duplicate()结果是一个值为布尔的Seris,通过obj[ df.duplicated()==False ]来取出Seris和DataFrame中的重复值、行,新结果的index为False对应的索引。 对于Seris,从第一个值开始判断,如果元素第一次出现,则duplicate后的值为True,否则为False 对应于DataFrame,从第一行开始判断,如果行的内容第一次出现(要求行内元素...
returning a new object (a copy) with all the original columns in addition to the new ones.DataFrame.join(other[, on, how, lsuffix, …])Join columns with other DataFrame either on index or on a key column.DataFrame.merge(right[, how, on, left_on, ...
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, …])For DataFrame with multi-level index, return new DataFrame with lab...
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, …]) #For DataFrame with multi-level index, return new DataFrame with...
# print(pd.merge(df1,df2))#没有任何数据能够merge # # Empty DataFrame # # Columns: [key, data_set_1, data_set_2] # # Index: [] #改变数值 # df1 = DataFrame({"key":["X","Y","Z"],"data_set_1":[1,2,3]}) # df2 = DataFrame({"key":["X","B","C"],"data_set_2...
df['Is_Duplicate'] = df.duplicated() 查看添加了新列的DataFrame: 代码语言:txt 复制 print(df) 这样,新的列"Is_Duplicate"将会显示每一行数据是否为重复数据,True表示重复,False表示不重复。 对于以上问题,腾讯云没有特定的产品和产品介绍链接地址与之相关。
labels 行、列索引的标签名称axis = 0 删除列 axis = 1 删除行index 行索引标签columns 列索引标签level 使用多级索引时,指定索引层级inplace 若为True则在原DataFrame上操作,返回空。若为False则返回新DataFrameerrors 当传入的标签不再DataFrame时,是否抛出异常。’raise’ or ‘ignore’ dropna 、 drop_duplicate...
drop_duplicates() Drops duplicate values from the DataFrame droplevel() Drops the specified index/column(s) dropna() Drops all rows that contains NULL values dtypes Returns the dtypes of the columns of the DataFrame duplicated() Returns True for duplicated rows, otherwise False empty Returns True...
drop([labels, axis, index, columns, level, ...]) 从行或列中删除指定的标签。 drop_duplicates([subset, keep, inplace, ...]) 返回删除重复行的DataFrame。 droplevel(level[, axis]) 返回请求的索引/列级别已被删除的Series/DataFrame。 dropna(*[, axis, how, thresh, subset, ...]) 删除缺失值...
maybe, to avoid breaking changes, we could add an additional errors parameter to pd.merge with 'ignore', 'raise' options to allow/disallow duplicate columns labels being generated. phofl mentioned this issue Apr 16, 2021 Deprecate suffixes in merge producing duplicate columns #40991 Merged 4...