Polars the nextgen dataframe library.

Polars (#polars) is a #DataFrame library written in Rust, which means it is fast and efficient. It supports multi-threaded operations, making it ideal for handling large data processing tasks. DataFrames are tabular data structures that are used to store and manipulate large datasets.

Polars provides a variety of features and functionalities that make it an ideal choice for working with structured data. It supports different data types such as integers, floats, booleans, dates, strings, and lists. Additionally, it has support for missing values (also known as NaN values) which can be commonly encountered in real-world datasets.

Some of the key features provided by Polars include:

1. Fast processing: As mentioned earlier, Polars is designed to handle large-scale datasets efficiently. It uses a combination of multi-threading and SIMD (Single Instruction Multiple Data) instructions to achieve high performance.

2. Easy-to-use API: The API provided by Polars is intuitive and easy to use. You can perform common data manipulation tasks such as filtering rows based on certain conditions or grouping rows by a particular column in just a few lines of code.

3. Joining operations: Polars provides robust support for joining two DataFrames together using various join algorithms such as hash join and sort merge join.

4. Aggregation functions: It also provides numerous built-in aggregation functions such as mean(), sum(), min(), max() etc.

5. Flexibility: With its flexible API and powerful functionality, you can use Polars for a wide range of tasks such as data cleaning, analysis, machine learning or visualization.

In conclusion, if you're looking for a fast and flexible DataFrame library for your data processing needs, then Polars should definitely be on your radar!

Need some examples? Absolutely! Here are a few examples of how Polars can be used:

1. Data Manipulation: Polars can be used to manipulate and transform dataframes in various ways. For example, you can use it to filter rows based on certain condition, aggregate data by group, merge/join datasets and so on:

#python

import polars as p




df = pl.DataFrame({

? ? ? ?'A': [1, 2, 3],

? ? ? ?'B': [4, 5, 6],

? ? ? ?'C': ['foo', 'bar', 'baz']

? ?})




# Filter Rows

filtered_df = df[df['A'] > 1]




# Group By and Aggregate

grouped_df = df.groupby('C').agg({'B': ['sum', 'mean']})




# Merge Datasets

other_df = pl.DataFrame({'D': [9, 10]})

merged_df = df.join(other_df)        

2. Statistical Analysis: Polars also provides built-in support for statistical analysis of DataFrame columns using descriptive statistics like mean(), sum(), std() etc. This makes it easy to perform exploratory data analysis (EDA) while working with large datasets:

#python

import polars as p




df = pl.read_csv('my_data.csv')




print(df['column_name'].mean())

print(df[['col1', 'col2']].corr())

print(df['column_name'].quantile(0.95))l        

3. Machine Learning: With the help of Polars' integration with Rust ecosystem and other Python libraries like scikit-learn or Tensorflow, it is possible to build machine learning models on top of large dataframes as well.

import polars as p

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression




df = pl.read_csv('my_data.csv')

X_train, X_test, y_train,y_test = train_test_split(df[['col1', 'col2']], df['target'], test_size=0.3)




model = LinearRegression()

model.fit(X_train, y_train)

y_pred = model.predict(X_test)l        

I hope this gives you a good overview of what Polars can do and how it can be used. Do let me know if there is anything more I can help you with!

Now spark has spark sql and yes polars has its own too!

Polars is a powerful dataframe library that offers a variety of features for data manipulation and analysis. One such feature is its ability to execute SQL queries on Parquet files directly using the `sql` method.

Here's an example of how you can use Polars to execute SQL queries on Parquet files:

import polars as pl



# Read in a Parquet file

df = pl.read_parquet('path/to/parquet_file.parquet')




# Execute an SQL query on the dataframe

result = df.sql("SELECT * FROM table WHERE column='value'")




# Print the result

print(result)

        

In this example, we first read in a Parquet file using the `read_parquet()` function. Next, we use the `sql()` method to execute an SQL query - in this case, selecting all rows from a table where a specific column has a certain value. Finally, we print out the result.

This is just one example of how Polars can be used for data manipulation and analysis - there are many other methods and functions available that make it easy to clean, reshape, and analyze data.

Interesting Article on Rust CLI for SQL By Luca Zanna : Read on

Recent Polars Benchmark

#polars #dataengineering #dataframe

Big shout out to Ritchie Vink or (Ritchie) and Chitral Verma