Python编程入门学习笔记（十）

Python 学习笔记（十）

<h1 style="text-align:center">泰坦尼克数据处理与分析 </h1>

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


```python
import pandas as pd

%matplotlib inline
```

#### 导入数据


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

#### 快速预览


```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>



|单词|翻译|
|---|---|
|Passenger|社会阶层（1、精英；2、中层；3、船员/劳苦大众）|
|Survived|是否幸存|
|name|名字|
|sex|性别|
|age|年龄|
|sibsp|兄弟姐妹配偶个数 sibling spouse|
|parch|父母儿女个数|
|ticket|船票号|
|fare|船票价格|
|cabin|船舱|
|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
# 把所有数值类型的数据做一个简单的统计
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函数统计null值的个数
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



#### 处理空值


```python
# 可以填充整个dataframe里面的空值,可以取消注释，试验一下
#titanic.fillna(0)
# 单独选择一列进行填充
#titanic.Age.fillna(0)

# 求年龄的中位数
titanic.Age.median()

#按年龄的中位数进行填充，此时返回一个新的series
# titanic.Age.fillna(titanic.Age.median())

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

# 在次查看Age的空值
titanic.isnull().sum()
```

### 尝试从性别进行分析


```python
# 做简单的汇总统计，经常用到
titanic.Sex.value_counts()
```




    male      577
    female    314
    Name: Sex, dtype: int64




```python
# 生还者中，男女的人数
survived = titanic[titanic.Survived==1].Sex.value_counts()  
```


```python
# 未生还者中，男女的人数
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
# 绘图成功，但不是想要的效果
# 把dataframe转置一下，行列相互替换
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')等价的
```




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




![png](output_19_1.png)



```python
# 仍然不是我们想要的结果
df.plot(kind = 'bar',stacked = True)
```




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




![png](output_20_1.png)



```python
# 男女中生还者的比例情况
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)


#### 通过上面图片可以看出：性别特征对是否生还的影响还是挺大的

### 尝试从年龄进行分析


```python
# 简单统计
# 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
# 直方图柱子显示多一点
df.plot.hist(stacked = True,bins = 30)
# 中间很高的柱子，是因为我们把空值都替换为了中位数
```




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




![png](output_26_1.png)



```python
# 密度图，更直观一点
df.plot.kde()
```




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




![png](output_27_1.png)



```python
# 可以查看年龄的分布，来决定图片横轴的取值范围
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
# 限定范围
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
# 大于16岁和小于等于16岁中生还者的比例情况
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)


### 分析票价


```python
# 票价和年龄特征相似
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
# 设定xlim范围，先查看票价的范围
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)


#### 可以看出低票价的人生还率比较低

### 组合特征


```python
# 比如同时查看年龄和票价对生还率的影响
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
# 不美观
ax = plt.subplot()

# 未生还者
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')

#生还者
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
# 生还者
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)


### 隐含特征


```python
#提取称呼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()
# 比如有一个人称呼是Mr，而年龄是不可知的，这个时候可以用所有Mr的年龄平均值来替代，
# 而不是用我们之前最简单的所有数据的中位数。
```




    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
### 夜光图，简单用灯光图的亮度来模拟这个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