WebMar 13, 2024 · 具体操作如下: df.drop_duplicates() 其中,df 是您的数据框名称。这个函数会返回一个新的数据框,其中所有重复的行都被删除了。如果您想要在原始数据框上进行修改,可以使用 inplace=True 参数: df.drop_duplicates(inplace=True) 希望这个回答能够帮助 … WebDataFrame.drop_duplicates(subset=None, *, keep='first', inplace=False, ignore_index=False) [source] #. Return DataFrame with duplicate rows removed. … pandas.DataFrame.duplicated# DataFrame. duplicated (subset = None, keep = 'first') … pandas.DataFrame.drop# DataFrame. drop (labels = None, *, axis = 0, index = … pandas.DataFrame.droplevel# DataFrame. droplevel (level, axis = 0) [source] # … copy bool, default True. If False, avoid copy if possible. indicator bool or str, default … pandas.DataFrame.groupby# DataFrame. groupby (by = None, axis = 0, level = …
What Does inplace = True Mean in Python? - AskPython
WebDec 14, 2024 · 函数pandas.DataFrame.drop_duplicates(subset=None, keep='first', inplace=False, ignore_index= False)主要用来去除重复项,返回DataFrame类型的数据。. 有几个参数要注意一下 subset:默认为None 去除重复项时要考虑的标签,当subset=None时所有标签都相同才认为是重复项. keep: {‘first’, ‘last’, False},默认为‘first’ WebApr 14, 2024 · by default, drop_duplicates () function has keep=’first’. Syntax: In this syntax, subset holds the value of column name from which the duplicate values will be removed and keep can be ‘first’,’ last’ or ‘False’. keep if set to ‘first’, then will keep the first occurrence of data & remaining duplicates will be removed. rawlings st louis jobs
数据清理_pandas中提供了插补缺失值的方法interpolate_独角兽没 …
WebNov 23, 2024 · Remember: by default, Pandas drop duplicates looks for rows of data where all of the values are the same. In this dataframe, that applied to row 0 and row 1. But here, instead of keeping the first duplicate row, it kept the last duplicate row. It should be pretty obvious that this was because we set keep = 'last'. WebMar 13, 2024 · 具体操作如下: ```python import pandas as pd # 读取 Excel 表 df = pd.read_excel('example.xlsx') # 删除重复行 df.drop_duplicates(inplace=True) # 保存 Excel 表 df.to_excel('example.xlsx', index=False) ``` 以上代码会读取名为 `example.xlsx` 的 Excel 表,删除其中的重复行,并将结果保存回原表中。 WebAug 24, 2024 · Since you will drop everything but the firsts elements of each group, you can change only the ones at subdf.index [0]. This yield: df = pd.read_csv ('pra.csv') # Sort the data by Login Date since we always need the latest # Login date first. We're making a copy so as to keep the # original data intact, while still being able to sort by datetime ... simple green sunshine