使用Pandas查找2个不同大小的数据框之间的不同行 [英] Find different rows between 2 dataframes of different size with Pandas

问题描述

我有2个大小不同的数据帧df1和df2.

I have 2 dataframes df1 and df2 of different size.

df1 = pd.DataFrame({'A':[np.nan, np.nan, np.nan, 'AAA','SSS','DDD'], 'B':[np.nan,np.nan,'ciao',np.nan,np.nan,np.nan]})
df2 = pd.DataFrame({'C':[np.nan, np.nan, np.nan, 'SSS','FFF','KKK','AAA'], 'D':[np.nan,np.nan,np.nan,1,np.nan,np.nan,np.nan]})

我的目标是确定df1中哪些元素不会出现在df2中.

My goal is to identify the elements of df1 which do not appear in df2.

使用以下几行代码，我能够实现自己的目标.

I was able to achieve my goal using the following lines of code.

df = pd.DataFrame({})
for i, row1 in df1.iterrows():

    found = False
    for j, row2, in df2.iterrows():

        if row1['A']==row2['C']:

            found = True
            print(row1.to_frame().T)

    if found==False and pd.isnull(row1['A'])==False:
        df = pd.concat([df, row1.to_frame().T], axis=0)

df.reset_index(drop=True)

有没有更优雅，更有效的方法来实现我的目标?

Is there a more elegant and efficient way to achieve my goal?

注意:解决方法是

    A   B
0   DDD NaN

推荐答案

我认为需要 isin 与

I believe need isin withboolean indexing :

在默认情况下，也省略NaN的行以链接新条件:

Also omit NaNs rows by default chain new condition:

#changed df2 with no NaN in C column
df2 = pd.DataFrame({'C':[4, 5, 5, 'SSS','FFF','KKK','AAA'], 
                    'D':[np.nan,np.nan,np.nan,1,np.nan,np.nan,np.nan]})
print (df2)
     C    D
0    4  NaN
1    5  NaN
2    5  NaN
3  SSS  1.0
4  FFF  NaN
5  KKK  NaN
6  AAA  NaN

df = df1[~(df1['A'].isin(df2['C']) | (df1['A'].isnull()))]
print (df)
     A    B
5  DDD  NaN

---

如果没有必要，请省略C列中的NaN:

---

If not necessary omit NaNs if not exist in C column:

df = df1[~df1['A'].isin(df2['C'])]
print (df)
     A     B
0  NaN   NaN
1  NaN   NaN
2  NaN  ciao
5  DDD   NaN

---

如果两列中均存在NaN，则使用第二种解决方案:

---

If exist NaNs in both columns use second solution:

(输入DataFrame来自问题)

df = df1[~df1['A'].isin(df2['C'])]
print (df)
     A    B
5  DDD  NaN

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