逐行、逐個單元地比較 2 個 Pandas 數據幀

Question

我有 2 個數據幀， df1和df2 ，並且想要執行以下操作，將結果存儲在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，類似於：

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)

我從這個開始：

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

但我不確定如何處理它，並希望得到一些幫助。

Answer 1

因此，要繼續在評論中進行討論，您可以使用矢量化，這是像 pandas 或 numpy 這樣的庫的賣點之一。 理想情況下，您不應該調用iterrows() 。 更明確一點我的建議：

# 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]) 

現在，您甚至可以通過創建 lambda 函數然后使用列名壓縮它們來將不同的函數應用於不同的列：

# 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“問題”是您需要確保您的函數適用於您的列中的數據。 例如，如果您要執行類似df1['A'] - df2['A'] （使用您提供的 df1, df2）之類的操作，則會引發ValueError因為兩個字符串的減法未定義。 只是需要注意的事情。

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編輯，回復：您的評論：這也是可行的。 遍歷就是更大，這樣你就不會碰到的dfX.columns KeyError ，並拋出一個if語句有：

# 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])