Python程式設計入門學習筆記(十)

阿新 • • 發佈：2018-12-25

<h1 style="text-align:center">泰坦尼克資料處理與分析 </h1>

![](http://www.allengao.cn/wp-content/uploads/2018/06/Titanic.jpg)


```python
import pandas as pd

%matplotlib inline
```

#### 匯入資料


```python
titanic = pd.read_csv('K:/Code/jupyter-notebook/Python Study/train.csv')
```

#### 快速預覽


```python
titanic.head()
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>PassengerId</th>
      <th>Survived</th>
      <th>Pclass</th>
      <th>Name</th>
      <th>Sex</th>
      <th>Age</th>
      <th>SibSp</th>
      <th>Parch</th>
      <th>Ticket</th>
      <th>Fare</th>
      <th>Cabin</th>
      <th>Embarked</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>0</td>
      <td>3</td>
      <td>Braund, Mr. Owen Harris</td>
      <td>male</td>
      <td>22.0</td>
      <td>1</td>
      <td>0</td>
      <td>A/5 21171</td>
      <td>7.2500</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>1</td>
      <td>1</td>
      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>
      <td>female</td>
      <td>38.0</td>
      <td>1</td>
      <td>0</td>
      <td>PC 17599</td>
      <td>71.2833</td>
      <td>C85</td>
      <td>C</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>1</td>
      <td>3</td>
      <td>Heikkinen, Miss. Laina</td>
      <td>female</td>
      <td>26.0</td>
      <td>0</td>
      <td>0</td>
      <td>STON/O2. 3101282</td>
      <td>7.9250</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>1</td>
      <td>1</td>
      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>
      <td>female</td>
      <td>35.0</td>
      <td>1</td>
      <td>0</td>
      <td>113803</td>
      <td>53.1000</td>
      <td>C123</td>
      <td>S</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>0</td>
      <td>3</td>
      <td>Allen, Mr. William Henry</td>
      <td>male</td>
      <td>35.0</td>
      <td>0</td>
      <td>0</td>
      <td>373450</td>
      <td>8.0500</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
  </tbody>
</table>
</div>



|單詞|翻譯|
|---|---|
|Passenger|社會階層（1、精英；2、中層；3、船員/勞苦大眾）|
|Survived|是否倖存|
|name|名字|
|sex|性別|
|age|年齡|
|sibsp|兄弟姐妹配偶個數 sibling spouse|
|parch|父母兒女個數|
|ticket|船票號|
|fare|船票價格|
|cabin|船艙|
|embarked|登船口|


```python
titanic.info()
```

    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 891 entries, 0 to 890
    Data columns (total 12 columns):
    PassengerId    891 non-null int64
    Survived       891 non-null int64
    Pclass         891 non-null int64
    Name           891 non-null object
    Sex            891 non-null object
    Age            714 non-null float64
    SibSp          891 non-null int64
    Parch          891 non-null int64
    Ticket         891 non-null object
    Fare           891 non-null float64
    Cabin          204 non-null object
    Embarked       889 non-null object
    dtypes: float64(2), int64(5), object(5)
    memory usage: 83.6+ KB
    


```python
# 把所有數值型別的資料做一個簡單的統計
titanic.describe()
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>PassengerId</th>
      <th>Survived</th>
      <th>Pclass</th>
      <th>Age</th>
      <th>SibSp</th>
      <th>Parch</th>
      <th>Fare</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>891.000000</td>
      <td>891.000000</td>
      <td>891.000000</td>
      <td>714.000000</td>
      <td>891.000000</td>
      <td>891.000000</td>
      <td>891.000000</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>446.000000</td>
      <td>0.383838</td>
      <td>2.308642</td>
      <td>29.699118</td>
      <td>0.523008</td>
      <td>0.381594</td>
      <td>32.204208</td>
    </tr>
    <tr>
      <th>std</th>
      <td>257.353842</td>
      <td>0.486592</td>
      <td>0.836071</td>
      <td>14.526497</td>
      <td>1.102743</td>
      <td>0.806057</td>
      <td>49.693429</td>
    </tr>
    <tr>
      <th>min</th>
      <td>1.000000</td>
      <td>0.000000</td>
      <td>1.000000</td>
      <td>0.420000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>223.500000</td>
      <td>0.000000</td>
      <td>2.000000</td>
      <td>20.125000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>7.910400</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>446.000000</td>
      <td>0.000000</td>
      <td>3.000000</td>
      <td>28.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>14.454200</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>668.500000</td>
      <td>1.000000</td>
      <td>3.000000</td>
      <td>38.000000</td>
      <td>1.000000</td>
      <td>0.000000</td>
      <td>31.000000</td>
    </tr>
    <tr>
      <th>max</th>
      <td>891.000000</td>
      <td>1.000000</td>
      <td>3.000000</td>
      <td>80.000000</td>
      <td>8.000000</td>
      <td>6.000000</td>
      <td>512.329200</td>
    </tr>
  </tbody>
</table>
</div>




```python
# isnull函式統計null值的個數
titanic.isnull().sum()
```




