WebAug 6, 2024 · I am updating a data frame using apply of function. But now I need to modify multiple columns using this function, Here is my sample code: def update_row (row): listy = [1,2,3] return listy dp_data_df [ ['A', 'P','Y']] = dp_data_df.apply (update_row, axis=1) It is throwing the following error: ValueError: shape mismatch: value array of shape ...

Apply a Function to Multiple Columns in Pandas DataFrame

WebAug 31, 2024 · Using pandas.DataFrame.apply() method you can execute a function to a single column, all and list of multiple columns (two or more). In this article, I will cover how to apply() a function on values of a selected single, multiple, all columns. For example, let’s say we have three columns and would like to apply a function on a single column … WebJul 7, 2016 · pipe + comprehension. If your dataframes contain related data, as in this case, you should store them in a list (if numeric ordering is sufficient) or dict (if you need to provide custom labels to each dataframe). Then you can pipe each dataframe through a function foo via a comprehension.. List example df_list = [df1, df2, df3] df_list = [df.pipe(foo) for df … simple thanksgiving table centerpiece ideas

How to return multiple columns using apply in Pandas dataframe

WebSep 8, 2024 · Objects passed to the pandas.apply() are Series objects whose index is either the DataFrame’s index (axis=0) or the DataFrame’s columns (axis=1). By default (result_type=None), the final return type is inferred from the return type of the applied function. Otherwise, it depends on the result_type argument. WebDec 15, 2015 · df ['NewCol'] = df.apply (lambda x: segmentMatch (x ['TimeCol'], x ['ResponseCol']), axis=1) Rather than trying to pass the column as an argument as in your example, we now simply pass the appropriate entries in each row as argument, and store the result in 'NewCol'. Thank you! I can even use this with arguments! WebAug 30, 2024 · 1. You can use a dictionary comprehension and feed to the pd.DataFrame constructor: res = pd.DataFrame ( {col: [x.rstrip ('f') for x in df [col]] for col in df}) Currently, the Pandas str methods are inefficient. Regex is even more inefficient, but more easily extendible. As always, you should test with your data. simple thanksgiving recipes