Time series easier, faster, more fun. Pytimetk.
Please ⭐ us on GitHub (it takes 2-seconds and means a lot).
Time series analysis is fundamental in many fields, from business forecasting to scientific research. While the Python ecosystem offers tools like pandas, they sometimes can be verbose and not optimized for all operations, especially for complex time-based aggregations and visualizations.
Enter pytimetk. Crafted with a blend of ease-of-use and computational efficiency, pytimetk significantly simplifies the process of time series manipulation and visualization. By leveraging the polars backend, you can experience speed improvements ranging from 3X to a whopping 3500X. Let's dive into a comparative analysis.
| Features/Properties | pytimetk | pandas (+matplotlib) |
|---|---|---|
| Speed | 🚀 3X to 3500X Faster | 🐢 Standard |
| Code Simplicity | 🎉 Concise, readable syntax | 📜 Often verbose |
plot_timeseries() |
🎨 2 lines, no customization | 🎨 16 lines, customization needed |
summarize_by_time() |
🕐 2 lines, 13.4X faster | 🕐 6 lines, 2 for-loops |
pad_by_time() |
⛳ 2 lines, fills gaps in timeseries | ❌ No equivalent |
anomalize() |
📈 2 lines, detects and corrects anomalies | ❌ No equivalent |
augment_timeseries_signature() |
📅 1 line, all calendar features | 🕐 29 lines of dt extractors |
augment_rolling() |
🏎️ 10X to 3500X faster | 🐢 Slow Rolling Operations |
As evident from the table, pytimetk is not just about speed; it also simplifies your codebase. For example, summarize_by_time(), converts a 6-line, double for-loop routine in pandas into a concise 2-line operation. And with the polars engine, get results 13.4X faster than pandas!
Similarly, plot_timeseries() dramatically streamlines the plotting process, encapsulating what would typically require 16 lines of matplotlib code into a mere 2-line command in pytimetk, without sacrificing customization or quality. And with plotly and plotnine engines, you can create interactive plots and beautiful static visualizations with just a few lines of code.
For calendar features, pytimetk offers augment_timeseries_signature() which cuts down on over 30 lines of pandas dt extractions. For rolling features, pytimetk offers augment_rolling(), which is 10X to 3500X faster than pandas. It also offers pad_by_time() to fill gaps in your time series data, and anomalize() to detect and correct anomalies in your time series data.
Join the revolution in time series analysis. Reduce your code complexity, increase your productivity, and harness the speed that pytimetk brings to your workflows.
Explore more at our pytimetk homepage.
Install the latest stable version of pytimetk using pip:
pip install pytimetkAlternatively you can install the development version:
pip install git+https://github.com/business-science/pytimetk.gitThis is a simple code to test the function summarize_by_time:
import pytimetk as tk
import pandas as pd
df = tk.datasets.load_dataset('bike_sales_sample')
df['order_date'] = pd.to_datetime(df['order_date'])
df \
.groupby("category_2") \
.summarize_by_time(
date_column='order_date',
value_column= 'total_price',
freq = "MS",
agg_func = ['mean', 'sum'],
engine = "polars"
)Get started with the pytimetk documentation
To install pytimetk using Poetry, follow these steps:
Make sure you have Python 3.9 or later installed on your system.
To install Poetry, you can use the official installer provided by Poetry. Do not use pip.
Clone the pytimetk repository from GitHub:
git clone https://github.com/business-science/pytimetkUse Poetry to install the package and its dependencies:
poetry installor you can create a virtualenv with poetry and install the dependencies
poetry shell
poetry installWe are in the early stages of development. But it's obvious the potential for pytimetk now in Python. 🐍
- Please ⭐ us on GitHub (it takes 2-seconds and means a lot).
- To make requests, please see our Project Roadmap GH Issue #2. You can make requests there.
- Want to contribute? See our contributing guide here.