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)
To apply a function to multiple columns of a Pandas DataFrame, you can simply use the DataFrame.apply() method by specifying the column names. The method itself takes a function as a parameter that has to be applied on the columns.
importpandasaspd# 创建一个示例DataFramedf=pd.DataFrame({'A':[1,2,3],'B':[4,5,6]})# 定义一个函数,计算两列的和defsum_two_columns(row):returnrow['A']+row['B']# 使用apply函数df['Sum']=df.apply(sum_two_columns,axis=1)print(df) Python Copy Output: 示例代码2:根据条件创建新列 ...
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....
Python program to apply function that returns multiple values to rows in pandas DataFrame # Importing Pandas packageimportpandasaspd# Create a dictionaryd={'Num': [ iforiinrange(10)]}# Create DataFramedf=pd.DataFrame(d)# Display DataFrameprint("Original DataFrame:\n",df,"\n")# Defi...
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(lambda x: apply_md5(x[col]), axis=1) 显示结果数据,df_data.head() 2. Polars测试 Polars特点: Polars库在io上优势明显,非常快; Polars是Rust编写的,内存模型是基于Apache Arrow,python只是一个前端的封装...
方法append_to_multiple和select_as_multiple可以同时从多个表中执行追加/选择操作。其思想是有一个表(称之为选择器表),你在这个表中索引大部分/全部列,并执行你的查询。其他表是数据表,其索引与选择器表的索引匹配。然后你可以在选择器表上执行非常快速的查询,同时获取大量数据。这种方法类似于拥有一个非常宽的...
When working with pandas DataFrames you are often required to rename multiple columns of pandas DataFrame, you can do this by using the rename() method.
(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/...
apply(lambda x: upper(x)) 5. 数据导出 一旦我们把数据处理完,下一步要做的就是如何保存数据。 Pandas也提供了非常便捷的功能。 Excel df.to_excel('myData.xlsx',index=False, sheet_name='Sheet1') CSV df.to_csv('myData.csv',index=False) 数据库 import pyodbc server = "10.21.120.88" data...