    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



#### 處理空值


```python
# 可以填充整個dataframe裡面的空值,可以取消註釋，試驗一下
#titanic.fillna(0)
# 單獨選擇一列進行填充
#titanic.Age.fillna(0)

# 求年齡的中位數
titanic.Age.median()

#按年齡的中位數進行填充，此時返回一個新的series
# titanic.Age.fillna(titanic.Age.median())

#直接填充，並不返回新的series
titanic.Age.fillna(titanic.Age.median(),inplace=True)

# 在次檢視Age的空值
titanic.isnull().sum()
```

### 嘗試從性別進行分析


```python
# 做簡單的彙總統計，經常用到
titanic.Sex.value_counts()
```




    male      577
    female    314
    Name: Sex, dtype: int64




```python
# 生還者中，男女的人數
survived = titanic[titanic.Survived==1].Sex.value_counts()  
```


```python
# 未生還者中，男女的人數
dead = titanic[titanic.Survived==0].Sex.value_counts() 
```


```python
df = pd.DataFrame([survived,dead],index=['survived','dead'])
df.plot.bar()
```




    <matplotlib.axes._subplots.AxesSubplot at 0x1496afd27f0>




![png](output_17_1.png)



```python
# 繪圖成功，但不是想要的效果
# 把dataframe轉置一下，行列相互替換
df = df.T
df
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>survived</th>
      <th>dead</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>female</th>
      <td>233</td>
      <td>81</td>
    </tr>
    <tr>
      <th>male</th>
      <td>109</td>
      <td>468</td>
    </tr>
  </tbody>
</table>
</div>




```python
df.plot.bar() # df.plot(kind='bar')等價的
```




    <matplotlib.axes._subplots.AxesSubplot at 0x1496d1d7940>




![png](output_19_1.png)



```python
# 仍然不是我們想要的結果
df.plot(kind = 'bar',stacked = True)
```




    <matplotlib.axes._subplots.AxesSubplot at 0x1496d22aef0>




![png](output_20_1.png)



```python
# 男女中生還者的比例情況
df['p_survived'] = df.survived / (df.survived + df.dead)
df['p_dead'] = df.dead / (df.survived + df.dead)
df[['p_survived','p_dead']].plot.bar(stacked=True)
```




    <matplotlib.axes._subplots.AxesSubplot at 0x1496d2b7470>




![png](output_21_1.png)


#### 通過上面圖片可以看出：性別特徵對是否生還的影響還是挺大的

### 嘗試從年齡進行分析


```python
# 簡單統計
# titanic.Age.value_counts()
```


```python
survived = titanic[titanic.Survived==1].Age
dead = titanic[titanic.Survived==0].Age
df =pd.DataFrame([survived,dead],index=['survived','dead'])
df = df.T
df.plot.hist(stacked=True)
```




    <matplotlib.axes._subplots.AxesSubplot at 0x1496d3c4be0>




![png](output_25_1.png)



```python
# 直方圖柱子顯示多一點
df.plot.hist(stacked = True,bins = 30)
# 中間很高的柱子，是因為我們把空值都替換為了中位數
```




    <matplotlib.axes._subplots.AxesSubplot at 0x1496e42f588>




![png](output_26_1.png)



```python
# 密度圖，更直觀一點
df.plot.kde()
```




    <matplotlib.axes._subplots.AxesSubplot at 0x1496e4c7dd8>




![png](output_27_1.png)



```python
# 可以檢視年齡的分佈，來決定圖片橫軸的取值範圍
titanic.Age.describe()
```




    count    891.000000
    mean      29.361582
    std       13.019697
    min        0.420000
    25%       22.000000
    50%       28.000000
    75%       35.000000
    max       80.000000
    Name: Age, dtype: float64




```python
# 限定範圍
df.plot.kde(xlim=(0,80))
```




    <matplotlib.axes._subplots.AxesSubplot at 0x1496e511c18>




![png](output_29_1.png)



```python
age = 16
young = titanic[titanic.Age<=age]['Survived'].value_counts()
old = titanic[titanic.Age>age]['Survived'].value_counts()
df = pd.DataFrame([young,old],index = ['young','old'])
df.columns = ['dead','survived']
df.plot.bar(stacked = True)
```




    <matplotlib.axes._subplots.AxesSubplot at 0x1496f3a3b70>




![png](output_30_1.png)



