如何使用python根据一列将整个数据集划分为4个范围

Question

我有一个带有7k records [telecom dataset]的数据集。

我想根据一个特定的列["tenure column"]将该数据集分割成4个范围，其中包含1到72个数字。

需要根据这个任期列来分割整个数据，例如：-

1至18范围1-数据集，19至36范围2-数据集，37至54范围3-数据集，55至72范围4-数据集

我的带头的样本集(5)

out.head(5)
Out[51]: 
   customerID      Date  gender  age  region  SeniorCitizen  Partner  \
0  9796-BPKIW  1/2/2008       1   57       1              1        0   
1  4298-OYIFC  1/4/2008       1   50       2              0        1   
2  9606-PBKBQ  1/6/2008       1   85       0              1        1   
3  1704-NRWYE  1/9/2008       0   55       0              1        0   
4  9758-MFWGD  1/6/2008       0   52       1              1        1   

   Dependents  tenure  PhoneService  ...    DeviceProtection  TechSupport  \
0           0       8             1  ...                   0            0   
1           0      15             1  ...                   1            1   
2           0      32             1  ...                   0            0   
3           0       9             1  ...                   0            0   
4           1      48             0  ...                   0            0   

   StreamingTV  StreamingMovies  Contract  PaperlessBilling  PaymentMethod  \
0            0                0         0                 1              1   
1            1                1         0                 1              2   
2            0                1         0                 1              2   
3            1                0         0                 1              2   
4            0                0         1                 0              0   

   MonthlyCharges  TotalCharges  Churn  
0           69.95        562.70      0  
1          103.45       1539.80      0  
2           85.00       2642.05      1  
3           80.85        751.65      1  
4           29.90       1388.75      0  

Answer 1

用熊猫来做这件事很容易。

import pandas as pd

df = pd.read_csv('your_dataset_file.csv', sep=',', header=0)
# Sort it according to tenure
df.sort_values(by=['tenure'], inplace=True)
# Create bin edges 
step_size = int(df.tenure.max()/4)
bin_edges = list(range(0,df.tenure.max()+step_size, step_size))
lbls = ['a','b','c','d']
df['bin'] = pd.cut(df.tenure,bin_edges, labels= lbls)
# Create separate dataframes from it
df1 = df[df.bin == 'a']
df2 = df[df.bin == 'b']
df3 = df[df.bin == 'c']
df4 = df[df.bin == 'd']

Answer 2

我将创建数据集列表。

dflist = [df[df["tenure column"].isin(range(i*18 + 1,(i+1)*18+1))] for i in range(4)] 

Answer 3

易于理解的代码

i = 1
m = 0
out["tenure column"] = out["tenure column"].astype(int)
df = [None]*4
while i<72:
   df[m] = out[(out["tenure column"]>=i) & (out["tenure column"]<=(i+17))]
   m += 1
   i += 18

希望这能解决你的问题