获取数据框中匹配和不匹配列数据的计数 [英] Get the count of matching and not matching columns data in a dataframe

问题描述

我有两个类似的数据框， 这是输入的csv数据.

I have two dataframes which are like, This is the input csv data .

Document_ID OFFSET  PredictedFeature
    0         0            2000
    0         8            2000
    0         16           2200
    0         23           2200
    0         30           2200
    1          0            2100
    1          5            2100
    1          7            2100

现在我也有输出数据

 Document_ID    OFFSET   PredictedFeature
        0         0            2000
        0         8            2100
        0         16           2100
        0         23           2100
        0         30           2200
        1          0           2000
        1          5           2000
        1          7           2100

现在，在这里我要尝试的是匹配结果(无论是否获得).

Now, Here what I am trying to do is that matching the results weather they are getting or not.

所以我做到了，

df1_inputPredictedFeature_column['new'] = df1_inputPredictedFeature_column['PredictedFeature'] == df1_predictedFeature_column['PredictedFeature']

这将添加一列，以告诉天气它是否与预测的功能列匹配.

This adds one columns which will tell weather it is matching the predictedfeature column or not.

现在我正在尝试的是，

总共有2个特征，其中输入csv的预测特征为2000.但是在输出csv中，它仅匹配第一位，而不是第二位.

there are a total of 2 features where 2000 is in the predictedfeature of input csv. But in the output csv only first place it is matching and not in the second place.

所以我正在尝试获取像这样的数据，

SO I am trying to get this data like ,

predictedFeatureClass  inputCsvOccured   outputcsvmatched  

 2000                        2                1

2200                         3                 1

那么，我将如何获得这些数据?任何帮助都会很棒.

SO, How will I get this data ? any help will be great.

推荐答案

您可以使用groupby进行操作

you can do it using groupby like below

df1_inputPredictedFeature_column = pd.DataFrame([['0', '0', '2000'], ['0', '8', '2000'], ['0', '16', '2200'], ['0', '23', '2200'], ['0', '30', '2200'], ['1', '0', '2100'], ['1', '5', '2100'], ['1', '7', '2100']], columns=('Document_ID', 'OFFSET', 'PredictedFeature'))
df1_predictedFeature_column = pd.DataFrame([['0', '0', '2000'], ['0', '8', '2100'], ['0', '16', '2100'], ['0', '23', '2100'], ['0', '30', '2200'], ['1', '0', '2000'], ['1', '5', '2000'], ['1', '7', '2100']], columns=('Document_ID', 'OFFSET', 'PredictedFeature'))

df1_inputPredictedFeature_column['new'] = (df1_inputPredictedFeature_column['PredictedFeature'] == df1_predictedFeature_column['PredictedFeature']).astype(np.int)

result = df1_inputPredictedFeature_column.groupby("PredictedFeature").agg({"PredictedFeature":"count", "new":np.sum})

result.columns = ["inputCsvOccured", "outputcsvmatched"]
result.index.name = "predictedFeatureClass"

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

结果

predictedFeatureClass  inputCsvOccured  outputcsvmatched
0                  2000                2                 1
1                  2100                3                 1
2                  2200                3                 1

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