* python装饰函数中的args等参数
* python装饰函数中的args等参数
提问于 2019-05-24 02:06:13
我有以下脚本，可以很好地工作。装饰器on_or_off从函数中获取来自on_switch的值，如果它的值为True，则执行。
import pandas as pd
import ctypes
import sec4_analysis as analysis
# Main class
######################################################################
class Analysis_ProjectX_Demographic(analysis.Analysis_ProjectX):
def __init__(self):
super()
super().__init__()
def demographic_analytic_steps(self):
self.import_parent_ref_data()
self.import_master_data()
self.recategorize_var(on_switch=True)
self.result_in_plaintext(on_switch=True)
self.result_in_csv(on_switch=True)
# Decorators
def on_or_off(func):
def wrapper(self, on_switch, *args):
if on_switch:
func(self, on_switch, *args)
return wrapper
# Core class functions
@on_or_off
def recategorize_var(self, on_switch=False):
self.df_master_filtered = self.recat_binary(self.df_master_filtered, 'INDEX_RURAL', 'INDEX_RURAL_CAT', 0, 'URBAN', 1, 'RURAL')
self.df_master_filtered = self.recat_age(self.df_master_filtered, 'INDEX_AGE', 'INDEX_AGE_CAT')
@on_or_off
def result_in_plaintext(self, on_switch=False):
df_dict = {
'TxGroup':self.df_master_filtered,
}
for df_key, df in df_dict.items():
print ('Dataset name: {}'.format(df_key))
print ('Unique patients, n: {}'.format(df['PHN_ENC'].nunique()))
self.descriptive_num_var_results(df_key, df, 'INDEX_AGE')
self.descriptive_cat_var_results(df_key, df, 'INDEX_AGE_CAT')
self.descriptive_cat_var_results(df_key, df, 'INDEX_RURAL_CAT')
self.descriptive_cat_var_results(df_key, df, 'INDEX_SEX')
@on_or_off
def result_in_csv(self, on_switch=False):
pass
# Helper functions
def recat_binary(self, df, old_var, new_var, old_val1, new_val1, old_val2, new_val2):
df.loc[df[old_var] == old_val1, new_var] = new_val1
df.loc[df[old_var] == old_val2, new_var] = new_val2
return df
def recat_age(self, df, old_var, new_var):
df.loc[(df[old_var]>=19.00)&(df[old_var]<25.00), new_var] = '19-24'
df.loc[(df[old_var]>=25.00)&(df[old_var]<30.00), new_var] = '25-29'
df.loc[(df[old_var]>=30.00)&(df[old_var]<35.00), new_var] = '30-34'
df.loc[(df[old_var]>=35.00)&(df[old_var]<40.00), new_var] = '35-39'
df.loc[(df[old_var]>=40.00)&(df[old_var]<45.00), new_var] = '40-44'
df.loc[(df[old_var]>=45.00)&(df[old_var]<50.00), new_var] = '45-49'
df.loc[(df[old_var]>=50.00)&(df[old_var]<55.00), new_var] = '50-54'
df.loc[(df[old_var]>=55.00)&(df[old_var]<60.00), new_var] = '55-59'
df.loc[(df[old_var]>=60.00)&(df[old_var]<65.00), new_var] = '60-64'
df.loc[(df[old_var]>=65.00)&(df[old_var]<300.00), new_var] = '65/above'
return df
x = Analysis_ProjectX_Demographic()
x.demographic_analytic_steps()但是，修饰后的函数应该可以自由地拥有除on_switch之外的任意数量的参数。当我在result_in_plaintext.()中引入更多参数some_text时。
import pandas as pd
import ctypes
import sec4_analysis as analysis
# Main class
######################################################################
class Analysis_ProjectX_Demographic(analysis.Analysis_ProjectX):
def __init__(self):
super()
super().__init__()
def demographic_analytic_steps(self):
self.import_parent_ref_data()
self.import_master_data()
self.recategorize_var(on_switch=True)
self.result_in_plaintext(some_text='This is done', on_switch=True)
self.result_in_csv(on_switch=True)
# Decorators
def on_or_off(func):
def wrapper(self, on_switch, *args):
if on_switch:
func(self, on_switch, *args)
return wrapper
# Core class functions
@on_or_off
def recategorize_var(self, on_switch=False):
self.df_master_filtered = self.recat_binary(self.df_master_filtered, 'INDEX_RURAL', 'INDEX_RURAL_CAT', 0, 'URBAN', 1, 'RURAL')
self.df_master_filtered = self.recat_age(self.df_master_filtered, 'INDEX_AGE', 'INDEX_AGE_CAT')
@on_or_off
def result_in_plaintext(self, some_text, on_switch=False):
df_dict = {
'TxGroup':self.df_master_filtered,
}
for df_key, df in df_dict.items():
print ('Dataset name: {}'.format(df_key))
print ('Unique patients, n: {}'.format(df['PHN_ENC'].nunique()))
self.descriptive_num_var_results(df_key, df, 'INDEX_AGE')
self.descriptive_cat_var_results(df_key, df, 'INDEX_AGE_CAT')
self.descriptive_cat_var_results(df_key, df, 'INDEX_RURAL_CAT')
self.descriptive_cat_var_results(df_key, df, 'INDEX_SEX')
print(some_text)
@on_or_off
def result_in_csv(self, on_switch=False):
pass
# Helper functions
def recat_binary(self, df, old_var, new_var, old_val1, new_val1, old_val2, new_val2):
df.loc[df[old_var] == old_val1, new_var] = new_val1
df.loc[df[old_var] == old_val2, new_var] = new_val2
return df
def recat_age(self, df, old_var, new_var):
df.loc[(df[old_var]>=19.00)&(df[old_var]<25.00), new_var] = '19-24'
df.loc[(df[old_var]>=25.00)&(df[old_var]<30.00), new_var] = '25-29'
df.loc[(df[old_var]>=30.00)&(df[old_var]<35.00), new_var] = '30-34'
df.loc[(df[old_var]>=35.00)&(df[old_var]<40.00), new_var] = '35-39'
df.loc[(df[old_var]>=40.00)&(df[old_var]<45.00), new_var] = '40-44'
df.loc[(df[old_var]>=45.00)&(df[old_var]<50.00), new_var] = '45-49'
df.loc[(df[old_var]>=50.00)&(df[old_var]<55.00), new_var] = '50-54'
df.loc[(df[old_var]>=55.00)&(df[old_var]<60.00), new_var] = '55-59'
df.loc[(df[old_var]>=60.00)&(df[old_var]<65.00), new_var] = '60-64'
df.loc[(df[old_var]>=65.00)&(df[old_var]<300.00), new_var] = '65/above'
return df
x = Analysis_ProjectX_Demographic()
x.demographic_analytic_steps()它给出了这个错误：
line 16, in demographic_analytic_steps
self.result_in_plaintext(some_text='This is done', on_switch=True)
TypeError: wrapper() got an unexpected keyword argument 'some_text'回答 1
Stack Overflow用户
发布于 2019-05-24 02:51:38
class MyClass:
def on_or_off(func):
def wrapper(self,*args, **kwargs):
if kwargs['on_switch']:
func(**kwargs)
return wrapper
@on_or_off
def test(on_switch = False,some_text="This is done"):
print(f' "Test" Function executed with on_switch = {on_switch} and some_text {some_text}')
obj = MyClass()
obj.test(on_switch = True, some_text="Abhijeet")页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/56280749
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