Python：使用熊猫将一个数组连接到另一个数组

您可以使用isin：

import pandas as pd
allFeatures = [101, 179, 181, 183, 185, 843, 845, 847, 849, 851, 853, 855]
allReadings = [0.03, 0.01, 0.01, 0.03, 0.03, 0.01, 0.03, 0.02, 0.07, 0.06, 0.01, 0.04]
aoiFeatures = [183, 845, 853]

df = pd.DataFrame({'features':allFeatures, 'readings':allReadings})
result = df.loc[df['features'].isin(aoiFeatures)]
print(result)

产量

    features  readings
3        183      0.03
6        845      0.03
10       853      0.01

如果您计划feature经常根据值选择行，并且features可以将其制成唯一的索引，并且如果DataFrame至少中等大小（例如约10,000行），那么（为提高性能）创建features索引可能会更好：

import pandas as pd
allFeatures = [101, 179, 181, 183, 185, 843, 845, 847, 849, 851, 853, 855]
allReadings = [0.03, 0.01, 0.01, 0.03, 0.03, 0.01, 0.03, 0.02, 0.07, 0.06, 0.01, 0.04]
aoiFeatures = [183, 845, 853]

df = pd.DataFrame({'readings':allReadings}, index=allFeatures)
result = df.loc[aoiFeatures]
print(result)

产量

     readings
183      0.03
845      0.03
853      0.01

这是我用来进行IPython％timeit测试的设置：

import pandas as pd
N = 10000
allFeatures = np.repeat(np.arange(N), 1)
allReadings = np.random.random(N)
aoiFeatures = np.random.choice(allFeatures, N//10, replace=False)

def using_isin():
    df = pd.DataFrame({'features':allFeatures, 'readings':allReadings})
    for i in range(1000):
        result = df.loc[df['features'].isin(aoiFeatures)]
    return result


def using_index():
    df = pd.DataFrame({'readings':allReadings}, index=allFeatures)
    for i in range(1000):
        result = df.loc[aoiFeatures]
    return result

这显示using_index可能会更快一些：

In [108]: %timeit using_isin()
1 loop, best of 3: 697 ms per loop

In [109]: %timeit using_index()
1 loop, best of 3: 432 ms per loop

但是请注意，如果allFeatures包含重复项，则将其作为索引是不利的。例如，如果您将以上设置更改为使用：

allFeatures = np.repeat(np.arange(N//2), 2)    # repeat every value twice

然后

In [114]: %timeit using_isin()
1 loop, best of 3: 667 ms per loop

In [115]: %timeit using_index()
1 loop, best of 3: 3.47 s per loop