ENH: df.assign accepting dependent **kwargs (#14207) #18852

datajanko · 2017-12-19T20:12:25Z

Specifically, 'df.assign(b=1, c=lambda x:x['b'])'
does not throw an exception in python 3.6 and above.
Further details are discussed in Issues #14207 and #18797.

closes #14207
closes #18797

tests added / passed
passes git diff upstream/master -u -- "*.py" | flake8 --diff
whatsnew entry

gfyoung · 2017-12-19T23:33:39Z

pandas/core/frame.py

+        columns created within the same ``assign`` call. For python 3.6 and
+        above it is possible to reference columns created in an assignment.
+        To this end you have to respect the order of |*|*kwargs and use
+        callables referencing the assigned columns.


python --> Python
eralier --> earlier, <-- note the comma

Add comma after "To this end"
"|*|*kwargs" <-- what's this?

sorry I fear the |*|*kwargs part is an artifact of somewhere else, will also change the earlier typo

gfyoung · 2017-12-19T23:34:20Z

pandas/tests/frame/test_mutate_columns.py

-        with pytest.raises(KeyError):
-            df.assign(C=df.A, D=lambda x: x['A'] + x['C'])
+        if not PY36:
+            with pytest.raises(KeyError):


Add note as to why this is occurring. Also, I presume there is no real error message for this KeyError.

gfyoung · 2017-12-19T23:35:06Z

pandas/tests/frame/test_mutate_columns.py

+
+    def test_assign_dependent(self):
+        df = DataFrame({'A': [1, 2], 'B': [3, 4]})
+        if PY36:


Reference issue number below function declaration

You might want to consider decorating with a test-skipper so that we don't run this test on anything below 3.6 (also no need to create the DataFrame if there's nothing afterwards).

jreback · 2017-12-20T00:58:21Z

doc/source/whatsnew/v0.22.0.txt

@@ -139,6 +139,7 @@ Other Enhancements
 - :func:`read_excel()` has gained the ``nrows`` parameter (:issue:`16645`)
 - :func:``DataFrame.to_json`` and ``Series.to_json`` now accept an ``index`` argument which allows the user to exclude the index from the JSON output (:issue:`17394`)
 - ``IntervalIndex.to_tuples()`` has gained the ``na_tuple`` parameter to control whether NA is returned as a tuple of NA, or NA itself (:issue:`18756`)
+- :func:``DataFrame.assign()`` now acceepts dependent kwargs, e.g. `df.assign(b=1, c=lambda x:x['b'])` does not throw an exception anymore. (:issue: `14207)


previously this would raise (put the what it did raise), write the issue like

(:issue:`14207`)

Also:

:func:``DataFrame.assign()`` --> :func:`DataFrame.assign` (single ticks, no parenthesis)

acceepts --> accepts

`df.assign(b=1, c=lambda x:x['b'])` --> ``df.assign(b=1, c=lambda x: x['b'])`` (double ticks)

I think it'd be worth breaking this out into its own section and highlight a case where this is an API change. Something like

.. warning:: This may subtly change the behavior of your code when you're using ``assign`` to update an existing column. Previously, callables refering to other variables being updated would get the "old" values .. code-block:: ipython In [2]: df = pd.DataFrame({"A": [1, 2, 3]}) In [3]: df.assign(A=lambda df: df.A + 1, C=lambda df: df.A * -1) Out[3]: A C 0 2 -1 1 3 -2 2 4 -3 Now, callables will get the "new" value .. ipython:: python df = pd.DataFrame({"A": [1, 2, 3]}) df.assign(A=lambda df: df.A + 1, C=lambda df: df.A * -1)

jreback · 2017-12-20T01:00:02Z

pandas/core/frame.py

+                results[k] = com._apply_if_callable(v, data)
+
+            # sort by key for 3.5 and earlier
+                results = sorted(results.items())


this is incorrectly indented

jreback · 2017-12-20T01:02:49Z

pandas/tests/frame/test_mutate_columns.py

+            with pytest.raises(KeyError):
+                df.assign(C=df.A, D=lambda x: x['A'] + x['C'])
+
+    def test_assign_dependent(self):


add a skipif decorator here if not PY36

jreback · 2017-12-20T01:03:39Z

pandas/tests/frame/test_mutate_columns.py

-            df.assign(C=lambda df: df.A, D=lambda df: df['A'] + df['C'])
-        with pytest.raises(KeyError):
-            df.assign(C=df.A, D=lambda x: x['A'] + x['C'])
+        if not PY36:


make this into a separate test (from the PY36) part and add a skipif PY36 decorator

datajanko · 2017-12-20T07:54:51Z

thanks for the valuable feedback. If I want to recommit my pull request, is a rebase required or can I also just add a new commit with the appropriate changes. (Sorry I'm new to that). I think I will issue a new pull request (with the appropriate change) later today

gfyoung · 2017-12-20T08:00:45Z

@datajanko :

Don't issue a new PR! You are more than welcome to push new changes to your current PR. The fewer PR's we have to review, the better 😄
Making your PR merge-able is of secondary concern because your tests aren't passing. Once you get them to pass, then worry about it. For the time being, just keep pushing new commits.

datajanko · 2017-12-21T08:41:35Z

The tests pass and hopefully I fixed the errors. However, in the whatsnew section the exception traceback is printed out (when I ran it) due different python versions. What could be done there to avoid printing the traceback?

jreback · 2017-12-21T17:39:49Z

doc/source/whatsnew/v0.22.0.txt

@@ -119,6 +119,56 @@ Current Behavior

    s.rank(na_option='top')

+.. _whatsnew_0220.enhancements.assign_dependent:


rebase on master. can you move to 0.23 (docs were renamed), prob easiest to just check this file from master and past in new one

(also 0220 -> 0230)

I think I'll manage to rebase until Christmas.
the whatsnew page I constructed contains the traceback (since a change between python versions is shown) Is this desired? If not, is there a way to just print the code and output as in the document and not run the code actually?

datajanko · 2017-12-23T21:11:03Z

I hopefully rebased correctly. Additionally, I could fix the "errors" in the whatsnew description and squashed all commits into one

jreback

doc comments. you don't need to squash fyi.

jreback · 2017-12-23T21:23:38Z

doc/source/whatsnew/v0.22.0.txt

@@ -7,8 +7,3 @@ This is a major release from 0.21.1 and includes a number of API changes,
 deprecations, new features, enhancements, and performance improvements along
 with a large number of bug fixes. We recommend that all users upgrade to this
 version.


revert this file

jreback · 2017-12-23T21:24:22Z

doc/source/whatsnew/v0.23.0.txt

+``.assign()`` accepts dependent arguments
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The :func:`DataFrame.assign()` now accepts dependent kwargs. In earlier versions this throws a Keyerror exception anymore. (:issue: `14207)


you don't need to say what it did in prior version (here)

jreback · 2017-12-23T21:24:52Z

doc/source/whatsnew/v0.23.0.txt

+
+The :func:`DataFrame.assign()` now accepts dependent kwargs. In earlier versions this throws a Keyerror exception anymore. (:issue: `14207)
+
+Specifically, defining a new column inside assign may be referenced in the same assign statement if a callable is used. For example


.assign(); use double-backticks.

jreback · 2017-12-23T21:25:07Z

doc/source/whatsnew/v0.23.0.txt

+
+Specifically, defining a new column inside assign may be referenced in the same assign statement if a callable is used. For example
+
+.. code-block:: ipython     


use an ipython block (this executes the code), you don't need to show the results they will be rendered when they execute

jreback · 2017-12-23T21:25:46Z

doc/source/whatsnew/v0.23.0.txt

+using ``assign`` to update an existing column. Previously, callables
+refering to other variables being updated would get the "old" values
+
+.. code-block:: ipython


you can use a code block here as this is previous behavior

use
Previous Behaviour:

jreback · 2017-12-23T21:26:49Z

doc/source/whatsnew/v0.23.0.txt

+    1  3 -2
+    2  4 -3
+
+Now, callables will get the "new" value


New Behaviour:

jreback · 2017-12-23T21:26:54Z

doc/source/whatsnew/v0.23.0.txt

+Now, callables will get the "new" value
+
+.. code-block:: ipython
+


ipython block

jreback · 2017-12-23T21:27:15Z

doc/source/whatsnew/v0.23.0.txt

+``.assign()`` accepts dependent arguments
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The :func:`DataFrame.assign()` now accepts dependent kwargs. In earlier versions this throws a Keyerror exception anymore. (:issue: `14207)


put a reference to the doc-section on .assign in the docs which also needs updating.

Apologies, but what is the correct path to refer to? What should be the updated content of the .assign docs?

jreback · 2017-12-23T21:28:14Z

doc/source/whatsnew/v0.23.0.txt

+
+This may subtly change the behavior of your code when you're
+using ``assign`` to update an existing column. Previously, callables
+refering to other variables being updated would get the "old" values


codecov · 2017-12-24T03:43:01Z

Codecov Report

❗ No coverage uploaded for pull request base (master@5c76f33). Click here to learn what that means.
The diff coverage is 28.57%.

@@            Coverage Diff            @@
##             master   #18852   +/-   ##
=========================================
  Coverage          ?   91.61%           
=========================================
  Files             ?      150           
  Lines             ?    48796           
  Branches          ?        0           
=========================================
  Hits              ?    44704           
  Misses            ?     4092           
  Partials          ?        0

Flag	Coverage Δ
#multiple	`89.98% <28.57%> (?)`
#single	`41.72% <28.57%> (?)`

Impacted Files	Coverage Δ
pandas/core/frame.py	`97.17% <28.57%> (ø)`

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 5c76f33...4184732. Read the comment docs.

datajanko · 2017-12-26T09:15:41Z

Why is the coverage changing in only the documentation is changed?

jreback · 2017-12-27T20:29:34Z

coverage is a little bit bogus :<

jreback · 2017-12-27T20:30:05Z

doc/source/whatsnew/v0.23.0.txt

+
+Specifically, defining a new column inside ``.assign()`` may be referenced in the same assign statement if a callable is used. For example
+
+.. ipython:: python     


if you use ipython, then you don't need to show the code, it executes

jreback · 2017-12-27T20:30:15Z

doc/source/whatsnew/v0.23.0.txt

+
+Previous Behaviour:
+
+.. code-block:: ipython


so this is correct here

jreback · 2017-12-27T20:31:12Z

doc/source/whatsnew/v0.23.0.txt

+
+New Behaviour:
+
+.. ipython:: python


but you don't need all this, just

df.assign(....) # complete the line of course

generally we would simply execute and show the DataFrame creation in an ipython block at the top of the sub-section, then do a code-block for previous, and an ipython block for enw

jreback · 2017-12-27T20:31:30Z

pandas/tests/frame/test_mutate_columns.py

+
+    @pytest.mark.skipif(PY36, reason="""Issue #14207: valid for python
+                        3.6 and above""")
+    def test_assign_bad_old_version(self):


can you name this something more relevant

do you mean the name of the function or the reason?

name of the function

jreback · 2018-01-01T17:58:56Z

doc/source/whatsnew/v0.23.0.txt

+
+Specifically, defining a new column inside ``.assign()`` may be referenced in the same assign statement if a callable is used. For example
+
+.. 	ipython:: python     


pls see how other things are formatted in the whatsnew.

you need only to do

.. ipython:: python df = pd.DataFrame({'A': [1, 2, 3]}) df df.assign(B=df.A, C=lambda x: x['A'] + x['B'])

jreback · 2018-01-01T17:59:10Z

doc/source/whatsnew/v0.23.0.txt

+``.assign()`` accepts dependent arguments
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The :func:`DataFrame.assign()` now accepts dependent kwargs. (:issue:`14207`)


a little more expl of what a dependenet kwarg is

jreback · 2018-01-01T17:59:24Z

doc/source/whatsnew/v0.23.0.txt

+
+New Behaviour:
+
+.. ipython:: python


jreback · 2018-01-02T11:08:51Z

doc/source/whatsnew/v0.23.0.txt

+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The :func:`DataFrame.assign()` now accepts dependent kwargs for python version later than 3.6 (see also `PEP 468
+<https://www.python.org/dev/peps/pep-0468/>`_). Now the keyword-value pairs passed to `.assign()` may depend on their predecessors if the values are callables. (:issue:`14207`)


use :func:`DataFrame.assign` here.

