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💡 Pro tip: This is one of those techniques that will make you look like a data science wizard! Introduction to Polars Polars is a fast and efficient DataFrame library for Python, written in Rust. It provides a user-friendly interface similar to pandas but with better performance and memory efficiency. - Made Simple!

Let’s break this down together! Here’s how we can tackle this:

import polars as pl

# Create a DataFrame from a Python dictionary
data = {"names": ["Alice", "Bob", "Charlie"], "ages": [25, 32, 18]}
df = pl.DataFrame(data)
print(df)

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🎉 You’re doing great! This concept might seem tricky at first, but you’ve got this! Reading Data from CSV Polars can read data from various sources, including CSV files. It provides a convenient function to read CSV data into a DataFrame. - Made Simple!

Let’s break this down together! Here’s how we can tackle this:

# Read a CSV file
df = pl.read_csv("data.csv")
print(df)

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✨ Cool fact: Many professional data scientists use this exact approach in their daily work! Data Selection Polars supports familiar indexing and selection operations, similar to pandas, to access specific rows and columns of a DataFrame. - Made Simple!

Let’s break this down together! Here’s how we can tackle this:

# Select a column
ages = df["ages"]
print(ages)

# Select rows based on a condition
young_people = df[df["ages"] < 25]
print(young_people)

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🔥 Level up: Once you master this, you’ll be solving problems like a pro! Data Manipulation Polars provides various methods to manipulate and transform data in a DataFrame, such as filtering, sorting, and applying functions. - Made Simple!

Don’t worry, this is easier than it looks! Here’s how we can tackle this:

# Filter rows
filtered_df = df.filter(pl.col("ages") > 25)
print(filtered_df)

# Sort DataFrame
sorted_df = df.sort("ages", descending=True)
print(sorted_df)

🚀 Aggregations Polars supports efficient aggregations on DataFrames, including sum, mean, min, max, and more. - Made Simple!

Here’s where it gets exciting! Here’s how we can tackle this:

# Calculate the mean age
mean_age = df.select(pl.col("ages").mean().alias("mean_age"))
print(mean_age)

# Group by and aggregate
grouped = df.groupby("ages").agg(pl.count().alias("count"))
print(grouped)

🚀 Joining DataFrames Polars allows you to join multiple DataFrames based on common columns, similar to SQL-like joins. - Made Simple!

Let me walk you through this step by step! Here’s how we can tackle this:

# Create another DataFrame
df2 = pl.DataFrame({"names": ["Alice", "Eve", "Frank"], "cities": ["New York", "London", "Paris"]})

# Join DataFrames
joined_df = df.join(df2, on="names", how="left")
print(joined_df)

🚀 Missing Data Handling Polars provides methods to handle missing data in DataFrames, such as filling or dropping rows/columns with null values. - Made Simple!

Don’t worry, this is easier than it looks! Here’s how we can tackle this:

# Drop rows with null values
df_no_nulls = df.drop_nulls()
print(df_no_nulls)

# Fill null values
filled_df = df.fill_null(0)
print(filled_df)

🚀 Data Visualization Polars integrates with popular data visualization libraries like Matplotlib and Plotly, allowing you to create visualizations directly from DataFrames. - Made Simple!

Here’s where it gets exciting! Here’s how we can tackle this:

import matplotlib.pyplot as plt

# Plot a histogram
df.plot.histogram("ages", buckets=5)
plt.show()

🚀 Performance Comparison Polars aims to provide better performance compared to pandas, especially for larger datasets. Here’s an example to compare the performance of common operations. - Made Simple!

Let me walk you through this step by step! Here’s how we can tackle this:

import pandas as pd
import timeit

# Create a large DataFrame
large_df = pl.DataFrame({"values": [i for i in range(1_000_000)]})
pandas_df = pd.DataFrame(large_df.to_dict())

# Compare performance
polars_time = timeit.timeit(lambda: large_df.filter(pl.col("values") > 500_000), number=1000)
pandas_time = timeit.timeit(lambda: pandas_df[pandas_df["values"] > 500_000], number=1000)

print(f"Polars time: {polars_time:.6f} seconds")
print(f"Pandas time: {pandas_time:.6f} seconds")

🚀 Lazy Evaluation Polars supports lazy evaluation, which means operations are not executed until necessary. This can lead to improved performance for complex operations. - Made Simple!

Here’s a handy trick you’ll love! Here’s how we can tackle this:

# Create a lazy expression
lazy_df = pl.scan_csv("data.csv") \
            .filter(pl.col("age") > 25) \
            .select(["name", "age"])

# Execute the lazy expression
result = lazy_df.collect()
print(result)

🚀 Data Types Polars supports a variety of data types, including numeric, string, date/time, and categorical types. It also provides methods for type conversion and handling missing values. - Made Simple!

Here’s where it gets exciting! Here’s how we can tackle this:

# Create a DataFrame with mixed data types
data = {"names": ["Alice", "Bob", "Charlie"], "ages": [25, None, 18], "dates": ["2022-01-01", "2021-05-15", None]}
df = pl.DataFrame(data)

# Convert data types
df = df.with_columns(
    pl.col("ages").cast(pl.Int64),
    pl.col("dates").str.strptime(pl.Date, "%Y-%m-%d")
)
print(df)

🚀 Parallel Processing Polars can leverage multiple CPU cores for parallel processing, leading to faster computations on larger datasets. - Made Simple!

Let me walk you through this step by step! Here’s how we can tackle this:

# Set the number of threads for parallel processing
pl.set_env_threaded(True)
pl.set_env_threading_max_num_threads(4)

# Perform a computation in parallel
result = df.select(pl.col("values").apply(lambda x: x * 2).parallel())
print(result)

🚀 User-Defined Functions (UDFs) Polars allows you to define custom functions (UDFs) and apply them to DataFrames. UDFs can be written in Rust or Python. - Made Simple!

Let’s break this down together! Here’s how we can tackle this:

# Define a Python UDF
import polars as pl

def square(x):
    return x ** 2

# Apply the UDF to a column
df = pl.DataFrame({"values": [1, 2, 3, 4, 5]})
squared_df = df.with_column(pl.col("values").apply(square))
print(squared_df)

🚀 Integration with Other Libraries Polars can integrate with other Python libraries, such as NumPy, Scikit-learn, and Dask, enabling seamless data processing workflows. - Made Simple!

Ready for some cool stuff? Here’s how we can tackle this:

import numpy as np
from sklearn.linear_model import LinearRegression

# Convert Polars DataFrame to NumPy array
X = df["features"].to_numpy()
y = df["target"].to_numpy()

# Train a linear regression model
model = LinearRegression().fit(X, y)

# Make predictions on a new DataFrame
new_df = pl.DataFrame({"features": [[1, 2], [3, 4]]})
predictions = model.predict(new_df["features"].to_numpy())
print(predictions)

This slideshow covers various topics related to Polars, including data manipulation, aggregations, joining, visualization, performance comparison, lazy evaluation, data types, parallel processing, user-defined functions, and integration with other libraries. The examples are designed to be actionable and suitable for beginner to intermediate level users.

🎊 Awesome Work!

You’ve just learned some really powerful techniques! Don’t worry if everything doesn’t click immediately - that’s totally normal. The best way to master these concepts is to practice with your own data.

What’s next? Try implementing these examples with your own datasets. Start small, experiment, and most importantly, have fun with it! Remember, every data science expert started exactly where you are right now.

Keep coding, keep learning, and keep being awesome! 🚀