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....
To group a Pandas DataFrame by multiple columns, you can pass a list of column names to thegroupby()function. This will allow you to group the data based on the unique combinations of values from the specified columns. Can I apply multiple aggregation functions to different columns? You can ...
df.groupby(df.columns, axis=1).agg(lambdax: x.apply(lambday:','.join([str(l)forlinyifstr(l) !="nan"]), axis=1)) Copy This will result into: How does it work? First is grouping the columns which share the same name:
df['修改的列'] = df['条件列'].apply(调用函数名) import pandas as pd def test(): # 读取Excel文件 df = pd.read_excel('测试数据.xlsx') def modify_value(x): if x < 5: return '是' elif x < 10: return '否' else: return 'x' # 插入列 for col_num in range(4, 9): df....
DataFrame(data = weather_data, columns=['date', 'temperature', 'humidity']) weather_df 注意上述我们没有指定行索引,所以他们只有默认的所以0,1,和2.如果需要指定行索引的话,我们可以在加入index参数,如: weather_df = pd.DataFrame(data = weather_data, columns=['date', 'temperature', 'humidity']...
# 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
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...
你可以使用pandas的apply函数。最终df如下所示。https://pandas.pydata.org/docs/reference/api/pandas....