df_no_duplicates = df[~duplicate_rows] print(df_no_duplicates) 输出结果如下: A B C 0 1 5 9 1 2 6 10 3 4 8 12 4 5 9 13 七、删除特定列的重复值并保留顺序 如果希望删除特定列的重复值并保留行的顺序,可以结合drop_duplicates和sort_index方法。 # 删除列'A'的重复值,并保留顺序 df_uni...
官方解释:https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop_duplicates.html#pandas.DataFrame.drop_duplicates DataFrame.drop_duplicates(subset=None, keep='first', inplace=False) Return DataFrame with duplicate rows removed, optionally only considering certain columns. #返回...
python drop_duplicate去除重复行 python # 导入pandas库 import pandas as pd # 读取csv文件 df = pd.read_csv('data.csv') # 去除重复行 df.drop_duplicates()发布于 3 月前 本站已为你智能检索到如下内容,以供参考: 🐻 相关问答 6 个 1、python数组去重,去除后面重复的,不改变原数组顺序 2、list中...
duplicate_rows=data[data.duplicated()]# 找到重复的行print(duplicate_rows)# 打印重复的行以便查看 1. 2. 4. 删除重复值所在行 我们可以使用drop_duplicates()方法来删除重复值所在的行。默认情况下,这个方法会保留第一次出现的行,而删除后面的重复行。 data_cleaned=data.drop_duplicates()# 删除重复值所在...
#drop rows that contain only NaN df.dropna(axis=0, how='all') #drop columns that contain only NaN df.dropna(axis=1, how='all') .replace #replace all nan with blank df.replace(np.nan," ") .drop_duplicates() #remove duplicate rows based on all columns df.drop_duplicates() #remove...
(student_df)# drop duplicate rowsstudent_df0 = student_df.drop_duplicates(keep=False)print("drop duplicate rows with ignore_index=False:")print(student_df0)# drop duplicate rowsstudent_df1 = student_df.drop_duplicates(keep=False,ignore_index=True)print("drop duplicate rows with ignore_index=...
(df)# converts existing to more efficient dtypes,also called insidedata_cleaning()-klib.drop_missing(df)# drops missing values,also calledindata_cleaning()-klib.mv_col_handling(df)# drops featureswithhigh ratioofmissing vals based on informational content-klib.pool_duplicate_subsets(df)# pools...
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.equals(other) 两个数据框是否相同 ...
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.equals(other)两个数据框是否相同 ...
Only keep one value if the value in these two columns are duplicate concurrently, and delete repeatable data. ''' kpi1_Df = salesDf.drop_duplicates( subset = ['date sold', 'social security card number'] ) # Total consumption times: How many rows totalI = kpi1_Df.shape[0] print('...