시험 대비 (21.12.27)

시험 대비¶

101 pandas tips for beginners titanic¶

Part 1 - Exploring Data¶

In [ ]:

pwd

In [2]:

import numpy as np
import pandas as pd

# 시각화 관련 라이브러리
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
import missingno

In [3]:

test = pd.read_csv('resources/titanic/data_titanic/test.csv')
train = pd.read_csv('resources/titanic/data_titanic/train.csv')

In [4]:

print(train.shape)
train.head(3)

(891, 12)

Out[4]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S

In [5]:

print(test.shape)
test.head(3)

(418, 11)

Out[5]:

	PassengerId	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	892	3	Kelly, Mr. James	male	34.5	0	0	330911	7.8292	NaN	Q
1	893	3	Wilkes, Mrs. James (Ellen Needs)	female	47.0	1	0	363272	7.0000	NaN	S
2	894	2	Myles, Mr. Thomas Francis	male	62.0	0	0	240276	9.6875	NaN	Q

In [6]:

# To read lines from 101 to 110
train[101:111]

Out[6]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
101	102	0	3	Petroff, Mr. Pastcho ("Pentcho")	male	NaN	0	0	349215	7.8958	NaN	S
102	103	0	1	White, Mr. Richard Frasar	male	21.0	0	1	35281	77.2875	D26	S
103	104	0	3	Johansson, Mr. Gustaf Joel	male	33.0	0	0	7540	8.6542	NaN	S
104	105	0	3	Gustafsson, Mr. Anders Vilhelm	male	37.0	2	0	3101276	7.9250	NaN	S
105	106	0	3	Mionoff, Mr. Stoytcho	male	28.0	0	0	349207	7.8958	NaN	S
106	107	1	3	Salkjelsvik, Miss. Anna Kristine	female	21.0	0	0	343120	7.6500	NaN	S
107	108	1	3	Moss, Mr. Albert Johan	male	NaN	0	0	312991	7.7750	NaN	S
108	109	0	3	Rekic, Mr. Tido	male	38.0	0	0	349249	7.8958	NaN	S
109	110	1	3	Moran, Miss. Bertha	female	NaN	1	0	371110	24.1500	NaN	Q
110	111	0	1	Porter, Mr. Walter Chamberlain	male	47.0	0	0	110465	52.0000	C110	S

In [7]:

train.describe()

Out[7]:

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	446.000000	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	257.353842	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	1.000000	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	223.500000	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	446.000000	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	668.500000	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	891.000000	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

In [8]:

train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object 
 4   Sex          891 non-null    object 
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object 
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object 
 11  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB

In [9]:

train.dtypes

Out[9]:

PassengerId      int64
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 [10]:

len(train)

Out[10]:

891

In [11]:

train.columns

Out[11]:

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

Part 2 - Indexing¶

In [12]:

# To see a certain column head
train.columns[3]

Out[12]:

'Name'

In [13]:

# To see multiple column heads
train.columns[3:5]

Out[13]:

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

In [14]:

# To see the values of a certain column
train['Name'].head()

Out[14]:

0                              Braund, Mr. Owen Harris
1    Cumings, Mrs. John Bradley (Florence Briggs Th...
2                               Heikkinen, Miss. Laina
3         Futrelle, Mrs. Jacques Heath (Lily May Peel)
4                             Allen, Mr. William Henry
Name: Name, dtype: object

In [15]:

# isnull().any() / isna().any()
# To check if there are any null values (empty or NaN (Not a Number))
train.isnull().any()

Out[15]:

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

In [16]:

# isnull().sum() / isna().sum()
# To check the sum counts of null values
train.isnull().sum()

Out[16]:

PassengerId      0
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 [17]:

# Locating - loc - To read a certain row
train.loc[3]

Out[17]:

PassengerId                                               4
Survived                                                  1
Pclass                                                    1
Name           Futrelle, Mrs. Jacques Heath (Lily May Peel)
Sex                                                  female
Age                                                    35.0
SibSp                                                     1
Parch                                                     0
Ticket                                               113803
Fare                                                   53.1
Cabin                                                  C123
Embarked                                                  S
Name: 3, dtype: object

