500	S
2	1	1	female	38.0	1	0	71.2833	C
3	1	3	female	26.0	0	0	7.9250	S
4	1	1	female	35.0	1	0	53.1000	S
5	0	3	male	35.0	0	0	8.0500	S
...	...	...	...	...	...	...	...	...
887	0	2	male	27.0	0	0	13.0000	S
888	1	1	female	19.0	0	0	30.0000	S
889	0	3	female	28.0	1	2	23.4500	S
890	1	1	male	26.0	0	0	30.0000	C
891	0	3	male	32.0	0	0	7.7500	Q

891 rows × 8 columns

In [67]:

df['Age'].median()

Out[67]:

28.0

In [68]:

df1.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 891 entries, 1 to 891
Data columns (total 8 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Survived  891 non-null    int64  
 1   Pclass    891 non-null    int64  
 2   Sex       891 non-null    object 
 3   Age       891 non-null    float64
 4   SibSp     891 non-null    int64  
 5   Parch     891 non-null    int64  
 6   Fare      891 non-null    float64
 7   Embarked  891 non-null    object 
dtypes: float64(2), int64(4), object(2)
memory usage: 94.9+ KB

In [69]:

plt.hist(df1.Age, bins = 100)
plt.show()

In [70]:

sns.boxplot(x = df.Pclass, y = df.Age)

Out[70]:

<AxesSubplot:xlabel='Pclass', ylabel='Age'>

In [71]:

sns.boxplot(x = df.Pclass, y = df.Age, hue = df.Sex)

Out[71]:

<AxesSubplot:xlabel='Pclass', ylabel='Age'>

In [93]:

df

Out[93]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 11 columns

In [166]:

df2 = df.copy()
df2

Out[166]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 11 columns

In [167]:

df2.corr()

Out[167]:

	Survived	Pclass	Age	SibSp	Parch	Fare
Survived	1.000000	-0.338481	-0.077221	-0.035322	0.081629	0.257307
Pclass	-0.338481	1.000000	-0.369226	0.083081	0.018443	-0.549500
Age	-0.077221	-0.369226	1.000000	-0.308247	-0.189119	0.096067
SibSp	-0.035322	0.083081	-0.308247	1.000000	0.414838	0.159651
Parch	0.081629	0.018443	-0.189119	0.414838	1.000000	0.216225
Fare	0.257307	-0.549500	0.096067	0.159651	0.216225	1.000000

In [169]:

sns.heatmap(df2.corr(), annot = True, cmap = 'coolwarm')

Out[169]:

<AxesSubplot:>

In [74]:

sns.heatmap(df2.isnull(), yticklabels=False)

Out[74]:

<AxesSubplot:ylabel='PassengerId'>

In [ ]:

In [75]:

def fun(c) : 
    age = c[0]
    pclass = c[1]
    
    if pd.isnull(age) : 
        if pclass == 1 : 
            return 39
        elif pclass == 2 : 
            return 34
        else : 
            return 27
    else : 
        return age

In [76]:

df2['Age'] = df2[["Age", 'Pclass']].apply(fun, axis = 1)

In [77]:

plt.hist(df2.Age, bins = 100)
plt.show()

In [78]:

df2.fillna({"Embarked" : "S"}, inplace = True)

In [79]:

df2.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 891 entries, 1 to 891
Data columns (total 8 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Survived  891 non-null    int64  
 1   Pclass    891 non-null    int64  
 2   Sex       891 non-null    object 
 3   Age       891 non-null    float64
 4   SibSp     891 non-null    int64  
 5   Parch     891 non-null    int64  
 6   Fare      891 non-null    float64
 7   Embarked  891 non-null    object 
dtypes: float64(2), int64(4), object(2)
memory usage: 94.9+ KB

In [ ]:

In [80]:

df2.head()

Out[80]:

	Survived	Pclass	Sex	Age	SibSp	Parch	Fare	Embarked
PassengerId
1	0	3	male	22.0	1	0	7.2500	S
2	1	1	female	38.0	1	0	71.2833	C
3	1	3	female	26.0	0	0	7.9250	S
4	1	1	female	35.0	1	0	53.1000	S
5	0	3	male	35.0	0	0	8.0500	S

In [81]:

# univariate analysis : exploring one column at a time

In [82]:

# numerical

# sns.distplot(df.Age, kde = False, bins = 50 )

sns.distplot(df2.Age, kde = False, bins = 5 )

d:\installed softwares\python\lib\site-packages\seaborn\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

Out[82]:

<AxesSubplot:xlabel='Age'>

In [83]:

