catabra-pandas 0.0.3

CaTabRa-pandas is a library with additional functionality for pandas

CaTabRa-pandas

About • Quickstart • References • Contact • Acknowledgments

About

CaTabRa-pandas is a Python library with a couple of useful functions for efficiently working with pandas DataFrames. In particular, many of these functions are concerned with DataFrames containing intervals, i.e., DataFrames with (at least) two columns "start" and "stop" defining the left and right endpoints of intervals.

Highlights:

• Resample observations with respect to arbitrary (possibly irregular, possibly overlapping) windows: catabra_pandas.resample_eav and catabra_pandas.resample_interval.
• Compute the intersection, union, difference, etc. of intervals: catabra_pandas.combine_intervals.
• Group intervals by their distance to each other: catabra_pandas.group_intervals.
• For each point in a given DataFrame, find the interval that contains it: catabra_pandas.find_containing_interval.
• Find the previous/next observation for each entry in a DataFrame of timestamped observations: catabra_pandas.prev_next_values.

Each of these functions lacks a native pandas implementation, and is implemented extremely efficiently in CaTabRa-pandas. DataFrames with 10M+ rows are no problem!

Dask DataFrames are partly supported, too.

If you are interested in CaTabRa-pandas, you might be interested in CaTabRa, too: CaTabRa is a full-fledged tabular data analysis framework that enables you to calculate statistics, generate appealing visualizations and train machine learning models with a single command.

Quickstart

CaTabRa-pandas has minimal requirements (Python >= 3.6, pandas >= 1.0) and can be easily installed using pip:

pip install catabra-pandas

Once installed, CaTabRa-pandas can be readily used.

Use-Case: Merge DataFrames based on Overlapping Intervals

import pandas as pd
import catabra_pandas

left = pd.DataFrame(data=dict(start=[0, 7, 1, 8], stop=[2, 8, 5, 9]))
right = pd.DataFrame(data=dict(start=[10, 4, 0], stop=[11, 5, 3]))

catabra_pandas.merge_intervals(
    left,
    right,
    how="inner",
    left_start="start",
    left_stop="stop",
    right_start="start",
    right_stop="stop"
)

Note: This is a special case of a conditional join. Conditional joins are not supported by pandas by default, but are available in pyjanitor and Polars. The catabra-pandas implementation of interval-overlap- and interval-containment-joins is extremely fast and memory-efficient, as can be seen in these benchmarks.

Use-Case: Resample Observations wrt. Observation Windows

import pandas as pd
import catabra_pandas

observations = pd.DataFrame(
    data={
        "subject_id": [0, 0, 0, 0, 1, 1],
        "attribute": ["HR", "Temp", "HR", "HR", "Temp", "HR"],
        "timestamp": [1, 1, 5, 7, 2, 3],
        "value": [82.7, 36.9, 79.5, 78.7, 37.2, 89.4]
    }
)
windows = pd.DataFrame(
    data={
        ("subject_id", ""): [0, 0, 1],
        ("timestamp", "start"): [0, 4, 1],
        ("timestamp", "stop"): [6, 8, 4]
    }
)
catabra_pandas.resample_eav(
    observations,
    windows,
    agg={
        "HR": ["mean", "p75", "r-1"],   # mean value, 75-th percentile, last observed value
        "Temp": ["count", "mode"]     # standard deviation, mode
    },
    entity_col="subject_id",
    time_col="timestamp",
    attribute_col="attribute",
    value_col="value"
)

Use-Case: Find Containing Intervals

import pandas as pd
import catabra_pandas

intervals = pd.DataFrame(
    data={
        "subject_id": [0, 0, 1],
        "start": [0.5, 3.0, -10.7],
        "stop": [2.3, 10., 10.7]
    }
)
points = pd.DataFrame(
    data={
        "subject_id": [0, 0, 0, 1, 1],
        "point": [1.0, 2.5, 9.9, 0.0, -8.8]
    }
)
catabra_pandas.find_containing_interval(
    points,
    intervals,
    ["point"],
    start_col="start",
    stop_col="stop",
    group_by="subject_id"
)

References

If you use CaTabRa-pandas in your research, we would appreciate citing the following conference paper:

• A. Maletzky, S. Kaltenleithner, P. Moser and M. Giretzlehner. CaTabRa: Efficient Analysis and Predictive Modeling of Tabular Data. In: I. Maglogiannis, L. Iliadis, J. MacIntyre and M. Dominguez (eds), Artificial Intelligence Applications and Innovations (AIAI 2023). IFIP Advances in Information and Communication Technology, vol 676, pp 57-68, 2023. DOI:10.1007/978-3-031-34107-6_5

  @inproceedings{CaTabRa2023,
    author = {Maletzky, Alexander and Kaltenleithner, Sophie and Moser, Philipp and Giretzlehner, Michael},
    editor = {Maglogiannis, Ilias and Iliadis, Lazaros and MacIntyre, John and Dominguez, Manuel},
    title = {{CaTabRa}: Efficient Analysis and Predictive Modeling of Tabular Data},
    booktitle = {Artificial Intelligence Applications and Innovations},
    year = {2023},
    publisher = {Springer Nature Switzerland},
    address = {Cham},
    pages = {57--68},
    isbn = {978-3-031-34107-6},
    doi = {10.1007/978-3-031-34107-6_5}
  }
  

Contact

If you have any inquiries, please open a GitHub issue.

Acknowledgments

This project is financed by research subsidies granted by the government of Upper Austria. RISC Software GmbH is Member of UAR (Upper Austrian Research) Innovation Network.