2022年泰迪杯数据分析_B题：银行客户忠诚度分析赛题数据_任务一

另有题目文件，第四题第五题全部可实现文件均放在了我的资源里面

任务1.1（1）首先，使用pandas将数据导入，查看数据的详细信息，发现education、default、marital、job等字段存在明显缺失值，使用dropna()删除缺失值所在的行；其次查看数据是否存在重复值，整体不存在重复值，单独查看‘user_id’发现，存在56个重复值，drop_duplicates()删除重复值所在的行；最后 将处理好的数据保存到“result1_1.xlsx”中。

import pandas as pd
import numpy as np
import re

#导入数据
short_data1=pd.read_csv('short-customer-data.csv')
short_data1

	user_id	age	job	marital	education	default	housing	loan	contact	month	day_of_week	duration	poutcome	y
0	BA2200001	56	housemaid	married	postgraduate	no	no	no	telephone	may	mon	261	nonexistent	no
1	BA2200002	57	services	married	high school	NaN	no	no	telephone	may	mon	149	nonexistent	no
2	BA2200077	37	services	married	high school	no	yes	no	telephone	may	mon	226	nonexistent	no
3	BA2200004	40	admin.	married	postgraduate	no	no	no	telephone	may	mon	151	nonexistent	no
4	BA2200005	56	services	married	high school	no	no	yes	telephone	may	mon	307	nonexistent	no
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
41171	BA2241172	73	retired	married	junior college	no	yes	no	cellular	nov	fri	334	nonexistent	yes
41172	BA2241173	46	blue-collar	married	junior college	no	no	no	cellular	nov	fri	383	nonexistent	no
41173	BA2241174	56	retired	married	undergraduate	no	yes	no	cellular	nov	fri	189	nonexistent	no
41174	BA2241175	44	technician	married	junior college	no	no	no	cellular	nov	fri	442	nonexistent	yes
41175	BA2241176	74	retired	married	junior college	no	yes	no	cellular	nov	fri	239	failure	no

41176 rows × 14 columns

#查看数据详细信息
short_data1.info()

RangeIndex: 41176 entries, 0 to 41175
Data columns (total 14 columns):
 #   Column       Non-Null Count  Dtype 
---  ------       --------------  ----- 
 0   user_id      41176 non-null  object
 1   age          41176 non-null  int64 
 2   job          40846 non-null  object
 3   marital      41096 non-null  object
 4   education    39446 non-null  object
 5   default      32580 non-null  object
 6   housing      40186 non-null  object
 7   loan         40186 non-null  object
 8   contact      41176 non-null  object
 9   month        41176 non-null  object
 10  day_of_week  41176 non-null  object
 11  duration     41176 non-null  int64 
 12  poutcome     41176 non-null  object
 13  y            41176 non-null  object
dtypes: int64(2), object(12)
memory usage: 4.4+ MB

#查看是否存在重复值
short_data1.duplicated().sum()

0

#查看'user_id'是否存在重复值
short_data1['user_id'].duplicated().sum()

56

#删除重复值
short_data1=short_data1.drop_duplicates(subset=['user_id'])
short_data1

	user_id	age	job	marital	education	default	housing	loan	contact	month	day_of_week	duration	poutcome	y
0	BA2200001	56	housemaid	married	postgraduate	no	no	no	telephone	may	mon	261	nonexistent	no
1	BA2200002	57	services	married	high school	NaN	no	no	telephone	may	mon	149	nonexistent	no
2	BA2200077	37	services	married	high school	no	yes	no	telephone	may	mon	226	nonexistent	no
3	BA2200004	40	admin.	married	postgraduate	no	no	no	telephone	may	mon	151	nonexistent	no
4	BA2200005	56	services	married	high school	no	no	yes	telephone	may	mon	307	nonexistent	no
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
41170	BA2241171	29	unemployed	single	postgraduate	no	yes	no	cellular	nov	fri	112	success	no
41172	BA2241173	46	blue-collar	married	junior college	no	no	no	cellular	nov	fri	383	nonexistent	no
41173	BA2241174	56	retired	married	undergraduate	no	yes	no	cellular	nov	fri	189	nonexistent	no
41174	BA2241175	44	technician	married	junior college	no	no	no	cellular	nov	fri	442	nonexistent	yes
41175	BA2241176	74	retired	married	junior college	no	yes	no	cellular	nov	fri	239	failure	no

