meld_along_columns

trashpanda.meld_along_columns(left: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], right: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], copy_at_meld: bool = True, keep: Union[str, bool] = 'raise') → pandas.core.frame.DataFrame

Melds two DataFrames of Series into a single DataFrame with a common index.

Notes

This method is called meld because it doesn’t fit into existing categories of pandas join, merge or concat.

Warning

This method will override values of equal named columns in left with right.

Parameters

• left (DataFrame) – Left curve to be merged with the right one.

• right (DataFrame) – Right curve, which will merge to the left one.

• copy_at_meld (bool) – Makes a copy during the concat process, creating a new DataFrame instead of overriding the source.

• keep (Union[str, bool]) – Determines which duplicates to keep. - first: Default; keeps all first occurrences of duplicated indexes. - last: Keeps all last occurrences of duplicated indexes. - False: Drops all duplicated indexes.

Returns

DataFrame

Examples

Melding of two DataFrame.

>>> from pathlib import Path
>>> from pandas import DataFrame, Series
>>> import numpy as np
>>> import examplecurves
>>> left_curve, right_curve = examplecurves.Static.create(
...     family_name="nonlinear0", cut_curves_at=3, curve_selection=[1, 2]
... )
>>> left_curve.columns = ["left"]
>>> right_curve.columns = ["right"]
>>> from doctestprinter import doctest_print
>>> doctest_print(meld_along_columns(left_curve, right_curve))
         left     right
x
0.000  0.0000  0.000000
0.100  1.5625       NaN
0.111     NaN  1.607654
0.200  3.0000       NaN
0.222     NaN  3.085479

Melding of 2 Series.

>>> left_series = left_curve["left"]
>>> right_series = right_curve["right"]
>>> doctest_print(meld_along_columns(left_curve, right_curve))
         left     right
x
0.000  0.0000  0.000000
0.100  1.5625       NaN
0.111     NaN  1.607654
0.200  3.0000       NaN
0.222     NaN  3.085479

Example of melding two DataFrames with duplicated indexes and one duplicated column. Left values of the intersecting columns are being overriden with the right values.

>>> left_sample = pandas.DataFrame(
...     np.arange(10).reshape(5, 2),
...     columns=["b", "a"],
...     index=pandas.Index(np.linspace(0.1, 0.5, num=5), name="x")
... )
>>> doctest_print(left_sample)
     b  a
x
0.1  0  1
0.2  2  3
0.3  4  5
0.4  6  7
0.5  8  9
>>> right_sample = pandas.DataFrame(
...     np.linspace(0.5, 9.5, num=10).reshape(5, 2),
...     columns=["a", "c"],
...     index=pandas.Index([0.1, 0.1, 0.15, 0.15, 0.2], name="x")
... )
>>> doctest_print(right_sample)
        a    c
x
0.10  0.5  1.5
0.10  2.5  3.5
0.15  4.5  5.5
0.15  6.5  7.5
0.20  8.5  9.5
>>> merged_frame = meld_along_columns(left_sample, right_sample, keep='first')
>>> doctest_print(merged_frame)
        b    a    c
x
0.10  0.0  0.5  1.5
0.15  NaN  4.5  5.5
0.20  2.0  8.5  9.5
0.30  4.0  5.0  NaN
0.40  6.0  7.0  NaN
0.50  8.0  9.0  NaN