但这通常不是drop_duplicates的直接用途,而需要一些额外的逻辑处理: # duplicate_indices = df[df.duplicated(keep=False)].index # 找出所有重复行的索引(包括所有重复出现的行) # df_no_duplicates_at_all = df.drop(index=duplicate_indices) # 删除这些索引对应的行,但注意这会删除所有标记为重复的行,包括...
您可以尝试regular expression检查Match列中是否有ab,然后您可以删除那些indices 正如Jezreal所建议的,更优雅的解决方案 df[~df['Match'].str.contains(r'(?i)^ab')] Old solution df.drop(df[df['Match'].str.contains(r'(?i)^ab')].index, inplace=True) ...
Dropping Duplicate Pairs In that case, we need to consider more than just name when dropping duplicates. Since Max and Max are different breeds, we can drop the rows with pairs of names and breeds listed earlier in the dataset. unique_dogs = vet_visits.drop_duplicates(subset=["name", "br...
set_flags(*[, copy, allows_duplicate_labels]) 返回具有更新标志的新对象。 shift([periods, freq, axis, fill_value, suffix]) 按指定的周期数移动索引,可选择使用时间频率。 skew([axis, skipna, numeric_only]) 返回请求轴上的无偏偏度。 sort_index(*[, axis, level, ascending, ...]) 根据索引标...
drop(labels[, axis, level,…]) #返回删除的列 DataFrame.drop_duplicates([subset, keep,…]) #Return DataFrame with duplicate rows removed, optionally only DataFrame.duplicated([subset, keep]) #Return boolean Series denoting duplicate rows, optionally only DataFrame选取以及标签操作 代码语言:javascript...
set_flags(*[, copy, allows_duplicate_labels]) 返回更新后的flags的新对象。 set_index(keys, *[, drop, append, inplace, ...]) 使用现有列设置DataFrame的索引。 shift([periods, freq, axis, fill_value, suffix]) 以所需的周期数移动索引,可以选择带有时间频率。 skew([axis, skipna, numeric_onl...
[, method, …])Return an object with matching indices to myself.DataFrame.rename([index, columns])Alter axes input function or functions.DataFrame.rename_axis(mapper[, axis, copy, …])Alter index and / or columns using input function or functions.DataFrame.reset_index([level, drop, …])...
使用drop_duplicates()函数删除重复的行 df.drop_duplicates() 如果使用pd.concat([df1,df2],axis = 1)生成新的DataFrame,新的df中columns相同,使用duplicate()和drop_duplicates()不会出错!!! df2 =pd.concat((df,df),axis =1) df2 df2.duplicated() 0False...
reset_index(drop=True, inplace=True) df_concat Powered By Handling duplicates Manage duplicate indices by resetting and reindexing appropriately. Suppose you have the data with duplicate indices. Using reset_index() with drop=True and inplace=True ensures that the resulting DataFrame will have ...
DataFrame.drop_duplicates([subset, keep, …]) Return DataFrame with duplicate rows removed, optionally only ([subset, keep]) Return boolean Series denoting duplicate rows, optionally only (other) 两个数据框是否相同 ([items, like, regex, axis]) 过滤特定的子数据框 (offset) Convenience method for...