使用字典灵活选择 pandas 数据框行 [英] Flexibly select pandas dataframe rows using dictionary

问题描述

假设我具有以下数据框:

Suppose I have the following dataframe:

df = pd.DataFrame({'color':['red', 'green', 'blue'], 'brand':['Ford','fiat', 'opel'], 'year':[2016,2016,2017]})

        brand   color   year
0       Ford    red     2016
1       fiat    green   2016
2       opel    blue    2017

我知道要使用多个列进行选择，我可以执行以下操作:

I know that to select using multiple columns I can do something like:

new_df = df[(df['color']=='red')&(df['year']==2016)]

现在我想做的是找到一种使用字典来选择我想要的行的方法，其中字典的键代表映射到允许值的列.例如，在df上应用以下字典{'color':'red', 'year':2016}将产生与new_df相同的结果.

Now what I would like to do is find a way to use a dictionary to select the rows I want where the keys of the dictionary represent the columns mapping to the allowed values. For example applying the following dictionary {'color':'red', 'year':2016} on df would yield the same result as new_df.

我已经可以使用for循环来做到这一点，但我想知道是否有任何更快和/或更多的 pythonic 方式！

I can already do it with a for loop, but I'd like to know if there are any faster and/or more 'pythonic' ways of doing it!

请附上方法花费的时间.

Please include time taken of method.

推荐答案

使用单个表达式:

In [728]: df = pd.DataFrame({'color':['red', 'green', 'blue'], 'brand':['Ford','fiat', 'opel'], 'year':[2016,2016,2017]})

In [729]: d = {'color':'red', 'year':2016}

In [730]: df.loc[np.all(df[list(d)] == pd.Series(d), axis=1)]
Out[730]: 
  brand color  year
0  Ford   red  2016

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