# fastDigest
[](https://pypi.org/project/fastdigest)
[](https://www.python.org/downloads/)
[](https://github.com/moritzmucha/fastdigest/actions)
[](./LICENSE)
***fastDigest*** is a Python extension module that provides a lightning-fast implementation of the [t-digest algorithm](https://github.com/tdunning/t-digest). Built on top of highly optimized Rust code, *fastDigest* enables lightweight and accurate quantile and rank estimation for streaming data.
## Table of Contents
- [Features](#features)
- [Installation](#installation)
- [Installing from PyPI](#installing-from-pypi)
- [Installing from source](#installing-from-source)
- [Usage](#usage)
- [Creating a TDigest from values](#creating-a-tdigest-from-values)
- [Estimating quantiles and ranks](#estimating-quantiles-and-ranks)
- [Estimating the trimmed mean](#estimating-the-trimmed-mean)
- [Compressing the TDigest](#compressing-the-tdigest)
- [Merging TDigest objects](#merging-tdigest-objects)
- [Updating a TDigest](#updating-a-tdigest)
- [Exporting a TDigest to a dict](#exporting-a-tdigest-to-a-dict)
- [Restoring a TDigest from a dict](#restoring-a-tdigest-from-a-dict)
- [Benchmarks](#benchmarks)
- [License](#license)
- [Acknowledgements](#acknowledgements)
## Features
- **Quantile & rank estimation**: Compute highly accurate quantile and rank estimates.
- **Trimmed mean**: Calculate the truncated mean in close approximation.
- **Merging digests**: Merge many t-digests into one, enabling parallel computing and Big Data workflows such as MapReduce.
- **Updating**: Update a t-digest incrementally with streaming data, or batches of a dataset too large to fit in memory otherwise.
- **Serialization**: Use the `to_dict`/`from_dict` methods (e.g. for JSON conversion) or the `pickle` module for easy serialization.
- **Pythonic API**: Use built-in Python operators and functions such as `+` for merging, `==` for checking equality, or `len` to return the number of centroids in a digest.
- **Blazing fast**: Say goodbye to performance headaches — thanks to its Rust backbone, this module is hundreds of times faster than existing Python implementations.
## Installation
### Installing from PyPI
Compiled wheels are available on PyPI. Simply install via pip:
```bash
pip install fastdigest
```
### Installing from source
If you want to build and install *fastDigest* from source, you need **Rust** and **maturin**.
1. Install *maturin* via pip:
```bash
pip install maturin
```
2. Install the Rust toolchain: see https://rustup.rs
3. Build and install the package:
```bash
maturin build --release
pip install target/wheels/fastdigest-0.5.0-<platform-tag>.whl
```
## Usage
### Creating a TDigest from values
Initialize a TDigest directly from any non-empty sequence of numbers:
```python
import numpy as np
from fastdigest import TDigest
digest = TDigest([1.42, 2.71, 3.14]) # from list
digest = TDigest((42,)) # from tuple
digest = TDigest(range(101)) # from range
digest = TDigest(np.linspace(0, 100, 101)) # from numpy array
```
Specify the `max_centroids` parameter to enable automatic compression:
```python
import numpy as np
from fastdigest import TDigest
data = np.random.random(10_000)
digest = TDigest(data, max_centroids=1000)
print(f"Compressed: {len(digest)} centroids") # 988 centroids
```
### Estimating quantiles and ranks
Estimate the value at a given quantile `q` using `quantile(q)` or `percentile(100 * q)`:
```python
from fastdigest import TDigest
digest = TDigest(range(1001), max_centroids=3)
print(" Median:", digest.quantile(0.5))
print("99th percentile:", digest.quantile(0.99))
# same thing, different method:
print(" Median:", digest.percentile(50))
print("99th percentile:", digest.percentile(99))
```
Or do the reverse - find the cumulative probability (`rank`) of a given value:
```python
from fastdigest import TDigest
digest = TDigest(range(1001), max_centroids=3)
print("Rank of 500:", digest.rank(500))
print("Rank of 990:", digest.rank(990))
```
### Estimating the trimmed mean
Estimate the truncated mean, i.e. the arithmetic mean of all data points between two quantiles:
```python
from fastdigest import TDigest
values = list(range(10))
