Pandas read parquet slow. You signed out in another tab or window. H

Pandas read parquet slow. You signed out in another tab or window. However, the performance You signed in with another tab or window. Feb 15, 2024 · I'm using Azure Data Lake Gen2 to store data in parquet file format. We can use Dask’s read_parquet function, but provide a globstring of files to read in Sep 25, 2023 · However, memory usage of polars is the same as pandas 2 which is 753MB. concurrent import process_map def _read_parquet (filename, columns = None): """Wrapper to pass to a ProcessPoolExecutor to read parquet files as fast as possible. contrib. You switched accounts on another tab or window. So far I am constructing it each day anew from an Excel file, which is slow. Below I work through a use case to speed things up. to_parquet(parquet_f, engine='pyarrow', compression=None) pickle_f = os. 0 on a regular basis. May 21, 2025 · A practical guide to solving memory optimization issues when working with Parquet files in Pandas. Dec 22, 2023 · As title. parquet file, so I can quickly safe the SEDF on disk and load it whenever in a fast manner. If True, use dtypes that use pd. 7. A lot. dtypes) # Output # col1 object # col2 object # col3 object. This can . The article then delves into efficient data loading techniques, such as using the Dec 17, 2024 · Hi there, I am using an SEDF in ArcGIS Pro 3. (only applicable for the pyarrow engine) As new dtypes are added that support pd. use_nullable_dtypes bool, default False. Are you using a fast disk? Network-attached disks (such as cloud block storage) cause write-intensive and larger than memory workloads to slow down. if I save csv file into parquet file with pyarrow engine. 8GB parquet file on HDFS cluster. Recently Conor O Oct 31, 2020 · Apache Parquet is a columnar storage format with support for data partitioning Introduction. To read a Parquet file into a Pandas DataFrame, you can use the pd. The best you can do with parquet files is to use numeric columns (like you did in your update) and increase the number of row groups (or, equivalently, specify a smaller row_group_size in parquet. Now I would like to safe the data as a . 4GB parquet file that has ~100MM rows. I have split the data using partitions by year, month and day to benefit from the filtering functionality. Reload to refresh your session. More detailed instructions are linked for each point. 0. Aug 24, 2022 · I have a 4. Using Dask read_parquet and then compute() to Pandas DataFrame is much slower. Then I use the Apr 27, 2022 · I love the versatility of pandas as much as anyone. read_parquet() function. 0) is faster than the fastparquet engine (v0. from functools import partial import pandas as pd import pyarrow as pa from tqdm. I tried two approaches May 2, 2023 · Pandas defaults to flexible but memory-intensive object dtype when reading in new data: import pandas as pd df = pd. csv') print (df. pkl') df. parquet') df. Do you have enough memory? DuckDB works best if you have 1-4 GB memory per thread. The PyArrow engine (v4. NA in the future, the output with this option will change to use those dtypes. Learn how to handle dictionary columns efficiently, reduce memory consumption from 90GB to manageable levels, and implement best practices for big data processing in Python. Pandas 2 has same speed as Polars or pandas is even slightly faster which is also very interesting, which make me feel better if I stay with Pandas but just save csv file into parquet file. I'm reading the data from python directly (no Spark or Synapse here) using the pyarrofs_adlgen2 library as suggested in other Q&A of this forum. Aug 12, 2024 · This article is designed to help you enhance the performance of your data manipulation tasks using Pandas, a powerful Python library. da We’ll import dask. How to Read a Parquet File Using Pandas read_parquet. dataframe and notice that the API feels similar to pandas. read_csv ('data. Now that you have a strong understanding of what options the function offers, let’s start learning how to read a parquet file using Pandas. 1. It starts with an introduction to the importance of performance optimization, explaining how it can impact your data analysis and why it’s crucial to implement performance tips. to_pickle(pickle_f) How come I consistently get the opposite withpickle file being read about 3 times faster than parquet with 130 million Feb 11, 2020 · I am trying to read a decently large Parquet file (~2 GB with about ~30 million rows) into my Jupyter Notebook (in Python 3) using the Pandas read_parquet function. For running Oct 12, 2010 · parquet_f = os. I'd like to understand what's going on here. This causes two problems: Slow inference: Pandas processes all values in each column to infer types like int, float, string etc. I have also installed the pyarro Sep 9, 2022 · Understanding the Pandas read_parquet() function. NA as missing value indicator for the resulting DataFrame. read_parquet() effectively just wraps the PyArrow version here as far as I can tell. When it’s slow, however, pandas frustrates me as much as anyone. Jul 13, 2022 · Loading a Parquet file via PyArrow and then converting to Pandas is consistently faster than loading directly via Pandas on my machine (I have verified on both Windows and Linux machines). join(parent_dir, 'df. The Jul 14, 2022 · You already found the answer. I'm really struggling to see why, as the source for pd. read_parquet case there seems to be a lot of overhead before the workers even start to do something, and there are quite a few transfer- operations scattered across the task stream plot. write_table). path. What could be the reason that the read_parquet approach is so much slower? What does it do If you find that your workload in DuckDB is slow, we recommend performing the following checks. import dask. 0) as it can read Jan 27, 2020 · In the dd. And running Python script on a local machine. I have done the following to load the file to memory: sudo mkdir /ram/ sudo mount ramfs -t ramfs /ram This way IO is not a problem. I have recently gotten more familiar with how to work with Parquet datasets across the six major tools used to read and write from Parquet in the Python ecosystem: Pandas, PyArrow, fastparquet, AWS Data Wrangler, PySpark and Dask. For a about 6. putdi uipoufcmq xmb jcdbwja oxb szofe tdjoaru zaptl lyz ujprc