The following syntax shows to apply a function to multiple columns of DataFrame:df[['column1','column1']].apply(anyFun); Where, column1 and column2 are the column names on which we have to apply the function, and "function" has some operations that will be performed on the columns....
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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....
We can create a Pandas pivot table with multiple columns and return reshaped DataFrame. By manipulating given index or column values we can reshape the
#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/...
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....
How do I select multiple rows and columns from a pandas DataFrame? When should I use the "inplace" parameter in pandas? How do I make my pandas DataFrame smaller and faster? How do I use pandas with scikit-learn to create Kaggle submissions? More of your pandas questions answered! How ...
columns=['numbers', 'colors']) df['colName'] = 'colors' from pathos.multiprocessing import Pool tic = time.perf_counter() result = Pool(8).imap(enrich_row, df.iterrows(), chunksize=1) df = pd.DataFrame(result) toc = time.perf_counter() ...
As you can see, the columnsA,B,C, andDare automatically tab completed.Eis there as well; the rest of the attributes have been truncated for brevity. Viewing Data See theBasics section See the top & bottom rows of the frame In [14]:df.head()Out[14]:A B C D2013-01-01 0.469112 -...