下面我们再来看看各种舱级别情况下各性别的获救情况
1 fig = plt.figure() 2 fig.set(alpha=0.5) 3 plt.title(u"根据舱等级和性别的获救情况",fontproperties=getChineseFont()) 4 5 ax1 = fig.add_subplot(141) 6 data_train.Survived[data_train.Sex == 'female'][data_train.Pclass != 3].value_counts().plot(kind='bar', label="female highclass", color='#FA2479') 7 8 ax1.set_xticklabels(['survived','unsurvived'],rotation=0) 9 ax1.legend(["female/hight_level"], loc='best') 10 11 ax2=fig.add_subplot(142, sharey=ax1) 12 data_train.Survived[data_train.Sex == 'female'][data_train.Pclass == 3].value_counts().plot(kind='bar', label='female, low class', color='pink') 13 ax2.set_xticklabels(["unsurvived", "survived"], rotation=0) 14 plt.legend(["female/low_level"], loc='best') 15 16 ax3=fig.add_subplot(143, sharey=ax1) 17 data_train.Survived[data_train.Sex == 'male'][data_train.Pclass != 3].value_counts().plot(kind='bar', label='male, high class',color='lightblue') 18 ax3.set_xticklabels(["unsurvived", "survived"], rotation=0) 19 plt.legend(["male/hight_level"], loc='best') 20 21 ax4=fig.add_subplot(144, sharey=ax1) 22 data_train.Survived[data_train.Sex == 'male'][data_train.Pclass == 3].value_counts().plot(kind='bar', label='male low class', color='steelblue') 23 ax4.set_xticklabels(["unsurvived", "survived"], rotation=0) 24 plt.legend(["male/low_level"], loc='best') 25 26 plt.show()
得到下图
下面再看看大家族对结果有什么影响
1 g = data_train.groupby(['SibSp','Survived']) 2 df = pd.DataFrame(g.count()['PassengerId']) 3 4 print(df)
|
|
|
PassengerId |
|
SibSp |
Survived |
|
|
0 |
0 |
398 |
|
1 |
210 |
|
|
1 |
0 |
97 |
|
1 |
112 |
|
|
2 |
0 |
15 |
|
1 |
13 |
|
|
3 |
0 |
12 |
|
1 |
4 |
|
|
4 |
0 |
15 |
|
1 |
3 |
|
|
5 |
0 |
5 |
|
8 |
0 |
7 |
1 g = data_train.groupby(['Parch','Survived']) 2 df = pd.DataFrame(g.count()['PassengerId']) 3 print(df)
|
PassengerId |
||
|
Parch |
Survived |
|
|
0 |
0 |
445 |
|
1 |
233 |
|
|
1 |
0 |
53 |
|
1 |
65 |
|
|
2 |
0 |
40 |
|
1 |
40 |
|
|
3 |
0 |
2 |
|
1 |
3 |
|
|
4 |
0 |
4 |
|
5 |
0 |
4 |
|
1 |
1 |
|
|
6 |
0 |
1 |
基本没看出什么特殊关系，暂时作为备选特征。
ticket是船票编号，应该是unique的，和最后的结果没有太大的关系，不纳入考虑的特征范畴
cabin只有204个乘客有值，我们先看看它的一个分布
分布不均匀，应该算作类目型的，本身缺失值就多，还如此不集中，注定很棘手。如果直接按照类目特征处理，太散了，估计每个因子化后的特征都得不到什么权重。加上这么多缺失值，要不先把cabin缺失与否作为条件（虽然这部分信息缺失可能并非未登记，可能只是丢失而已，所以这样做未必妥当）。先在有无cabin信息这个粗粒度上看看Survived的情况。