```python
# 大於16歲和小於等於16歲中生還者的比例情況
df['p_survived'] = df.survived / (df.survived + df.dead)
df['p_dead'] = df.dead / (df.survived + df.dead)
df[['p_survived','p_dead']].plot.bar(stacked=True)
```




    <matplotlib.axes._subplots.AxesSubplot at 0x1496f407c50>




![png](output_31_1.png)


### 分析票價


```python
# 票價和年齡特徵相似
survived = titanic[titanic.Survived==1].Fare
dead = titanic[titanic.Survived==0].Fare
df = pd.DataFrame([survived,dead],index = ['survived','dead'])
df = df.T
df.plot.kde()
```




    <matplotlib.axes._subplots.AxesSubplot at 0x1496f47b978>




![png](output_33_1.png)



```python
# 設定xlim範圍，先檢視票價的範圍
titanic.Fare.describe()
```




    count    891.000000
    mean      32.204208
    std       49.693429
    min        0.000000
    25%        7.910400
    50%       14.454200
    75%       31.000000
    max      512.329200
    Name: Fare, dtype: float64




```python
df.plot(kind = 'kde',xlim = (0,513))
```




    <matplotlib.axes._subplots.AxesSubplot at 0x1496f45bba8>




![png](output_35_1.png)


#### 可以看出低票價的人生還率比較低

### 組合特徵


```python
# 比如同時檢視年齡和票價對生還率的影響
import matplotlib.pyplot as plt

plt.scatter(titanic[titanic.Survived==0].Age, titanic[titanic.Survived==0].Fare)
```




    <matplotlib.collections.PathCollection at 0x1496f597a58>




![png](output_38_1.png)



```python
# 不美觀
ax = plt.subplot()

# 未生還者
age = titanic[titanic.Survived==0].Age
fare = titanic[titanic.Survived==0].Fare
plt.scatter(age, fare,s=20,alpha=0.3,linewidths=1,edgecolors='gray')

#生還者
age = titanic[titanic.Survived==1].Age
fare = titanic[titanic.Survived==1].Fare
plt.scatter(age, fare,s=20,alpha=0.3,linewidths=1,edgecolors='red')
ax.set_xlabel('age')
ax.set_ylabel('fare')
```




    Text(0,0.5,'fare')




![png](output_39_1.png)



```python
# 生還者
ax = plt.subplot()
age = titanic[titanic.Survived==1].Age
fare = titanic[titanic.Survived==1].Fare
plt.scatter(age, fare,s=20,alpha=0.5,linewidths=1,edgecolors='red')
ax.set_xlabel('age')
ax.set_ylabel('fare')
```




    Text(0,0.5,'fare')




![png](output_40_1.png)


### 隱含特徵


```python
#提取稱呼Mr Mrs Miss
titanic.Name
```




    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
    5                                       Moran, Mr. James
    6                                McCarthy, Mr. Timothy J
    7                         Palsson, Master. Gosta Leonard
    8      Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)
    9                    Nasser, Mrs. Nicholas (Adele Achem)
    10                       Sandstrom, Miss. Marguerite Rut
    11                              Bonnell, Miss. Elizabeth
    12                        Saundercock, Mr. William Henry
    13                           Andersson, Mr. Anders Johan
    14                  Vestrom, Miss. Hulda Amanda Adolfina
    15                      Hewlett, Mrs. (Mary D Kingcome) 
    16                                  Rice, Master. Eugene
    17                          Williams, Mr. Charles Eugene
    18     Vander Planke, Mrs. Julius (Emelia Maria Vande...
    19                               Masselmani, Mrs. Fatima
    20                                  Fynney, Mr. Joseph J
    21                                 Beesley, Mr. Lawrence
    22                           McGowan, Miss. Anna "Annie"
    23                          Sloper, Mr. William Thompson
    24                         Palsson, Miss. Torborg Danira
    25     Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...
    26                               Emir, Mr. Farred Chehab
    27                        Fortune, Mr. Charles Alexander
    28                         O'Dwyer, Miss. Ellen "Nellie"
    29                                   Todoroff, Mr. Lalio
                                 ...                        
    861                          Giles, Mr. Frederick Edward
    862    Swift, Mrs. Frederick Joel (Margaret Welles Ba...
    863                    Sage, Miss. Dorothy Edith "Dolly"
    864                               Gill, Mr. John William
    865                             Bystrom, Mrs. (Karolina)
    866                         Duran y More, Miss. Asuncion
    867                 Roebling, Mr. Washington Augustus II
    868                          van Melkebeke, Mr. Philemon
    869                      Johnson, Master. Harold Theodor
    870                                    Balkic, Mr. Cerin
    871     Beckwith, Mrs. Richard Leonard (Sallie Monypeny)
    872                             Carlsson, Mr. Frans Olof
    873                          Vander Cruyssen, Mr. Victor
    874                Abelson, Mrs. Samuel (Hannah Wizosky)
    875                     Najib, Miss. Adele Kiamie "Jane"
    876                        Gustafsson, Mr. Alfred Ossian
    877                                 Petroff, Mr. Nedelio
    878                                   Laleff, Mr. Kristo
    879        Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)
    880         Shelley, Mrs. William (Imanita Parrish Hall)
    881                                   Markun, Mr. Johann
    882                         Dahlberg, Miss. Gerda Ulrika
    883                        Banfield, Mr. Frederick James
    884                               Sutehall, Mr. Henry Jr
    885                 Rice, Mrs. William (Margaret Norton)
    886                                Montvila, Rev. Juozas
    887                         Graham, Miss. Margaret Edith
    888             Johnston, Miss. Catherine Helen "Carrie"
    889                                Behr, Mr. Karl Howell
    890                                  Dooley, Mr. Patrick
    Name: Name, Length: 891, dtype: object




```python
titanic['title'] = titanic.Name.apply(lambda name: name.split(',')[1].split('.')[0].strip())
```


```python
s= 'Williams, Mr.Howard Hugh "harry"'
s.split(',')[-1].split('.')[0].strip()
```




    'Mr'




```python
titanic.title.value_counts()
# 比如有一個人稱呼是Mr，而年齡是不可知的，這個時候可以用所有Mr的年齡平均值來替代，
# 而不是用我們之前最簡單的所有資料的中位數。
```




    Mr              517
    Miss            182
    Mrs             125
    Master           40
    Dr                7
    Rev               6
    Mlle              2
    Major             2
    Col               2
    Capt              1
    Ms                1
    Mme               1
    Jonkheer          1
    the Countess      1
    Don               1
    Lady              1
    Sir               1
    Name: title, dtype: int64



### GDP


```python
### 夜光圖，簡單用燈光圖的亮度來模擬這個GDP
```


```python
titanic.head()
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>PassengerId</th>
      <th>Survived</th>
      <th>Pclass</th>
      <th>Name</th>
      <th>Sex</th>
      <th>Age</th>
      <th>SibSp</th>
      <th>Parch</th>
      <th>Ticket</th>
      <th>Fare</th>
      <th>Cabin</th>
      <th>Embarked</th>
      <th>title</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>0</td>
      <td>3</td>
      <td>Braund, Mr. Owen Harris</td>
      <td>male</td>
      <td>22.0</td>
      <td>1</td>
      <td>0</td>
      <td>A/5 21171</td>
      <td>7.2500</td>
      <td>NaN</td>
      <td>S</td>
      <td>Mr</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>1</td>
      <td>1</td>
      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>
      <td>female</td>
      <td>38.0</td>
      <td>1</td>
      <td>0</td>
      <td>PC 17599</td>
      <td>71.2833</td>
      <td>C85</td>
      <td>C</td>
      <td>Mrs</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>1</td>
      <td>3</td>
      <td>Heikkinen, Miss. Laina</td>
      <td>female</td>
      <td>26.0</td>
      <td>0</td>
      <td>0</td>
      <td>STON/O2. 3101282</td>
      <td>7.9250</td>
      <td>NaN</td>
      <td>S</td>
      <td>Miss</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>1</td>
      <td>1</td>
      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>
      <td>female</td>
      <td>35.0</td>
      <td>1</td>
      <td>0</td>
      <td>113803</td>
      <td>53.1000</td>
      <td>C123</td>
      <td>S</td>
      <td>Mrs</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>0</td>
      <td>3</td>
      <td>Allen, Mr. William Henry</td>
      <td>male</td>
      <td>35.0</td>
      <td>0</td>
      <td>0</td>
      <td>373450</td>
      <td>8.0500</td>
      <td>NaN</td>
      <td>S</td>
      <td>Mr</td>
    </tr>
  </tbody>
</table>
</div>




```python
titanic['family_size'] = titanic.SibSp + titanic.Parch + 1
```


```python
titanic
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

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




```python
titanic.family_size.value_counts()
```




    1     537
    2     161
    3     102
    4      29
    6      22
    5      15
    7      12
    11      7
    8       6
    Name: family_size, dtype: int64




```python
def func(family_size):
    if family_size == 1:
        return 'Singleton'
    if family_size <= 4 and family_size >= 2:
        return 'SmallFamily'
    if family_size > 4:
        return 'LargeFamily'
titanic['family_type'] = titanic.family_size.apply(func)
```


```python
titanic.family_type.value_counts()
```




    Singleton      537
    SmallFamily    292
    LargeFamily     62
    Name: family_type, dtype: int64

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