jreback · 2018-01-02T11:09:00Z

doc/source/whatsnew/v0.23.0.txt

+
+    df = pd.DataFrame({'A': [1, 2, 3]})
+    df
+	df.assign(B=df.A, C=lambda x:x['A']+ x['B'])


indent the same

jreback · 2018-01-02T11:09:34Z

pandas/core/frame.py

-            results[k] = com._apply_if_callable(v, data)
-
-        # preserve order for 3.6 and later, but sort by key for 3.5 and earlier
+        # for 3.6 preserve order of kwargs


jreback · 2018-01-02T11:09:41Z

pandas/core/frame.py

        else:
+            # for 3.5 or earlier: do all calculations first...


jreback · 2018-01-02T11:10:23Z

doc/source/whatsnew/v0.23.0.txt

+
+.. warning::
+
+This may subtly change the behavior of your code when you're


add this warning to the main docs as well in dsintro.rst

What are the Main docs? Docstring of the assign function?

http://pandas-docs.github.io/pandas-docs-travis/dsintro.html#assigning-new-columns-in-method-chains

That's in doc/source/dsintro.rst.

jreback · 2018-01-02T11:10:55Z

pandas/core/frame.py

-        the same ``assign`` call.
+        is possible, but for python 3.5 and earlier, you cannot reference
+        other columns created within the same ``assign`` call.
+        For python 3.6 and above it is possible to reference columns created


and Python 3.6 and later
Python 3.5 and earlier

I would talk about 3.6 and later first, since they're the future.

Assigning multiple columns within the same ``assign`` is possible. For Python 3.6 and above, later items in '\*\*kwargs' may refer to newly created or modified columns in 'df'; items are computed and assigned into 'df' in order. For Python 3.5 and below, the order of keyword arguments is not specified, you cannot refer to newly created or modified columns. All items are computed first, and then assigned in alphabetical order.

This can replace the entire "Notes" section I think.

Also, could you add the example you've been using to the "Examples" section.

TomAugspurger · 2018-01-02T11:47:43Z

doc/source/whatsnew/v0.23.0.txt

+``.assign()`` accepts dependent arguments
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The :func:`DataFrame.assign()` now accepts dependent kwargs for python version later than 3.6 (see also `PEP 468


"kwargs" -> "keyword arguments"

TomAugspurger · 2018-01-02T11:49:08Z

doc/source/whatsnew/v0.23.0.txt

+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The :func:`DataFrame.assign()` now accepts dependent kwargs for python version later than 3.6 (see also `PEP 468
+<https://www.python.org/dev/peps/pep-0468/>`_). Now the keyword-value pairs passed to `.assign()` may depend on their predecessors if the values are callables. (:issue:`14207`)


"keyword-value pairs" -> "keyword arguments".

I'm not sure if "predecessors" is the right word. How about

"Later keyword arguments may now refer to earlier ones. (:issue:`14207`)"

Sounds indeed better. Should I drop the reference to callables? (Sorry, obviously my auto correct messed up)

Maybe keep "callables", so

"Later keyword arguments may now refer to earlier ones if the argument is a callable. (:issue:`14207`)"

TomAugspurger · 2018-01-02T11:57:24Z

pandas/core/frame.py

-        the same ``assign`` call.
+        is possible, but for python 3.5 and earlier, you cannot reference
+        other columns created within the same ``assign`` call.
+        For python 3.6 and above it is possible to reference columns created


I would talk about 3.6 and later first, since they're the future.

