DOC: update the pandas.DataFrame.cummax docstring
State: closed

Checklist for the pandas documentation sprint (ignore this if you are doing
an unrelated PR):

- [X] PR title is "DOC: update the <your-function-or-method> docstring"
- [X] The validation script passes: `scripts/validate_docstrings.py <your-function-or-method>`
- [x] The PEP8 style check passes: `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] The html version looks good: `python doc/make.py --single <your-function-or-method>`
- [X] It has been proofread on language by another sprint participant

Please include the output of the validation script below between the "```" ticks:

```
################################################################################
##################### Docstring (pandas.DataFrame.cummax)  #####################
################################################################################

Return cumulative maximum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative
maximum.

Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
    The index or the name of the axis. 0 is equivalent to None or 'index'.
skipna : boolean, default True
    Exclude NA/null values. If an entire row/column is NA, the result
    will be NA.
*args, **kwargs :
    Additional keywords have no effect but might be accepted for
    compatibility with NumPy.

Returns
-------
cummax : Series or DataFrame

Examples
--------
**Series**

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummax()
0    2.0
1    NaN
2    5.0
3    5.0
4    5.0
dtype: float64

To include NA values in the operation, use ``skipna=False``

>>> s.cummax(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

**DataFrame**

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the maximum
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.

>>> df.cummax()
     A    B
0  2.0  1.0
1  3.0  NaN
2  3.0  1.0

To iterate over columns and find the maximum in each row,
use ``axis=1``

>>> df.cummax(axis=1)
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  1.0

See also
--------
pandas.core.window.Expanding.max : Similar functionality
    but ignores ``NaN`` values.
DataFrame.max : Return the maximum over
    DataFrame axis.
DataFrame.cummax : Return cumulative maximum over DataFrame axis.
DataFrame.cummin : Return cumulative minimum over DataFrame axis.
DataFrame.cumsum : Return cumulative sum over DataFrame axis.
DataFrame.cumprod : Return cumulative product over DataFrame axis.

################################################################################
################################## Validation ##################################
################################################################################

Errors found:
	Errors in parameters section
		Parameters {'kwargs', 'args'} not documented
		Unknown parameters {'*args, **kwargs :'}
		Parameter "*args, **kwargs :" has no type


################################################################################
##################### Docstring (pandas.DataFrame.cummin)  #####################
################################################################################

Return cumulative minimum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative
minimum.

Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
    The index or the name of the axis. 0 is equivalent to None or 'index'.
skipna : boolean, default True
    Exclude NA/null values. If an entire row/column is NA, the result
    will be NA.
*args, **kwargs :
    Additional keywords have no effect but might be accepted for
    compatibility with NumPy.

Returns
-------
cummin : Series or DataFrame

Examples
--------
**Series**

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummin()
0    2.0
1    NaN
2    2.0
3   -1.0
4   -1.0
dtype: float64

To include NA values in the operation, use ``skipna=False``

>>> s.cummin(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

**DataFrame**

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the minimum
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.

>>> df.cummin()
     A    B
0  2.0  1.0
1  2.0  NaN
2  1.0  0.0

To iterate over columns and find the minimum in each row,
use ``axis=1``

>>> df.cummin(axis=1)
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

See also
--------
pandas.core.window.Expanding.min : Similar functionality
    but ignores ``NaN`` values.
DataFrame.min : Return the minimum over
    DataFrame axis.
DataFrame.cummax : Return cumulative maximum over DataFrame axis.
DataFrame.cummin : Return cumulative minimum over DataFrame axis.
DataFrame.cumsum : Return cumulative sum over DataFrame axis.
DataFrame.cumprod : Return cumulative product over DataFrame axis.

################################################################################
################################## Validation ##################################
################################################################################

Errors found:
	Errors in parameters section
		Parameters {'args', 'kwargs'} not documented
		Unknown parameters {'*args, **kwargs :'}
		Parameter "*args, **kwargs :" has no type


################################################################################
##################### Docstring (pandas.DataFrame.cumsum)  #####################
################################################################################

