问题
I am using the scholarly
module in python to search for a keyword. I am getting back a generator object as follows:
import pandas as pd
import numpy as np
import scholarly
search_query = scholarly.search_keyword('Python')
print(next(search_query))
{'_filled': False,
'affiliation': 'Juelich Center for Neutron Science',
'citedby': 75900,
'email': '@fz-juelich.de',
'id': 'zWxqzzAAAAAJ',
'interests': ['Physics', 'C++', 'Python'],
'name': 'Gennady Pospelov',
'url_picture': 'https://scholar.google.com/citations?view_op=medium_photo&user=zWxqzzAAAAAJ'}
I want to access the element 'citedby' but when I try to do next(search_query)['citedby']
it returns TypeError: 'Author' object is not subscriptable
.
My question is how can I access elements in the generator object? and How can I convert that object to a Pandas dataframe?
回答1:
This is not a generator problem. The objects the generator produces are not dictionaries.
Granted, the scholary
library does not help matters by giving the Author
instances that you are given a dictionary-like string conversion, and not actually documenting what API that class does support.
Each of the 'keys' in the Author
representation is actually an attribute on the object:
author = next(search_query)
print(author.citedby)
You can get a dictionary for the object by using the vars() function:
author_dict = vars(author)
The data doesn't necessarily map to a dataframe directly, though. How would the interests
list be represented in the dataframe tabular data structure, for example? And you wouldn't want to include the _filled
internal attribute either (that's a flag to record if author.fill()
has been called yet).
That said, you could just create a dataframe from the dictionaries by mapping the generator over the vars
function:
search_query = scholarly.search_keyword('Python')
df = pd.DataFrame(map(vars, search_query))
and then drop the _filled
column if necessary, and convert the interests
column into something a bit more structured, such as separate columns with 0 / 1 values or similar.
Note that this is going to be slow, because the scholarly
library pages through the Google search results sequentially, and the library deliberately delays requests with a random sleep interval of 5-10 seconds each time to avoid Google blocking the requests. So you'll have to be patient as the Python
keyword search easily produces nearly 30 pages of results.
来源:https://stackoverflow.com/questions/57167543/python-how-to-access-the-elements-in-a-generator-object-and-put-them-in-a-panda