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[ENH] Coalesce fix #1042

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merged 5 commits into from
Mar 14, 2022
Merged

[ENH] Coalesce fix #1042

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0b7e275
use bfill only for coalesce
Mar 13, 2022
dd11ced
use bfill only for coalesce
Mar 13, 2022
302653d
Update coalesce.py
samukweku Mar 13, 2022
6734963
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Mar 13, 2022
e819bed
Update coalesce.py
samukweku Mar 13, 2022
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33 changes: 20 additions & 13 deletions janitor/functions/coalesce.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,14 +17,18 @@ def coalesce(
) -> pd.DataFrame:
"""Coalesce two or more columns of data in order of column names provided.

Given the list of column names, `coalesce` finds and returns the first
non-missing value from these columns, for every row in the input dataframe.
If all the column values are null for a particular row, then the
`default_value` will be filled in.
Given the variable arguments of column names,
`coalesce` finds and returns the first non-missing value
from these columns, for every row in the input dataframe.
If all the column values are null for a particular row,
then the `default_value` will be filled in.

If `target_column_name` is not provided,
then the first column is coalesced.

This method does not mutate the original DataFrame.

Example: Using `coalesce` with 3 columns, "a", "b" and "c".
Example: Use `coalesce` with 3 columns, "a", "b" and "c".

>>> import pandas as pd
>>> import numpy as np
Expand All @@ -34,13 +38,21 @@ def coalesce(
... "b": [2, 3, np.nan],
... "c": [4, np.nan, np.nan],
... })
>>> df.coalesce("a", "b", "c")
a b c
0 2.0 2.0 4.0
1 1.0 3.0 NaN
2 NaN NaN NaN

Example: Provide a target_column_name.

>>> df.coalesce("a", "b", "c", target_column_name="new_col")
a b c new_col
0 NaN 2.0 4.0 2.0
1 1.0 3.0 NaN 1.0
2 NaN NaN NaN NaN

Example: Providing a default value.
Example: Provide a default value.

>>> import pandas as pd
>>> import numpy as np
Expand Down Expand Up @@ -93,13 +105,8 @@ def coalesce(

if target_column_name is None:
target_column_name = column_names[0]
# bfill/ffill combo is faster than combine_first
outcome = (
df.filter(column_names)
.bfill(axis="columns")
.ffill(axis="columns")
.iloc[:, 0]
)

outcome = df.filter(column_names).bfill(axis="columns").iloc[:, 0]
if outcome.hasnans and (default_value is not None):
outcome = outcome.fillna(default_value)

Expand Down