nearest(df1, df2, overlap_filter=FilterOp.Strict, suffixes=('_1', '_2'), on_cols=None, col1=None, col2=None, output_type='polars.LazyFrame')
+
+Find pairs of overlapping genomic intervals. +Bioframe inspired API.
+ + +Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
+ df1
+ |
+
+ Union[str, DataFrame, LazyFrame, DataFrame]
+ |
+
+
+
+ Can be a path to a file, a polars DataFrame, or a pandas DataFrame. CSV with a header and Parquet are supported. + |
+ + required + | +
+ df2
+ |
+
+ Union[str, DataFrame, LazyFrame, DataFrame]
+ |
+
+
+
+ Can be a path to a file, a polars DataFrame, or a pandas DataFrame. CSV with a header and Parquet are supported. + |
+ + required + | +
+ overlap_filter
+ |
+
+ FilterOp
+ |
+
+
+
+ FilterOp, optional. The type of overlap to consider(Weak or Strict). default is FilterOp.Weak. + |
+
+ Strict
+ |
+
+ col1
+ |
+
+ Union[list[str], None]
+ |
+
+
+
+ The names of columns containing the chromosome, start and end of the +genomic intervals, provided separately for each set. The default +values are 'chrom', 'start', 'end'. + |
+
+ None
+ |
+
+ col2
+ |
+
+ Union[list[str], None]
+ |
+
+
+
+ The names of columns containing the chromosome, start and end of the +genomic intervals, provided separately for each set. The default +values are 'chrom', 'start', 'end'. + |
+
+ None
+ |
+
+ suffixes
+ |
+
+ tuple[str, str]
+ |
+
+
+
+ Suffixes for the columns of the two overlapped sets. + |
+
+ ('_1', '_2')
+ |
+
+ on_cols
+ |
+
+ Union[list[str], None]
+ |
+
+
+
+ List of additional column names to join on. default is None. + |
+
+ None
+ |
+
+ output_type
+ |
+
+ str
+ |
+
+
+
+ Type of the output. default is "polars.LazyFrame", "polars.DataFrame", or "pandas.DataFrame" are also supported. + |
+
+ 'polars.LazyFrame'
+ |
+
Returns:
+| Type | +Description | +
|---|---|
+ Union[LazyFrame, DataFrame, DataFrame]
+ |
+
+
+
+ polars.LazyFrame or polars.DataFrame or pandas.DataFrame of the overlapping intervals. + |
+
The default output format, i.e. LazyFrame, is recommended for large datasets as it supports output streaming and lazy evaluation. +This enables efficient processing of large datasets without loading the entire output dataset into memory.
+Example:
+ + +Support for col1, col2, and on_cols and suffixes parameters.
+polars_bio/range_op.pyoverlap(df1, df2, how='inner', overlap_filter=FilterOp.Strict, suffixes=('_1', '_2'), on_cols=None, col1=None, col2=None, output_type='polars.LazyFrame')
+
+Find pairs of overlapping genomic intervals. +Bioframe inspired API.
+ + +Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
+ df1
+ |
+
+ Union[str, DataFrame, LazyFrame, DataFrame]
+ |
+
+
+
+ Can be a path to a file, a polars DataFrame, or a pandas DataFrame. CSV with a header and Parquet are supported. + |
+ + required + | +
+ df2
+ |
+
+ Union[str, DataFrame, LazyFrame, DataFrame]
+ |
+
+
+
+ Can be a path to a file, a polars DataFrame, or a pandas DataFrame. CSV with a header and Parquet are supported. + |
+ + required + | +
+ how
+ |
+
+ str
+ |
+
+
+
+ How to handle the overlaps on the two dataframes. inner: use intersection of the set of intervals from df1 and df2, optional. + |
+
+ 'inner'
+ |
+
+ overlap_filter
+ |
+
+ FilterOp
+ |
+
+
+
+ FilterOp, optional. The type of overlap to consider(Weak or Strict). default is FilterOp.Weak. + |
+
+ Strict
+ |
+
+ col1
+ |
+
+ Union[list[str], None]
+ |
+
+
+
+ The names of columns containing the chromosome, start and end of the +genomic intervals, provided separately for each set. The default +values are 'chrom', 'start', 'end'. + |
+
+ None
+ |
+
+ col2
+ |
+
+ Union[list[str], None]
+ |
+
+
+
+ The names of columns containing the chromosome, start and end of the +genomic intervals, provided separately for each set. The default +values are 'chrom', 'start', 'end'. + |
+
+ None
+ |
+
+ suffixes
+ |
+
+ tuple[str, str]
+ |
+
+
+
+ Suffixes for the columns of the two overlapped sets. + |
+
+ ('_1', '_2')
+ |
+
+ on_cols
+ |
+ + | +
+
+
+ List of additional column names to join on. default is None. + |
+
+ None
+ |
+
+ output_type
+ |
+
+ str
+ |
+
+
+
+ Type of the output. default is "polars.LazyFrame", "polars.DataFrame", or "pandas.DataFrame" are also supported. + |
+
+ 'polars.LazyFrame'
+ |
+
Returns:
+| Type | +Description | +
|---|---|
+ Union[LazyFrame, DataFrame, DataFrame]
+ |
+
+
+
+ polars.LazyFrame or polars.DataFrame or pandas.DataFrame of the overlapping intervals. + |
+
The default output format, i.e. LazyFrame, is recommended for large datasets as it supports output streaming and lazy evaluation. +This enables efficient processing of large datasets without loading the entire output dataset into memory.
+import polars_bio as pb
+import pandas as pd
+
+df1 = pd.DataFrame([
+ ['chr1', 1, 5],
+ ['chr1', 3, 8],
+ ['chr1', 8, 10],
+ ['chr1', 12, 14]],
+columns=['chrom', 'start', 'end']
+)
+
+df2 = pd.DataFrame(
+[['chr1', 4, 8],
+ ['chr1', 10, 11]],
+columns=['chrom', 'start', 'end' ]
+)
+overlapping_intervals = pb.overlap(df1, df2, output_type="pandas.DataFrame")
+
+overlapping_intervals
+ chrom_1 start_1 end_1 chrom_2 start_2 end_2
+0 chr1 1 5 chr1 4 8
+1 chr1 3 8 chr1 4 8
+Support for col1, col2, and on_cols and suffixes parameters.
+polars_bio/range_op.py18 +19 +20 +21 +22 +23 +24 +25 +26 +27 +28 +29 +30 +31 +32 +33 +34 +35 +36 +37 +38 +39 +40 +41 +42 +43 +44 +45 +46 +47 +48 +49 +50 +51 +52 +53 +54 +55 +56 +57 +58 +59 +60 +61 +62 +63 +64 +65 +66 +67 +68 +69 +70 +71 +72 +73 +74 +75 +76 +77 +78 +79 +80 +81 +82 +83 +84 +85 +86 +87 +88 +89 +90 +91 +92 +93 +94 +95 +96 +97 | |