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DOC: Docstring updated for DataFrame.equals #34508

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Merged
merged 7 commits into from
Aug 7, 2020
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DOC: Docstring updated for DataFrame.equals #34508

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c442c13
Docstring updated for DataFrame.equals
pandeydeepak0 Jun 1, 2020
025cdf5
Docstring updated for DataFrame.equals
pandeydeepak0 Jun 1, 2020
f6909fd
removed Notes
pandeydeepak0 Jun 1, 2020
17d21db
removed Notes
pandeydeepak0 Jun 1, 2020
defaf2e
Update pandas/core/generic.py
jorisvandenbossche Jul 6, 2020
d086235
fix whitespace
jorisvandenbossche Aug 7, 2020
a00a1b9
Merge remote-tracking branch 'upstream/master' into freakypandit-master
jorisvandenbossche Aug 7, 2020
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16 changes: 6 additions & 10 deletions pandas/core/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1247,9 +1247,12 @@ def equals(self, other):

This function allows two Series or DataFrames to be compared against
each other to see if they have the same shape and elements. NaNs in
the same location are considered equal. The column headers do not
need to have the same type, but the elements within the columns must
be the same dtype.
the same location are considered equal.

The row/column index do not need to have the same type, as long
as the values are still considered equal.
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I feel the "still" can be removed. This was used in a different context under "Notes". It makes the sentence slightly confusing when used here. So we will have "as long as the values are considered equal".

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@fujiaxiang added your suggestion, thanks!


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you can leave the blank line here

The elements within the columns must be the same dtype.

Parameters
----------
Expand Down Expand Up @@ -1278,13 +1281,6 @@ def equals(self, other):
numpy.array_equal : Return True if two arrays have the same shape
and elements, False otherwise.

Notes
-----
This function requires that the elements have the same dtype as their
respective elements in the other Series or DataFrame. However, the
column labels do not need to have the same type, as long as they are
still considered equal.

Examples
--------
>>> df = pd.DataFrame({1: [10], 2: [20]})
Expand Down