In [18]:

train.loc[ train['Pclass']==1 ].head(3)

Out[18]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S

In [19]:

train.loc[train['Pclass']==1].reset_index(drop=True).head(3)

Out[19]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
1	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
2	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S

In [20]:

train.loc[train['Age'] < 1]
# train.loc[train.Age.values < 1]
# train.loc[train.Age < 1]
# 다 똑같다

Out[20]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
78	79	1	2	Caldwell, Master. Alden Gates	male	0.83	0	2	248738	29.0000	NaN	S
305	306	1	1	Allison, Master. Hudson Trevor	male	0.92	1	2	113781	151.5500	C22 C26	S
469	470	1	3	Baclini, Miss. Helene Barbara	female	0.75	2	1	2666	19.2583	NaN	C
644	645	1	3	Baclini, Miss. Eugenie	female	0.75	2	1	2666	19.2583	NaN	C
755	756	1	2	Hamalainen, Master. Viljo	male	0.67	1	1	250649	14.5000	NaN	S
803	804	1	3	Thomas, Master. Assad Alexander	male	0.42	0	1	2625	8.5167	NaN	C
831	832	1	2	Richards, Master. George Sibley	male	0.83	1	1	29106	18.7500	NaN	S

In [21]:

train.loc[9, 'Name']

Out[21]:

'Nasser, Mrs. Nicholas (Adele Achem)'

In [22]:

train.loc[9, ['Name', 'Age']]

Out[22]:

Name    Nasser, Mrs. Nicholas (Adele Achem)
Age                                    14.0
Name: 9, dtype: object

In [23]:

# iloc - integer loc
train.iloc[11:16]
# train.loc[11:16]
# 똑같다

Out[23]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
11	12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.5500	C103	S
12	13	0	3	Saundercock, Mr. William Henry	male	20.0	0	0	A/5. 2151	8.0500	NaN	S
13	14	0	3	Andersson, Mr. Anders Johan	male	39.0	1	5	347082	31.2750	NaN	S
14	15	0	3	Vestrom, Miss. Hulda Amanda Adolfina	female	14.0	0	0	350406	7.8542	NaN	S
15	16	1	2	Hewlett, Mrs. (Mary D Kingcome)	female	55.0	0	0	248706	16.0000	NaN	S

In [24]:

# If there are too many columns, you can reduce the number of columns it displays
pd.set_option("display.max_columns", 12)
train.head(3)

Out[24]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S

Part 3 - Sorting¶

In [25]:

# Sorting columns with descending order
train.sort_values('Fare', ascending=False).head(3)

Out[25]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
258	259	1	1	Ward, Miss. Anna	female	35.0	0	0	PC 17755	512.3292	NaN	C
737	738	1	1	Lesurer, Mr. Gustave J	male	35.0	0	0	PC 17755	512.3292	B101	C
679	680	1	1	Cardeza, Mr. Thomas Drake Martinez	male	36.0	0	1	PC 17755	512.3292	B51 B53 B55	C

In [26]:

# Sorting columns with ascending order
train.sort_values('Fare', ascending=True).head(3)

Out[26]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
271	272	1	3	Tornquist, Mr. William Henry	male	25.0	0	0	LINE	0.0	NaN	S
597	598	0	3	Johnson, Mr. Alfred	male	49.0	0	0	LINE	0.0	NaN	S
302	303	0	3	Johnson, Mr. William Cahoone Jr	male	19.0	0	0	LINE	0.0	NaN	S

In [27]:

# Sorting columns - Looking at certain rows
train.sort_values('Fare', ascending=True)[101:105]

Out[27]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
421	422	0	3	Charters, Mr. David	male	21.0	0	0	A/5. 13032	7.7333	NaN	Q
156	157	1	3	Gilnagh, Miss. Katherine "Katie"	female	16.0	0	0	35851	7.7333	NaN	Q
778	779	0	3	Kilgannon, Mr. Thomas J	male	NaN	0	0	36865	7.7375	NaN	Q
727	728	1	3	Mannion, Miss. Margareth	female	NaN	0	0	36866	7.7375	NaN	Q

