课程

Data Transformation with Polars

中级技能水平

更新时间 2026年3月

Take Polars further with text manipulation, rolling statistics, DataFrame joins, and advanced analytics.

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PythonData Manipulation4 小时14 视频54 练习4,700 经验值成就声明

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课程描述

Take your Polars skills further with intermediate techniques for data transformation and analysis. Learn to clean and search text, work with time series using rolling and window functions, and combine DataFrames through joins and concatenation. You'll also create reusable custom expressions and apply advanced analytics, such as quantiles, histograms, and correlations. By the end, you'll be equipped to handle complex data workflows with confidence.

先决条件

Introduction to Polars Intermediate Python for Developers

1

Transforming Data with Expressions

In this chapter, you'll learn how to clean and search text data, extract patterns from strings, and use conditional expressions to transform columns.

Transforming Data with Expressions

50 经验值

Converting column data types

100 经验值

Removing whitespace

100 经验值

Concatenating text columns

100 经验值

Searching and extracting text

50 经验值

Searching text

100 经验值

Extracting strings from text

100 经验值

Replacing multiple patterns

100 经验值

Creating conditional expressions

50 经验值

Categorizing customers with conditions

100 经验值

Chaining multiple conditions

100 经验值

Filling missing values

100 经验值

2

Working with Time Series and Window Functions

Learn how to parse and manipulate dates, filter and aggregate data by time periods, and calculate rolling and cumulative statistics.

Parsing and manipulating dates

50 经验值

Parsing datetime strings

100 经验值

Extracting the hour component

100 经验值

Calculating rental period end times

100 经验值

Filtering and aggregating by time period

50 经验值

Filtering for a specific datetime

100 经验值

Filtering for a time range

100 经验值

Calculating weekly rental totals

100 经验值

Rolling and cumulative aggregations

50 经验值

Calculating a rolling sum

100 经验值

Calculating cumulative rentals

100 经验值

Rolling aggregations on multiple columns

100 经验值

Transforming data by group

50 经验值

Calculating the hourly total with window functions

100 经验值

Calculating the difference from hourly mean

100 经验值

Grouping by multiple columns with window functions

100 经验值

3

Combining and Joining DataFrames

This chapter covers combining DataFrames via concatenation, joining on single or multiple columns, and matching rows based on conditions or nearest values.

Concatenating DataFrames

50 经验值

Stacking regional listings

100 经验值

Adding flag columns

100 经验值

Adding new rows efficiently

100 经验值

Joining DataFrames

50 经验值

Adding host information

100 经验值

Matching on unique combinations

100 经验值

Preserving all listings

100 经验值

Finding gaps in both DataFrames

100 经验值

Joining DataFrames by condition or nearest match

50 经验值

Matching listings to budget requests

100 经验值

Filtering with multiple conditions

100 经验值

Matching to the nearest price update

100 经验值

4

Custom Transformations and Advanced Analytics

Learn how to build reusable custom expressions, categorize data using quantiles and fixed thresholds, and analyze rankings and correlations.

Applying custom transformations

50 经验值

Converting streams to billions

100 经验值

Creating reusable expressions

100 经验值

Adding a custom expression

100 经验值

Calculating quantiles and histograms

50 经验值

Calculating popularity quantiles

100 经验值

Binning streams into tiers

100 经验值

Building a popularity histogram

100 经验值

Calculating ranks and correlations

50 经验值

Ranking albums with tie handling

100 经验值

Building a correlation matrix

100 经验值

Custom summaries with describe

100 经验值

Wrapping up

50 经验值

Data Transformation with Polars

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