How to Read a Parquet File Using Pandas read_parquet 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. Understanding the Pandas read_parquet() function If True, use data types that use pd.Na as missing value indicators for the resulting DataFrame. The path to the file, which can be a URL (such as S3 or FTP)Īdditional options that can be applied to particular storage connections, such as S3 The table below breaks down the function’s parameters and provides descriptions of how they can be used. We can see that the function offers 5 parameters, 4 of which have default arguments provided. Let’s take a look at the pd.read_parquet() function: # Understanding the Pandas read_parquet() Function This will give you a strong understanding of the function’s abilities. Understanding the Pandas read_parquet Functionīefore diving into using the Pandas read_parquet() function, let’s take a look at the different parameters and default arguments of the function. Because of this, its encoding schema is designed for handling massive amounts of data, especially spread across different files.
The format is an open-source format that is specifically designed for data storage and retrieval. This is because only particular can be read, rather than entire records. The benefits of this include significantly faster access to data, especially when querying only a subset of columns. This means data are stored based on columns, rather than by rows. The Apache Parquet format is a column-oriented data file format.
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