1 #cabin的值计数太分散了，绝大多数Cabin值只出现一次。感觉上作为类目，加入特征未必会有效 2 #那我们一起看看这个值的有无，对于survival的分布状况，影响如何吧 3 fig = plt.figure() 4 fig.set(alpha=0.2) # 设定图表颜色alpha参数 5 6 Survived_cabin = data_train.Survived[pd.notnull(data_train.Cabin)].value_counts() 7 Survived_nocabin = data_train.Survived[pd.isnull(data_train.Cabin)].value_counts() 8 df=pd.DataFrame({'Notnull':Survived_cabin, 'null':Survived_nocabin}).transpose() 9 df.plot(kind='bar', stacked=True) 10 plt.title(u"按Cabin有无看获救情况",fontproperties=getChineseFont()) 11 plt.xlabel(u"Cabin有无",fontproperties=getChineseFont()) 12 plt.ylabel(u"人数",fontproperties=getChineseFont()) 13 plt.show() 14 15 #似乎有cabin记录的乘客survival比例稍高，那先试试把这个值分为两类，有cabin值/无cabin值，一会儿加到类别特征好了
似乎有cabin的存活率高一些。
因此，我们从最明显突出的数据属性开始，cabin和age，有丢失数据对进一步研究影响较大。
Cabin：暂时按照上面分析的，按Cabin有无数据，将这个属性处理成Ye和No两种类型。
Age：对于年龄缺失，我们会有以下几种处理方法
1.如果缺失的样本占总数比例极高，可能就要直接舍弃了，作为特征加入的话，可能导致噪声的产生，影响最终结果。
2.如果缺失值样本适中，并且该属性非连续值特征属性，那就把NaN作为一个新类别，加到类别特征中。
3.如果缺失值样本适中，而该属性为连续值特征属性，有时候我们会考虑给定一个step（比如这里的age，可以考虑每隔2/3岁为一个步长），然后把它离散化之后把NaN作为一个type加到属性类目中。
4.有些情况下，缺失值个数并不多，也可以试着根据已有的值，拟合一下数据补充上。
本例中，后两种方式应该都是可行的，我们先试着补全。
我们使用scikit-learn中的RandomForest拟合一下缺失的年龄数据
1 def set_missing_ages(df): 2 ''' 3 使用RandomForestClassifier填充缺失的年龄 4 :param df: 5 :return: 6 ''' 7 #把已有的数值型特征取出来丢进Random Forest Regressor中 8 age_df = df[['Age','Fare','Parch','SibSp','Pclass']] 9 #乘客分成已知年龄和未知年龄两部分 10 known_age = age_df[age_df.Age.notnull()].as_matrix() 11 unknown_age = age_df[age_df.Age.isnull()].as_matrix() 12 13 y = known_age[:,0]#y即目标年龄 14 X = known_age[:,1:]#X即特征属性值 15 16 rfr = RandomForestRegressor(random_state=0,n_estimators=2000,n_jobs=-1) 17 rfr.fit(X,y) 18 19 predictedAges = rfr.predict(unknown_age[:,1::]) 20 df.loc[(df.Age.isnull()),'Age'] = predictedAges 21 return df,rfr 22 23 24 def set_Cabin_type(df): 25 #有客舱信息的为Yes，无客舱信息的为No 26 df.loc[(df.Cabin.notnull()), 'Cabin'] = "Yes" 27 df.loc[(df.Cabin.isnull()), 'Cabin'] = "No" 28 return df 29 30 data_train, rfr = set_missing_ages(data_train) 31 data_train = set_Cabin_type(data_train) 32 print(data_train)
|
|
PassengerId |
Survived |
Pclass |
Name |
Sex |
Age |
SibSp |
Parch |
Ticket |
Fare |
Cabin |
Embarked |
|
0 |
1 |
0 |
3 |
Braund, Mr. Owen Harris |
male |
22.000000 |
1 |
0 |
A/5 21171 |
7.2500 |
No |
S |
|
1 |
2 |
1 |
1 |
Cumings, Mrs. John Bradley (Florence Briggs Th... |
female |
38.000000 |
1 |
0 |
PC 17599 |
71.2833 |
Yes |
C |
|
2 |
3 |
1 |
3 |
Heikkinen, Miss. Laina |
female |
26.000000 |
0 |
0 |
STON/O2. 3101282 |
7.9250 |
No |
S |
|
3 |
4 |
1 |
1 |
Futrelle, Mrs. Jacques Heath (Lily May Peel) |
female |
35.000000 |
1 |
0 |
113803 |
53.1000 |
Yes |
S |
|
4 |
5 |
0 |
3 |
Allen, Mr. William Henry |
male |
35.000000 |
0 |
0 |
373450 |
8.0500 |
No |
S |
|
5 |
6 |
0 |
3 |
Moran, Mr. James |
male |
23.828953 |
0 |
0 |
330877 |
8.4583 |
No |
Q |
|
6 |
7 |
0 |
1 |
McCarthy, Mr. Timothy J |
male |
54.000000 |
0 |
0 |
17463 |
51.8625 |
Yes |
S |
|
7 |
8 |
0 |
3 |
Palsson, Master. Gosta Leonard |
