Python Pandas - 使用 apply() 时，“Series”对象没有属性“colNames”
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Pandas - 'Series' object has no attribute 'colNames' when using apply()
提问by Runner Bean
I need to use a lambda function to do a row by row computation. For example create some dataframe
我需要使用 lambda 函数进行逐行计算。例如创建一些数据框
import pandas as pd
import numpy as np
def myfunc(x, y):
return x + y
colNames = ['A', 'B']
data = np.array([np.arange(10)]*2).T
df = pd.DataFrame(data, index=[range(0, 10)], columns=colNames)
using 'myfunc' this does work
使用'myfunc'这确实有效
df['D'] = (df.apply(lambda x: myfunc(x.A, x.B), axis=1))
but this second case does not work!
但这第二种情况不起作用！
df['D'] = (df.apply(lambda x: myfunc(x.colNames[0], x.colNames[1]), axis=1))
giving the error
给出错误
AttributeError: ("'Series' object has no attribute 'colNames'", u'occurred at index 0')
I really need to use the second case (access the colNames using the list) which gives an error, any clues on how to do this?
我真的需要使用第二种情况（使用列表访问 colNames），这会产生错误，有关如何执行此操作的任何线索？
thanks
谢谢
采纳答案by foglerit
When you use df.apply(), each row of your DataFrame will be passed to your lambda function as a pandas Series. The frame's columns will then be the index of the series and you can access values using series[label].
当您使用 时df.apply()，DataFrame 的每一行都将作为熊猫系列传递给您的 lambda 函数。框架的列将成为系列的索引，您可以使用series[label].
So this should work:
所以这应该有效：
df['D'] = (df.apply(lambda x: myfunc(x[colNames[0]], x[colNames[1]]), axis=1))
