You can usedf[df.duplicated()]without any arguments to get rows with the same values on all columns. It takes defaults valuessubset=Noneandkeep=‘first’. The below example returns two rows as these are duplicate rows in our DataFrame. In this example, theduplicated()method without specifying...
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# 检查数据帧中是否有重复行 duplicate_rows = df[df.duplicated()] print("Duplicate Rows:") print(duplicate_rows) 结果是一个空数据帧。这意味着数据集中没有重复记录: Output >>> Duplicate Rows: Empty DataFrame Columns: [MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup, Latitude, Lon...
您可以循环访问A列的唯一值，并显示A列具有特定值的数据 代码：
上述代码中，duplicate_rows_col是一个布尔Series，表示在列'B'中是否有重复的行。```python 基于特定...
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rows = data[data.duplicated()] # 在原始数据中添加一列用于标记重复行 data['is_duplicate'] =...
We hope this article has helped you find duplicate rows in a Dataframe using all or a subset of the columns by checking all the examples we have discussed here. Then, using the above-discussed easy steps, you can quickly determine how Pandas can be used to find duplicates....
duplicate_rows = df[df.duplicated()] print("Duplicate Rows:") print(duplicate_rows) 1. 2. 3. 4. 结果是一个空数据帧。这意味着数据集中没有重复记录: 复制 Output >>> Duplicate Rows: Empty DataFrame Columns: [MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup, Latitude, Longitude...
# return the 5 largest (you can change this to any number you want to return)