如何根据 groupby.groups.keys() 筛选 pandas groupby 对象

Question

我有 pandas 个数据帧 df1 和 df2

df1:
     City  Pop Homes Other
0  City_1  100     1     0
1  City_1  100     2     6
2  City_1  100     2     2
3  City_1  100     3     9
4  City_1  200     1     6
5  City_1  200     2     6
6  City_1  200     3     7
7  City_1  300     1     0

df2:
     City  Pop Homes Other
0  City_1  100     1     0
1  City_1  100     2     6
2  City_1  100     2     2
3  City_1  100     8     9
4  City_1  200     1     6
5  City_1  200     2     6
6  City_1  800     3     7
7  City_1  800     8     0

我想创建 df3，它具有与 df1 和 df2 相同的列，但只包含成对的 Pop 和 Homes 值相同的行。

df3:
     City  Pop Homes Other
0  City_1  100     1     0
1  City_1  100     2     6
2  City_1  100     2     2
4  City_1  200     1     6
5  City_1  200     2     6

为了得到 df1 和 df2 中的对，我做了：

df1_string = """
City_1      100      1     0
City_1      100      2     6
City_1      100      2     2
City_1      100      3     9
City_1      200      1     6
City_1      200      2     6
City_1      200      3     7
City_1      300      1     0"""

df2_string = """
City_1      100      1     0
City_1      100      2     6
City_1      100      2     2
City_1      100      8     9
City_1      200      1     6
City_1      200      2     6
City_1      800      3     7
City_1      800      8     0"""

df1 = pd.DataFrame([x.split() for x in df1_string.split('\n')], columns=['City', 'Pop', 'Homes', 'Other'])
df2 = pd.DataFrame([x.split() for x in df2_string.split('\n')], columns=['City', 'Pop', 'Homes', 'Other'])

df1_keys = [x for x in df1.groupby(['Pop', 'Homes']).groups.keys()]
df2_keys = [x for x in df2.groupby(['Pop', 'Homes']).groups.keys()]

print(df1_keys)
[('100', '1'), ('100', '2'), ('100', '3'), ('200', '1'), ('200', '2'), ('200', '3'), ('300', '1')]
print(df2_keys)
[('100', '1'), ('100', '2'), ('100', '8'), ('200', '1'), ('200', '2'), ('800', '3'), ('800', '8')]

但我不知道如何从这里过滤 df1。我以为会是这样的：

df1 = df1[df1.groupby(['Pop', 'Homes']).groups.keys().isin(df2.groupby(['Pop', 'Homes']).groups.keys())]

但这不起作用。

我还要提一下，df1 和 df2 的长度并不总是相同。

解决方案

df1.set_index(['Pop', 'Homes'], inplace=True)
df2.set_index(['Pop', 'Homes'], inplace=True)

df1 = df1[df2.index.isin(df1.index)]

df1.reset_index(inplace=True)

Answer 1

将索引设置为 Pop 和 Home 生成值 'pairs' 并使用 isin() 应用所需的过滤器：

df1.set_index(['Pop', 'Homes'], inplace=True)
df2.set_index(['Pop', 'Homes'], inplace=True)

df1 = df1[df2.index.isin(df1.index)]

df1.reset_index(inplace=True)
print(df1)

如何根据 groupby.groups.keys() 筛选 pandas groupby 对象

How to filter pandas groupby object based on groupby.groups.keys()

concat

filter

python-3.x

pandas-groupby