仅外连接 python pandas

Question

我有两个 DataFrame，它们具有相同的列名以及一些匹配数据和一些唯一数据。

我想排除中间部分，只保存两个 DataFrame 独有的部分。

我将如何连接、合并或加入这两个数据框？

例如这张图我不想要这张图的中间，我想要两边而不是中间:

这是我现在的代码：

def query_to_df(query):
    ...
    df_a = pd.DataFrame(data_a)
    df_b = pd.DataFrame(data_b)
    outer_results = pd.concat([df_a, df_b], axis=1, join='outer')
    return df

让我给你举个我需要的例子：

df_a = 
col_a  col_b  col_c
   a1     b1     c1
   a2     b2     c2

df_b = 
col_a  col_b  col_c
   a2     b2     c2
   a3     b3     c3

# they only share the 2nd row:    a2     b2     c2 
# so the outer result should be:
col_a  col_b  col_c  col_a  col_b  col_c
   a1     b1     c1     NA     NA     NA
   NA     NA     NA     a3     b3     c3

或者我对 2 个数据帧同样满意

result_1 =
col_a  col_b  col_c
   a1     b1     c1

result_2 =
col_a  col_b  col_c
   a3     b3     c3

最后，您会注意到 a2 b2 c2 被排除在外，因为所有列都匹配 - 我如何指定我想根据所有列加入，而不仅仅是 1？如果 df_a 有 a2 foo c2，我希望该行也位于 result_1 中。

Answer 1

使用merge with indicator parameter and outer join first and then filter by query or boolean indexing:

df = df_a.merge(df_b, how='outer', indicator=True)
print (df)
  col_a col_b col_c      _merge
0    a1    b1    c1   left_only
1    a2    b2    c2        both
2    a3    b3    c3  right_only

a = df.query('_merge == "left_only"').drop('_merge', 1)
print (a)
  col_a col_b col_c
0    a1    b1    c1

b = df.query('_merge == "right_only"').drop('_merge', 1)
print (b)
  col_a col_b col_c
2    a3    b3    c3

或：

a = df[df['_merge'] == "left_only"].drop('_merge', 1)
print (a)
  col_a col_b col_c
0    a1    b1    c1

b = df[df['_merge'] == "right_only"].drop('_merge', 1)
print (b)
  col_a col_b col_c
2    a3    b3    c3

Answer 2

使用pd.DataFrame.drop_duplicates
这假设行在它们各自的数据框中是唯一的。

df_a.append(df_b).drop_duplicates(keep=False)

  col_a col_b col_c
0    a1    b1    c1
1    a3    b3    c3

您甚至可以使用 pd.concat 和 keys 参数来给出行所在的上下文。

pd.concat([df_a, df_b], keys=['a', 'b']).drop_duplicates(keep=False)

    col_a col_b col_c
a 0    a1    b1    c1
b 1    a3    b3    c3

Answer 3

concat 和 drop_duplicates with keep = False

new_df = pd.concat([df_a, df_b]).drop_duplicates(keep=False)

    col_a   col_b   col_c
0   a1      b1      c1
1   a3      b3      c3

使用 numpy setdiff1

df_a = pd.DataFrame(np.setdiff1d(np.array(df_a.values), np.array(df_b.values))\
.reshape(-1, df_a.shape[1]), columns = df_a.columns)

df_b = pd.DataFrame(np.setdiff1d(np.array(df_b.values), np.array(df_a.values))\
.reshape(-1, df_b.shape[1]), columns = df_b.columns)

df_a

    col_a   col_b   col_c
0   a1      b1      c1

df_b

    col_a   col_b   col_c
0   a3      b3      c3

仅外连接 python pandas

only outer join python pandas

python

outer-join

dataframe

pandas