用包含数组条目的列查询 pandas [英] pandas query with a column consisting of array entries

问题描述

ykp.data
Out[182]: 
    state  action  reward  
0    [41]       5      59  
1     [5]      52      48  
2    [46]      35      59  
3    [42]      16      12  
4    [43]      37      48   
5    [36]       5      59   
6    [49]      52      48 
7    [39]      11      23 

我想找到与[ 42]在状态输入项中运行，

I would like to find the row that matches [42] in the state entry so I ran

ykp.data.query('state == [42]')

但是我得到

Empty DataFrame
Columns: [state, action, reward]
Index: []

当我应该看到 [42]，16、12 时。

有人可以请告诉我如何解决这个问题？我需要将状态值存储为数组。

Can someone please tell me how I can workaround this? I need my state-values to be stored as arrays.

推荐答案

最好避免 pd.Series。在此处申请。相反，您可以使用 itertools.chain 构造一个常规的NumPy数组。然后将数组与整数进行比较以形成布尔数组以进行索引：

Best to avoid pd.Series.apply here. Instead, you can use itertools.chain to construct a regular NumPy array. Then compare the array to an integer to form a Boolean array for indexing:

from itertools import chain

df = pd.DataFrame(np.random.randint(0, 100, size=(100000, 1)), columns=['state'])
df = df.assign(state=df.state.apply(lambda x: [x]), axis=1)

def wen(df):
    df.state=df.state.astype(str)
    return df.query("state == '[42]'")

%timeit df[np.array(list(chain.from_iterable(df['state'].values))) == 42]  # 14.2 ms
%timeit df[df.state.apply(tuple) == (42,)]                                 # 41.9 ms
%timeit df.loc[df.state.apply(lambda x: x==[42])]                          # 33.9 ms
%timeit wen(df)                                                            # 19.9 ms

更好的是，不要在数据框中使用列表。只需使用常规的 int 系列。这样可以提高内存效率和性能。

Better still, don't use lists in your dataframe. Just use regular int series. This will be memory and performance efficient.

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