Question

我有一个包含两列的简单数据框。

+---------+-------+ | subject | score |
+---------+-------+ | wow     | 0     |
+---------+-------+ | cool    | 0     |
+---------+-------+ | hey     | 0     |
+---------+-------+ | there   | 0     |
+---------+-------+ | come on | 0     |
+---------+-------+ | welcome | 0     |
+---------+-------+

对于“主题”列中的每条记录，我正在调用一个函数并更新“得分”列中的结果：

df['score'] = df['subject'].apply(find_score)

Here find_score is a function, which processes strings and returns a score :

def find_score (row):
    # Imports the Google Cloud client library
    from google.cloud import language

    # Instantiates a client
    language_client = language.Client()

    import re
    pre_text = re.sub('<[^>]*>', '', row)
    text = re.sub(r'[^\w]', ' ', pre_text)

    document = language_client.document_from_text(text)

    # Detects the sentiment of the text
    sentiment = document.analyze_sentiment().sentiment

    print("Sentiment score - %f " % sentiment.score) 

    return sentiment.score

这可以正常工作，但是它逐渐处理记录时速度很慢。

有没有办法，这可以并行化吗？没有手动将数据帧拆分成更小的块？有没有自动执行此操作的库？

干杯

Answer 1

每次调用language.Client函数时find_score的实例化可能是一个主要的瓶颈。您无需为每次使用该函数创建新的客户端实例，因此在调用之前尝试在函数外部创建它：

# Instantiates a client
language_client = language.Client()

def find_score (row):
    # Imports the Google Cloud client library
    from google.cloud import language


    import re
    pre_text = re.sub('<[^>]*>', '', row)
    text = re.sub(r'[^\w]', ' ', pre_text)

    document = language_client.document_from_text(text)

    # Detects the sentiment of the text
    sentiment = document.analyze_sentiment().sentiment

    print("Sentiment score - %f " % sentiment.score) 

    return sentiment.score

df['score'] = df['subject'].apply(find_score)

如果你坚持，你可以使用这样的多处理：

from multiprocessing import Pool
# <Define functions and datasets here>
pool = Pool(processes = 8) # or some number of your choice
df['score'] = pool.map(find_score, df['subject'])
pool.terminate()

自动多处理数据帧列上的“函数应用”

1 个答案: