关于 pandas 插值函数的不确定性 [英] Uncertainity about the Interpolate Function in Pandas

问题描述

我正在使用pandas中的插值函数.这是一个玩具例子，用来说明问题:

I am working with the interpolate function in pandas. Here is a toy example to make an illustrative case:

df=pd.DataFrame({'Data':np.random.normal(size=200), 'Data2':np.random.normal(size=200)}) 

df.iloc[1, 0] = np.nan

print df

print df.interpolate('nearest')

我的问题:interpolate函数可以在多列上工作吗?也就是说，它是否使用多元分析来确定缺失字段的值?还是仅查看各个列?

My question: Does the interpolate function work over multiple columns? That is, does it use multivariate analysis to determine the value for a missing field? Or does it simply look at individual columns?

推荐答案

文档引用了各种可用的方法-大多数只是依靠index，可能是通过单变量scipy.interp1d或其他单变量scipy方法进行的:

The docs reference the various available methods - most just rely on the index, possibly via the univariate scipy.interp1d or other univariate scipy methods:

方法:{线性"，时间"，索引"，值"，最近"，零"， 线性"，二次"，三次"，重心"，克罗格"，多项式"， 样条曲线"，"piecewise_polynomial"，"pchip"}

method : {‘linear’, ‘time’, ‘index’, ‘values’, ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘barycentric’, ‘krogh’, ‘polynomial’, ‘spline’ ‘piecewise_polynomial’, ‘pchip’}

• 线性":忽略索引，并将值等距地对待.这是MultiIndexes支持的唯一方法.
• 默认的时间":插值适用于每日或更高分辨率的数据，以插值给定长度的间隔索引"，值":使用索引的实际数值
• 最近"，零"，线性"，二次"，三次"，重心"，多项式"被传递给scipy.interpolate.interp1d. 多项式"和样条"都要求您还指定一个顺序(int)，例如df.interpolate(方法=多项式"，顺序= 4).这些使用索引的实际数值.
• "krogh"，"piecewise_polynomial"，"spline"和"pchip"都是类似名称的scipy插值方法的包装.这些使用索引的实际数值.
• ‘linear’: ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes.
• default ‘time’: interpolation works on daily and higher resolution data to interpolate given length of interval ‘index’, ‘values’: use the actual numerical values of the index
• ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘barycentric’, ‘polynomial’ is passed to scipy.interpolate.interp1d. Both ‘polynomial’ and ‘spline’ require that you also specify an order (int), e.g. df.interpolate(method=’polynomial’, order=4). These use the actual numerical values of the index.
• ‘krogh’, ‘piecewise_polynomial’, ‘spline’, and ‘pchip’ are all wrappers around the scipy interpolation methods of similar names. These use the actual numerical values of the index.

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