In [ ]:

import pandas as pd

In [ ]:

data = pd.read_csv("WA_Fn-UseC_-Telco-Customer-Churn.csv")
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']
data.head()

Out[ ]:

	用户ID	性别	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	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	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	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	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	No	No	2	Yes	No	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes

5 rows × 21 columns

In [ ]:

# 4.4.1 整体流失情况：人数、比例、流失率

In [ ]:

data['是否流失'].drop_duplicates()

Out[ ]:

0     No
2    Yes
Name: 是否流失, dtype: object

In [ ]:

# 流失人数、非流失人数
churn1 = data[data['是否流失']=='Yes']['是否流失'].count()
churn0 = data[data['是否流失']=='No']['是否流失'].count()
churn0,churn1

Out[ ]:

(5174, 1869)

In [ ]:

# 流失率 = 流失人数 / 总人数
churn_per = churn1 / len(data)
churn_per

Out[ ]:

0.2653698707936959

In [ ]:

# 4.4.2 性别：人数、比例、流失率
data['性别'].drop_duplicates()

Out[ ]:

0    Female
1      Male
Name: 性别, dtype: object

In [ ]:

gender0 = data[data['性别']=='Female']['性别'].count()
gender1 = data[data['性别']=='Male']['性别'].count()
gender0,gender1

Out[ ]:

(3488, 3555)

In [ ]:

gender0 / len(data)   # 女性占人数总量

Out[ ]:

0.495243504188556

In [ ]:

# 女性流失率：女性流失人数/女性总人数
## 创建一个churn=yes的子数据集
df_churn = data[data['是否流失']=='Yes']
## 计算女性流失率
gender0_churn = df_churn[df_churn['性别']=='Female']['性别'].count()
gender0_churn_per = gender0_churn / gender0
gender0_churn_per

Out[ ]:

0.26920871559633025

In [ ]:

# 男性流失率：男性流失人数/男性总人数
## 计算男性流失率
gender0_churn = df_churn[df_churn['性别']=='Male']['性别'].count()
gender0_churn_per = gender0_churn / gender1
gender0_churn_per

Out[ ]:

0.2616033755274262

In [ ]:

# 4.4.3 老人：人数、比例、流失率
data['是否老人'].drop_duplicates()

Out[ ]:

0     0
20    1
Name: 是否老人, dtype: int64

In [ ]:

senior0 = len(data[data['是否老人']==0])
senior1 = len(data[data['是否老人']==1])
senior0,senior1

Out[ ]:

(5901, 1142)

In [ ]:

senior1 / len(data) # 老年人比例

Out[ ]:

0.1621468124378816

In [ ]:

# 计算对应的流失率：对应流失人数/对应总人数
s0_churn = len(df_churn[df_churn['是否老人']==0])
s1_churn = len(df_churn[df_churn['是否老人']==1])
s0_churn_per = s0_churn / senior0
s1_churn_per = s1_churn / senior1
s0_churn_per, s1_churn_per

Out[ ]:

(0.23606168446026096, 0.4168126094570928)

In [ ]:

# 4.4.4 伴侣：人数、比例、流失率
data['是否有伴侣'].drop_duplicates()

Out[ ]:

0    Yes
1     No
Name: 是否有伴侣, dtype: object

In [ ]:

partner0 = len(data[data['是否有伴侣']=='No'])
partner1 = len(data[data['是否有伴侣']=='Yes'])
partner0,partner1

Out[ ]:

(3641, 3402)

In [ ]:

partner0 / len(data)   # 没有伴侣的人的比例

Out[ ]:

0.5169672014766434

In [ ]:

partner1 / len(data)   # 有伴侣的人的比例

Out[ ]:

0.4830327985233565

In [ ]:

# 计算对应的流失率：对应流失人数/对应总人数
partner0_churn = len(df_churn[df_churn['是否有伴侣']=='No'])
partner1_churn = len(df_churn[df_churn['是否有伴侣']=='Yes'])
partner0_churn_per = partner0_churn / partner0
partner1_churn_per = partner1_churn / partner1
partner0_churn_per, partner1_churn_per

Out[ ]:

(0.32957978577313923, 0.1966490299823633)

In [ ]:

# 4.4.5 亲属：人数、比例、流失率
data['是否有孩子'].drop_duplicates()

Out[ ]:

0     No
6    Yes
Name: 是否有孩子, dtype: object

In [ ]:

dependents0 = len(data[data['是否有孩子']=='No'])
dependents1 = len(data[data['是否有孩子']=='Yes'])
dependents0,dependents1

Out[ ]:

(4933, 2110)

In [ ]:

# 是否有孩子的比例
dependents0 / len(data),dependents1 / len(data)

Out[ ]:

(0.7004117563538265, 0.2995882436461735)

In [ ]:

# 计算对应的流失率：对应流失人数/对应总人数
dependents0_churn = len(df_churn[df_churn['是否有孩子']=='No'])
dependents1_churn = len(df_churn[df_churn['是否有孩子']=='Yes'])
dependents0_churn_per = dependents0_churn / dependents0
dependents1_churn_per = dependents1_churn / dependents1
dependents0_churn_per, dependents1_churn_per

Out[ ]:

(0.3127914048246503, 0.15450236966824646)

In [ ]:

# 4.4.6 数据洞察：现象、朔源、建议
# 高流失率用户特征:
# 1.性别无特殊性
# 2.老年人相对于年轻人更容易流失
# 3.单身用户更倾向于流失
# 4.无孩子相对于有孩子更容易流失

4.4-用户维度

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