Advanced Features: Parallel Processing and Lazy Evaluation
Polars naturally provides parallel processing to expedite calculations and permits lazy evaluation, which may be useful for query plan optimization.
Lazy Evaluation
Lazy evaluation allows you to build a query plan without executing it immediately. This can lead to significant performance improvements.
# Lazy Evaluation
lazy_df = combined_df.lazy()
# Lazy filtering and aggregation
lazy_male_ages = lazy_df.filter(pl.col("Gender") == "Male").select("Age")
lazy_average_male_age = lazy_male_ages.mean()
# Collect the results (execute the lazy computation)
result = lazy_average_male_age.collect()
print(result)
Output:
shape: (1, 1)
┌──────┐
│ Age │
│ --- │
│ f64 │
╞══════╡
│ 25.0 │
└──────┘
Parallel Processing
Polars can automatically parallelize operations, making it highly efficient for large datasets.
We’ll use the multiprocessing module to filter the dataset in parallel. The task will be to filter rows where the age is greater than a certain value and then process the filtered data.
Step 1: Define the Function for Parallel Processing
First, define a function that will filter the DataFrame based on age and perform some processing:
import polars as pl
def filter_and_process(df, age_threshold):
# Filter rows where Age is greater than the threshold
df_filtered = df.filter(pl.col("Age") > age_threshold)
# Perform some processing, e.g., adding a new column
df_processed = df_filtered.with_column((pl.col("Age") + 5).alias("Age_in_5_years"))
return df_processed
Step 2: Set Up Multiprocessing
Next, set up the multiprocessing environment to run the function in parallel:
- Multiprocessing Setup: We create a pool of worker processes using
multiprocessing.Pool. - Parallel Execution: The
starmapmethod is used to apply thefilter_and_processfunction to the DataFrame in parallel for different age thresholds.
import multiprocessing
# Define the age thresholds for parallel processing
age_thresholds = [20, 25, 30]
# Create a pool of worker processes
pool = multiprocessing.Pool(processes=3)
# Use the pool to apply the function in parallel
results = pool.starmap(filter_and_process, [(df, age) for age in age_thresholds])
# Close the pool and wait for the work to finish
pool.close()
pool.join()
for result in results:
print(result)
Output:
shape: (3, 4)
┌───────┬─────┬────────┬──────────────┐
│ Name │ Age │ Gender │ Age_in_5_years │
│ --- │ --- │ --- │ --- │
│ str │ i64 │ str │ i64 │
├───────┼─────┼────────┼──────────────┤
│ John │ 25 │ Male │ 30 │
│ Alice │ 30 │ Female │ 35 │
│ Bob │ 28 │ Male │ 33 │
└───────┴─────┴────────┴──────────────┘
shape: (2, 4)
┌───────┬─────┬────────┬──────────────┐
│ Name │ Age │ Gender │ Age_in_5_years │
│ --- │ --- │ --- │ --- │
│ str │ i64 │ str │ i64 │
├───────┼─────┼────────┼──────────────┤
│ Alice │ 30 │ Female │ 35 │
│ Bob │ 28 │ Male │ 33 │
└───────┴─────┴────────┴──────────────┘
shape: (1, 4)
┌───────┬─────┬────────┬──────────────┐
│ Name │ Age │ Gender │ Age_in_5_years │
│ --- │ --- │ --- │ --- │
│ str │ i64 │ str │ i64 │
├───────┼─────┼────────┼──────────────┤
│ Alice │ 30 │ Female │ 35 │
└───────┴─────┴────────┴──────────────┘
Mastering Polars: High-Efficiency Data Analysis and Manipulation
In the ever-evolving landscape of data science and analytics, efficient data manipulation and analysis are paramount. While pandas has been the go-to library for many Python enthusiasts, a new player, Polars, is making waves with its performance and efficiency. This article delves into the world of Polars, providing a comprehensive introduction, highlighting its features, and showcasing practical examples to get you started.
Table of Content
- Understanding Polars Library
- Why is Polars Used for Data Science?
- Getting Started with Polars : Implementation
- Advanced Features: Parallel Processing and Lazy Evaluation
- Integration with Other Libraries
- Advantages and Disadvantages of Polars
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