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....
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...
To apply a function that returns multiple values to rows in pandas DataFrame, we will define a function for performing some operations on the values, and then finally we will return all the values in the form of a series. Note To work with pandas, we need to importpandaspackage fi...
importpandasaspd# 创建一个DataFramedf=pd.DataFrame({'A':[5,15,25],'B':[10,20,30],'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...
使用Pandas apply()方法返回多列原文:https://www . geesforgeks . org/return-multi-columns-use-pandas-apply-method/传递给pants . apply()的对象是系列对象,其索引是数据框的索引(轴=0)或数据框的列(轴=1)。默认情况下(result_type=None),最终的返回类型是从应用的函数的返回类型推断出来的。否则,它...
hexdigest(), 16) h = k % 2147483648 return h 1. Pandas测试 读取数据集,记录该操作耗时: import pandas as pd df_data = pd.read_csv(data_file, names=col_list) 显示原始数据,df_data.head() 运行apply函数,并记录该操作耗时: for col in df_data.columns: df_data[col] = df_data.apply(...
# Groupby & multiple aggregations on different columns 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....
#apply()函数使用案例# # 导入 numpy 库 import numpy as np # 导入 pandas 库 import pandas as pd # 定义 DataFrame # 数据为 3 行 4 列 s_data = pd.DataFrame([[5.1,3.5,1.4,0.2], [6.1,3.7,4.1,1.5], [5.8,2.7,5.1,1.9]], columns=['feature_one','feature_two','feature_three','fea...
(self, key, value) 1284 ) 1285 1286 check_dict_or_set_indexers(key) 1287 key = com.apply_if_callable(key, self) -> 1288 cacher_needs_updating = self._check_is_chained_assignment_possible() 1289 1290 if key is Ellipsis: 1291 key = slice(None) ~/work/pandas/pandas/pandas/core/...
3 Applying different functions to DataFrame columns By passing a dict toaggregateyou can apply a different aggregation to the columns of a DataFrame: In [94]: grouped.agg({"C": np.sum,"D":lambdax: np.std(x, ddof=1)}) Out[94]: ...