41120 rows × 14 columns

#查看重复值是否删除
short_data1['user_id'].duplicated().sum()

0

#查看缺失值
short_data1.isnull().sum()

user_id           0
age               0
job             328
marital          80
education      1726
default        8578
housing         988
loan            988
contact           0
month             0
day_of_week       0
duration          0
poutcome          0
y                 0
dtype: int64

#删除缺失值所在的行
short_data1=short_data1.dropna()
short_data1

	user_id	age	job	marital	education	default	housing	loan	contact	month	day_of_week	duration	poutcome	y
0	BA2200001	56	housemaid	married	postgraduate	no	no	no	telephone	may	mon	261	nonexistent	no
2	BA2200077	37	services	married	high school	no	yes	no	telephone	may	mon	226	nonexistent	no
3	BA2200004	40	admin.	married	postgraduate	no	no	no	telephone	may	mon	151	nonexistent	no
4	BA2200005	56	services	married	high school	no	no	yes	telephone	may	mon	307	nonexistent	no
6	BA2200007	59	admin.	married	junior college	no	no	no	telephone	may	mon	139	nonexistent	no
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
41170	BA2241171	29	unemployed	single	postgraduate	no	yes	no	cellular	nov	fri	112	success	no
41172	BA2241173	46	blue-collar	married	junior college	no	no	no	cellular	nov	fri	383	nonexistent	no
41173	BA2241174	56	retired	married	undergraduate	no	yes	no	cellular	nov	fri	189	nonexistent	no
41174	BA2241175	44	technician	married	junior college	no	no	no	cellular	nov	fri	442	nonexistent	yes
41175	BA2241176	74	retired	married	junior college	no	yes	no	cellular	nov	fri	239	failure	no

30444 rows × 14 columns

#查看缺失值是否删除
short_data1.isnull().sum()

user_id        0
age            0
job            0
marital        0
education      0
default        0
housing        0
loan           0
contact        0
month          0
day_of_week    0
duration       0
poutcome       0
y              0
dtype: int64

#将处理好的数据导出
short_data1.to_excel('result1_1.xlsx',index = False)
short_data1

	user_id	age	job	marital	education	default	housing	loan	contact	month	day_of_week	duration	poutcome	y
0	BA2200001	56	housemaid	married	postgraduate	no	no	no	telephone	may	mon	261	nonexistent	no
2	BA2200077	37	services	married	high school	no	yes	no	telephone	may	mon	226	nonexistent	no
3	BA2200004	40	admin.	married	postgraduate	no	no	no	telephone	may	mon	151	nonexistent	no
4	BA2200005	56	services	married	high school	no	no	yes	telephone	may	mon	307	nonexistent	no
6	BA2200007	59	admin.	married	junior college	no	no	no	telephone	may	mon	139	nonexistent	no
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
41170	BA2241171	29	unemployed	single	postgraduate	no	yes	no	cellular	nov	fri	112	success	no
41172	BA2241173	46	blue-collar	married	junior college	no	no	no	cellular	nov	fri	383	nonexistent	no
41173	BA2241174	56	retired	married	undergraduate	no	yes	no	cellular	nov	fri	189	nonexistent	no
41174	BA2241175	44	technician	married	junior college	no	no	no	cellular	nov	fri	442	nonexistent	yes
41175	BA2241176	74	retired	married	junior college	no	yes	no	cellular	nov	fri	239	failure	no

30444 rows × 14 columns

任务1.1.2

import pandas as pd
import numpy as np
import re

long_data1=pd.read_csv('long-customer-train.csv')
long_data1.info()

