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>
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</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 890Data columns (total 12 columns):PassengerId    891 non-null int64Survived       891 non-null int64Pclass         891 non-null int64Name           891 non-null objectSex            891 non-null objectAge            714 non-null float64SibSp          891 non-null int64Parch          891 non-null int64Ticket         891 non-null objectFare           891 non-null float64Cabin          204 non-null objectEmbarked       889 non-null objectdtypes: 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>
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<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      0Survived         0Pclass           0Name             0Sex              0Age            177SibSp            0Parch            0Ticket           0Fare             0Cabin          687Embarked         2dtype: 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      577female    314Name: 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>
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<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    891.000000mean      29.361582std       13.019697min        0.42000025%       22.00000050%       28.00000075%       35.000000max       80.000000Name: 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    891.000000mean      32.204208std       49.693429min        0.00000025%        7.91040050%       14.45420075%       31.000000max      512.329200Name: 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 pltplt.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 Harris1      Cumings, Mrs. John Bradley (Florence Briggs Th...2                                 Heikkinen, Miss. Laina3           Futrelle, Mrs. Jacques Heath (Lily May Peel)4                               Allen, Mr. William Henry5                                       Moran, Mr. James6                                McCarthy, Mr. Timothy J7                         Palsson, Master. Gosta Leonard8      Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)9                    Nasser, Mrs. Nicholas (Adele Achem)10                       Sandstrom, Miss. Marguerite Rut11                              Bonnell, Miss. Elizabeth12                        Saundercock, Mr. William Henry13                           Andersson, Mr. Anders Johan14                  Vestrom, Miss. Hulda Amanda Adolfina15                      Hewlett, Mrs. (Mary D Kingcome) 16                                  Rice, Master. Eugene17                          Williams, Mr. Charles Eugene18     Vander Planke, Mrs. Julius (Emelia Maria Vande...19                               Masselmani, Mrs. Fatima20                                  Fynney, Mr. Joseph J21                                 Beesley, Mr. Lawrence22                           McGowan, Miss. Anna "Annie"23                          Sloper, Mr. William Thompson24                         Palsson, Miss. Torborg Danira25     Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...26                               Emir, Mr. Farred Chehab27                        Fortune, Mr. Charles Alexander28                         O'Dwyer, Miss. Ellen "Nellie"29                                   Todoroff, Mr. Lalio...                        861                          Giles, Mr. Frederick Edward862    Swift, Mrs. Frederick Joel (Margaret Welles Ba...863                    Sage, Miss. Dorothy Edith "Dolly"864                               Gill, Mr. John William865                             Bystrom, Mrs. (Karolina)866                         Duran y More, Miss. Asuncion867                 Roebling, Mr. Washington Augustus II868                          van Melkebeke, Mr. Philemon869                      Johnson, Master. Harold Theodor870                                    Balkic, Mr. Cerin871     Beckwith, Mrs. Richard Leonard (Sallie Monypeny)872                             Carlsson, Mr. Frans Olof873                          Vander Cruyssen, Mr. Victor874                Abelson, Mrs. Samuel (Hannah Wizosky)875                     Najib, Miss. Adele Kiamie "Jane"876                        Gustafsson, Mr. Alfred Ossian877                                 Petroff, Mr. Nedelio878                                   Laleff, Mr. Kristo879        Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)880         Shelley, Mrs. William (Imanita Parrish Hall)881                                   Markun, Mr. Johann882                         Dahlberg, Miss. Gerda Ulrika883                        Banfield, Mr. Frederick James884                               Sutehall, Mr. Henry Jr885                 Rice, Mrs. William (Margaret Norton)886                                Montvila, Rev. Juozas887                         Graham, Miss. Margaret Edith888             Johnston, Miss. Catherine Helen "Carrie"889                                Behr, Mr. Karl Howell890                                  Dooley, Mr. PatrickName: 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              517Miss            182Mrs             125Master           40Dr                7Rev               6Mlle              2Major             2Col               2Capt              1Ms                1Mme               1Jonkheer          1the Countess      1Don               1Lady              1Sir               1Name: 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     5372     1613     1024      296      225      157      1211      78       6Name: 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      537SmallFamily    292LargeFamily     62Name: family_type, dtype: int64

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