Python program to apply Pandas function to column to create multiple new columns # Importing Pandas packageimportpandasaspd# Create a dictionaryd={'Num': [ iforiinrange(10)]}# Create DataFramedf1=pd.DataFrame(d)# Display DataFrameprint("Original DataFrame:\n",df1,"\n")# Defining...
然而,当apply函数的结果是一个 Series 时,Pandas 会自动将结果转置。这是因为 Pandas 设计的初衷是让每一列代表一个变量,每一行代表一个观察值。 如果你希望避免这种转置,你可以在aid函数中直接返回一个 Pandas Series,而不是一个元组。这样,apply函数就会将每一行的结果组合成一个新的 DataFrame,而不是转置它们。
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....
"two"], ["foo", "one"], ["foo", "two"]], ...: columns=["first", "second"], ...: ) ...: In [11]: pd.MultiIndex.from_frame(df) Out[11]: MultiIndex([('bar', 'one'), ('bar', 'two'), ('foo', 'one'), ('foo', 'two')], names=['first', 'second']) 作...
However, when you use df.rolling with df.apply function, the function can not recognise both columns. Expected Behavior I expect the rolling function can return multiple columns as it shows in for loop print, into apply function after it, when we use dataframe instead of series or array as...
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
In [1]: dates = pd.date_range('1/1/2000', periods=8) In [2]: df = pd.DataFrame(np.random.randn(8, 4), ...: index=dates, columns=['A', 'B', 'C', 'D']) ...: In [3]: df Out[3]: A B C D 2000-01-01 0.469112 -0.282863 -1.509059 -1.135632 2000-01-02 1.212112...
4. 使用apply函数进行自定义操作 apply函数允许对DataFrame的列或行应用自定义函数。这在处理复杂逻辑时非常有用。例如,计算每行的平均年龄(假设有多列年龄数据): def calculate_average_age(row): age_columns = ['Age1', 'Age2', 'Age3'] # 假设这些列存在 ...
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
Pandas支持向量化操作,这意味着你可以对整个Series或DataFrame应用一个函数,而不需要显式地循环遍历每个元素。这种操作通常比使用循环或apply()方法更快。 python 复制代码 df['new_column'] = df['column_name'].apply(lambda x: x**2) # 较慢的方式 df['new_column'] = df['column_name']**2 # 更...