基于元组的子集 pandas 数据帧

本文介绍了基于元组的子集 pandas 数据帧的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我有一个像这样的数据集

I have a dataset like this

Firstnames = ['AA','BB','CC','AA','CC']
Lastnames = ['P', 'Q', 'R', 'P', 'R']
values = [10, 13, 3, 22, 45]

df = pd.DataFrame(data = list(zip(Firstnames,Lastnames,values)), \
                  columns=['Firstnames','Lastnames','values'])
df

    Firstnames  Lastnames   values
0   AA          P           10
1   BB          Q           13
2   CC          R           3
3   AA          P           22
4   CC          R           45

我有一个这样的元组数组

I have an array of tuple like this

lst = array([('AA', 'P'), ('BB', 'Q')])

我想对df进行子集化，这样Firstname == 'AA' & Lastnames == 'P'或Firstname == 'BB' & Lastnames == 'Q'

I want to subset df, such that Firstname == 'AA' & Lastnames == 'P'or Firstname == 'BB' & Lastnames == 'Q'

我可以手动执行此操作，但是我的数组很大，我想以编程方式进行操作

I can do this manually, however my array is quite large and I want to do it programmatically

我的预期输出将是

Firstnames  Lastnames   values
AA          P           10
AA          P           22
BB          Q           13

`agg` + `isin`

由于元组是可哈希的，因此可以使用isin并将汇总的值与last进行比较.直接使用lst和列表而不是np.array有帮助.

`agg`+`isin`

Since tuples are hashable, you can use isin and compare the aggregated values to your last. Using lst and a list directly instead of np.array helps.

>>> lst = [('AA', 'P'), 
           ('BB', 'Q')]

>>> mask = df[['Firstnames', 'Lastnames']].agg(tuple, 1).isin(lst)
>>> df[mask]

    Firstnames  Lastnames   values
0   AA          P           10
1   BB          Q           13
3   AA          P           22

如果需要，可以按名称sort_values

If you want, you can sort_values by the names

>>> df[mask].sort_values(by=['Firstnames', 'Lastnames'])

    Firstnames  Lastnames   values
0   AA          P           10
3   AA          P           22
1   BB          Q           13

`pd.concat`

对于较小的lst s，您还可以使用列表理解和pd.concat

`pd.concat`

You can also use a list comprehension and pd.concat for smaller lsts

>>> pd.concat([df[df.Firstnames.eq(a) & df.Lastnames.eq(b)] for a,b in lst])

    Firstnames  Lastnames   values
0   AA          P           10
3   AA          P           22
1   BB          Q           13

时间:

Timings:

小lst，大df

df = pd.concat([df]*10000).reset_index(drop=True)

%timeit mask = df[['Firstnames', 'Lastnames']].agg(tuple, 1).isin(lst); df[mask].sort_values(by=['Firstnames', 'Lastnames'])
942 ms ± 71.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit pd.concat([df[df.Firstnames.eq(a) & df.Lastnames.eq(b)] for a,b in lst])
16.2 ms ± 355 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

对于大lst和小df

c = list(map(''.join, itertools.product(string.ascii_uppercase, string.ascii_uppercase)))
lst = [(a,b) for a,b in zip(c, list(string.ascii_uppercase)*26)]
df = pd.DataFrame({'Firstnames': c, 'Lastnames': list(string.ascii_uppercase)*26, 'values': 10})

%timeit mask = df[['Firstnames', 'Lastnames']].agg(tuple, 1).isin(lst); df[mask].sort_values(by=['Firstnames', 'Lastnames'])
15.1 ms ± 301 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

%timeit pd.concat([df[df.Firstnames.eq(a) & df.Lastnames.eq(b)] for a,b in lst])
781 ms ± 33.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

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