In [28]:

# Sorting columns - multi columns in ascending order
train.sort_values(['Fare', 'Survived', 'Pclass'], ascending=[True, False, False]).head(3)

Out[28]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
271	272	1	3	Tornquist, Mr. William Henry	male	25.0	0	0	LINE	0.0	NaN	S
179	180	0	3	Leonard, Mr. Lionel	male	36.0	0	0	LINE	0.0	NaN	S
302	303	0	3	Johnson, Mr. William Cahoone Jr	male	19.0	0	0	LINE	0.0	NaN	S

In [29]:

# Sorting a certain column with a certain condition
train[train['Fare']>80].head(3)

Out[29]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
27	28	0	1	Fortune, Mr. Charles Alexander	male	19.0	3	2	19950	263.0000	C23 C25 C27	S
31	32	1	1	Spencer, Mrs. William Augustus (Marie Eugenie)	female	NaN	1	0	PC 17569	146.5208	B78	C
34	35	0	1	Meyer, Mr. Edgar Joseph	male	28.0	1	0	PC 17604	82.1708	NaN	C

In [30]:

# Sort columns by alphabetical order
train.reindex(sorted(train.columns), axis=1).head(3)
# train.sort_index(axis=1)
# 똑같다

Out[30]:

	Age	Cabin	Embarked	Fare	Name	Parch	PassengerId	Pclass	Sex	SibSp	Survived	Ticket
0	22.0	NaN	S	7.2500	Braund, Mr. Owen Harris	0	1	3	male	1	0	A/5 21171
1	38.0	C85	C	71.2833	Cumings, Mrs. John Bradley (Florence Briggs Th...	0	2	1	female	1	1	PC 17599
2	26.0	NaN	S	7.9250	Heikkinen, Miss. Laina	0	3	3	female	0	1	STON/O2. 3101282

In [31]:

# Multi column filtering
train[(train.Age >= 30) & (train.Sex == 'female')].head()
train[(train['Age'] >= 30) & (train['Sex'] == 'female')]
# 똑같다

Out[31]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
11	12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.5500	C103	S
15	16	1	2	Hewlett, Mrs. (Mary D Kingcome)	female	55.0	0	0	248706	16.0000	NaN	S
18	19	0	3	Vander Planke, Mrs. Julius (Emelia Maria Vande...	female	31.0	1	0	345763	18.0000	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...	...
862	863	1	1	Swift, Mrs. Frederick Joel (Margaret Welles Ba...	female	48.0	0	0	17466	25.9292	D17	S
865	866	1	2	Bystrom, Mrs. (Karolina)	female	42.0	0	0	236852	13.0000	NaN	S
871	872	1	1	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	female	47.0	1	1	11751	52.5542	D35	S
879	880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C
885	886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.1250	NaN	Q

114 rows × 12 columns

In [32]:

# isin() - filtering by conditions
train[train.Embarked.isin(['C'])].head(3)
# train[train['Embarked'].isin(['C'])]
# 똑같다

Out[32]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
9	10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C
19	20	1	3	Masselmani, Mrs. Fatima	female	NaN	0	0	2649	7.2250	NaN	C

In [33]:

train[train.Cabin.isin(['C50', 'C85'])]
# train[train['Cabin'].isin(['C50', 'C85'])]
# 똑같다

Out[33]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
879	880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C

In [34]:

train[train.Ticket.isin(['113572']) & train.Parch.isin([0])]

Out[34]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
61	62	1	1	Icard, Miss. Amelie	female	38.0	0	0	113572	80.0	B28	NaN
829	830	1	1	Stone, Mrs. George Nelson (Martha Evelyn)	female	62.0	0	0	113572	80.0	B28	NaN

In [35]:

len(train[train.Fare == 80])

Out[35]:

2

In [36]:

# Sorting with conditions and if there are any that matches
(train.Fare > 80).any()

Out[36]:

True

In [37]:

# Sorting with conditions and get the percentage
(train.Fare > 80).mean()

Out[37]:

0.08305274971941638

Part 4 Creating¶

In [38]:

# Making a new empty DataFrame
sample = pd.DataFrame()
sample

Out[38]:

In [39]:

# Copying and creating a new DataFrame
temp = train.copy()
temp.head(3)

Out[39]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S

In [40]:

# Creating a new column
pd.set_option("display.max_columns", 13)
temp['New'] = 0
temp.head(3)

Out[40]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	New
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	0
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	0
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	0

In [41]:

# 특정 위치에 새로운 컬럼 추가하기
pd.set_option('display.max_columns', 14)
temp.insert(0, 'New2', 0)
temp.head(3)

Out[41]:

	New2	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	New
0	0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	0
1	0	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	0
2	0	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	0

In [42]:

# Creating a new column by copying an existing column
temp['New'] = temp['Age']
temp.head(3)

Out[42]:

	New2	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	New
0	0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	22.0
1	0	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	38.0
2	0	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	26.0

In [43]:

# Creating a new Dataframe with a certain column
y_train = train['Survived']
y_train.head(3)

Out[43]:

0    0
1    1
2    1
Name: Survived, dtype: int64

In [44]:

# Creating a new Dataframe with certain row
sample1 = train[0:5]
sample1

Out[44]:

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

In [45]:

# Creating a new Dataframe with certain columns
sample2 = train[['Pclass', 'Name', 'Sex']]
sample2.head(3)

Out[45]:

	Pclass	Name	Sex
0	3	Braund, Mr. Owen Harris	male
1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female
2	3	Heikkinen, Miss. Laina	female

In [46]:

# Creating a new Dataframe with certain columns and certain rows
sample3 = train[['Pclass', 'Name', 'Sex']][1:4]
sample3

Out[46]:

	Pclass	Name	Sex
1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female
2	3	Heikkinen, Miss. Laina	female
3	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female

In [47]:

# Creating a new Dataframe with exisiting rows that matches a certain condition
sample4 = train.loc[train.Survived == 1]
# train.loc[train.Survived.values == 1]
# train.loc[train['Survived'] == 1]
sample4.head(3)

Out[47]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S

In [48]:

# Creating a new Dataframe with exisiting rows that matches multiple conditions
sample5 = train.loc[(train.Survived == 1) & (train.Pclass == 3) & (train.Sex == 'male')]
sample5.head(3)

Out[48]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
36	37	1	3	Mamee, Mr. Hanna	male	NaN	0	0	2677	7.2292	NaN	C
65	66	1	3	Moubarek, Master. Gerios	male	NaN	1	1	2661	15.2458	NaN	C
74	75	1	3	Bing, Mr. Lee	male	32.0	0	0	1601	56.4958	NaN	S

In [49]:

# Concat - Merging and creating a new data frame
data = pd.concat((train, test), sort=True)
print(data.shape)
data.head(3)

(1309, 12)

Out[49]:

	Age	Cabin	Embarked	Fare	Name	Parch	PassengerId	Pclass	Sex	SibSp	Survived	Ticket
0	22.0	NaN	S	7.2500	Braund, Mr. Owen Harris	0	1	3	male	1	0.0	A/5 21171
1	38.0	C85	C	71.2833	Cumings, Mrs. John Bradley (Florence Briggs Th...	0	2	1	female	1	1.0	PC 17599
2	26.0	NaN	S	7.9250	Heikkinen, Miss. Laina	0	3	3	female	0	1.0	STON/O2. 3101282

In [50]:

data.tail(3)

Out[50]:

	Age	Cabin	Embarked	Fare	Name	Parch	PassengerId	Pclass	Sex	SibSp	Survived	Ticket
415	38.5	NaN	S	7.2500	Saether, Mr. Simon Sivertsen	0	1307	3	male	0	NaN	SOTON/O.Q. 3101262
416	NaN	NaN	S	8.0500	Ware, Mr. Frederick	0	1308	3	male	0	NaN	359309
417	NaN	NaN	C	22.3583	Peter, Master. Michael J	1	1309	3	male	1	NaN	2668