0 or ‘index’:函数按列处理(apply function to each column) 1 or ‘columns’:函数按行处理( apply function to each row) # 只处理指定行、列,可以用行或者列的 name 属性进行限定df5=df.apply(lambdad:np.square(d)ifd.name=="a"elsed,axis=1)print("-"*30,"\n",df5)# 仅对行"a"进行操作...
例如,可以通过在apply()中指定一个权重列来计算加权平均值。 代码语言:javascript 代码运行次数:0 运行 复制 In [8]: def weighted_mean(x): ...: arr = np.ones((1, x.shape[1])) ...: arr[:, :2] = (x[:, :2] * x[:, 2]).sum(axis=0) / x[:, 2].sum() ...: return arr...
apply 在Series的值上调用函数。func既可以是Numpy的一元通用函数(ufunc),也可以是只用于单个值的python函数。 DataFrame对象的函数 apply 在DataFrame的行或列上应用函数 Apply a function along an axis of the DataFrame. Objects passed to the function are Series objects whose index is either the DataFrame'...
经过查看引用,发现apply函数可以对dataframe和Series类型使用,此处我们查看dataframe的apply: defapply(self, func, axis=0, raw=False, result_type=None, args=(), **kwds):""" Apply a function along an axis of the DataFrame. Objects passed to the function are Series objects whose index is either ...
DataFrame.apply(func, axis=0, broadcast=False, raw=False, reduce=None, args=(), **kwds) 在给定轴方向应用函数 参数 func : function|要应用在行和列的函数 axis : {0 or ‘index’, 1 or ‘columns’}, default 0|选择是行还是列
df['weight']=df.apply(lambda x:np.random.normal(loc=param_map[x['gender']][x['age_cohort']][0],scale=param_map[x['gender']][x['age_cohort']][1]),axis=1)#set500values missingforiinrange(500):df.loc[np.random.randint(0,len(df)),'weight']=np.nan ...
# 运行以下代码deffix_century(x): year = x.year - 100if x.year > 1989else x.yearreturn datetime.date(year, x.month, x.day)# apply the function fix_century on the column and replace the values to the right onesdata['Yr_Mo_Dy'] = data['Yr_Mo_Dy'].apply(fix_century)# data...
Example - Square the values by passing an anonymous function as an argument to apply(): Python-Pandas Code: import numpy as np import pandas as pd s = pd.Series([31, 27, 11], index=['Beijing', 'Los Angeles', 'Berlin']) def square(x): ...
例如,可以通过在apply()中指定一个权重列来计算加权平均值。 In [8]: def weighted_mean(x):...: arr = np.ones((1, x.shape[1]))...: arr[:, :2] = (x[:, :2] * x[:, 2]).sum(axis=0) / x[:, 2].sum()...: return arr...:In [9]: df = pd.DataFrame([[1, 2, ...
大部分时间现在花在apply_integrate_f上。禁用 Cython 的boundscheck和wraparound检查可以提高性能。 In [15]: %prun -l 4 apply_integrate_f(df["a"].to_numpy(), df["b"].to_numpy(), df["N"].to_numpy())78 function calls in 0.001 secondsOrdered by: internal timeList reduced from 21 to ...