我有兩個類似的數(shù)據(jù)框，這是輸入的 csv 數(shù)據(jù)。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現(xiàn)在我也有輸出數(shù)據(jù) 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現(xiàn)在，我在這里要做的是匹配他們得到或沒有得到的結(jié)果。所以我做了 ，df1_inputPredictedFeature_column['new'] = df1_inputPredictedFeature_column['PredictedFeature'] == df1_predictedFeature_column['PredictedFeature']這會添加一列來告訴天氣它是否與預(yù)測特征列匹配?，F(xiàn)在我正在嘗試的是，共有 2 個特征，其中 2000 在輸入 csv 的預(yù)測特征中。但在輸出 csv 中，它僅排在第一位，而不是排在第二位。所以我正在嘗試獲取這些數(shù)據(jù)，例如，predictedFeatureClass inputCsvOccured outputcsvmatched 2000 2 12200 3 1那么，我將如何獲得這些數(shù)據(jù)？任何幫助都會很棒。
2 回答
小唯快跑啊
TA貢獻1863條經(jīng)驗 獲得超2個贊
您可以使用 groupby 來完成，如下所示
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)
結(jié)果
predictedFeatureClass inputCsvOccured outputcsvmatched
0 2000 2 1
1 2100 3 1
2 2200 3 1
慕哥9229398
TA貢獻1877條經(jīng)驗 獲得超6個贊
一個想法是通過元組列表將new列轉(zhuǎn)換為整數(shù)，Series.view然后通過元組列表聚合列new以指定新列名：sizesum
df1['new'] = (df1['PredictedFeature'] == df2['PredictedFeature']).view('i1')
df = (df1.groupby("PredictedFeature")['new']
.agg([('inputCsvOccured','size'), ('outputcsvmatched','sum')])
.reset_index())
print (df)
PredictedFeature inputCsvOccured outputcsvmatched
0 2000 2 1
1 2100 3 1
2 2200 3 1
熊貓 0.25+ 解決方案：
df1['new'] = (df1['PredictedFeature'] == df2['PredictedFeature']).view('i1')
df = (df1.groupby("PredictedFeature")
.agg(inputCsvOccured=pd.NamedAgg(column='new', aggfunc='size'),
outputcsvmatched=pd.NamedAgg(column='new', aggfunc='sum'))
.reset_index())
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