pandas_intervals-1.0.1.tar.gz

# pandas-intervals Pandas extension for `DataFrame`s of intervals (AKA `spandas`). This library provides `ivl` extension: a standard interface and methods for `DataFrames` of "interval"-like objects, where each object is specified by a `start` and an `end`. * The columns `"start"` and `"end"` are automatically formatted as `float` types, * The order of the columns is strictly as specified, * Interval set operations (intersections, combinations, differences) can be called via the `ivl` property on any DataFrame after importing this module. The `IntervalsAccessor` object is also extensible and allows adding columns with default values and specifying how columns are aggregated when combining close intervals. ## Installation Minimal requirements: * `Python >= 3.9` * `Pandas >= 1.5` * `Numpy >= 1.24` Install the latest stable version from `PyPi`: ``` $ pip install pandas-intervals ``` Or clone this repo and install: ``` $ git clone https://github.com/BlakeJC94/pandas_intervals $ cd pandas_intervals # If Poetry is avaliable: $ poetry install # Otherwise Pip requirements can be used: $ pip install -r requirements.txt $ pip install . ``` ## Quick start To enable this extension, we need to register this extension using the provided setup function. ```python # Top of script/notebook, or top of `__init__.py` in module import pandas as pd from pandas_intervals import setup_ivl_accessor setup_ivl_accessor() ``` The core documentation can be accessed within Python: ```python >>> import pandas as pd >>> from pandas_intervals import setup_ivl_accessor >>> setup_ivl_accessor() >>> help(pd.DataFrame.ivl) ``` ## Usage Say we have two sets of intervals `A` and `B` as specified by: ``` A: (----] (----] (--------------] B: (--] (---] (----] (---] (------] (---] ``` We can represent these sets in `python` using `DataFrames`, and ```python import pandas as pd from pandas_intervals import setup_ivl_accessor setup_ivl_accessor() df_a = pd.DataFrame( [ (50, 100), (150, 200), (300, 450), ], ).ivl() print(df_a) # start end # 0 50.0 100.0 # 1 150.0 200.0 # 2 300.0 450.0 df_b = pd.DataFrame( [ (10, 40), (80, 120), (230, 280), (330, 370), (420, 490), (510, 550), ], ).ivl() print(df_b) # start end # 0 10.0 40.0 # 1 80.0 120.0 # 2 230.0 280.0 # 3 330.0 370.0 # 4 420.0 490.0 # 5 510.0 550.0 ``` We have all the standard methods available to DataFrames, but we also now have native interval set operations implemented through the `ivl` accessor: ```python union = df_a.ivl.union(df_b) # A : [----) [----) # B : [----) [----) # # Result : [----) [----) [----) overlapping = df_a.ivl.overlap() # A : [----) [----) # [----) # # Result : [----) # [----) non_overlapping = df_a.ivl.non_overlap() # A : [----) [----) # [----) # # Result : [----) intersection = df_a.ivl.intersection(df_b) # A : [----) [----) # [----) # B : [----) [----) [----) # # Result : [----) [----) # [----) diff = df_a.ivl.diff(df_b) # A : [----) [----) # [----) # B : [----) [----) [----) # # Result : [----) symdiff = df_a.ivl.symdiff(df_b) # A : [----) [----) # [----) # B : [----) [----) [----) # # Result : [----) [----) combined = df_a.ivl.combine() # Optionally accepts intervals DataFrame as arg # A : [----) [----) # [----) # # Result : [-------) [----) complement = df_a.ivl.complement() # Optional kwargs: `left_bound`, `right_bound` # A : [----) [----) [---) # [----) # # Result : [-----) [----) trunc = df_a.ivl.truncate(df_b) # A : [------------) [----) [---) # B : [----) [----) [---) # # Result : [--) [----) [---) [---) ``` There's also a few additional methods for filtering and calculating durations and distances on intervals: ```python intervals_between_bounds = df_a.ivl.between(200, 400) short_intervals = df_a.ivl.shorter_than(50) long_intervals = df_a.ivl.longest_than(500) padded = df_a.ivl.pad(10) # Optional kwargs: `left_pad`, `right_pad` df_a_min_dist_to_b = df_a.ivl.nearest(df_b) ``` We can also plot intervals using a Plotly backend (if available): ```python df_a.ivl.plot() ```  Multiple groups of intervals can also be plotted on the same graph as well: ```python results = [] for df in [df_a, df_b, df_c]: df['group'] = i results.append(df) results = pd.concat(results) results.ivl.plot(groupby_cols=['group']) ```  ## Extensions This interface can easily be extended, we can add additional columns with default values and types. For example, if we want to create an intervals accessor called `"regions"` which * Has 2 extra columns ("tag" and "note"), * Column "tag" must be specified, but "note" is optional, * Column "tag" is an integer, and "note" is a string, * Aggregations are done across different values of "tag", and "note" values are combined into a comma-separated string. We can accomplish this in a relatively small class: ```python import json from os import PathLike from typing import List import pandas as pd from pandas_intervals import IntervalsAccessor # Let's define a function to be used when aggregating results across the `notes` column def comma_join(x: List[str]) -> str: return ", ".join(sorted({i.strip() for n in x if n for i in n.split(",")})) @pd.api.extensions.register_dataframe_accessor("reg") # Name of new accessor, pd.DataFrame.<name> class RegionsAccessor(IntervalsAccessor): # Additional required columns can be specified in a list of tuple # where each tuple is `(column_name, dtype, aggregation)` additional_fields = [ ("tag", "int64", "groupby"), # Operations will apply to DFs of tag values separately ("note", "object", lambda x: ",".join(x)), ] # Default values for columns can be specified as a dictionary, # columns that don't appear in this list are assumed to be necessary default_values = { "note": "", } # Add whatever methods/properties you want! @property def all_notes(self): return self.df["note"] def concat_and_sort(self, df: pd.DataFrame) -> pd.DataFrame: df = self.format(df) return pd.concat([self.df, df], axis=0).sort_values("tag") # Create constructors to parse and validate data from other sources @classmethod def from_json(cls, filepath: PathLike) -> pd.DataFrame: with open(filepath, 'r', encoding='utf-8') as f: intervals = json.load(f) assert isinstance(intervals, list) assert len(intervals) > 0 assert isinstance(intervals[0], dict) assert all(k in intervals[0] for k in ["start", "end", "tag", "note"]) df = pd.DataFrame(intervals) return cls.format(df) ``` After defining and running this snippet, we now have * `pd.DataFrame(..).reg.all_notes()` available as a method on any DataFrame, * `pd.DataFrame(..).reg()` will return a formatted DataFrame as specified by the fields in `RegionsAccessor.additional_cols`, * All the methods on the `ivl` accessor come along for free! ## Contributing Pull requests are most welcome! Dependencies and environments are managed with `[poetry](https://python-poetry.org)`. To get started developing for this package: ``` $ git clone https://github.com/BlakeJC94/pandas_intervals $ cd pandas_intervals $ poetry install $ pytest ``` Bench