Python按照某些列去重，可用drop_duplicates函数轻松处理。本文致力用简洁的语言介绍该函数。版权声明：本文...
By usingpandas.DataFrame.T.drop_duplicates().Tyou can drop/remove/delete duplicate columns with the same name or a different name. This method removes all columns of the same name beside the first occurrence of the column and also removes columns that have the same data with a different colu...
You would do this using the drop_duplicates method. It takes an argument subset, which is the column we want to find or duplicates based on - in this case, we want all the unique names. vet_visits.drop_duplicates(subset="name") Powered By date name breed weight_kg 0 2018-09-02 ...
In [1]: dates = pd.date_range('1/1/2000', periods=8) In [2]: df = pd.DataFrame(np.random.randn(8, 4), ...: index=dates, columns=['A', 'B', 'C', 'D']) ...: In [3]: df Out[3]: A B C D 2000-01-01 0.469112 -0.282863 -1.509059 -1.135632 2000-01-02 1.212112...
df = pd.read_excel("test.xlsx", dtype=str, keep_default_na='') df.drop(columns=['寄件地区'], inplace=True) 5、列表头改名(补充) 如下:将某列表头【到件地区】修改为【对方地区】 df = pd.read_excel("test.xlsx", dtype=str, keep_default_na='') df = df.rename(columns={'到件地区...
💡 提示:使用如下命令创建一个脏数据文件,df.fillna(df['年龄'].mean())按照平均年龄做缺失值填充,df.drop_duplicates()删除重复值数据。 评论 In [40]: #使用字典创建一个数据集 import pandas as pd df = pd.DataFrame({'用户ID':['1000','1001','1002','1003','1004','1004'], '姓名':['...
df.drop(columns = ['col1','col2'...]) df.pop('col_name') del df['col_name'] In the last section, we have shown the comparison of these functions. So stay tuned… Also, See: Drop duplicates in pandas DataFrame Drop columns with NA in pandas DataFrame ...
Given a Pandas DataFrame, we have to remove duplicate columns. Removing duplicate columns in Pandas DataFrame For this purpose, we are going to usepandas.DataFrame.drop_duplicates()method. This method is useful when there are more than 1 occurrence of a single element in a column. It will re...
2. 示例:使用 pipe() 进行自定义数据清洗 假设我们有一个自定义函数 clean_text_column(df, column_name) 用于清洗 DataFrame 中的某个文本列(例如转换为小写、去除特殊字符)。 复制 importpandasaspdimportre # 示例 DataFrame data={'ID':[1,2,3],'Description':['Product A - NEW!','Item B (Old ...
user")# 去掉自己和自己的组合.reset_index()# 重新整理索引列，方便后面的groupby.rename(columns={"...