Question

我有一个数据框df：

          AID   JID   CID
0         1     A     NaN
1         1     A     NaN
2         1     B     NaN
3         1     NaN   X
4         3     A     NaN
5         4     NaN   NaN
6         4     C     X
7         5     C     Y
8         5     C     X
9         6     A     NaN
10        6     B     NaN

我想计算AID使用JID或CID的次数。

结果数据框应该是这样的，其中索引是AID值，列是CID和JID值：

      A     B     C    X    Y
1     2     1     0    1    0
3     1     0     0    0    0
4     0     0     1    1    0
5     0     0     2    1    1
6     1     1     0    0    0

我知道如何通过手动循环和计数来完成它。但我想知道更有效的方法是什么？

Answer 1

我melt然后使用pivot_table：

In [80]: d2 = pd.melt(df, id_vars="AID")

In [81]: d2.pivot_table(index="AID", columns="value", values="variable", 
                        aggfunc="count", fill_value=0)
Out[81]: 
value  A  B  C  X  Y
AID                 
1      2  1  0  1  0
3      1  0  0  0  0
4      0  0  1  1  0
5      0  0  2  1  1
6      1  1  0  0  0

这是有效的，因为melt将数据框“扁平化”成我们可以更轻松地一起访问这些值的内容，pivot_table正是您想要的聚合类型：

In [90]: pd.melt(df, "AID")
Out[90]: 
   AID variable value
0    1      JID     A
1    1      JID     A
2    1      JID     B
3    1      JID   NaN
4    3      JID     A
[... skipped]
17    4      CID     X
18    5      CID     Y
19    5      CID     X
20    6      CID   NaN
21    6      CID   NaN

Answer 2

您可以先stack创建Series，然后groupby创建df = df.set_index('AID').stack().groupby(level=0).value_counts().unstack(1, fill_value=0) print (df) A B C X Y AID 1 2 1 0 1 0 3 1 0 0 0 0 4 0 0 1 1 0 5 0 0 2 1 1 6 1 1 0 0 0。最后重塑value_counts：

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生成一个数据框，用于计算pandas

2 个答案: