默认保留第一条，故删除最后一条得到新数据框。 想要根据更多列数去重，可以在subset中添加列。...但是对于两列中元素顺序相反的数据框去重，drop_duplicates函数无能为力。 如需处理这种类型的数据去重问题，参见本公众号中的文章【Python】基于多列组合删除数据框中的重复值。 -end- ...
list_=['a','a','b','b','c']list(set(list_))['c','a','b']DataFrame去重 df.drop_...
importxlwingsasxw#下載下來的Excel,列和行有很多空白的,要先刪除掉.# 打开Excel文件workbook = xw.Book('test.xlsx') sheet = workbook.sheets[0]# 删除1-12行sheet.range('1:12').api.EntireRow.Delete()# 删除A-G列sheet.range('A:G').api.EntireColumn.Delete()# 保存并关闭工作簿workbook.save()...
df = df.drop_duplicates() 保存删除重复行后的表格为一个新文件: 代码语言:txt 复制 df.to_excel("删除重复行的表格.xlsx", index=False) 这样,重复行将会被从漂亮表格中删除,并且保存为一个新的表格文件。 对于表格数据处理的优势,它可以帮助我们快速处理大量的数据,并进行数据分析和可视化。常见的应用场...
importosimporthashlibdefcalc_md5(file_path):withopen(file_path,'rb')asf:md5obj=hashlib.md5()md5obj.update(f.read())hash=md5obj.hexdigest()returnhashdefdelete_duplicates(dir_path):hash_keys=dict()forroot,dirs,filesinos.walk(dir_path):forfileinfiles:file_path=os.path.join(root,file)file_...
(seq)) def odict(seq): return list(OrderedDict.fromkeys(seq)) from simple_benchmark import benchmark b = benchmark([f7, iteration_utilities_unique_everseen, more_itertools_unique_everseen, odict], {2**i: list(range(2**i)) for i in range(1, 20)}, 'list size (no duplicates)')...
1 How to remove duplicates in a list of strings in a pandas column Python 3 python - how to delete duplicate list in each row (pandas)? 2 Remove duplicates from python dataframe list 2 Remove Duplicated Values From List In Pandas Dataframe 0 Remove duplicated elements ...
def delete_out3sigma(df, list_norm_T): out_index = [] #保存要删除的行索引 for col in list_norm_T: rule = (df[col].mean()-3*df[col].std()>df[col])|(df[col].mean()+3*df[col].std()<df[col]) index = df.index[rule] ...
This post will discuss how to remove duplicate values from a list in Python. The solution should find and delete the values that appear more than once in the list. 1. Using a Set A simple solution is to insert all elements from the list into a set that would eliminate duplicates. A ...
# df.reset_index(inplace=True) 1. 2. 3. 4. 5. 1.3索引类型(略) 1.4索引对象(行和列在panda里其实是一个index对象,可以传入构建数据和读取数据) pd.Index([1,2,3]) #Int64Index([1, 2, 3], dtype='int64') pd.Index(list('abc')) #Index(['a', 'b', 'c'], dtype='object') ...