Return cumulative sum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative
sum.

Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
    The index or the name of the axis. 0 is equivalent to None or 'index'.
skipna : boolean, default True
    Exclude NA/null values. If an entire row/column is NA, the result
    will be NA.
*args, **kwargs :
    Additional keywords have no effect but might be accepted for
    compatibility with NumPy.

Returns
-------
cumsum : Series or DataFrame

Examples
--------
**Series**

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumsum()
0    2.0
1    NaN
2    7.0
3    6.0
4    6.0
dtype: float64

To include NA values in the operation, use ``skipna=False``

>>> s.cumsum(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

**DataFrame**

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the sum
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.

>>> df.cumsum()
     A    B
0  2.0  1.0
1  5.0  NaN
2  6.0  1.0

To iterate over columns and find the sum in each row,
use ``axis=1``

>>> df.cumsum(axis=1)
     A    B
0  2.0  3.0
1  3.0  NaN
2  1.0  1.0

See also
--------
pandas.core.window.Expanding.sum : Similar functionality
    but ignores ``NaN`` values.
DataFrame.sum : Return the sum over
    DataFrame axis.
DataFrame.cummax : Return cumulative maximum over DataFrame axis.
DataFrame.cummin : Return cumulative minimum over DataFrame axis.
DataFrame.cumsum : Return cumulative sum over DataFrame axis.
DataFrame.cumprod : Return cumulative product over DataFrame axis.

################################################################################
################################## Validation ##################################
################################################################################

Errors found:
	Errors in parameters section
		Parameters {'args', 'kwargs'} not documented
		Unknown parameters {'*args, **kwargs :'}
		Parameter "*args, **kwargs :" has no type

################################################################################
##################### Docstring (pandas.DataFrame.cumprod) #####################
################################################################################

Return cumulative product over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative
product.

Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
    The index or the name of the axis. 0 is equivalent to None or 'index'.
skipna : boolean, default True
    Exclude NA/null values. If an entire row/column is NA, the result
    will be NA.
*args, **kwargs :
    Additional keywords have no effect but might be accepted for
    compatibility with NumPy.

Returns
-------
cumprod : Series or DataFrame

Examples
--------
**Series**

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumprod()
0     2.0
1     NaN
2    10.0
3   -10.0
4    -0.0
dtype: float64

To include NA values in the operation, use ``skipna=False``

>>> s.cumprod(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

**DataFrame**

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the product
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.

>>> df.cumprod()
     A    B
0  2.0  1.0
1  6.0  NaN
2  6.0  0.0

To iterate over columns and find the product in each row,
use ``axis=1``

>>> df.cumprod(axis=1)
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  0.0

See also
--------
pandas.core.window.Expanding.prod : Similar functionality
    but ignores ``NaN`` values.
DataFrame.prod : Return the product over
    DataFrame axis.
DataFrame.cummax : Return cumulative maximum over DataFrame axis.
DataFrame.cummin : Return cumulative minimum over DataFrame axis.
DataFrame.cumsum : Return cumulative sum over DataFrame axis.
DataFrame.cumprod : Return cumulative product over DataFrame axis.

################################################################################
################################## Validation ##################################
################################################################################

Errors found:
	Errors in parameters section
		Parameters {'args', 'kwargs'} not documented
		Unknown parameters {'*args, **kwargs :'}
		Parameter "*args, **kwargs :" has no type
```

If the validation script still gives errors, but you think there is a good reason
to deviate in this case (and there are certainly such cases), please state this
explicitly.

The *args/**kwargs error is a bug in the parameter evaluation

Labels:
name: Docs
name: Numeric Operations
description: Arithmetic, Comparison, and Logical operations

Comments:
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==========================================
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------

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@datapythonista Thank you for the helpful comments :) Please let me know if I need to change anything else.
Hello @arminv! Thanks for updating the PR.



Cheers ! There are no PEP8 issues in this Pull Request. :beers: 

##### Comment last updated on March 17, 2018 at 16:19 Hours UTC
I also checked HTML rendering of the corresponding `Series` cumulative methods (`pandas.Series.cummax` ,etc.) and they appear to be ok. There is a duplication in their 'See also' section but again I couldn't think of a simple way to fix this through templating.
@arminv Thanks a lot for this PR!
Thank you guys for being so helpful. I look forward to contributing again.