我如何根据数字的前几个数字切割字符串

Question

如何根据数字的第一个特定数字和其余的数字切割字符串

这是我的数据

Install-Package PushSharp -Version 4.0.10

这是预期的输出

Id actual_pattern 1 100101 2 10101 3 1010101 4 101 的

是cut_pattern1的前4位数字 因为actual_pattern是cut_pattern2的余数形式，如果cut_pattern1的余数不存在，则使cut_pattern1 = 0

如果cut_pattern2中有任何1，则使cut_pattern2 = 1，否则使binary_cut2 = 0

binary_cut2

Answer 1

通过使用str，replace进行索引来创建新列，以更改空字符串，对于新列，请使用Series.str.contains并将其转换为整数：

df['actual_pattern'] = df['actual_pattern'].astype(str)

df['cut_pattern1'] = df['actual_pattern'].str[:4]
df['cut_pattern2'] = df['actual_pattern'].str[4:].replace('','0')
df['binary_cut2'] = df['cut_pattern2'].str.contains('1').astype(int)
print (df)
   Id actual_pattern cut_pattern1 cut_pattern2  binary_cut2
0   1         100101         1001           01            1
1   2          10101         1010            1            1
2   3        1010101         1010          101            1
3   4            101          101            0            0

编辑：

@Rick Hitchcock的解决方案：

df['actual_pattern'] = df['actual_pattern'].astype(str)

df['cut_pattern1'] = df['actual_pattern'].str[:4]
df['cut_pattern2'] = df['actual_pattern'].str[4:].replace('','0')
df['binary_cut2'] = df['cut_pattern2'].str.contains('1').astype(int)
print (df)
  Id actual_pattern cut_pattern1 cut_pattern2  binary_cut2
0  1         100101         1001           01            1
1  2          10101         1010            1            1
2  3        1010101         1010          101            1
3  4       00001111         0000         1111            1

Answer 2

这是我的处理方法：

s = df.actual_pattern.astype(str).str
# Split into 2 lists, the first containing the first 4 digits
out = s.split(r'(\d{4})').str[-2:].values.tolist()
# [['1001', '01'], ['1010', '1'], ['1010', '101'], ['101']]
# build a dataframe from the lists
out = pd.DataFrame(out, columns=['cut_pattern1', 'cut_pattern2'])
# fill missing values (absense of string in list) with 0
out['cut_pattern2'] = out.cut_pattern2.fillna('0')
out['binary_cut2'] = out.cut_pattern2.str.contains('1').view('i1')
print(out)

     cut_pattern1 cut_pattern2 binary_cut2
0         1001           01           1
1         1010            1           1
2         1010          101           1
3          101            0           0

Answer 3

在此处使用一些正则表达式和字符串提取：

m=df.actual_pattern.str.extract('(?P<cut_pattern1>.{,4})(?P<cut_pattern2>.*)').replace('',0)

---

   cut_pattern1 cut_pattern2
0         1001           01
1         1010            1
2         1010          101
3          101            0

然后做：

m.assign(binary_cut2=m.cut_pattern2.str.contains('1',na=False).astype(int))

---

  cut_pattern1 cut_pattern2  binary_cut2
0         1001           01            1
1         1010            1            1
2         1010          101            1
3          101            0            0

最后将其连接到原始df：

m=df.actual_pattern.str.extract('(?P<cut_pattern1>.{,4})(?P<cut_pattern2>.*)').replace('',0)
m=m.assign(binary_cut2=m.cut_pattern2.str.contains('1',na=False).astype(int))
pd.concat([df,m],axis=1)

---

  Id actual_pattern cut_pattern1 cut_pattern2  binary_cut2
0  1         100101         1001           01            1
1  2          10101         1010            1            1
2  3        1010101         1010          101            1
3  4            101          101            0            0