4.2-数据清洗

标签：... No 4.2 NaN check 7043 清洗数据 Yes

4.2-数据清洗 In [ ]:

import pandas as pd

In [ ]:

# 4.2.1 数据导入、预览: read_csv、head
data = pd.read_csv("WA_Fn-UseC_-Telco-Customer-Churn.csv")
data

Out[ ]:

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7590-VHVEG	Female	0	Yes	No	1	No	No phone service	DSL	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	5575-GNVDE	Male	0	No	No	34	Yes	No	DSL	Yes	...	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	3668-QPYBK	Male	0	No	No	2	Yes	No	DSL	Yes	...	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	7795-CFOCW	Male	0	No	No	45	No	No phone service	DSL	Yes	...	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	9237-HQITU	Female	0	No	No	2	Yes	No	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	6840-RESVB	Male	0	Yes	Yes	24	Yes	Yes	DSL	Yes	...	Yes	Yes	Yes	Yes	One year	Yes	Mailed check	84.80	1990.5	No
7039	2234-XADUH	Female	0	Yes	Yes	72	Yes	Yes	Fiber optic	No	...	Yes	No	Yes	Yes	One year	Yes	Credit card (automatic)	103.20	7362.9	No
7040	4801-JZAZL	Female	0	Yes	Yes	11	No	No phone service	DSL	Yes	...	No	No	No	No	Month-to-month	Yes	Electronic check	29.60	346.45	No
7041	8361-LTMKD	Male	1	Yes	No	4	Yes	Yes	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Mailed check	74.40	306.6	Yes
7042	3186-AJIEK	Male	0	No	No	66	Yes	No	Fiber optic	Yes	...	Yes	Yes	Yes	Yes	Two year	Yes	Bank transfer (automatic)	105.65	6844.5	No

7043 rows × 21 columns

In [ ]:

# 4.2.2 数据结构、列标签: shape、columns
data.shape

Out[ ]:

(7043, 21)

In [ ]:

data.columns

Out[ ]:

Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',
       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',
       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',
       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],
      dtype='object')

In [ ]:

data.columns = ['用户ID', '性别', '是否老人', '是否有伴侣', '是否有孩子',
       '合同期限', '通话服务', '多线程', '网络服务',
       '在线安全', '在线备份', '设备安全', '技术支持',
       '流媒体电视', '流媒体电影', '合同类型', '电子账单',
       '支付方式', '月消费', '总消费', '是否流失']      # 设置字段名为中文
data

Out[ ]:

	用户ID	性别	是否老人	是否有伴侣	是否有孩子	合同期限	通话服务	多线程	网络服务	在线安全	...	设备安全	技术支持	流媒体电视	流媒体电影	合同类型	电子账单	支付方式	月消费	总消费	是否流失
0	7590-VHVEG	Female	0	Yes	No	1	No	No phone service	DSL	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	5575-GNVDE	Male	0	No	No	34	Yes	No	DSL	Yes	...	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	3668-QPYBK	Male	0	No	No	2	Yes	No	DSL	Yes	...	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	7795-CFOCW	Male	0	No	No	45	No	No phone service	DSL	Yes	...	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	9237-HQITU	Female	0	No	No	2	Yes	No	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	6840-RESVB	Male	0	Yes	Yes	24	Yes	Yes	DSL	Yes	...	Yes	Yes	Yes	Yes	One year	Yes	Mailed check	84.80	1990.5	No
7039	2234-XADUH	Female	0	Yes	Yes	72	Yes	Yes	Fiber optic	No	...	Yes	No	Yes	Yes	One year	Yes	Credit card (automatic)	103.20	7362.9	No
7040	4801-JZAZL	Female	0	Yes	Yes	11	No	No phone service	DSL	Yes	...	No	No	No	No	Month-to-month	Yes	Electronic check	29.60	346.45	No
7041	8361-LTMKD	Male	1	Yes	No	4	Yes	Yes	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Mailed check	74.40	306.6	Yes
7042	3186-AJIEK	Male	0	No	No	66	Yes	No	Fiber optic	Yes	...	Yes	Yes	Yes	Yes	Two year	Yes	Bank transfer (automatic)	105.65	6844.5	No

7043 rows × 21 columns

In [ ]:

# 4.2.3 汇总统计（数量、分布指标）:describe
data.describe(include='all')

Out[ ]:

	用户ID	性别	是否老人	是否有伴侣	是否有孩子	合同期限	通话服务	多线程	网络服务	在线安全	...	设备安全	技术支持	流媒体电视	流媒体电影	合同类型	电子账单	支付方式	月消费	总消费	是否流失
count	7043	7043	7043.000000	7043	7043	7043.000000	7043	7043	7043	7043	...	7043	7043	7043	7043	7043	7043	7043	7043.000000	7043	7043
unique	7043	2	NaN	2	2	NaN	2	3	3	3	...	3	3	3	3	3	2	4	NaN	6531	2
top	7590-VHVEG	Male	NaN	No	No	NaN	Yes	No	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	NaN		No
freq	1	3555	NaN	3641	4933	NaN	6361	3390	3096	3498	...	3095	3473	2810	2785	3875	4171	2365	NaN	11	5174
mean	NaN	NaN	0.162147	NaN	NaN	32.371149	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	64.761692	NaN	NaN
std	NaN	NaN	0.368612	NaN	NaN	24.559481	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	30.090047	NaN	NaN
min	NaN	NaN	0.000000	NaN	NaN	0.000000	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	18.250000	NaN	NaN
25%	NaN	NaN	0.000000	NaN	NaN	9.000000	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	35.500000	NaN	NaN
50%	NaN	NaN	0.000000	NaN	NaN	29.000000	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	70.350000	NaN	NaN
75%	NaN	NaN	0.000000	NaN	NaN	55.000000	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	89.850000	NaN	NaN
max	NaN	NaN	1.000000	NaN	NaN	72.000000	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	118.750000	NaN	NaN

11 rows × 21 columns

In [ ]:

# 4.2.4 缺失值查询与处理：isnull.sum、dropnan
data.isnull

Out[ ]:

<bound method DataFrame.isnull of             用户ID      性别  是否老人 是否有伴侣 是否有孩子  合同期限 通话服务               多线程  \
0     7590-VHVEG  Female     0   Yes    No     1   No  No phone service   
1     5575-GNVDE    Male     0    No    No    34  Yes                No   
2     3668-QPYBK    Male     0    No    No     2  Yes                No   
3     7795-CFOCW    Male     0    No    No    45   No  No phone service   
4     9237-HQITU  Female     0    No    No     2  Yes                No   
...          ...     ...   ...   ...   ...   ...  ...               ...   
7038  6840-RESVB    Male     0   Yes   Yes    24  Yes               Yes   
7039  2234-XADUH  Female     0   Yes   Yes    72  Yes               Yes   
7040  4801-JZAZL  Female     0   Yes   Yes    11   No  No phone service   
7041  8361-LTMKD    Male     1   Yes    No     4  Yes               Yes   
7042  3186-AJIEK    Male     0    No    No    66  Yes                No   

             网络服务 在线安全  ... 设备安全 技术支持 流媒体电视 流媒体电影            合同类型 电子账单  \
0             DSL   No  ...   No   No    No    No  Month-to-month  Yes   
1             DSL  Yes  ...  Yes   No    No    No        One year   No   
2             DSL  Yes  ...   No   No    No    No  Month-to-month  Yes   
3             DSL  Yes  ...  Yes  Yes    No    No        One year   No   
4     Fiber optic   No  ...   No   No    No    No  Month-to-month  Yes   
...           ...  ...  ...  ...  ...   ...   ...             ...  ...   
7038          DSL  Yes  ...  Yes  Yes   Yes   Yes        One year  Yes   
7039  Fiber optic   No  ...  Yes   No   Yes   Yes        One year  Yes   
7040          DSL  Yes  ...   No   No    No    No  Month-to-month  Yes   
7041  Fiber optic   No  ...   No   No    No    No  Month-to-month  Yes   
7042  Fiber optic  Yes  ...  Yes  Yes   Yes   Yes        Two year  Yes   

                           支付方式     月消费      总消费 是否流失  
0              Electronic check   29.85    29.85   No  
1                  Mailed check   56.95   1889.5   No  
2                  Mailed check   53.85   108.15  Yes  
3     Bank transfer (automatic)   42.30  1840.75   No  
4              Electronic check   70.70   151.65  Yes  
...                         ...     ...      ...  ...  
7038               Mailed check   84.80   1990.5   No  
7039    Credit card (automatic)  103.20   7362.9   No  
7040           Electronic check   29.60   346.45   No  
7041               Mailed check   74.40    306.6  Yes  
7042  Bank transfer (automatic)  105.65   6844.5   No  

[7043 rows x 21 columns]>

In [ ]:

data.isnull().sum()

Out[ ]:

用户ID     0
性别       0
是否老人     0
是否有伴侣    0
是否有孩子    0
合同期限     0
通话服务     0
多线程      0
网络服务     0
在线安全     0
在线备份     0
设备安全     0
技术支持     0
流媒体电视    0
流媒体电影    0
合同类型     0
电子账单     0
支付方式     0
月消费      0
总消费      0
是否流失     0
dtype: int64

In [ ]:

# 4.2.5 重复值查询与处理：duplicated、drop_duplicated
data.duplicated().sum()

Out[ ]:

标签：...,No,4.2,NaN,check,7043,清洗,数据,Yes
From： https://www.cnblogs.com/mlzxdzl/p/17782417.html

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