Assigning multiple columns within the same ``assign`` is possible. For Python 3.6 and above, later items in '\*\*kwargs' may refer to newly created or modified columns in 'df'; items are computed and assigned into 'df' in order. For Python 3.5 and below, the order of keyword arguments is not specified, you cannot refer to newly created or modified columns. All items are computed first, and then assigned in alphabetical order.

This can replace the entire "Notes" section I think.

TomAugspurger · 2018-01-02T11:57:41Z

pandas/core/frame.py

-        the same ``assign`` call.
+        is possible, but for python 3.5 and earlier, you cannot reference
+        other columns created within the same ``assign`` call.
+        For python 3.6 and above it is possible to reference columns created


Also, could you add the example you've been using to the "Examples" section.

TomAugspurger · 2018-01-02T12:05:14Z

Could you add a

.. versionmodified :: 0.23.0
   Keyword argument order is maintained for Python 3.6 and later.

to https://github.com/pandas-dev/pandas/pull/18852/files#diff-1e79abbbdd150d4771b91ea60a4e1cc7R2648

datajanko · 2018-01-10T06:03:06Z

Sorry, I am not sure why travis failed on some 3.5 builds. Any ideas? Maybe I messed something up when reading?

gfyoung · 2018-01-10T06:25:46Z

I restarted the builds to check, but you might need to rebase onto master. There has been some numpy weirdness lately (see d5194e5 from @TomAugspurger where we pinned numpy).

datajanko · 2018-01-10T07:42:45Z

I saw some issues yesterday and already performed a rebase (I hope correctly). I think I know where the issues are coming from: I added an example in frame.py (as @TomAugspurger suggested) as a doctest which will only work for python 3.6 and above. Is it possible to skip the doctest for older python versions? If not can I post examples in the doctoring of a function also using restructured texts code-blocks?

jreback · 2018-01-10T13:03:45Z

doc/source/dsintro.rst

@@ -507,9 +507,41 @@ of one argument to be called on the ``DataFrame``. A *copy* of the original
 DataFrame is returned, with the new values inserted.

 .. warning::
+  Starting from Python 3.6 ``**kwargs`` is an ordered dictionary and ``assign`` 


use :func`DataFrame.assign`

jreback · 2018-01-10T13:04:01Z

doc/source/dsintro.rst

@@ -507,9 +507,41 @@ of one argument to be called on the ``DataFrame``. A *copy* of the original
 DataFrame is returned, with the new values inserted.

 .. warning::
+  Starting from Python 3.6 ``**kwargs`` is an ordered dictionary and ``assign`` 
+  respects the order of the keyword arguments. It is allowed to write


reword It is allowed to write

jreback · 2018-01-10T13:04:41Z

doc/source/dsintro.rst

+  .. ipython::
+      :verbatim:
+
+      In [1]: # Allowed for Python 3.6 and later


comments on a separate line. don't user verbatim, just write the expression

jreback · 2018-01-10T13:04:58Z

doc/source/dsintro.rst

+  This may subtly change the behavior of your code when you're
+  using ``.assign()`` to update an existing column. Prior to Python 3.6,
+  callables referring to other variables being updated would get the "old" values
+


This previous does not need to be here, ONLY in the whatsnew new

remove from here down

Ok, so implicitly, if one uses pandas 0.23 it is assumed that also python >= 3.6, right?

why would you say that?

remove it because it is redundant (you said it above)

jreback

2 minor doc changes. ping on green.

jreback · 2018-01-13T22:15:39Z

doc/source/dsintro.rst


-  All expressions are computed first, and then assigned. So you can't refer
-  to another column being assigned in the same call to ``assign``. For example:
+      In [1]: df.assign(C = lambda x: x['A'] + x['B'],


when using an ipython:: python block, it executes the code, thus we cannot have the In [1]: there

using a simple python:: block will always execute the code and yield errors since df is (currently) not defined appropriately. So - it seems - there are two possibilities to resolve this:

use verbatim and just show the syntax of the call (which you refused in my last commit)

use the iris DataFrame from the section or df from a section before and modify the call w.r.t those frames.

