Hi, I just installed tensorflow-io and try to load parquet. import pandas as pd import tensorflow as tf import tensorflow_io as tfio pd.DataFrame({'a':[.1,.2], 'b':[.01,.02]}).to_parquet('file.parquet') df = pd.read_parquet('file.parquet...
import pathlib import zipfile import pandas as pd import requests def download_benchmark( benchmark_zip_file="orderly_benchmark.zip", benchmark_directory="orderly_benchmark/", version=2, ): figshare_url = ( f"https://figshare.com/ndownloader/articles/23298467/versions/{version}" ) print(...
import pandas as pd from tqdm.auto import tqdm base = "https://synapseaisolutionsa.blob.core.windows.net/public/AdventureWorks" # load list of tables df_tables = pd.read_csv(f"{base}/adventureworks.csv", names=["table"]) for table in (pbar := tqdm(df_tables['...
pandas.DataFrame.from_records 是一个非常有用的函数,它可以从各种记录格式的数据中创建 DataFrame。可以从列表、元组、字典等创建 DataFrame。它对于从结构化数据(如数据库结果集)创建 DataFrame 非常有用。本文主要介绍一下Pandas中pandas.DataFrame.from_records方法的使用。 DataFrame.from_records(data,index = None...
import pandas as pd from matplotlib import pyplot as plt df = pd.read_csv("out_filtered.log", sep=' ', header=None) df = df.assign( ts=lambda x: pd.to_datetime( x[1]+"T"+ x[2]), bytes=lambda x: x[5], thread=lambda x: x[4], ...
import boto3 import pandas as pd # Import the csv into a DataFrame df = pd.read_csv('stock_prices.csv') # Create an Amazon Bedrock client bedrock_client = boto3.client('bedrock') # Define the prompt user_query = "Show me all wells that produced oil on...
importmltable path = {'file':'abfss://<filesystem>@<account>.dfs.core.windows.net/<folder>/<file_name>.csv'} tbl = mltable.from_delimited_files(paths=[path]) df = tbl.to_pandas_dataframe() df.head() Read parquet files in a folder ...
import pandas as pd import os # # Check to see if file exists # fpath="import/Sample.csv" if os.path.isfile(fpath): print("File: ", fpath, " exists!") else: print ("File: ", fpath, " does not exist!") df_data = pd.read_csv(fpath, sep = ',', encoding='cp1252')...
(file) df = spark.read.json(<json_path>).select('add.path') target_list = list(df.select('path').toPandas()['path']) final_list.extend(target_list) return list(filter(None, final_list)) def copy_files(final_list,source_folder,backup_folder): ''' copy parquet files from source ...
number_rows_read: int = -1, return_transform_objects: bool = False, blocks_per_read: int = None, report_progress: int = None, xdf_compression_level: int = 0, strings_as_factors: bool = None, **kwargs) -> typing.Union[revoscalepy.datasource.RxXdfData.RxXdfData, pandas.core.frame...