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# Introduction
Pandas Undoubtedly a robust and versatile library Manage and analyze data workflowssomething fundamental in the big picture of data science. Still, when dataset sizes get very large, this may not be the most efficient option because it runs primarily in a single thread and relies heavily on the Python interpreter, which can lead to significant processing time.
This article focuses on a new library that accelerates pandas-like operations: Polar. In particular, I will share with you 10 Insightful Polaroid One-Liners For daily smoothing and sharpening Data manipulation and processing functions.
Before you begin, don’t forget import polars as pl First!
# 1. Loading CSV files
Pollers’ method of reading a dataset from a CSV file seems similar to its Pandas counterpart, except that it is multi-threaded (and written internally in Rust), allowing it to load data more efficiently. This example shows how to load a CSV file into a polar data frame.
df = pl.read_csv("dataset.csv")Even for medium datasets (not just extremely large ones), the difference in time taken to read a file with Polar can be up to 5 times faster than using Pandas.
# 2. Slow loading for more scalable workflows
One approach is to create so-called “lazy data frames” instead of eagerly reading them all at once, which enables subsequent operations in the data workflow to be chained, only executing them when collect() The method is finally called—a very simple strategy for large-scale data pipelines! Here’s how to implement slow data frame loading using scan_csv() Method:
df_lazy = pl.scan_csv("dataset.csv")# 3. Selecting and renaming the relevant columns
To make things easier and clearer in post-processing, it’s a good idea to make sure you’re only dealing with the columns of the dataset that are relevant to your data science or analysis project. Here’s how to do it efficiently with Polar DataFrames. Suppose you are using a customer dataset like This one. You can then use the following one-liner to select the relevant columns of your choice, like this:
df = df.select((pl.col("Customer Id"), pl.col("First Name")))# 4. Filtering for a subset of rows
Of course, we can also filter the polar path for specific rows, such as users. This one-liner is used to filter users residing in a specific city.
df_filtered = df.filter(pl.col("City") == "Hatfieldshire")You may want to use this method display() or head() To see the result of this “query”, i.e. rows that meet certain criteria.
# 5. Grouping and computing aggregates by category
With tasks like grouping and aggregation, the performance value of Poles really starts to show in larger datasets. Take this one-liner for example: The key here is connecting group_by On a discrete column, with agg() To aggregate for all rows in each group, such as the average over a numeric column, or just the row count in each group, as shown below:
df_city = df.group_by("City").agg((pl.len().alias("num_customers")))Be careful! In pandas, groupby() It doesn’t have an underscore symbol, but in polar regions, it does.
# 6. Creating Derived Columns (Simple Feature Engineering)
Thanks to the vectorized computation capabilities of pollers, generating new columns from arithmetic operations on existing ones is significantly faster. This one-liner demonstrates that (considering the now popular California Housing dataset for the examples that follow!):
df = df.with_columns((pl.col("total_rooms") / pl.col("households")).alias("rooms_per_household"))# 7. Applying conditional logic
Constant attributes such as income level or similar attributes can be converted into classes and labeled classes, all in a vectorized and overhead-free manner. To create this example. It does so income_category Columns based on median income per district in California:
df = df.with_columns(pl.when(pl.col("median_income") > 5).then(pl.lit("High")).otherwise(pl.lit("Low")).alias("income_category"))
# 8. Slow pipeline execution
This one-liner, while a bit long, pulls together multiple ideas shown in the previous examples to create a lazy pipeline to be executed with the Lord. collect Remember the method: For this slow approach to work, you need to use one-liner #2 to read your dataset file “the slow way”.
result = (pl.scan_csv("
.filter(pl.col("median_house_value") > 200000)
.with_columns((pl.col("total_rooms") / pl.col("households")).alias("rooms_per_household"))
.group_by("ocean_proximity").agg(pl.mean("rooms_per_household").alias("avg_rooms_per_household"))
.sort("avg_rooms_per_household", descending=True)
.collect())# 9. Joining datasets
Suppose we have an additional dataset region_stats.csv With aggregated statistical information for California counties. We can then use a one-liner like this to apply join operations to a specific category column, like this:
df_joined = df.join(pl.read_csv("region_stats.csv"), on="ocean_proximity", how="left")
This will result in an efficient combination of residential data with district-level metadata, through a multi-threaded join of pollers that preserves performance even in large datasets.
# 10. Performing rolling computations
In highly fluctuating data variables, rolling is useful for aggregate smoothing, for example, average house values ​​across latitude and longitude. This one-liner illustrates how to implement such a fast, vectorized operation: perfect for spatial or geographic ordering.
df = df.sort("longitude").with_columns(pl.col("median_house_value").rolling_mean(window_size=7).alias("rolling_value_avg"))
# wrap up
In this article, we have listed 10 handy one-liners to efficiently use Polar as a fast alternative to Pandas for handling large datasets. These one-liners cover faster, better strategies for handling large amounts of data in less time. The next time you work with poles in your projects, you’ll undoubtedly notice a variety of improvements.
Ivan Palomares Carrascosa Is a leader, author, speaker, and consultant in AI, Machine Learning, Deep Learning, and LLMS. He trains and guides others in real-world applications of AI.