# categorical : 
sns.countplot(x = "Survived", data = df2)
plt.show()

# sns.countplot(x = df2["Survived"])

In [84]:

df1.Survived.value_counts()

Out[84]:

0    549
1    342
Name: Survived, dtype: int64

In [85]:

#sns.countplot(x = "Survived", data = df2, hue = "Pclass")
#plt.show()
sns.countplot(x = "Survived", data = df2, hue = "Sex")

Out[85]:

<AxesSubplot:xlabel='Survived', ylabel='count'>

In [86]:

# sns.countplot(x = "Survived", data = df2, hue = "Age")

In [ ]:

In [87]:

# bivariate analysis - exploring 2 columns at a time
# num vs num, cat vs num, cat vs cat

In [88]:

df2.head()

Out[88]:

	Survived	Pclass	Sex	Age	SibSp	Parch	Fare	Embarked
PassengerId
1	0	3	male	22.0	1	0	7.2500	S
2	1	1	female	38.0	1	0	71.2833	C
3	1	3	female	26.0	0	0	7.9250	S
4	1	1	female	35.0	1	0	53.1000	S
5	0	3	male	35.0	0	0	8.0500	S

In [89]:

sns.jointplot(x = "Age", y = "Fare", data = df2)
plt.show()

In [90]:

sns.jointplot(x = "Age", y = "Fare", data = df2, color = 'red')
plt.show()

In [91]:

sns.jointplot(x = "Age", y = "Fare", data = df2, color = 'red', kind = "kde")
plt.show()

In [ ]:

In [92]:

# cat vs num : 

# boxplot, barplot, striplot, swarmplot, violinplot 

In [93]:

sns.boxplot(x = "Survived", y = "Age", data  = df2)
plt.show()

In [94]:

sns.boxplot(x = "Survived", y = "Age", data  = df2, hue = "Pclass")
plt.show()

In [95]:

sns.swarmplot(x = "Survived", y = "Age", data  = df2, hue = "Pclass")
plt.show()

d:\installed softwares\python\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 26.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
d:\installed softwares\python\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 7.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)

In [96]:

sns.violinplot(x = "Survived", y = "Age", data  = df2, hue = "Pclass", width = 0.5)
plt.show()

In [97]:

sns.stripplot(x = "Survived", y = "Age", data  = df2, hue = "Pclass", jitter = False)
# sns.color_palette("crest")
plt.show()

In [98]:

sns.stripplot(x = "Survived", y = "Age", data  = df2, hue = "Sex", palette = 'magma')

Out[98]:

<AxesSubplot:xlabel='Survived', ylabel='Age'>

In [99]:

df2.head()

Out[99]:

	Survived	Pclass	Sex	Age	SibSp	Parch	Fare	Embarked
PassengerId
1	0	3	male	22.0	1	0	7.2500	S
2	1	1	female	38.0	1	0	71.2833	C
3	1	3	female	26.0	0	0	7.9250	S
4	1	1	female	35.0	1	0	53.1000	S
5	0	3	male	35.0	0	0	8.0500	S

In [100]:

df2.groupby(['Embarked']).sum()

df2.groupby(['Embarked']).sum().plot(kind = 'pie',y = "Fare")

Out[100]:

<AxesSubplot:ylabel='Fare'>

In [101]:

# plt.pie()

df2.groupby(['Embarked']).sum()

Out[101]:

	Survived	Pclass	Age	SibSp	Parch	Fare
Embarked
C	93	317	5177.92	65	61	10072.2962
Q	30	224	2116.50	33	13	1022.2543
S	219	1516	19126.75	368	266	17599.3988

In [102]:

df2.groupby(['Pclass']).sum().plot(kind = 'pie',y = "Fare")

Out[102]:

<AxesSubplot:ylabel='Fare'>

In [103]:

df2.groupby(['Pclass']).sum()

Out[103]:

	Survived	Age	SibSp	Parch	Fare
Pclass
1	136	8281.42	90	77	18177.4125
2	87	5542.83	74	70	3801.8417
3	119	12596.92	302	193	6714.6951

In [ ]:

In [104]:

# cat to num
# concat...
# joins, merge

In [105]:

df = df2.copy()

In [106]:

df.head()

Out[106]:

	Survived	Pclass	Sex	Age	SibSp	Parch	Fare	Embarked
PassengerId
1	0	3	male	22.0	1	0	7.2500	S
2	1	1	female	38.0	1	0	71.2833	C
3	1	3	female	26.0	0	0	7.9250	S
4	1	1	female	35.0	1	0	53.1000	S
5	0	3	male	35.0	0	0	8.0500	S

In [107]:

# labelencoder, onehotencoder
# pandas get dummies

In [108]:

s_ = pd.get_dummies(df.Sex, drop_first=True)
s_.head()

Out[108]:

	male
PassengerId
1	1
2	0
3	0
4	0
5	1

In [109]:

e_ = pd.get_dummies(df.Embarked, drop_first=True)
e_.head()

Out[109]:

	Q	S
PassengerId
1	0	1
2	0	0
3	0	1
4	0	1
5	0	1

In [110]:

df = pd.concat([df, s_, e_], axis = 1)

In [111]:

df.head()

Out[111]:

	Survived	Pclass	Sex	Age	SibSp	Parch	Fare	Embarked	male	Q	S
PassengerId
1	0	3	male	22.0	1	0	7.2500	S	1	0	1
2	1	1	female	38.0	1	0	71.2833	C	0	0	0
3	1	3	female	26.0	0	0	7.9250	S	0	0	1
4	1	1	female	35.0	1	0	53.1000	S	0	0	1
5	0	3	male	35.0	0	0	8.0500	S	1	0	1

In [112]:

df.drop(['Sex', "Embarked"], axis = 1, inplace = True)

In [113]:

df.head()

Out[113]:

	Survived	Pclass	Age	SibSp	Parch	Fare	male	Q	S
PassengerId
1	0	3	22.0	1	0	7.2500	1	0	1
2	1	1	38.0	1	0	71.2833	0	0	0
3	1	3	26.0	0	0	7.9250	0	0	1
4	1	1	35.0	1	0	53.1000	0	0	1
5	0	3	35.0	0	0	8.0500	1	0	1

In [114]:

df2.head()

Out[114]:

	Survived	Pclass	Sex	Age	SibSp	Parch	Fare	Embarked
PassengerId
1	0	3	male	22.0	1	0	7.2500	S
2	1	1	female	38.0	1	0	71.2833	C
3	1	3	female	26.0	0	0	7.9250	S
4	1	1	female	35.0	1	0	53.1000	S
5	0	3	male	35.0	0	0	8.0500	S

In [115]:

df2['S1'] = df2.Sex.replace(to_replace = ["male", 'female'], value = [0, 1])

In [116]:

df2.head()

Out[116]:

	Survived	Pclass	Sex	Age	SibSp	Parch	Fare	Embarked	S1
PassengerId
1	0	3	male	22.0	1	0	7.2500	S	0
2	1	1	female	38.0	1	0	71.2833	C	1
3	1	3	female	26.0	0	0	7.9250	S	1
4	1	1	female	35.0	1	0	53.1000	S	1
5	0	3	male	35.0	0	0	8.0500	S	0

In [ ]:

In [117]:

df.head()

Out[117]:

	Survived	Pclass	Age	SibSp	Parch	Fare	male	Q	S
PassengerId
1	0	3	22.0	1	0	7.2500	1	0	1
2	1	1	38.0	1	0	71.2833	0	0	0
3	1	3	26.0	0	0	7.9250	0	0	1
4	1	1	35.0	1	0	53.1000	0	0	1
5	0	3	35.0	0	0	8.0500	1	0	1

In [118]:

df['Family'] = df['SibSp'] + df['Parch']

In [119]:

df.head()

Out[119]:

	Survived	Pclass	Age	SibSp	Parch	Fare	male	Q	S	Family
PassengerId
1	0	3	22.0	1	0	7.2500	1	0	1	1
2	1	1	38.0	1	0	71.2833	0	0	0	1
3	1	3	26.0	0	0	7.9250	0	0	1	0
4	1	1	35.0	1	0	53.1000	0	0	1	1
5	0	3	35.0	0	0	8.0500	1	0	1	0

In [120]:

df.drop(['SibSp', "Parch"], axis = 1, inplace = True)

In [121]:

df.head()

Out[121]:

	Survived	Pclass	Age	Fare	male	Q	S	Family
PassengerId
1	0	3	22.0	7.2500	1	0	1	1
2	1	1	38.0	71.2833	0	0	0	1
3	1	3	26.0	7.9250	0	0	1	0
4	1	1	35.0	53.1000	0	0	1	1
5	0	3	35.0	8.0500	1	0	1	0

In [122]:

df.shape

Out[122]:

(891, 8)

In [123]:

df.drop_duplicates(inplace = True)    # Pandas drop_duplicates() method helps in removing duplicates from the data frame.

In [124]:

df.shape

Out[124]:

(776, 8)

Exploratory Data Analysis on Titanic Data in Python

Titanic : EDA¶

Observation:¶

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