通过系统命令访问R中python脚本的输出

我现在的问题是下面链接的后续问题

我已经用system命令在R中执行了python代码。现在在python脚本的末尾，我想访问在R中创建的数据帧。一种方法是使用df.to_csv保存在python中创建的数据帧，然后将其导入到R中。但是我想知道是否有有效的方法可以直接访问R中的输出

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

输出数据帧为：

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

X仅包含0/1（状态）。如上所述，如何在R中直接访问数据帧而不保存它

Python script used is:


import pandas as pd 
import numpy as np
import sys


from difflib import SequenceMatcher

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

arg1 = sys.argv[1]
arg2 = sys.argv[2]
arg3 = sys.argv[3]
print (arg1)
print (arg2)
print (arg3)

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

temp= get_similar_CRs (arg1, arg2, arg3)

print temp

---

我建议查看

网状
包（请参阅）

您可以使用
py\u run\u file（）
运行文件，并使用
py
访问python主模块。假设您的文件名为“Untitled3.py”，它创建的数据帧名为
df
，那么

library(reticulate)

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

py_run_file("Untitled3.py")

py$df


编辑

或者，您可以只从python文件导入函数，然后从R内部调用它们。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

然后跑

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

我听说过网状包装。因此，您建议避免使用系统命令，最好使用网状软件包。是的。它将为您的python代码提供一个更易于使用的界面；您还可以创建python函数并从R.py_调用它们。run_文件给了我与前面问题中相同的问题。没有找到熊猫。它正在执行系统python，而不是Anaconda python。您可以指定要与
use\u python（）
一起使用的python。请参阅更新的答案。如果没有一个最小的可复制示例，则很难进一步提供帮助。你能分享你的python代码吗？