RangeIndex: 9300 entries, 0 to 9299
Data columns (total 11 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   CustomerId       9300 non-null   int64  
 1   CreditScore      9300 non-null   int64  
 2   Gender           9300 non-null   int64  
 3   Age              9300 non-null   object 
 4   Tenure           9300 non-null   int64  
 5   Balance          9300 non-null   float64
 6   NumOfProducts    9300 non-null   int64  
 7   HasCrCard        9300 non-null   int64  
 8   IsActiveMember   9300 non-null   int64  
 9   EstimatedSalary  9300 non-null   float64
 10  Exited           9300 non-null   int64  
dtypes: float64(2), int64(8), object(1)
memory usage: 799.3+ KB

long_data1[long_data1['Age']=='-']

	CustomerId	CreditScore	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
464	15570485	558	0	-	4	161766.87	1	0	0	92378.54	0
566	15573452	663	0	-	7	115930.87	1	1	0	19862.78	0
651	15576000	765	0	-	6	138033.55	1	1	1	67972.45	0
696	15577064	592	0	-	2	104702.65	2	1	0	107948.72	0
796	15580068	526	0	-	5	0.00	2	1	1	105618.14	0
...	...	...	...	...	...	...	...	...	...	...	...
8176	15784161	583	0	-	8	102945.01	1	0	0	52861.89	0
8270	15787035	602	1	-	8	0.00	2	1	1	152843.53	0
8591	15795881	776	0	-	8	106365.29	1	1	1	148527.56	0
8776	15801062	557	1	-	4	0.00	2	0	1	105433.53	0
8794	15801417	657	0	-	4	82500.28	1	1	1	115260.72	0

78 rows × 11 columns

长期数据中的客户年龄“Age”列存在数值为-1、0 和“-”的异常值，删除存在该情况的行数据。
这里我们先将异常值赋值为空值

long_data2=long_data1.replace(to_replace=['-1','0','-','1'],value=[np.nan,np.nan,np.nan,np.nan])
long_data2.info()

RangeIndex: 9300 entries, 0 to 9299
Data columns (total 11 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   CustomerId       9300 non-null   int64  
 1   CreditScore      9300 non-null   int64  
 2   Gender           9300 non-null   int64  
 3   Age              9180 non-null   object 
 4   Tenure           9300 non-null   int64  
 5   Balance          9300 non-null   float64
 6   NumOfProducts    9300 non-null   int64  
 7   HasCrCard        9300 non-null   int64  
 8   IsActiveMember   9300 non-null   int64  
 9   EstimatedSalary  9300 non-null   float64
 10  Exited           9300 non-null   int64  
dtypes: float64(2), int64(8), object(1)
memory usage: 799.3+ KB

再用删除空值的方法将其所在的行删除

long_data3=long_data2.dropna()
long_data3.info()

Int64Index: 9180 entries, 0 to 9299
Data columns (total 11 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   CustomerId       9180 non-null   int64  
 1   CreditScore      9180 non-null   int64  
 2   Gender           9180 non-null   int64  
 3   Age              9180 non-null   object 
 4   Tenure           9180 non-null   int64  
 5   Balance          9180 non-null   float64
 6   NumOfProducts    9180 non-null   int64  
 7   HasCrCard        9180 non-null   int64  
 8   IsActiveMember   9180 non-null   int64  
 9   EstimatedSalary  9180 non-null   float64
 10  Exited           9180 non-null   int64  
dtypes: float64(2), int64(8), object(1)
memory usage: 860.6+ KB

# long_data3['age_new']=long_data3.Age.str.extract(r"(\d+)")
# #long_data3['new_Age']=long_data3.Age.str.replace('岁','')
# #long_data3[long_data3.age_new.str.contains(" ")]
# long_data3['age_new']=long_data3['age_new'].astype(int)
# long_data4=long_data3.drop(columns=['Age'])
# long_data4.rename(columns={'age_new':'Age'},inplace=True)
# long_data4.info()

“Age”列存在空格和“岁”等异常字符，删除这些异常
字符但须保留年龄数值，将处理后的数值存于“Age”列。
这里通过正则表达式匹配异常字符中的正确年龄，并将年龄保存在了Age列，数据类型用int

long_data3['Age']=long_data3.Age.str.extract(r"(\d+)")
long_data3['Age']=long_data3['Age'].astype(int)

