Getting Started with Polars : Implementation
Installing Polars
Before diving into examples, you need to install Polars. You can do this using pip:
pip install polars
Creating a DataFrame
Creating a DataFrame in Polars is straightforward. You can create a DataFrame from a dictionary, list of lists, or even from a CSV file.
import polars as pl
# Create a sample dataset
data = [["John", 25, "Male"], ["Alice", 30, "Female"], ["Bob", 28, "Male"]]
df = pl.DataFrame(data, schema=["Name", "Age", "Gender"])
# Basic data exploration
print(df)
Output:
shape: (3, 3)
┌───────┬─────┬────────┐
│ Name ┆ Age ┆ Gender │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ str │
╞═══════╪═════╪════════╡
│ John ┆ 25 ┆ Male │
│ Alice ┆ 30 ┆ Female │
│ Bob ┆ 28 ┆ Male │
└───────┴─────┴────────┘
Basic DataFrame Operations
Polars provides a rich set of functions for data manipulation. Here are some common operations:
1. Filtering and Aggregation
To filter rows based on a condition, use the filter method:
# Filtering and aggregation
male_ages = df.filter(pl.col("Gender") == "Male").select("Age")
average_male_age = male_ages.mean()
print(male_ages)
print(average_male_age)
Ouput:
shape: (2, 1)
┌─────┐
│ Age │
│ --- │
│ i64 │
╞═════╡
│ 25 │
│ 28 │
└─────┘
shape: (1, 1)
┌──────┐
│ Age │
│ --- │
│ f64 │
╞══════╡
│ 26.5 │
└──────┘
2. Concatenating DataFrames
# Concatenating DataFrames
more_data = [["Charlie", 22, "Male"], ["Diana", 26, "Female"]]
another_df = pl.DataFrame(more_data, schema=["Name", "Age", "Gender"])
combined_df = pl.concat([df, another_df], how="diagonal")
print(combined_df)
Ouput:
shape: (5, 3)
┌─────────┬─────┬────────┐
│ Name ┆ Age ┆ Gender │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ str │
╞═════════╪═════╪════════╡
│ John ┆ 25 ┆ Male │
│ Alice ┆ 30 ┆ Female │
│ Bob ┆ 28 ┆ Male │
│ Charlie ┆ 22 ┆ Male │
│ Diana ┆ 26 ┆ Female │
└─────────┴─────┴────────┘
3. Grouping and Aggregation
To group by a column and perform aggregation, use the groupby and agg methods:
# Grouping and aggregation
grouped_df = combined_df.groupby("Gender").agg(
pl.col("Age").mean().alias("Average Age")
)
print(grouped_df)
Output:
shape: (2, 2)
┌────────┬─────────────┐
│ Gender ┆ Average Age │
│ --- ┆ --- │
│ str ┆ f64 │
╞════════╪═════════════╡
│ Male ┆ 25.0 │
│ Female ┆ 28.0 │
└────────┴─────────────┘
<ipython-input-5-5bc52ea0a171>:2: DeprecationWarning: `groupby` is deprecated. It has been renamed to `group_by`.
grouped_df = combined_df.groupby("Gender").agg(
4. Selecting Columns
To select specific columns, you can use the select method:
# Select the "Name" and "Age" columns
df_selected = df.select(["Name", "Age"])
print(df_selected)
Output:
shape: (3, 2)
┌───────┬─────┐
│ Name │ Age │
│ --- │ --- │
│ str │ i64 │
├───────┼─────┤
│ John │ 25 │
│ Alice │ 30 │
│ Bob │ 28 │
└───────┴─────┘
5. Adding New Columns
To add a new column, use the with_column method:
# Add a new column "Age_in_5_years" which is Age + 5
df = df.with_column((pl.col("Age") + 5).alias("Age_in_5_years"))
print(df)
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 │
└───────┴─────┴────────┴──────────────┘
6. Sorting Data
To sort the DataFrame by a specific column, use the sort method:
# Sort by "Age" in descending order
df_sorted = df.sort("Age", reverse=True)
print(df_sorted)
Output:
shape: (3, 4)
┌───────┬─────┬────────┬──────────────┐
│ Name │ Age │ Gender │ Age_in_5_years │
│ --- │ --- │ --- │ --- │
│ str │ i64 │ str │ i64 │
├───────┼─────┼────────┼──────────────┤
│ Alice │ 30 │ Female │ 35 │
│ Bob │ 28 │ Male │ 33 │
│ John │ 25 │ Male │ 30 │
└───────┴─────┴────────┴──────────────┘
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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