我正在寻找一种添加numpy列的通用方法。nans到一个数据帧而不得到愚蠢的SettingWithCopyWarning。

从以下方面:

答案在这里 关于将变量作为关键字参数传递的问题 此方法用于生成一个numpy数组的NaNs

我想到了这个:

col = 'column_name'
df = df.assign(**{col:numpy.full(len(df), numpy.nan)})

首先创建一个包含相关数据的python的list_of_e。 用这个: Df ['e'] = list_of_e

当您将Series对象作为新列添加到现有DF时，您需要确保它们都具有相同的索引。 然后添加到DF中

e_series = pd.Series([-0.335485, -1.166658,-0.385571])
print(e_series)
e_series.index = d_f.index
d_f['e'] = e_series
d_f

我得到了可怕的SettingWithCopyWarning，它没有通过使用iloc语法修复。我的DataFrame是由read_sql从ODBC源创建的。根据上面low - tech的建议，以下方法对我来说是有效的:

df.insert(len(df.columns), 'e', pd.Series(np.random.randn(sLength),  index=df.index))

This worked fine to insert the column at the end. I don't know if it is the most efficient, but I don't like warning messages. I think there is a better solution, but I can't find it, and I think it depends on some aspect of the index. Note. That this only works once and will give an error message if trying to overwrite and existing column. Note As above and from 0.16.0 assign is the best solution. See documentation http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.assign.html#pandas.DataFrame.assign Works well for data flow type where you don't overwrite your intermediate values.

以下是我所做的…但我对熊猫和Python都很陌生，所以不能保证。

df = pd.DataFrame([[1, 2], [3, 4], [5,6]], columns=list('AB'))

newCol = [3,5,7]
newName = 'C'

values = np.insert(df.values,df.shape[1],newCol,axis=1)
header = df.columns.values.tolist()
header.append(newName)

df = pd.DataFrame(values,columns=header)

最简单的方法:

data['new_col'] = list_of_values

data.loc[ : , 'new_col'] = list_of_values

这样在pandas对象中设置新值时就避免了所谓的链式索引。点击这里进一步阅读。