逐行、逐个单元地比较 2 个 Pandas 数据帧

Question

我有 2 个数据帧，df1 和 df2，并且想要执行以下操作，将结果存储在 df3：

for each row in df1:

    for each row in df2:

        create a new row in df3 (called "df1-1, df2-1" or whatever) to store results 

        for each cell(column) in df1: 

            for the cell in df2 whose column name is the same as for the cell in df1:

                compare the cells (using some comparing function func(a,b) ) and, 
                depending on the result of the comparison, write result into the 
                appropriate column of the "df1-1, df2-1" row of df3)

例如：

df1
A   B    C      D
foo bar  foobar 7
gee whiz herp   10

df2
A   B   C      D
zoo car foobar 8

df3
df1-df2 A             B              C                   D
foo-zoo func(foo,zoo) func(bar,car)  func(foobar,foobar) func(7,8)
gee-zoo func(gee,zoo) func(whiz,car) func(herp,foobar)   func(10,8)

我从这个开始：

for r1 in df1.iterrows():
    for r2 in df2.iterrows():
        for c1 in r1:
            for c2 in r2:

但我不确定如何处理它，希望能得到一些帮助。

Answer 1

所以要在 cmets 中继续讨论，可以使用矢量化，这是 pandas 或 numpy 等库的卖点之一。理想情况下，您不应该打电话给iterrows()。我的建议更明确一点：

# with df1 and df2 provided as above, an example
df3 = df1['A'] * 3 + df2['A']

# recall that df2 only has the one row so pandas will broadcast a NaN there
df3
0    foofoofoozoo
1             NaN
Name: A, dtype: object

# more generally

# we know that df1 and df2 share column names, so we can initialize df3 with those names
df3 = pd.DataFrame(columns=df1.columns) 
for colName in df1:
    df3[colName] = func(df1[colName], df2[colName]) 

现在，您甚至可以将不同的函数应用于不同的列，例如，创建 lambda 函数，然后使用列名压缩它们：

# some example functions
colAFunc = lambda x, y: x + y
colBFunc = lambda x, y; x - y
....
columnFunctions = [colAFunc, colBFunc, ...]

# initialize df3 as above
df3 = pd.DataFrame(columns=df1.columns)
for func, colName in zip(columnFunctions, df1.columns):
    df3[colName] = func(df1[colName], df2[colName])

唯一想到的“问题”是您需要确保您的函数适用于列中的数据。例如，如果您要执行 df1['A'] - df2['A'] 之类的操作（使用您提供的 df1、df2），则会引发 ValueError，因为两个字符串的减法未定义。只是需要注意的事情。

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编辑，回复：您的评论：这也是可行的。遍历更大的 dfX.columns，这样您就不会遇到 KeyError，并在其中抛出 if 语句：

# all the other jazz
# let's say df1 is [['A', 'B', 'C']] and df2 is [['A', 'B', 'C', 'D']]
# so iterate over df2 columns
for colName in df2:
    if colName not in df1:
        df3[colName] = np.nan # be sure to import numpy as np
    else:
        df3[colName] = func(df1[colName], df2[colName])