C:\Users\31214\AppData\Local\Temp\ipykernel_10860\3539377572.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  long_data3['Age']=long_data3.Age.str.extract(r"(\d+)")
C:\Users\31214\AppData\Local\Temp\ipykernel_10860\3539377572.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  long_data3['Age']=long_data3['Age'].astype(int)

long_data3.to_excel('result1_2.xlsx',index = False)
long_data3.info()

Int64Index: 9180 entries, 0 to 9299
Data columns (total 11 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   CustomerId       9180 non-null   int64  
 1   CreditScore      9180 non-null   int64  
 2   Gender           9180 non-null   int64  
 3   Age              9180 non-null   int32  
 4   Tenure           9180 non-null   int64  
 5   Balance          9180 non-null   float64
 6   NumOfProducts    9180 non-null   int64  
 7   HasCrCard        9180 non-null   int64  
 8   IsActiveMember   9180 non-null   int64  
 9   EstimatedSalary  9180 non-null   float64
 10  Exited           9180 non-null   int64  
dtypes: float64(2), int32(1), int64(8)
memory usage: 824.8 KB

long_data3

	CustomerId	CreditScore	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
0	15553251	713	1	52	0	185891.54	1	1	1	46369.57	1
1	15553256	619	1	41	8	0.00	3	1	1	79866.73	1
2	15553283	603	1	42	8	91611.12	1	0	0	144675.30	1
3	15553308	589	1	61	1	0.00	1	1	0	61108.56	1
4	15553387	687	1	39	2	0.00	3	0	0	188150.60	1
...	...	...	...	...	...	...	...	...	...	...	...
9295	15815628	711	1	37	8	113899.92	1	0	0	80215.20	0
9296	15815645	481	0	37	8	152303.66	2	1	1	175082.20	0
9297	15815656	541	1	39	9	100116.67	1	1	1	199808.10	1
9298	15815660	758	1	34	1	154139.45	1	1	1	60728.89	0
9299	15815690	614	1	40	3	113348.50	1	1	1	77789.01	0

9180 rows × 11 columns

任务1.2

import pandas as pd

short_data=pd.read_excel('result1_1.xlsx')
short_data

	user_id	age	job	marital	education	default	housing	loan	contact	month	day_of_week	duration	poutcome	y
0	BA2200001	56	housemaid	married	postgraduate	no	no	no	telephone	may	mon	261	nonexistent	no
1	BA2200077	37	services	married	high school	no	yes	no	telephone	may	mon	226	nonexistent	no
2	BA2200004	40	admin.	married	postgraduate	no	no	no	telephone	may	mon	151	nonexistent	no
3	BA2200005	56	services	married	high school	no	no	yes	telephone	may	mon	307	nonexistent	no
4	BA2200007	59	admin.	married	junior college	no	no	no	telephone	may	mon	139	nonexistent	no
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
30439	BA2241171	29	unemployed	single	postgraduate	no	yes	no	cellular	nov	fri	112	success	no
30440	BA2241173	46	blue-collar	married	junior college	no	no	no	cellular	nov	fri	383	nonexistent	no
30441	BA2241174	56	retired	married	undergraduate	no	yes	no	cellular	nov	fri	189	nonexistent	no
30442	BA2241175	44	technician	married	junior college	no	no	no	cellular	nov	fri	442	nonexistent	yes
30443	BA2241176	74	retired	married	junior college	no	yes	no	cellular	nov	fri	239	failure	no

