如何使用条件索引获取单元格上的标量值 [英] How to get scalar value on a cell using conditional indexing

问题描述

我有如下所示的数据框.我需要获取列B的标量值，具体取决于A的值(这是我脚本中的变量).我正在尝试loc()函数，但它返回的是Series而不是标量值.如何获得标量value()?

I have the dataframe shown below. I need to get the scalar value of column B, dependent on the value of A (which is a variable in my script). I'm trying the loc() function but it returns a Series instead of a scalar value. How do I get the scalar value()?

>>> x = pd.DataFrame({'A' : [0,1,2], 'B' : [4,5,6]})
>>> x
   A  B
0  0  4
1  1  5
2  2  6

>>> x.loc[x['A'] == 2]['B']
2    6
Name: B, dtype: int64

>>> type(x.loc[x['A'] == 2]['B'])
<class 'pandas.core.series.Series'>

推荐答案

首先，最好从.loc访问行和列索引:

First of all, you're better off accessing both the row and column indices from the .loc:

x.loc[x['A'] == 2, 'B']

第二，您总是可以在序列或数据帧上使用.values来获取底层的numpy矩阵:

Second, you can always get at the underlying numpy matrix using .values on a series or dataframe:

In : x.loc[x['A'] == 2, 'B'].values[0]
Out: 6

最后，如果您对原始问题的条件索引"不感兴趣，则还可以使用特定的访问器来从DataFrame中获取单个标量值:

Finally, if you're not interested in the original question's "conditional indexing", there are also specific accessors designed to get a single scalar value from a DataFrame: dataframe.at[index, column] or dataframe.iat[i, j] (these are similar to .loc[] and .iloc[] but designed for quick access to a single value).

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