Which way to go?

Best to just make a small dataframe for the example:

.. ipython:: python df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) df.assign(C=lambda x: x['A'] + x['B'], D=lambda x: x['A'] + x['C'])

Call it something other than df though, since creating a new df will break examples further down.

jreback · 2018-01-13T22:15:58Z

doc/source/dsintro.rst

-               (df.assign(C = lambda x: x['A'] + x['B'])
-                  .assign(D = lambda x: x['A'] + x['C']))
+  This may subtly change the behavior of your code when you're
+  using ``.assign()`` to update an existing column. Prior to Python 3.6,


:func:`DataFrame`

datajanko · 2018-01-15T10:38:08Z

so what is the next thing I should do in this pull request? some rebasing required? Or just sit back and wait?

jreback · 2018-01-15T15:05:40Z

@TomAugspurger if you'd verify this reads ok.

datajanko · 2018-01-30T14:44:21Z

is there anything left I need to do?

jreback · 2018-01-31T12:16:56Z

can you rebase on master

datajanko · 2018-02-01T13:44:52Z

just for clarification: rebasing the changes you made to my branch on the current master branch of pandas-dev? I think I will be able to manage this over the weekend

jreback · 2018-02-01T13:46:26Z

yes just rebase against master and push again

Specifically, 'df.assign(b=1, c=lambda x:x['b'])' does not throw an exception in python 3.6 and above. Further details are discussed in Issues pandas-dev#14207 and pandas-dev#18797. populates dsintro and frame.py with examples and warning - adds example to frame.py - reworked warning in dsintro - reworked Notes in frame.py Remains open: frame.py probably is responsible vor travis not passing: doc test that requires python 3.6

datajanko · 2018-02-09T20:10:07Z

sorry, did the rebase just now. Had some urgent family matters to attend. Hope the rebase is correct.

jreback · 2018-02-10T16:20:22Z

thanks!

…s-dev#18852)

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datajanko force-pushed the assign-chained-kwargs branch from 25153d3 to b7ec738 Compare February 9, 2018 20:09

TomAugspurger and others added 3 commits February 9, 2018 14:35

Update docs

094f346

Merge branch 'master' into PR_TOOL_MERGE_PR_18852

07a9295

mod to docs

4184732

jreback merged commit bae38fc into pandas-dev:master Feb 10, 2018

datajanko deleted the assign-chained-kwargs branch February 10, 2018 17:08

harisbal pushed a commit to harisbal/pandas that referenced this pull request Feb 28, 2018

ENH: df.assign accepting dependent **kwargs (pandas-dev#14207) (panda…

b98e595

…s-dev#18852)

jschendel mentioned this pull request Jul 24, 2018

DOC: Updated the DataFrame.assign docstring #21917

Merged

		@@ -119,6 +119,56 @@ Current Behavior

		s.rank(na_option='top')

		.. _whatsnew_0220.enhancements.assign_dependent:


		The :func:`DataFrame.assign()` now accepts dependent kwargs. In earlier versions this throws a Keyerror exception anymore. (:issue: `14207)

		Specifically, defining a new column inside assign may be referenced in the same assign statement if a callable is used. For example


		Specifically, defining a new column inside assign may be referenced in the same assign statement if a callable is used. For example

		.. code-block:: ipython

		Now, callables will get the "new" value

		.. code-block:: ipython


		Specifically, defining a new column inside ``.assign()`` may be referenced in the same assign statement if a callable is used. For example

		.. ipython:: python


		.. warning::

		This may subtly change the behavior of your code when you're

ENH: df.assign accepting dependent **kwargs (#14207) #18852

ENH: df.assign accepting dependent **kwargs (#14207) #18852

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datajanko commented Dec 20, 2017

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codecov bot commented Dec 24, 2017 • edited Loading

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TomAugspurger Jan 2, 2018 • edited Loading

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TomAugspurger commented Jan 2, 2018

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datajanko commented Dec 19, 2017 •

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codecov bot commented Dec 24, 2017 •

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datajanko commented Jan 10, 2018 •

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