ValueError：无法将具有多列的 DataFrame 设置为单列

Question

我尝试使用下一个函数，但出现 ValueError: Cannot set a DataFrame with multiple columns to the single column.

我将此函数与其他列（CO01NUM022AH 和 CO01NUM022CT）一起使用，并且没有任何问题（创建了新列 R2 ... y R ...）。但对于其他列（CO01EXP001VE 和 CO01EXP001RO），只有我得到列 R2 ...

如有任何帮助或建议，我们将不胜感激。

这是我的代码。我收到第 19 行的错误：

VarMeses = ('CO01EXP001VE', 'CO01EXP001RO')

def RangDef0 (row):
    if row[column] == 0:
       rango = 'y. Sin antiguedad'
    elif row[column] > 0:
       rango = baseburo[NombrecolM]
    elif (row[column] == -1) | (row[column] == -2) | (row[column] == -3):
       rango = 'z. Sin informacion'
    else:
       rango = 'y. Sin antiguedad'
    return rango

binsM = [1, 12, 24, 48, 72, 96, 120, 144, 168, 99999]
labM = ['a. 1-12 m','b. 13-24 m','c. 25-48 m','d. 49-72 m','e. 73-96 m','f. 97-120 m','g. 121-144 m','h. 145-168 m','i. >168 m']

for column in VarMeses:
    NombrecolM = 'R2'+ str(column)  
    NombrecolM2 = 'R'+ str(column)
    baseburo[NombrecolM] = pd.cut(baseburo[column], bins = binsM, labels = labM, include_lowest = True)
    baseburo[NombrecolM2] = baseburo.apply(RangDef0, axis=1)

这是数据框：

Item    CO01NUM022AH    CO01NUM022CT    CO01EXP001VE    CO01EXP001RO    R2CO01NUM022AH  RCO01NUM022AH   R2CO01NUM022CT  RCO01NUM022CT   R2CO01EXP001VE  R2CO01EXP001RO
710059  0.0 0.0 206.0   239.0   b. 1    a. 0    b. 1    a. 0    i. >168 m   i. >168 m
710532  0.0 -1.0    -1.0    97.0    b. 1    a. 0    NaN z. Sin informacion  NaN f. 97-120 m
710895  0.0 -1.0    13.0    117.0   b. 1    a. 0    NaN z. Sin informacion  b. 13-24 m  f. 97-120 m
711302  0.0 -1.0    -1.0    54.0    b. 1    a. 0    NaN z. Sin informacion  NaN d. 49-72 m
711361  0.0 -1.0    -1.0    225.0   b. 1    a. 0    NaN z. Sin informacion  NaN i. >168 m

Answer 1

该错误可能来自于在正确定义或脱离上下文之前引用了 baseburo[NombrecolM]。你可以这样做：

import pandas as pd

data = {
    'Item': [710059, 710532, 710895, 711302, 711361],
    'CO01NUM022AH': [0.0, 0.0, 0.0, 0.0, 0.0],
    'CO01NUM022CT': [0.0, -1.0, -1.0, -1.0, -1.0],
    'CO01EXP001VE': [206.0, -1.0, 13.0, -1.0, -1.0],
    'CO01EXP001RO': [239.0, 97.0, 117.0, 54.0, 225.0]
}
baseburo = pd.DataFrame(data)

print(baseburo)

VarMeses = ('CO01EXP001VE', 'CO01EXP001RO')

binsM = [0, 1, 12, 24, 48, 72, 96, 120, 144, 168, 99999]
labM = ['y. Sin antiguedad', 'a. 1-12 m', 'b. 13-24 m', 'c. 25-48 m', 'd. 49-72 m', 'e. 73-96 m', 'f. 97-120 m', 'g. 121-144 m', 'h. 145-168 m', 'i. >168 m']

def RangDef0(row, column):
    if row[column] == 0:
       return 'y. Sin antiguedad'
    elif row[column] > 0:
       return pd.cut([row[column]], bins=binsM, labels=labM, right=False)[0]
    elif row[column] in [-1, -2, -3]:
       return 'z. Sin informacion'
    else:
       return 'y. Sin antiguedad'

for column in VarMeses:
    NombrecolM = 'R2' + column  
    NombrecolM2 = 'R' + column
    baseburo[NombrecolM] = pd.cut(baseburo[column], bins=binsM, labels=labM, right=False)
    baseburo[NombrecolM2] = baseburo.apply(lambda row: RangDef0(row, column), axis=1)

print(baseburo)

这给出了

  R2CO01EXP001VE       RCO01EXP001VE R2CO01EXP001RO RCO01EXP001RO  
0      i. >168 m           i. >168 m      i. >168 m     i. >168 m  
1            NaN  z. Sin informacion    f. 97-120 m   f. 97-120 m  
2     b. 13-24 m          b. 13-24 m    f. 97-120 m   f. 97-120 m  
3            NaN  z. Sin informacion     d. 49-72 m    d. 49-72 m  
4            NaN  z. Sin informacion      i. >168 m     i. >168 m