ValueError: 无法使用长度与 Python 中的值不同的多索引选择索引器进行设置

Question

我有一些示例代码，可以使用 Google 的自然语言 API 来分析实体及其情绪。对于 Pandas 数据框中的每条记录，我想返回一个字典列表，其中每个元素都是一个实体。然而，当我尝试让它在生产数据上工作时遇到了问题。这是示例代码

from google.cloud import language_v1 # version 2.0.0

import os 

os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'path/to/json'

import pandas as pd 

# establish client connection

client = language_v1.LanguageServiceClient()

# helper function 

def custom_analyze_entity(text_content):

    global client

    #print("Accepted Input::" + text_content)

    document = language_v1.Document(content=text_content, type_=language_v1.Document.Type.PLAIN_TEXT, language = 'en')

    response = client.analyze_entity_sentiment(request = {'document': document})

    # a document can have many entities

    # create a list of dictionaries, every element in the list is a dictionary that represents an entity

    # the dictionary is nested

    l = []

    #print("Entity response:" + str(response.entities))

    for entity in response.entities:

        #print('=' * 20)

        temp_dict = {}

        temp_meta_dict = {}

        temp_mentions = {}

        temp_dict['name'] = entity.name

        temp_dict['type'] = language_v1.Entity.Type(entity.type_).name

        temp_dict['salience'] = str(entity.salience)

        sentiment = entity.sentiment

        temp_dict['sentiment_score'] = str(sentiment.score)

        temp_dict['sentiment_magnitude'] = str(sentiment.magnitude)

        for metadata_name, metadata_value in entity.metadata.items():

            temp_meta_dict['metadata_name'] = metadata_name

            temp_meta_dict['metadata_value'] = metadata_value

        temp_dict['metadata'] = temp_meta_dict

        for mention in entity.mentions:

            temp_mentions['mention_text'] = str(mention.text.content)

            temp_mentions['mention_type'] = str(language_v1.EntityMention.Type(mention.type_).name)

        temp_dict['mentions'] = temp_mentions

        #print(u"Appended Entity::: {}".format(temp_dict))

        l.append(temp_dict)

    return l

Answer 1

尝试这样做：input_df.loc[0, 'entity_object'] = ""for i in range(len(input_df)):    op = custom_analyze_entity(input_df.loc[i,'freeform_text'])    input_df.loc[i, 'entity_object'] = op或者对于您的具体情况，您不需要使用loc函数。input_df["entity_object"] = ""    for i in range(len(input_df)):        op = custom_analyze_entity(input_df.loc[i,'freeform_text'])             input_df["entity_object"][i] = op