male |
2.000000 |
3 |
1 |
349909 |
21.0750 |
No |
S |
|
8 |
9 |
1 |
3 |
Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) |
female |
27.000000 |
0 |
2 |
347742 |
11.1333 |
No |
S |
|
9 |
10 |
1 |
2 |
Nasser, Mrs. Nicholas (Adele Achem) |
female |
14.000000 |
1 |
0 |
237736 |
30.0708 |
No |
C |
|
10 |
11 |
1 |
3 |
Sandstrom, Miss. Marguerite Rut |
female |
4.000000 |
1 |
1 |
PP 9549 |
16.7000 |
Yes |
S |
|
11 |
12 |
1 |
1 |
Bonnell, Miss. Elizabeth |
female |
58.000000 |
0 |
0 |
113783 |
26.5500 |
Yes |
S |
|
12 |
13 |
0 |
3 |
Saundercock, Mr. William Henry |
male |
20.000000 |
0 |
0 |
A/5. 2151 |
8.0500 |
No |
S |
|
13 |
14 |
0 |
3 |
Andersson, Mr. Anders Johan |
male |
39.000000 |
1 |
5 |
347082 |
31.2750 |
No |
S |
|
14 |
15 |
0 |
3 |
Vestrom, Miss. Hulda Amanda Adolfina |
female |
14.000000 |
0 |
0 |
350406 |
7.8542 |
No |
S |
|
15 |
16 |
1 |
2 |
Hewlett, Mrs. (Mary D Kingcome) |
female |
55.000000 |
0 |
0 |
248706 |
16.0000 |
No |
S |
|
16 |
17 |
0 |
3 |
Rice, Master. Eugene |
male |
2.000000 |
4 |
1 |
382652 |
29.1250 |
No |
Q |
|
17 |
18 |
1 |
2 |
Williams, Mr. Charles Eugene |
male |
32.066493 |
0 |
0 |
244373 |
13.0000 |
No |
S |
|
18 |
19 |
0 |
3 |
Vander Planke, Mrs. Julius (Emelia Maria Vande... |
female |
31.000000 |
1 |
0 |
345763 |
18.0000 |
No |
S |
|
19 |
20 |
1 |
3 |
Masselmani, Mrs. Fatima |
female |
29.518205 |
0 |
0 |
2649 |
7.2250 |
No |
C |
|
20 |
21 |
0 |
2 |
Fynney, Mr. Joseph J |
male |
35.000000 |
0 |
0 |
239865 |
26.0000 |
No |
S |
|
21 |
22 |
1 |
2 |
Beesley, Mr. Lawrence |
male |
34.000000 |
0 |
0 |
248698 |
13.0000 |
Yes |
S |
|
22 |
23 |
1 |
3 |
McGowan, Miss. Anna "Annie" |
female |
15.000000 |
0 |
0 |
330923 |
8.0292 |
No |
Q |
|
23 |
24 |
1 |
1 |
Sloper, Mr. William Thompson |
male |
28.000000 |
0 |
0 |
113788 |
35.5000 |
Yes |
S |
|
24 |
25 |
0 |
3 |
Palsson, Miss. Torborg Danira |
female |
8.000000 |
3 |
1 |
349909 |
21.0750 |
No |
S |
|
25 |
26 |
1 |
3 |
Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... |
female |
38.000000 |
1 |
5 |
347077 |
31.3875 |
No |
S |
|
26 |
27 |
0 |
3 |
Emir, Mr. Farred Chehab |
male |
29.518205 |
0 |
0 |
2631 |
7.2250 |
No |
C |
|
27 |
28 |
0 |
1 |
Fortune, Mr. Charles Alexander |
male |
19.000000 |
3 |
2 |
19950 |
263.0000 |
Yes |
S |
|
28 |
29 |
1 |
3 |
O'Dwyer, Miss. Ellen "Nellie" |
female |
22.380113 |
0 |
0 |
330959 |
7.8792 |
No |
Q |
|
29 |
30 |
0 |
3 |
Todoroff, Mr. Lalio |
male |
27.947206 |
0 |
0 |
349216 |
7.8958 |
No |
S |
|
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
|
861 |
862 |
0 |
2 |
Giles, Mr. Frederick Edward |
male |
21.000000 |
1 |
0 |
28134 |
11.5000 |
No |
S |
|
862 |
863 |
1 |
1 |
Swift, Mrs. Frederick Joel (Margaret Welles Ba... |
female |
48.000000 |
0 |
0 |
17466 |
25.9292 |
Yes |
S |
|
863 |
864 |
0 |
3 |
Sage, Miss. Dorothy Edith "Dolly" |
female |
10.888325 |
8 |
2 |
CA. 2343 |
69.5500 |
No |
S |
|
864 |
865 |
0 |
2 |
Gill, Mr. John William |
male |
24.000000 |
0 |
0 |
233866 |
13.0000 |
No |
S |
|
865 |
866 |
1 |
2 |
Bystrom, Mrs. (Karolina) |
female |
42.000000 |
0 |
0 |
236852 |
13.0000 |
No |
S |
|
866 |
867 |
1 |
2 |
Duran y More, Miss. Asuncion |
female |
27.000000 |
1 |
0 |
SC/PARIS 2149 |
13.8583 |
No |
C |
|
867 |
868 |
0 |
1 |
Roebling, Mr. Washington Augustus II |
male |
31.000000 |
0 |
0 |
PC 17590 |
50.4958 |
Yes |
S |
|
868 |
869 |
0 |
3 |
van Melkebeke, Mr. Philemon |
male |
25.977889 |
0 |
0 |
345777 |
9.5000 |
No |
S |
|
869 |
870 |
1 |
3 |
Johnson, Master. Harold Theodor |
male |
4.000000 |
1 |
1 |
347742 |
11.1333 |
No |
S |
|
870 |
871 |
0 |
3 |
Balkic, Mr. Cerin |
male |
26.000000 |
0 |
0 |
349248 |
7.8958 |
No |
S |
|
871 |
872 |
1 |
1 |
Beckwith, Mrs. Richard Leonard (Sallie Monypeny) |
female |
47.000000 |
1 |
1 |
11751 |
52.5542 |
Yes |
S |
|
872 |
873 |
0 |
1 |
Carlsson, Mr. Frans Olof |
male |
33.000000 |
0 |
0 |
695 |
5.0000 |
Yes |
S |
|
873 |
874 |
0 |
3 |
Vander Cruyssen, Mr. Victor |
male |
47.000000 |
0 |
0 |
345765 |
9.0000 |
No |
S |
|
874 |
875 |
1 |
2 |
Abelson, Mrs. Samuel (Hannah Wizosky) |
female |
28.000000 |
1 |
0 |
P/PP 3381 |
24.0000 |
No |
C |
|
875 |
876 |
1 |
3 |
Najib, Miss. Adele Kiamie "Jane" |
female |
15.000000 |
0 |
0 |
2667 |
7.2250 |
No |
C |
|
876 |
877 |
0 |
3 |
Gustafsson, Mr. Alfred Ossian |
male |
20.000000 |
0 |
0 |
7534 |
9.8458 |
No |
S |
|
877 |
878 |
0 |
3 |
Petroff, Mr. Nedelio |
male |
19.000000 |
0 |
0 |
349212 |
7.8958 |
No |
S |
|
878 |
879 |
0 |
3 |
Laleff, Mr. Kristo |
male |
27.947206 |
0 |
0 |
349217 |
7.8958 |
No |
S |
|
879 |
880 |
1 |
1 |
Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) |
female |
56.000000 |
0 |
1 |
11767 |
83.1583 |
Yes |
C |
|
880 |
881 |
1 |
2 |
Shelley, Mrs. William (Imanita Parrish Hall) |
female |
25.000000 |
0 |
1 |
230433 |
26.0000 |
No |
S |
|
881 |
882 |
0 |
3 |
Markun, Mr. Johann |
male |
33.000000 |
0 |
0 |
349257 |
7.8958 |
No |
S |
|
882 |
883 |
0 |
3 |
Dahlberg, Miss. Gerda Ulrika |
female |
22.000000 |
0 |
0 |
7552 |
10.5167 |
No |
S |
|
883 |
884 |
0 |
2 |
Banfield, Mr. Frederick James |
male |
28.000000 |
0 |
0 |
C.A./SOTON 34068 |
10.5000 |
No |
S |
|
884 |
885 |
0 |
3 |
Sutehall, Mr. Henry Jr |
male |
25.000000 |
0 |
0 |
SOTON/OQ 392076 |
7.0500 |
No |
S |
|
885 |
886 |
0 |