values.append(1000) # outlier that we want to ignore
digest = TDigest(values)
result = digest.trimmed_mean(0.1, 0.9)
print(f"Trimmed mean: {result}") # result: 5.0
```
### Compressing the TDigest
If you don't specify the `max_centroids` parameter at initialization, the TDigest object will be **uncompressed**; meaning it has one centroid per data point. You can manually call the `compress` method to shrink the object in-place, reducing memory usage while mostly maintaining accuracy:
```python
import numpy as np
from fastdigest import TDigest
data = np.random.gumbel(0, 0.1, 10_000)
digest = TDigest(data)
p99 = digest.quantile(0.99) # estimate the 99th percentile
print(f"{len(digest):5} centroids: {p99=:.3f}")
digest.compress(1000) # compress to 1000 or fewer centroids
p99 = digest.quantile(0.99)
print(f"{len(digest):5} centroids: {p99=:.3f}")
digest.compress(100) # compress to 100 or fewer centroids
p99 = digest.quantile(0.99)
print(f"{len(digest):5} centroids: {p99=:.3f}")
```
### Merging TDigest objects
#### Merging TDigests into a new instance
Use the `+` operator to merge two digests, creating a new TDigest instance:
```python
from fastdigest import TDigest
digest1 = TDigest(range(50))
digest2 = TDigest(range(50, 101))
merged_digest = digest1 + digest2 # alias for digest1.merge(digest2)
```
**Note:** If `max_centroids` is specified in both instances and the combined `n_centroids` is greater than `max_centroids`, compression will be performed immediately.
When merging two TDigests with different `max_centroids` values, the larger value is used. `None` counts as larger than any other value, since it means no compression.
#### Merging into a TDigest in-place
You can also merge in-place using the `+=` operator:
```python
from fastdigest import TDigest
digest = TDigest(range(50))
temp_digest = TDigest(range(50, 101))
digest += temp_digest # alias for digest.merge_inplace(temp_digest)
digest == TDigest(range(101)) # True
```
**Note:** When using `merge_inplace` or `+=`, the calling TDigest's `max_centroids` parameter always remains unchanged.
This means you can effectively chain-merge many uncompressed digests and perform a single compression step at the end by combining `+=` and `+`:
```python
from fastdigest import TDigest
digest = TDigest(range(101), max_centroids=3)
tmp_digest1 = TDigest(range(101, 201))
tmp_digest2 = TDigest(range(201, 301))
tmp_digest3 = TDigest(range(301, 401))
digest += tmp_digest1 + tmp_digest2 + tmp_digest3
print(f"Result: {len(digest)} centroids") # 3 centroids
```
#### Merging a list of TDigests
The `merge_all` function offers an easy way of batch-merging a list of TDigests. The `max_centroids` value for the new instance can be optionally specified as a keyword argument, otherwise it is determined from the input TDigests.
```python
from fastdigest import TDigest, merge_all
# create a list of 10 non-overlapping digests
digests = [TDigest(range(i, i+10)) for i in range(0, 100, 10)]
# merge all digests and create a new instance compressed to 3 centroids
merged = merge_all(digests, max_centroids=3)
merged == TDigest(range(100), max_centroids=3) # True
```
### Updating a TDigest
To update an existing TDigest in-place with a new sequence/array of values, use `batch_update`:
```python
from fastdigest import TDigest
digest = TDigest([1, 2, 3])
digest.batch_update([4, 5, 6])
```
To update with a single value, use `update`:
```python
from fastdigest import TDigest
digest = TDigest([1, 2, 3])
digest.update(4)
```
**Note:** If you have more than one value to add, it is always preferable to use `batch_update` rather than looping over `update`.
### Exporting a TDigest to a dict
Obtain a dictionary representation of the digest by calling `to_dict`:
```python
from fastdigest import TDigest
import json
digest = TDigest(range(101), max_centroids=3)
tdigest_dict = digest.to_dict()
print(json.dumps(tdigest_dict, indent=2))
```
### Restoring a TDigest from a dict
Use `TDigest.from_dict(d)` to create a new TDigest instance. The dict has to contain a list of `centroids`, with each centroid itself being a dict with keys `m` (mean) and `c` (weight or count). The `max_centroids` key is optional.
```python
from fastdigest import TDigest
data = {
"max_centroids": 3,
"centroids": [
{"m": 0.0, "c": 1.0},
{"m": 50.0, "c": 99.0},
{"m": 100.0, "c": 1.0}
]
}
digest = TDigest.from_dict(data)
```
**Note:** dicts created by the *tdigest* Python library can also natively be used by *fastDigest*. For functional continuity, set `max_centroids` to 1000 after importing:
```python
from fastdigest import TDigest
imported_digest = TDigest.from_dict(legacy_dict)
imported_digest.max_centroids = 1000
```
## Benchmarks
Constructing a TDigest and estimating the median of 1,000,000 uniformly distributed random values (average of 10 consecutive runs):
| Library | Time (ms) | Speedup |
|--------------------|-----------|-----------------|
| tdigest | ~12,800 | - |
| fastdigest | ~51 | **250x** faster |
*Environment*: Python 3.13.2, Fedora 41 (Workstation), AMD Ryzen 5 7600X
If you want to try it yourself, install *fastDigest* as well as [*tdigest*](https://github.com/CamDavidsonPilon/tdigest) and run:
```bash
python benchmark.py
```
## License
*fastDigest* is licensed under the MIT License. See the LICENSE file for details.
## Acknowledgements
Credit goes to Ted Dunning for inventing the [t-digest](https://github.com/tdunning/t-digest). Special thanks to Andy Lok for creating the efficient [*tdigests* Rust library](https://github.com/andylokandy/tdigests), as well as all [*PyO3* contributors](https://github.com/pyo3).
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"description": "# fastDigest\n\n[](https://pypi.org/project/fastdigest)\n[](https://www.python.org/downloads/)\n[](https://github.com/moritzmucha/fastdigest/actions)\n[](./LICENSE)\n\n***fastDigest*** is a Python extension module that provides a lightning-fast implementation of the [t-digest algorithm](https://github.com/tdunning/t-digest). Built on top of highly optimized Rust code, *fastDigest* enables lightweight and accurate quantile and rank estimation for streaming data.\n\n## Table of Contents\n\n- [Features](#features)\n- [Installation](#installation)\n - [Installing from PyPI](#installing-from-pypi)\n - [Installing from source](#installing-from-source)\n- [Usage](#usage)\n - [Creating a TDigest from values](#creating-a-tdigest-from-values)\n - [Estimating quantiles and ranks](#estimating-quantiles-and-ranks)\n - [Estimating the trimmed mean](#estimating-the-trimmed-mean)\n - [Compressing the TDigest](#compressing-the-tdigest)\n - [Merging TDigest objects](#merging-tdigest-objects)\n - [Updating a TDigest](#updating-a-tdigest)\n - [Exporting a TDigest to a dict](#exporting-a-tdigest-to-a-dict)\n - [Restoring a TDigest from a dict](#restoring-a-tdigest-from-a-dict)\n- [Benchmarks](#benchmarks)\n- [License](#license)\n- [Acknowledgements](#acknowledgements)\n\n## Features\n\n- **Quantile & rank estimation**: Compute highly accurate quantile and rank estimates.\n- **Trimmed mean**: Calculate the truncated mean in close approximation.\n- **Merging digests**: Merge many t-digests into one, enabling parallel computing and Big Data workflows such as MapReduce.\n- **Updating**: Update a t-digest incrementally with streaming data, or batches of a dataset too large to fit in memory otherwise.\n- **Serialization**: Use the `to_dict`/`from_dict` methods (e.g. for JSON conversion) or the `pickle` module for easy serialization.\n- **Pythonic API**: Use built-in Python operators and functions such as `+` for merging, `==` for checking equality, or `len` to return the number of centroids in a digest.\n- **Blazing fast**: Say goodbye to performance headaches \u2014 thanks to its Rust backbone, this module is hundreds of times faster than existing Python implementations.\n\n## Installation\n\n### Installing from PyPI\n\nCompiled wheels are available on PyPI. Simply install via pip:\n\n```bash\npip install fastdigest\n```\n\n### Installing from source\n\nIf you want to build and install *fastDigest* from source, you need **Rust** and **maturin**.\n\n1. Install *maturin* via pip:\n\n```bash\npip install maturin\n```\n\n2. Install the Rust toolchain: see https://rustup.rs\n\n3. Build and install the package:\n\n```bash\nmaturin build --release\npip install target/wheels/fastdigest-0.5.0-<platform-tag>.whl\n```\n\n## Usage\n\n### Creating a TDigest from values\n\nInitialize a TDigest directly from any non-empty sequence of numbers:\n\n```python\nimport numpy as np\nfrom fastdigest import TDigest\n\ndigest = TDigest([1.42, 2.71, 3.14]) # from list\ndigest = TDigest((42,)) # from tuple\ndigest = TDigest(range(101)) # from range\ndigest = TDigest(np.linspace(0, 100, 101)) # from numpy array\n```\n\nSpecify the `max_centroids` parameter to enable automatic compression:\n\n```python\nimport numpy as np\nfrom fastdigest import TDigest\n\ndata = np.random.random(10_000)\ndigest = TDigest(data, max_centroids=1000)\n\nprint(f\"Compressed: {len(digest)} centroids\") # 988 centroids\n```\n\n### Estimating quantiles and ranks\n\nEstimate the value at a given quantile `q` using `quantile(q)` or `percentile(100 * q)`:\n\n```python\nfrom fastdigest import TDigest\n\ndigest = TDigest(range(1001), max_centroids=3)\nprint(\" Median:\", digest.quantile(0.5))\nprint(\"99th percentile:\", digest.quantile(0.99))\n\n# same thing, different method:\nprint(\" Median:\", digest.percentile(50))\nprint(\"99th percentile:\", digest.percentile(99))\n```\n\nOr do the reverse - find the cumulative probability (`rank`) of a given value:\n\n```python\nfrom fastdigest import TDigest\n\ndigest = TDigest(range(1001), max_centroids=3)\nprint(\"Rank of 500:\", digest.rank(500))\nprint(\"Rank of 990:\", digest.rank(990))\n```\n\n### Estimating the trimmed mean\n\nEstimate the truncated mean, i.e. the arithmetic mean of all data points between two quantiles:\n\n```python\nfrom fastdigest import TDigest\n\nvalues = list(range(10))\nvalues.append(1000) # outlier that we want to ignore\ndigest = TDigest(values)\nresult = digest.trimmed_mean(0.1, 0.9)\n\nprint(f\"Trimmed mean: {result}\") # result: 5.0\n```\n\n### Compressing the TDigest\n\nIf you don't specify the `max_centroids` parameter at initialization, the TDigest object will be **uncompressed**; meaning it has one centroid per data point. You can manually call the `compress` method to shrink the object in-place, reducing memory usage while mostly maintaining accuracy:\n\n```python\nimport numpy as np\nfrom fastdigest import TDigest\n\ndata = np.random.gumbel(0, 0.1, 10_000)\n\ndigest = TDigest(data)\np99 = digest.quantile(0.99) # estimate the 99th percentile\nprint(f\"{len(digest):5} centroids: {p99=:.3f}\")\n\ndigest.compress(1000) # compress to 1000 or fewer centroids\np99 = digest.quantile(0.99)\nprint(f\"{len(digest):5} centroids: {p99=:.3f}\")\n\ndigest.compress(100) # compress to 100 or fewer centroids\np99 = digest.quantile(0.99)\nprint(f\"{len(digest):5} centroids: {p99=:.3f}\")\n```\n\n### Merging TDigest objects\n\n#### Merging TDigests into a new instance\n\nUse the `+` operator to merge two digests, creating a new TDigest instance:\n\n```python\nfrom fastdigest import TDigest\n\ndigest1 = TDigest(range(50))\ndigest2 = TDigest(range(50, 101))\nmerged_digest = digest1 + digest2 # alias for digest1.merge(digest2)\n```\n\n**Note:** If `max_centroids` is specified in both instances and the combined `n_centroids` is greater than `max_centroids`, compression will be performed immediately.\n\nWhen merging two TDigests with different `max_centroids` values, the larger value is used. `None` counts as larger than any other value, since it means no compression.\n\n#### Merging into a TDigest in-place\n\nYou can also merge in-place using the `+=` operator:\n\n```python\nfrom fastdigest import TDigest\n\ndigest = TDigest(range(50))\ntemp_digest = TDigest(range(50, 101))\ndigest += temp_digest # alias for digest.merge_inplace(temp_digest)\n\ndigest == TDigest(range(101)) # True\n```\n\n**Note:** When using `merge_inplace` or `+=`, the calling TDigest's `max_centroids` parameter always remains unchanged.