30444 rows × 14 columns

对短期数据中的字符型数据进行特征编码，如将信用违约情况
{‘否’,‘是’}编码为{0,1}

在这种情况下，我们必须将数据进行编码，即是说，将文字型数据转换为数值型。
这里我们采用preprocessing.LabelEncoder将汉字进行编码

短期客户数据中，需要进行特征编码的字段都包含{job,marital,education,default,housing,loan,contact,poutcome,y}

from sklearn.preprocessing import LabelEncoder
short_data6=short_data.copy()
short_data6

	user_id	age	job	marital	education	default	housing	loan	contact	month	day_of_week	duration	poutcome	y
0	BA2200001	56	housemaid	married	postgraduate	no	no	no	telephone	may	mon	261	nonexistent	no
1	BA2200077	37	services	married	high school	no	yes	no	telephone	may	mon	226	nonexistent	no
2	BA2200004	40	admin.	married	postgraduate	no	no	no	telephone	may	mon	151	nonexistent	no
3	BA2200005	56	services	married	high school	no	no	yes	telephone	may	mon	307	nonexistent	no
4	BA2200007	59	admin.	married	junior college	no	no	no	telephone	may	mon	139	nonexistent	no
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
30439	BA2241171	29	unemployed	single	postgraduate	no	yes	no	cellular	nov	fri	112	success	no
30440	BA2241173	46	blue-collar	married	junior college	no	no	no	cellular	nov	fri	383	nonexistent	no
30441	BA2241174	56	retired	married	undergraduate	no	yes	no	cellular	nov	fri	189	nonexistent	no
30442	BA2241175	44	technician	married	junior college	no	no	no	cellular	nov	fri	442	nonexistent	yes
30443	BA2241176	74	retired	married	junior college	no	yes	no	cellular	nov	fri	239	failure	no

30444 rows × 14 columns

t1=short_data6.loc[:,'job']    #要输入的是标签，不是特征矩阵，所以允许一维数据
t2=short_data6.loc[:,'marital']
t3=short_data6.loc[:,'education']
t4=short_data6.loc[:,'default']
t5=short_data6.loc[:,'housing']
t6=short_data6.loc[:,'loan']
t7=short_data6.loc[:,'contact']
t8=short_data6.loc[:,'poutcome']
t9=short_data6.loc[:,'y']

le1 = LabelEncoder()     #实例化
le1 = le1.fit(t1) # 导入数据
label1 = le1.transform(t1) # transform接口调取结果
short_data6.loc[:,"job"] = label1
short_data6['job'].unique()

array([ 3,  7,  0,  9,  1, 10,  5,  2,  4,  8,  6])

le2 = LabelEncoder()     #实例化
le2 = le2.fit(t2) # 导入数据
label2 = le2.transform(t2) # transform接口调取结果
short_data6.loc[:,"marital"] = label2
short_data6['marital'].unique()

array([1, 2, 0])

le3 = LabelEncoder()     #实例化
le3 = le3.fit(t3) # 导入数据
label3 = le3.transform(t3) # transform接口调取结果
short_data6.loc[:,"education"] = label3
short_data6['education'].unique()

array([3, 0, 2, 4, 1])

le4 = LabelEncoder()     #实例化
le4 = le4.fit(t4) # 导入数据
label4 = le4.transform(t4) # transform接口调取结果
short_data6.loc[:,"default"] = label4
short_data6['default'].unique()

array([0, 1])

le5 = LabelEncoder()     #实例化
le5 = le5.fit(t5) # 导入数据
label5 = le5.transform(t5) # transform接口调取结果
short_data6.loc[:,"housing"] = label5
short_data6['housing'].unique()

array([0, 1])

le6 = LabelEncoder()     #实例化
le6 = le6.fit(t6) # 导入数据
label6 = le6.transform(t6) # transform接口调取结果
short_data6.loc[:,"loan"] = label6
short_data6['loan'].unique()

array([0, 1])

le7 = LabelEncoder()     #实例化
le7 = le7.fit(t7) # 导入数据
label7 = le7.transform(t7) # transform接口调取结果
short_data6.loc[:,"contact"] = label7
short_data6['contact'].unique()

array([1, 0])

le8 = LabelEncoder()     #实例化
le8 = le8.fit(t8) # 导入数据
label8 = le8.transform(t8) # transform接口调取结果
short_data6.loc[:,"poutcome"] = label8
short_data6['poutcome'].unique()

array([1, 0, 2])

le9 = LabelEncoder()     #实例化
le9 = le9.fit(t9) # 导入数据
label9 = le9.transform(t9) # transform接口调取结果
short_data6.loc[:,"y"] = label9
short_data6['y'].unique()