3 |
Rice, Mrs. William (Margaret Norton) |
female |
39.000000 |
0 |
5 |
382652 |
29.1250 |
No |
Q |
|
886 |
887 |
0 |
2 |
Montvila, Rev. Juozas |
male |
27.000000 |
0 |
0 |
211536 |
13.0000 |
No |
S |
|
887 |
888 |
1 |
1 |
Graham, Miss. Margaret Edith |
female |
19.000000 |
0 |
0 |
112053 |
30.0000 |
Yes |
S |
|
888 |
889 |
0 |
3 |
Johnston, Miss. Catherine Helen "Carrie" |
female |
16.232379 |
1 |
2 |
W./C. 6607 |
23.4500 |
No |
S |
|
889 |
890 |
1 |
1 |
Behr, Mr. Karl Howell |
male |
26.000000 |
0 |
0 |
111369 |
30.0000 |
Yes |
C |
|
890 |
891 |
0 |
3 |
Dooley, Mr. Patrick |
male |
32.000000 |
0 |
0 |
370376 |
7.7500 |
No |
Q |
891 rows × 12 columns
使用逻辑回归模型时，需要输入的特征都是数值型特征，我们通常会先对类别型特征因子化／one-hot编码。
例如：
以Embarked为例，原本一个属性维度，因为其取值是[S，C，Q]中任意一个，将其平展开为 Embarked_C,Embarked_S,Embarked_Q三个属性
之前Embarked取值为S的，此时的Embarked_S取值为1，而Embarked_C,Embarked_Q则取值为0
之前Embarked取值为C的，此时的Embarked_C取值为1，而Embarked_S,Embarked_Q则取值为0
之前Embarked取值为Q的，此时的Embarked_Q取值为1，而Embarked_C,Embarked_S则取值为0
下面使用pandas的get_dummies来完成这个工作，并拼接在前面的data_train之上，如下所示：
1 dummies_Cabin = pd.get_dummies(data_train['Cabin'], prefix='Cabin') 2 dummies_Embarked = pd.get_dummies(data_train['Embarked'], prefix='Embarked') 3 dummies_Sex = pd.get_dummies(data_train['Sex'], prefix='Sex') 4 dummies_Pclass = pd.get_dummies(data_train['Pclass'], prefix='Pclass') 5 df = pd.concat([data_train, dummies_Cabin, dummies_Embarked, dummies_Sex, dummies_Pclass], axis=1) 6 df.drop(['Pclass', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1, inplace=True) 7 8 print(df)
|
|
PassengerId |
Survived |
Age |
SibSp |
Parch |
Fare |
Cabin_No |
Cabin_Yes |
Embarked_C |
Embarked_Q |
Embarked_S |
Sex_female |
Sex_male |
Pclass_1 |
Pclass_2 |
Pclass_3 |
|
0 |
1 |
0 |
22.000000 |
1 |
0 |
7.2500 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
|
1 |
2 |
1 |
38.000000 |
1 |
0 |
71.2833 |
0 |
1 |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
|
2 |
3 |
1 |
26.000000 |
0 |
0 |
7.9250 |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
1 |
|
3 |
4 |
1 |
35.000000 |
1 |
0 |
53.1000 |
0 |
1 |
0 |
0 |
1 |
1 |
0 |
1 |
0 |
0 |
|
4 |
5 |
0 |
35.000000 |
0 |
0 |
8.0500 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
|
5 |
6 |
0 |
23.828953 |
0 |
0 |
8.4583 |
1 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
1 |
|
6 |
7 |
0 |
54.000000 |
0 |
0 |
51.8625 |
0 |
1 |
0 |
0 |
1 |
0 |
1 |
1 |
0 |
0 |
|
7 |
8 |
0 |
2.000000 |
3 |
1 |
21.0750 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
|
8 |
9 |
1 |
27.000000 |
0 |
2 |
11.1333 |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
1 |
|
9 |
10 |
1 |
14.000000 |
1 |
0 |
