AttributeError: 'function' 对象没有属性 'sum' pandas
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AttributeError: 'function' object has no attribute 'sum' pandas
提问by Gilbert
I have the following data frame in Pandas...
我在 Pandas 中有以下数据框...
+-----------------------+
| | count |
+-----------------------+
| group | |
+-----------------------+
| 11- | 99435 |
+-----------------------+
| Bachelor+ | 64900 |
+-----------------------+
| Just 12 | 162483 |
+-----------------------+
| Some College | 61782 |
+-----------------------+
I want to perform the following code but I'm getting an error...
我想执行以下代码，但出现错误...
death_2013['percent_of_total'] = death_2013.count.apply(
lambda x: (x / death_2013.count.sum()))
I'm getting the following error...
我收到以下错误...
AttributeError: 'function' object has no attribute 'apply'
I checked the death_2013.dtypesand countis a int64. I can't figure out what is wrong with the code.
我检查了death_2013.dtypes并且count是一个int64。我无法弄清楚代码有什么问题。
采纳答案by miradulo
There is a pandas.DataFrame.countmethod, which is shadowing the name of your column. This is why you're getting this error message - the bound method countis being accessed, which then obviously doesn't work.
有一种pandas.DataFrame.count方法可以隐藏列的名称。这就是您收到此错误消息的原因 -count正在访问绑定方法，这显然不起作用。
In this case, you should simply use the ['name_of_column']syntax to access the countcolumn in both places, and be mindful of DataFrame method names when naming columns in the future.
在这种情况下，您应该简单地使用['name_of_column']语法count在两个地方访问列，并在以后命名列时注意 DataFrame 方法名称。
death_2013['percent_of_total'] = death_2013['count'].apply(
lambda x: (x / death_2013['count'].sum()))Note however that in this particular case there is no need to use apply- you can simply divide the entire Series by the mean.
但是请注意，在这种特殊情况下，不需要使用apply- 您可以简单地将整个系列除以平均值。
death_2013['count'] / death_2013['count'].sum()
回答by John Coleman
The problem is that dataframes have a countmethod. If you want to run apply()on a columns named countuse the syntax
问题是数据帧有一个count方法。如果要apply()在名为的列上运行，请count使用语法
death_2013['count'].apply()
Alternatively, rename the column.
或者，重命名列。
