Data frames are very powerful tools for data storage, not only in Python but also in R. DataFrame Why Should a Data Frame Be Converted Into Parquet?Īs you may know, a data frame is the most important and frequently used data structures of the Pandas library. In the following line, we are printing this data frame using print(). This dictionary is then converted to a Data Frame with the help of a method- pd.DataFrame(). The values corresponding to these keys are enclosed in square brackets. Next, we create a data dictionary with two keys: Height and Weight. In the first line, we import the Pandas library in our environment with its standard alias name-pd. Let us take a quick look at the explanation. The code for creating a data frame with the help of a dictionary is shown below. Check out this post on how to filter a data frame.Ī simple example of creating a data frame is given below. What is a Data Frame?Ī data frame is a 2D table structure that stores values in rows and columns.Ī data frame may have thousands of rows of data, of which only a few would be useful. Parquet files are self-describing, making them easy to work with using processing tools such as Apache Spark, Apache Impala, etc. Storing data in the Parquet file might also reduce the internal input-output operations of your system and reduce complexity.Īlso, unlike CSV, a parquet format supports nested data structures and is well-compatible with Apache Arrow tables. When stored in a columnar structure, the data becomes easy to query. So what is the use of columnar storage, you may ask? Well, when the data (complex and large) is stored in a column-wise structure rather than in rows, it allows for more efficient compression and encoding of the data. It is mainly used for big data processing as it can efficiently process and manipulate large, complex data. Parquet is a storage format based on columnar storage of data. The to_parquet of the Pandas library is a method that reads a DataFrame and writes it to a parquet format.īefore learning more about the to_parquet method, let us dig deep into what a Parquet format is and why we should use Parquet.
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