熊猫:有条件的2列的累计和

时间:2019-11-11 18:46:50

标签: python pandas

假设我有两个农场A和B。每个星期都有不同的动物。如何获得每个农场当前的动物累计数量?

gensim

通过groupby,我可以从每个农场中获得积蓄:

+---+-----+--------+-----+--------+
|   |  A  | Farm_A |  B  | Farm_B |
+---+-----+--------+-----+--------+
| 0 | dog |   1    | cat |   1    |
| 1 | cat |   0    | dog |   1    |
| 2 | cat |   0    | dog |   1    |
| 3 | cat |   1    | dog |   0    |
| 4 | dog |   1    | dog |   1    |
| 5 | dog |   0    | dog |   0    |
| 6 | dog |   1    | cat |   1    |
+---+-----+--------+-----+--------+

我的问题是,如何从农场A和B的每一行中获取动物的累积总数?

例如第3行: 农场A中的动物是猫,那么我想从行0、1、2、3和3中的A和B农场中得到的猫总和为2。

再次在第3行,农场B的动物是狗,那么我要从第0、1、2、3行= 3的两个农场得到的狗总数

这是我想要实现的:

df['A cumsum Farm_A'] = df.groupby(['A'])['Farm_A'].cumsum()
df['B cumsum Farm_B'] = df.groupby(['B'])['Farm_B'].cumsum()

+---+-----+--------+-----+--------+-----------------+-----------------+
|   |  A  | Farm_A |  B  | Farm_B | A cumsum Farm_A | B cumsum Farm_B |
+---+-----+--------+-----+--------+-----------------+-----------------+
| 0 | dog |   1    | cat |   1    |        1        |        1        |
| 1 | cat |   0    | dog |   1    |        0        |        1        |
| 2 | cat |   0    | dog |   1    |        0        |        2        |
| 3 | cat |   1    | dog |   0    |        1        |        2        |
| 4 | dog |   1    | dog |   1    |        2        |        3        |
| 5 | dog |   0    | dog |   0    |        2        |        3        |
| 6 | dog |   1    | cat |   1    |        3        |        2        |
+---+-----+--------+-----+--------+-----------------+-----------------+

1 个答案:

答案 0 :(得分:0)

可以创建最后两列与虚拟变量一起使用。这样,您就可以跨农场为每种动物创建一个cumsum,然后可以lookup为每一行获取适当的值。

import pandas as pd

res = pd.get_dummies(df, columns=['A', 'B'])
# Animals only count if dummy & exists, so need to multiply.
res = pd.concat([res.filter(like='A_').multiply(res.Farm_A, axis=0),
                 res.filter(like='B_').multiply(res.Farm_B, axis=0)],
                axis=1)
# Cumsum per animal
res = res.groupby(res.columns.str.split('_').str[1], axis=1).apply(lambda x: x.sum(1).cumsum())
#   cat  dog
#0    1    1
#1    1    2
#2    1    3
#3    2    3
#4    2    5
#5    2    5
#6    3    6

# Lookup
df['A at both'] = res.lookup(df.index, df.A)
df['B at both'] = res.lookup(df.index, df.B)

输出

     A  Farm_A    B  Farm_B  A at both  B at both
0  dog       1  cat       1          1          1
1  cat       0  dog       1          1          2
2  cat       0  dog       1          1          3
3  cat       1  dog       0          2          3
4  dog       1  dog       1          5          5
5  dog       0  dog       0          5          5
6  dog       1  cat       1          6          3