import pandas as pdfrom llama_index.query_engine.pandas_query_engine import PandasQueryEngine df = pd.DataFrame( {"city": ["New York", "Islamabad", "Mumbai"], "population": [8804190, 1009832, 12478447]})使用 ，我们将创建一个查询引擎来加载数据帧并为其编制索引。PandasQueryEngine 之后，我...
# 将新行添加到DataFrame并打印更新的DataFrame new_row = pd.DataFrame({'Bid': [bid], 'Ask': [ask], 'Mid_Price': [mid_price]}) df = pd.concat([df, new_row], ignore_index=True) print(f"消费者处理的买价: {bid:.4f}, 卖价: {ask:.4f}, 中间价: {mid_price:.4f}") print("...
我们首先要定义数据库连接和一个执行 Cypher 语句(图形数据库查询语言)并输出 Pandas DataFrame 的函数。 代码语言:javascript 代码运行次数:2 运行 AI代码解释 NEO4J_URI="bolt://localhost"NEO4J_USERNAME="neo4j"NEO4J_PASSWORD="password"driver=GraphDatabase.driver(NEO4J_URI,auth=(NEO4J_USERNAME,NEO4J_PASSWORD...
# 将DataFrame转换为逐行的字典格式，适用于LlamaIndex for index, row in self.data.iterrows():yield...
import pandas as pd from llama_index.query_engine.pandas_query_engine import PandasQueryEngine df = pd.DataFrame( {"city": ["New York", "Islamabad", "Mumbai"], "population": [8804190, 1009832, 12478447]} ) Using thePandasQueryEngine, we will create a query engine to load the dataframe...
DataFrame- data+group_by()+filter()+sort_by()+plot.bar()Series- data+unique()+count()+plot.pie()Plot+bar()+pie()+scatter() Sequence Diagram The following sequence diagram illustrates the flow of operations when working with thellama_indexlibrary:...
pandas.pandas_query_engine import PandasQueryEngine from llama_index.core.schema import QueryBundle # Ensure this import is correct app = FastAPI() # Load the CSV file df = pd.read_csv("sample.csv") # Initialize the PandasQueryEngine with the DataFrame query_engine = PandasQueryEngine(df) ...
3 importpandasaspd 4 5 # https://huggingface.co/datasets/AIatMongoDB/embedded\_movies 6 7 dataset=load_dataset("AIatMongoDB/embedded\_movies") 8 9 # Convert the dataset to a pandas dataframe 10 11 dataset_df=pd.DataFrame(dataset['train']) 12 13 dataset_df.head(5) Step ...
from llama_index.query_engine.pandas_query_engineimportPandasQueryEngine df=pd.DataFrame({"city":["New York","Islamabad","Mumbai"],"population":[8804190,1009832,12478447]}) 使用,我们将创建一个查询引擎来加载数据帧并为其编制索引。PandasQueryEngine ...
结果会以 pandas DataFrame(包含模型名称和原始问答输出)组织起来,并返回分数。 4. 运行完整评估并显示结果 我们对两个模型执行 evaluate_model 函数,并显示结果 DataFrame,其中包含 RAGAS 分数。 gpt4o_df =evaluate_model(chat_model_4o,"GPT-4o")