30.0708 |
1 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
|
10 |
11 |
1 |
4.000000 |
1 |
1 |
16.7000 |
0 |
1 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
1 |
|
11 |
12 |
1 |
58.000000 |
0 |
0 |
26.5500 |
0 |
1 |
0 |
0 |
1 |
1 |
0 |
1 |
0 |
0 |
|
12 |
13 |
0 |
20.000000 |
0 |
0 |
8.0500 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
|
13 |
14 |
0 |
39.000000 |
1 |
5 |
31.2750 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
|
14 |
15 |
0 |
14.000000 |
0 |
0 |
7.8542 |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
1 |
|
15 |
16 |
1 |
55.000000 |
0 |
0 |
16.0000 |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
1 |
0 |
|
16 |
17 |
0 |
2.000000 |
4 |
1 |
29.1250 |
1 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
1 |
|
17 |
18 |
1 |
32.066493 |
0 |
0 |
13.0000 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
1 |
0 |
|
18 |
19 |
0 |
31.000000 |
1 |
0 |
18.0000 |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
1 |
|
19 |
20 |
1 |
29.518205 |
0 |
0 |
7.2250 |
1 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
1 |
|
20 |
21 |
0 |
35.000000 |
0 |
0 |
26.0000 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
1 |
0 |
|
21 |
22 |
1 |
34.000000 |
0 |
0 |
13.0000 |
0 |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
1 |
0 |
|
22 |
23 |
1 |
15.000000 |
0 |
0 |
8.0292 |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
1 |
|
23 |
24 |
1 |
28.000000 |
0 |
0 |
35.5000 |
0 |
1 |
0 |
0 |
1 |
0 |
1 |
1 |
0 |
0 |
|
24 |
25 |
0 |
8.000000 |
3 |
1 |
21.0750 |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
1 |
|
25 |
26 |
1 |
38.000000 |
1 |
5 |
31.3875 |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
1 |
|
26 |
27 |
0 |
29.518205 |
0 |
0 |
7.2250 |
1 |
0 |
1 |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
|
27 |
28 |
0 |
19.000000 |
3 |
2 |
263.0000 |
0 |
1 |
0 |
0 |
1 |
0 |
1 |
1 |
0 |
0 |
|
28 |
29 |
1 |
22.380113 |
0 |
0 |
7.8792 |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
1 |
|
29 |
30 |
0 |
27.947206 |
0 |
0 |
7.8958 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
|
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
|
861 |
862 |
0 |
21.000000 |
1 |
0 |
11.5000 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
1 |
0 |
|
862 |
863 |
1 |
48.000000 |
0 |
0 |
25.9292 |
0 |
1 |
0 |
0 |
1 |
1 |
0 |
1 |
0 |
0 |
|
863 |
864 |
0 |
10.888325 |
8 |
2 |
69.5500 |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
1 |
|
864 |
865 |
0 |
24.000000 |
0 |
0 |
13.0000 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
1 |
0 |
|
865 |
866 |
1 |
42.000000 |
0 |
0 |
13.0000 |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
1 |
0 |
|
866 |
867 |
1 |
27.000000 |
1 |
0 |
13.8583 |
1 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
|
867 |
868 |
0 |
31.000000 |
0 |
0 |
50.4958 |
0 |
1 |
0 |
0 |
1 |
0 |
1 |
1 |
0 |
0 |
|
868 |
869 |
0 |
25.977889 |
0 |
0 |
9.5000 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
|
869 |
870 |
1 |
4.000000 |
1 |
1 |
11.1333 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
|
870 |
871 |
0 |
26.000000 |
0 |
0 |
7.8958 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
|
871 |
872 |
1 |
47.000000 |
1 |
1 |
52.5542 |
0 |
1 |
0 |
0 |
1 |
1 |
0 |
1 |
0 |
0 |
|
872 |
873 |
0 |
33.000000 |
0 |
0 |
5.0000 |
0 |
1 |
0 |
0 |
1 |
0 |
1 |
1 |
0 |
0 |
|
873 |
874 |
0 |
47.000000 |
0 |
0 |
9.0000 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
|
874 |
875 |
1 |
28.000000 |
1 |
0 |
24.0000 |
1 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
|
875 |
876 |
1 |
15.000000 |
0 |
0 |
7.2250 |
1 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
1 |
|
876 |
877 |
0 |
20.000000 |
0 |
0 |
9.8458 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
|
877 |
878 |
0 |
19.000000 |
0 |
0 |
7.8958 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
|
878 |
879 |
0 |
27.947206 |
0 |
0 |
7.8958 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
|
879 |
880 |
1 |
56.000000 |
0 |
1 |
83.1583 |
0 |
1 |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
|
880 |
881 |
1 |
25.000000 |
0 |
1 |
26.0000 |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
1 |
0 |
|
881 |
882 |
0 |
33.000000 |
0 |
0 |
7.8958 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
|
882 |
883 |
0 |
22.000000 |
0 |
0 |
10.5167 |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
1 |
|
883 |
884 |
0 |
28.000000 |
0 |
0 |
10.5000 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
1 |
0 |
|
884 |
885 |
0 |
25.000000 |
0 |
0 |
7.0500 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
|
885 |
886 |
0 |
39.000000 |
0 |
5 |
29.1250 |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
1 |
|
886 |
887 |
0 |
27.000000 |
0 |
0 |
13.0000 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
1 |
0 |
|
887 |
888 |
1 |
19.000000 |
0 |
0 |
30.0000 |
0 |
1 |
0 |
0 |
1 |
1 |
0 |
1 |
0 |
0 |
|
888 |
889 |
0 |
16.232379 |
1 |
2 |
23.4500 |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
1 |
|
889 |
890 |
1 |
26.000000 |
0 |
0 |
30.0000 |
0 |
1 |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
|
890 |
891 |
0 |
32.000000 |
0 |
0 |
7.7500 |
1 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
1 |
891 rows × 16 columns