\n\nThis means you can effectively chain-merge many uncompressed digests and perform a single compression step at the end by combining `+=` and `+`:\n\n```python\nfrom fastdigest import TDigest\n\ndigest = TDigest(range(101), max_centroids=3)\ntmp_digest1 = TDigest(range(101, 201))\ntmp_digest2 = TDigest(range(201, 301))\ntmp_digest3 = TDigest(range(301, 401))\ndigest += tmp_digest1 + tmp_digest2 + tmp_digest3\n\nprint(f\"Result: {len(digest)} centroids\") # 3 centroids\n```\n\n#### Merging a list of TDigests\n\nThe `merge_all` function offers an easy way of batch-merging a list of TDigests. The `max_centroids` value for the new instance can be optionally specified as a keyword argument, otherwise it is determined from the input TDigests.\n\n```python\nfrom fastdigest import TDigest, merge_all\n\n# create a list of 10 non-overlapping digests\ndigests = [TDigest(range(i, i+10)) for i in range(0, 100, 10)]\n\n# merge all digests and create a new instance compressed to 3 centroids\nmerged = merge_all(digests, max_centroids=3)\n\nmerged == TDigest(range(100), max_centroids=3) # True\n```\n\n### Updating a TDigest\n\nTo update an existing TDigest in-place with a new sequence/array of values, use `batch_update`:\n\n```python\nfrom fastdigest import TDigest\n\ndigest = TDigest([1, 2, 3])\ndigest.batch_update([4, 5, 6])\n```\n\nTo update with a single value, use `update`:\n\n```python\nfrom fastdigest import TDigest\n\ndigest = TDigest([1, 2, 3])\ndigest.update(4)\n```\n\n**Note:** If you have more than one value to add, it is always preferable to use `batch_update` rather than looping over `update`.\n\n### Exporting a TDigest to a dict\n\nObtain a dictionary representation of the digest by calling `to_dict`:\n\n```python\nfrom fastdigest import TDigest\nimport json\n\ndigest = TDigest(range(101), max_centroids=3)\ntdigest_dict = digest.to_dict()\nprint(json.dumps(tdigest_dict, indent=2))\n```\n\n### Restoring a TDigest from a dict\n\nUse `TDigest.from_dict(d)` to create a new TDigest instance. The dict has to contain a list of `centroids`, with each centroid itself being a dict with keys `m` (mean) and `c` (weight or count). The `max_centroids` key is optional.\n\n```python\nfrom fastdigest import TDigest\n\ndata = {\n \"max_centroids\": 3,\n \"centroids\": [\n {\"m\": 0.0, \"c\": 1.0},\n {\"m\": 50.0, \"c\": 99.0},\n {\"m\": 100.0, \"c\": 1.0}\n ]\n}\ndigest = TDigest.from_dict(data)\n```\n\n**Note:** dicts created by the *tdigest* Python library can also natively be used by *fastDigest*. For functional continuity, set `max_centroids` to 1000 after importing:\n\n```python\nfrom fastdigest import TDigest\n\nimported_digest = TDigest.from_dict(legacy_dict)\nimported_digest.max_centroids = 1000\n```\n\n## Benchmarks\n\nConstructing a TDigest and estimating the median of 1,000,000 uniformly distributed random values (average of 10 consecutive runs):\n\n| Library | Time (ms) | Speedup |\n|--------------------|-----------|-----------------|\n| tdigest | ~12,800 | - |\n| fastdigest | ~51 | **250x** faster |\n\n*Environment*: Python 3.13.2, Fedora 41 (Workstation), AMD Ryzen 5 7600X\n\nIf you want to try it yourself, install *fastDigest* as well as [*tdigest*](https://github.com/CamDavidsonPilon/tdigest) and run:\n\n```bash\npython benchmark.py\n```\n\n## License\n\n*fastDigest* is licensed under the MIT License. See the LICENSE file for details.\n\n## Acknowledgements\n\nCredit goes to Ted Dunning for inventing the [t-digest](https://github.com/tdunning/t-digest). Special thanks to Andy Lok for creating the efficient [*tdigests* Rust library](https://github.com/andylokandy/tdigests), as well as all [*PyO3* contributors](https://github.com/pyo3).\n\n",
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