从(行，列，值)数据创建Pandas DataFrame [英] Create Pandas DataFrame from (row, column, value) data

问题描述

我有一个Pandas数据框，其中包含三列:行，列，值.行值都是小于N的所有整数，列值都是小于M的所有整数.这些值都是正整数.

I have a Pandas Dataframe with three columns: row, column, value. The row values are all integers below some N, and the column values are all integers below some M. The values are all positive integers.

如何有效地创建具有N行和M列的数据框，如果(i, j , val)是我的原始数据框中的行，则索引i, j的值为val，并且默认值为(0)否则?此外，由于数据已经很大，但是N*M仍然是我的数据大小的10倍，是否可以立即创建 parse 数据框?

How do I efficiently create a Dataframe with N rows and M columns, with at index i, j the value val if (i, j , val) is a row in my original Dataframe, and some default value (0) otherwise? Furthermore, is it possible to create a sparse Dataframe immediately, since the data is already quite large, but N*M is still about 10 times the size of my data?

推荐答案

NumPy解决方案适合此处的性能-

A NumPy solution would suit here for performance -

a = df.values
m,n = a[:,:2].max(0)+1
out = np.zeros((m,n),dtype=a.dtype)
out[a[:,0], a[:,1]] = a[:,2]
df_out = pd.DataFrame(out)

样品运行-

In [58]: df
Out[58]: 
    row  col  val
0     7    1   30
1     3    3    0
2     4    8   30
3     5    8   18
4     1    3    6
5     1    6   48
6     0    2    6
7     4    7    6
8     5    0   48
9     8    1   48
10    3    2   12
11    6    8   18

In [59]: df_out
Out[59]: 
    0   1   2  3  4  5   6  7   8
0   0   0   6  0  0  0   0  0   0
1   0   0   0  6  0  0  48  0   0
2   0   0   0  0  0  0   0  0   0
3   0   0  12  0  0  0   0  0   0
4   0   0   0  0  0  0   0  6  30
5  48   0   0  0  0  0   0  0  18
6   0   0   0  0  0  0   0  0  18
7   0  30   0  0  0  0   0  0   0
8   0  48   0  0  0  0   0  0   0

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