python學習筆記（十）

<h1 style="text-align:center">泰坦尼克數據處理與分析 </h1>```python import pandas as pd%matplotlib inline ```#### 導入數據```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|是否幸存| |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 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 # 把所有數值類型的數據做一個簡單的統計 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函數統計null值的個數 titanic.isnull().sum() ```PassengerId 0Survived 0Pclass 0Name 0Sex 0Age 177SibSp 0Parch 0Ticket 0Fare 0Cabin 687Embarked 2dtype: int64#### 處理空值```python # 可以填充整個dataframe里面的空值,可以取消注釋，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，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() ```### 嘗試從性別進行分析```python # 做簡單的匯總統計，經常用到 titanic.Sex.value_counts() ```male 577female 314Name: 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>```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>```python # 仍然不是我們想要的結果 df.plot(kind = 'bar',stacked = True) ```<matplotlib.axes._subplots.AxesSubplot at 0x1496d22aef0>```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>#### 通過上面圖片可以看出：性別特征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>```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>```python # 密度圖，更直觀一點 df.plot.kde() ```<matplotlib.axes._subplots.AxesSubplot at 0x1496e4c7dd8>```python # 可以查看年齡的分布，來決定圖片橫軸的取值范圍 titanic.Age.describe() ```count 891.000000mean 29.361582std 13.019697min 0.42000025% 22.00000050% 28.00000075% 35.000000max 80.000000Name: Age, dtype: float64```python # 限定范圍 df.plot.kde(xlim=(0,80)) ```<matplotlib.axes._subplots.AxesSubplot at 0x1496e511c18>```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>```python # 大于16歲和小于等于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>### 分析票價```python # 票價和年齡特征相似 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>```python # 設定xlim范圍，先查看票價的范圍 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>#### 可以看出低票價的人生還率比較低### 組合特征```python # 比如同時查看年齡和票價對生還率的影響 import matplotlib.pyplot as pltplt.scatter(titanic[titanic.Survived==0].Age, titanic[titanic.Survived==0].Fare) ```<matplotlib.collections.PathCollection at 0x1496f597a58>```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')```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')### 隱含特征```python #提取稱呼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數據的中位數。 ```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 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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