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("...
# 将DataFrame转换为逐行的字典格式，适用于LlamaIndex for index, row in self.data.iterrows():yield...
Let’s get to the main topic of creating your own PandasAI. After installing the library and setting up the API key, we will create a simple city dataframe with the city name and population as the columns. import pandas as pd from llama_index.query_engine.pandas_query_engine import Pandas...
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 ...
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:...
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 ...
2 import pandas as pd 3 4 # https://huggingface.co/datasets/MongoDB/airbnb_embeddings 5 dataset = load_dataset("MongoDB/airbnb_embeddings", split="train", streaming=True) 6 dataset = dataset.take(4000) 7 # Convert the dataset to a pandas dataframe 8 dataset_df = pd.DataFrame(dataset...
# 把它转换成Pandas的DataFrame格式 df = pd.DataFrame(x, columns=['value']) # 检查原始数据的平稳性 test_stationarity(df['value']) # 进行一阶差分 df['first_difference'] = df['value'] - df['value'].shift(1) # 检查一阶差分后的数据的平稳性 ...
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) ...