20,30],'C':['pandasdataframe.com','modify','columns']})# 定义一个函数,如果数值大于10,加10defadd_ten(x):returnx+10ifx>10elsex# 对'A'和'B'列应用条件函数df[['A','B']]=df[['A','B']].applymap(add_ten)print(df)
示例代码 2: 使用 apply 返回多列 importpandasaspd# 创建一个 DataFramedf=pd.DataFrame({'A':range(1,6),'B':['pandasdataframe.com'for_inrange(5)]})# 定义一个函数,返回多个新的列值defmultiple_columns(row):returnpd.Series([row['A']*2,row['A']*3],index=['double','triple'])# 应用...
In Pandas, the apply() function can indeed be used to return multiple columns by returning a pandas Series or DataFrame from the applied function. In this article, I will explain how to return multiple columns from the pandas apply() function....
Combine Multiple columns into a single one in Pandas Nov 3, 2021 4 min read In this short guide, you'll see how to combine multiple columns into a single one in Pandas. Here you can find the short answer: (1) String concatenation df['Magnitude Type'] +', '+ df['Type'] Copy (2...
result = df.groupby('Courses').aggregate({'Duration':'count','Fee':['min','max']}) Pandas GroupBy Multiple Columns Example You can apply different aggregation functions to different columns in a singlegroupbyoperation using theagg()method.Most of the time when you are working on a real-ti...
将apply()函数应用于Pandas中的多个列？尝试使用以下代码，它应该会给出与在combined列上运行上述函数时...
评论 In [23]: #行列聚合,这里使用groupby数据分组内容,详细学习groupby函数可参考第三节内容,groupby函数指定分类对象分组 df_group = DP_table.groupby(['区域']).apply(lambda x: x['商品品类'].unique()).reset_index() df_group.rename(columns={0:'商品品类'},inplace=True)#重命名 df_group ....
columns=['user', 'another_user', 'mate_type']) result = (pairs_df.groupby(['user', 'anoth...
# 设置自定义索引df.index=['a','b','c']# 使用loc进行标签索引print(df.loc['b'])""" Name Bob Age 30 City Paris Name: b, dtype: object """# 选择多行print(df.loc[['a','c']])""" Name Age City a Alice 25 New York
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 Model)','Widget C*']}df_text=pd.DataFra...