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July 21, 2017 22:39
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Dask doesn't support the following argument(s). | |
* buf | |
* columns | |
* col_space | |
* header | |
* index | |
* na_rep | |
* formatters | |
* float_format | |
* sparsify | |
* index_names | |
* justify | |
* line_width | |
* max_cols | |
* show_dimensions | |
.. py:method:: DataFrame.to_timestamp(freq=None, how='start', axis=0) | |
:module: dask.dataframe | |
Cast to DatetimeIndex of timestamps, at *beginning* of period | |
:Parameters: | |
**freq** : string, default frequency of PeriodIndex | |
Desired frequency | |
**how** : {'s', 'e', 'start', 'end'} | |
Convention for converting period to timestamp; start of period | |
vs. end | |
**axis** : {0 or 'index', 1 or 'columns'}, default 0 | |
The axis to convert (the index by default) | |
**copy** : boolean, default True | |
If false then underlying input data is not copied | |
:Returns: | |
**df** : DataFrame with DatetimeIndex | |
.. rubric:: Notes | |
Dask doesn't support the following argument(s). | |
* copy | |
.. py:method:: DataFrame.truediv(other, axis='columns', level=None, fill_value=None) | |
:module: dask.dataframe | |
Floating division of dataframe and other, element-wise (binary operator `truediv`). | |
Equivalent to ``dataframe / other``, but with support to substitute a fill_value for | |
missing data in one of the inputs. | |
:Parameters: | |
**other** : Series, DataFrame, or constant | |
**axis** : {0, 1, 'index', 'columns'} | |
For Series input, axis to match Series index on | |
**fill_value** : None or float value, default None | |
Fill missing (NaN) values with this value. If both DataFrame | |
locations are missing, the result will be missing | |
**level** : int or name | |
Broadcast across a level, matching Index values on the | |
passed MultiIndex level | |
:Returns: | |
**result** : DataFrame | |
.. seealso:: | |
:obj:`DataFrame.rtruediv` | |
.. rubric:: Notes | |
Mismatched indices will be unioned together | |
.. py:attribute:: DataFrame.values | |
:module: dask.dataframe | |
Return a dask.array of the values of this dataframe | |
Warning: This creates a dask.array without precise shape information. | |
Operations that depend on shape information, like slicing or reshaping, | |
will not work. | |
.. py:method:: DataFrame.var(axis=None, skipna=True, ddof=1, split_every=False) | |
:module: dask.dataframe | |
Return unbiased variance over requested axis. | |
Normalized by N-1 by default. This can be changed using the ddof argument | |
:Parameters: | |
**axis** : {index (0), columns (1)} | |
**skipna** : boolean, default True | |
Exclude NA/null values. If an entire row/column is NA, the result | |
will be NA | |
**level** : int or level name, default None | |
If the axis is a MultiIndex (hierarchical), count along a | |
particular level, collapsing into a Series | |
**ddof** : int, default 1 | |
degrees of freedom | |
**numeric_only** : boolean, default None | |
Include only float, int, boolean columns. If None, will attempt to use | |
everything, then use only numeric data. Not implemented for Series. | |
:Returns: | |
**var** : Series or DataFrame (if level specified) | |
.. rubric:: Notes | |
Dask doesn't support the following argument(s). | |
* level | |
* numeric_only | |
.. py:method:: DataFrame.visualize(filename='mydask', format=None, optimize_graph=False, **kwargs) | |
:module: dask.dataframe | |
Render the computation of this object's task graph using graphviz. | |
Requires ``graphviz`` to be installed. | |
:Parameters: | |
**filename** : str or None, optional | |
The name (without an extension) of the file to write to disk. If | |
`filename` is None, no file will be written, and we communicate | |
with dot using only pipes. | |
**format** : {'png', 'pdf', 'dot', 'svg', 'jpeg', 'jpg'}, optional | |
Format in which to write output file. Default is 'png'. | |
**optimize_graph** : bool, optional | |
If True, the graph is optimized before rendering. Otherwise, | |
the graph is displayed as is. Default is False. | |
****kwargs** | |
Additional keyword arguments to forward to ``to_graphviz``. | |
:Returns: | |
**result** : IPython.diplay.Image, IPython.display.SVG, or None | |
See dask.dot.dot_graph for more information. | |
.. seealso:: | |
:obj:`dask.base.visualize`, :obj:`dask.dot.dot_graph` | |
.. rubric:: Notes | |
For more information on optimization see here: | |
http://dask.pydata.org/en/latest/optimize.html | |
.. py:method:: DataFrame.where(cond, other=nan) | |
:module: dask.dataframe | |
Return an object of same shape as self and whose corresponding | |
entries are from self where cond is True and otherwise are from | |
other. | |
:Parameters: | |
**cond** : boolean NDFrame, array-like, or callable | |
If cond is callable, it is computed on the NDFrame and | |
should return boolean NDFrame or array. The callable must | |
not change input NDFrame (though pandas doesn't check it). | |
.. versionadded:: 0.18.1 | |
A callable can be used as cond. | |
**other** : scalar, NDFrame, or callable | |
If other is callable, it is computed on the NDFrame and | |
should return scalar or NDFrame. The callable must not | |
change input NDFrame (though pandas doesn't check it). | |
.. versionadded:: 0.18.1 | |
A callable can be used as other. | |
**inplace** : boolean, default False | |
Whether to perform the operation in place on the data | |
**axis** : alignment axis if needed, default None | |
**level** : alignment level if needed, default None | |
**try_cast** : boolean, default False | |
try to cast the result back to the input type (if possible), | |
**raise_on_error** : boolean, default True | |
Whether to raise on invalid data types (e.g. trying to where on | |
strings) | |
:Returns: | |
**wh** : same type as caller | |
.. seealso:: | |
:func:`DataFrame.mask` | |
.. rubric:: Notes | |
The where method is an application of the if-then idiom. For each | |
element in the calling DataFrame, if ``cond`` is ``True`` the | |
element is used; otherwise the corresponding element from the DataFrame | |
``other`` is used. | |
The signature for :func:`DataFrame.where` differs from | |
:func:`numpy.where`. Roughly ``df1.where(m, df2)`` is equivalent to | |
``np.where(m, df1, df2)``. | |
For further details and examples see the ``where`` documentation in | |
:ref:`indexing <indexing.where_mask>`. | |
.. rubric:: Examples | |
>>> s = pd.Series(range(5)) # doctest: +SKIP | |
>>> s.where(s > 0) # doctest: +SKIP | |
0 NaN | |
1 1.0 | |
2 2.0 | |
3 3.0 | |
4 4.0 | |
>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B']) # doctest: +SKIP | |
>>> m = df % 3 == 0 # doctest: +SKIP | |
>>> df.where(m, -df) # doctest: +SKIP | |
A B | |
0 0 -1 | |
1 -2 3 | |
2 -4 -5 | |
3 6 -7 | |
4 -8 9 | |
>>> df.where(m, -df) == np.where(m, df, -df) # doctest: +SKIP | |
A B | |
0 True True | |
1 True True | |
2 True True | |
3 True True | |
4 True True | |
>>> df.where(m, -df) == df.mask(~m, -df) # doctest: +SKIP | |
A B | |
0 True True | |
1 True True | |
2 True True | |
3 True True | |
4 True True |
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