array([0, 1])

short_data7=short_data6.copy()
short_data7

	user_id	age	job	marital	education	default	housing	loan	contact	month	day_of_week	duration	poutcome	y
0	BA2200001	56	3	1	3	0	0	0	1	may	mon	261	1	0
1	BA2200077	37	7	1	0	0	1	0	1	may	mon	226	1	0
2	BA2200004	40	0	1	3	0	0	0	1	may	mon	151	1	0
3	BA2200005	56	7	1	0	0	0	1	1	may	mon	307	1	0
4	BA2200007	59	0	1	2	0	0	0	1	may	mon	139	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
30439	BA2241171	29	10	2	3	0	1	0	0	nov	fri	112	2	0
30440	BA2241173	46	1	1	2	0	0	0	0	nov	fri	383	1	0
30441	BA2241174	56	5	1	4	0	1	0	0	nov	fri	189	1	0
30442	BA2241175	44	9	1	2	0	0	0	0	nov	fri	442	1	1
30443	BA2241176	74	5	1	2	0	1	0	0	nov	fri	239	0	0

30444 rows × 14 columns

#short_data7.loc[short_data7['month']=='mar']['month'].unique()
short_data6['month'].unique()

array(['may', 'jun', 'jul', 'aug', 'oct', 'nov', 'dec', 'mar', 'apr',
       'sep'], dtype=object)

对上次拜访客户的月份(month)和最近拜访客户的月份(day_of_week)将不适用LabelEncoder库进行编码，因此这里将手动赋值，例如：一月：1，星期一：1

short_data8=short_data7.replace(to_replace=['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec'],value=['1','2','3','4','5','6','7','8','9','10','11','12'])
short_data8

	user_id	age	job	marital	education	default	housing	loan	contact	month	day_of_week	duration	poutcome	y
0	BA2200001	56	3	1	3	0	0	0	1	5	mon	261	1	0
1	BA2200077	37	7	1	0	0	1	0	1	5	mon	226	1	0
2	BA2200004	40	0	1	3	0	0	0	1	5	mon	151	1	0
3	BA2200005	56	7	1	0	0	0	1	1	5	mon	307	1	0
4	BA2200007	59	0	1	2	0	0	0	1	5	mon	139	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
30439	BA2241171	29	10	2	3	0	1	0	0	11	fri	112	2	0
30440	BA2241173	46	1	1	2	0	0	0	0	11	fri	383	1	0
30441	BA2241174	56	5	1	4	0	1	0	0	11	fri	189	1	0
30442	BA2241175	44	9	1	2	0	0	0	0	11	fri	442	1	1
30443	BA2241176	74	5	1	2	0	1	0	0	11	fri	239	0	0

30444 rows × 14 columns

short_data8['day_of_week'].unique()

array(['mon', 'tue', 'wed', 'thu', 'fri'], dtype=object)

short_data9=short_data8.replace(to_replace=['mon','tue','wed','thu','fri'],value=[1,2,3,4,5])
short_data9

	user_id	age	job	marital	education	default	housing	loan	contact	month	day_of_week	duration	poutcome	y
0	BA2200001	56	3	1	3	0	0	0	1	5	1	261	1	0
1	BA2200077	37	7	1	0	0	1	0	1	5	1	226	1	0
2	BA2200004	40	0	1	3	0	0	0	1	5	1	151	1	0
3	BA2200005	56	7	1	0	0	0	1	1	5	1	307	1	0
4	BA2200007	59	0	1	2	0	0	0	1	5	1	139	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
30439	BA2241171	29	10	2	3	0	1	0	0	11	5	112	2	0
30440	BA2241173	46	1	1	2	0	0	0	0	11	5	383	1	0
30441	BA2241174	56	5	1	4	0	1	0	0	11	5	189	1	0
30442	BA2241175	44	9	1	2	0	0	0	0	11	5	442	1	1
30443	BA2241176	74	5	1	2	0	1	0	0	11	5	239	0	0

30444 rows × 14 columns

short_data9.to_excel("result1_3.xlsx",encoding = 'openpyxl',index = False)