通过系统命令访问R中的python脚本输出

Question

我目前的问题是对以下链接的跟进问题。

Not able to import pandas in R

我用系统命令在R中执行了python代码。现在在python脚本结束时，我想访问在R中创建的Dataframe。一种方法是使用df.to_csv保存在python中创建的Dataframe，然后在R中导入它。但是我想知道任何直接访问输出的有效方法在R。

@shared_task
def my_task():
    time.sleep(5)
    Channel('my-background-task').send({"refresh": True})

输出数据帧是：

x=system("/Users/ravinderbhatia/anaconda/bin/python /Users/ravinderbhatia/Downloads/Untitled3.py EMEA regulatory '10% productivity saves SOW'")

X只包含0/1（状态）。如上所述，如何直接在R中访问Dataframe而不保存它。

description                       status region
10  10% productivity saves SOW   pending   EMEA
16  10% productivity saves SOW  approved   EMEA

Answer 1

我建议您查看reticulate包（请参阅online vignette）。

您可以使用py_run_file()运行文件，并使用py访问python主模块。因此，假设您的文件名为“Untitled3.py”，其创建的数据框称为df，然后

library(reticulate)

use_python("/Users/ravinderbhatia/anaconda/bin/python")

py_run_file("Untitled3.py")

py$df

修改

或者，你只能从python文件中导入函数，只需从里面调用它们。例如，将python文件作为

import pandas as pd 
import numpy as np
import sys
from difflib import SequenceMatcher

def similar(a, b):
    return SequenceMatcher(None, a, b).ratio()

def get_similar_CRs(arg1, arg2,arg3):
    ##create dummy data
    cr_id=range(1,41)

    description=['change in design','More robust system required',
                 'Grant system adminstrator rights',
                 'grant access to all products',
                 'Increase the credit limit',
                 'EDAP Scenario',
                 'Volume prpductivity for NA 2015',
                 '5% productivity saves SOW',
                 'effort reduction',
                 'reduction of false claims',
                 'Volume productivity EMEA',
                 'Volume productivity for NA 2016',
                 '10% productivity saves SOW',
                ]
    region=['EMEA','Asia Pacific','UK']

    business=['card','secured loan','mortgage']
    type=['regulatory','system','audit']

    status=['pending','approved']

    data=pd.DataFrame()
    data['description']=np.random.choice(description, 40)
    data['cr_id']=cr_id
    data['region']=np.random.choice(region,40)
    data['business']=np.random.choice(business, 40)
    data['status']=np.random.choice(status,40)
    data['type']=np.random.choice(type,40)

    subset_data=data.loc[data.region == arg1]
    print (subset_data.head())
    subset_data=subset_data.loc[subset_data.type ==arg2]

    ##This has to be captured dynamically
    new_cr=arg3

    cr_list=data['description'].unique().tolist()

    similar_CR=[] ###global variable
#     for new_cr in new_cr_lis
    for cr in cr_list:
        result=similar(new_cr,cr)
        if result >=0.8:
            similar_CR.append(cr)

    temp=subset_data.loc[subset_data.description.isin(similar_CR)]
    temp=temp[['description','status','region']]
    return temp

然后运行

library(reticulate)

# To install pandas and numpy in the regular python environment
py_install("pandas", "numpy")

py_run_file("Untitled3.py")

py$get_similar_CRs("EMEA", "regulatory", "10% productivity saves SOW")

#>                   description  status region
#> 2  10% productivity saves SOW pending   EMEA
#> 25 10% productivity saves SOW pending   EMEA