向 Pandas 数据框添加具有特定项目值的新列？
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adding new column to pandas dataframe with values for particular items?
提问by
I have this pandas dataframe:
我有这个Pandas数据框：
d=pandas.DataFrame([{"a": 1}, {"a": 3, "b": 2}])
and I'm trying to add a new column to it with non-null values only for certain rows, based on their numeric indices in the array. for example, adding a new column "c" only to the first row in d:
并且我试图根据它们在数组中的数字索引，仅针对某些行添加一个具有非空值的新列。例如，仅在 中的第一行添加新列“c” d：
# array of row indices
indx = np.array([0])
d.ix[indx]["c"] = "foo"
which should add "foo" as the column "c" value for the first row, and NaN for all other rows. but this doesn't seem to change the array:
它应该添加“foo”作为第一行的列“c”值，并为所有其他行添加 NaN。但这似乎并没有改变数组：
d.ix[np.array([0])]["c"] = "foo"
In [18]: d
Out[18]:
a b
0 1 NaN
1 3 2
what am I doing wrong here? how can it be done? thanks.
我在这里做错了什么？怎么做到呢？谢谢。
采纳答案by Jeff
In [11]: df = pd.DataFrame([{"a": 1}, {"a": 3, "b": 2}])
In [12]: df['c'] = np.array(['foo',np.nan])
In [13]: df
Out[13]:
a b c
0 1 NaN foo
1 3 2 nan
If you were assigning a numeric value, the following would work
如果您要分配一个数值，以下内容将起作用
In [16]: df['c'] = np.nan
In [17]: df.ix[0,'c'] = 1
In [18]: df
Out[18]:
a b c
0 1 NaN 1
1 3 2 NaN
