问题描述
设置:
pdf = pd.DataFrame(np.random.rand(4,5), columns = list('abcde'))
pdf['a'][2:]=pdf['a'][0]
pdf['a'][:2]=pdf['a'][1]
pdf.set_index(['a','b'])
输出:
c d e
a b
0.439502 0.115087 0.832546 0.760513 0.776555
0.609107 0.247642 0.031650 0.727773
0.995370 0.299640 0.053523 0.565753 0.857235
0.392132 0.832560 0.774653 0.213692
每个数据系列按索引 ID a 分组，b 表示 a 其他特征的时间索引.有没有办法让熊猫生成一个反映 a 分组的 numpy 3d 数组?目前它以二维形式读取数据，所以 pdf.shape 输出 (4, 5).我想要的是数组的变量形式:
Each data series is grouped by the index ID a and b represents a time index for the other features of a. Is there a way to get the pandas to produce a numpy 3d array that reflects the a groupings? Currently it reads the data as two dimensional so pdf.shape outputs (4, 5). What I would like is for the array to be of the variable form:
array([[[-1.38655912, -0.90145951, -0.95106951, 0.76570984],
[-0.21004144, -2.66498267, -0.29255182, 1.43411576],
[-0.21004144, -2.66498267, -0.29255182, 1.43411576]],
[[ 0.0768149 , -0.7566995 , -2.57770951, 0.70834656],
[-0.99097395, -0.81592084, -1.21075386, 0.12361382]]])
有没有原生 Pandas 的方法来做到这一点?请注意，实际数据中每个 a 分组的行数是可变的，因此我不能只是转置或重塑 pdf.values.如果没有原生方式，从数十万行和数百列迭代构建数组的最佳方法是什么?
Is there a native Pandas way to do this? Note that number of rows per a grouping in the actual data is variable, so I cannot just transpose or reshape pdf.values. If there isn't a native way, what's the best method for iteratively constructing the arrays from hundreds of thousands of rows and hundreds of columns?
推荐答案
panel.values
将直接返回一个 numpy 数组.这将必然是最高可接受的 dtype，因为所有内容都被压缩到单个 3-d numpy 数组中.它将是 new 数组，而不是 Pandas 数据的视图(无论 dtype).
will return a numpy array directly. this will by necessity be the highest acceptable dtype as everything is smushed into a single 3-d numpy array. It will be new array and not a view of the pandas data (no matter the dtype).
这篇关于Pandas 数据框或面板到 3d numpy 数组的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！