Refactoring of plotting + fixes for multiple charts (#117)

* Refactoring of plotting + fixes for multiple charts

Updates to plotting inline with pandas 0.25.3
Enables plotting of multiple histograms on the
same figure.

* Fix to setup.py to allow submodules

+ reformat of code and better Series.hist docs

docs/source/examples/demo_notebook.ipynb

@@ -753,7 +753,7 @@
     {
      "data": {
       "text/plain": [
-       "<eland.index.Index at 0x11ffd7f90>"
+       "<eland.index.Index at 0x11a122310>"
       ]
      },
      "execution_count": 17,
@@ -2707,7 +2707,7 @@
        "      <td>410.008918</td>\n",
        "      <td>2470.545974</td>\n",
        "      <td>...</td>\n",
-       "      <td>251.698552</td>\n",
+       "      <td>251.682199</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2724,7 +2724,7 @@
        "      <td>9735.660463</td>\n",
        "      <td>...</td>\n",
        "      <td>720.534532</td>\n",
-       "      <td>4.254967</td>\n",
+       "      <td>4.288079</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
@@ -2745,9 +2745,9 @@
        "mean       628.253689         7092.142457  ...     511.127842      2.835975\n",
        "std        266.386661         4578.263193  ...     334.741135      1.939365\n",
        "min        100.020531            0.000000  ...       0.000000      0.000000\n",
-       "25%        410.008918         2470.545974  ...     251.698552      1.000000\n",
+       "25%        410.008918         2470.545974  ...     251.682199      1.000000\n",
        "50%        640.387285         7612.072403  ...     503.148975      3.000000\n",
-       "75%        842.233478         9735.660463  ...     720.534532      4.254967\n",
+       "75%        842.233478         9735.660463  ...     720.534532      4.288079\n",
        "max       1199.729004        19881.482422  ...    1902.901978      6.000000\n",
        "\n",
        "[8 rows x 7 columns]"

docs/source/examples/online_retail_analysis.ipynb

@@ -1023,21 +1023,21 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
-       "      <td>14215.123301</td>\n",
+       "      <td>14221.960201</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>1.250100</td>\n",
+       "      <td>1.250000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
-       "      <td>15654.828552</td>\n",
+       "      <td>15671.712170</td>\n",
        "      <td>2.000000</td>\n",
        "      <td>2.510000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
-       "      <td>17218.003301</td>\n",
+       "      <td>17214.376367</td>\n",
-       "      <td>6.570576</td>\n",
+       "      <td>6.615042</td>\n",
-       "      <td>4.210000</td>\n",
+       "      <td>4.210533</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
@@ -1055,9 +1055,9 @@
        "mean   15590.776680      7.464000      4.103233\n",
        "std     1764.025160     85.924387     20.104873\n",
        "min    12347.000000  -9360.000000      0.000000\n",
-       "25%    14215.123301      1.000000      1.250100\n",
+       "25%    14221.960201      1.000000      1.250000\n",
-       "50%    15654.828552      2.000000      2.510000\n",
+       "50%    15671.712170      2.000000      2.510000\n",
-       "75%    17218.003301      6.570576      4.210000\n",
+       "75%    17214.376367      6.615042      4.210533\n",
        "max    18239.000000   2880.000000    950.990000"
       ]
      },

docs/source/index.rst

@@ -34,7 +34,6 @@ In general, the data resides in elasticsearch and not in memory, which allows el
   * :doc:`reference/general_utility_functions`
   * :doc:`reference/dataframe`
   * :doc:`reference/series`
-  * :doc:`reference/index`
   * :doc:`reference/indexing`
 
 * :doc:`implementation/index`

docs/source/reference/api/eland.Series.hist.rst

@@ -4,5 +4,5 @@ eland.Series.hist
 .. currentmodule:: eland
 
 .. automethod:: Series.hist
-.. image:: eland-Series-hist-1.png
+.. image:: eland-Series-hist-2.png
 

eland/__init__.py

@@ -23,7 +23,6 @@ from eland.field_mappings import *
 from eland.query import *
 from eland.operations import *
 from eland.query_compiler import *
-from eland.plotting import *
 from eland.ndframe import *
 from eland.series import *
 from eland.dataframe import *

eland/field_mappings.py

@@ -439,7 +439,8 @@ class FieldMappings:
 
         # extract relevant fields and convert to dict
         # <class 'dict'>: {'category.keyword': 'category', 'currency': 'currency', ...
-        return OrderedDict(aggregatables[['aggregatable_es_field_name', 'es_field_name']].to_dict(orient='split')['data'])
+        return OrderedDict(
+            aggregatables[['aggregatable_es_field_name', 'es_field_name']].to_dict(orient='split')['data'])
 
     def date_field_format(self, es_field_name):
         """

eland/plotting.py

@@ -1,133 +0,0 @@
-#  Copyright 2019 Elasticsearch BV
-#
-#      Licensed under the Apache License, Version 2.0 (the "License");
-#      you may not use this file except in compliance with the License.
-#      You may obtain a copy of the License at
-#
-#          http://www.apache.org/licenses/LICENSE-2.0
-#
-#      Unless required by applicable law or agreed to in writing, software
-#      distributed under the License is distributed on an "AS IS" BASIS,
-#      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#      See the License for the specific language governing permissions and
-#      limitations under the License.
-
-import numpy as np
-
-import pandas.core.common as com
-from pandas.core.dtypes.generic import (
-    ABCIndexClass)
-from pandas.plotting._matplotlib.tools import _flatten, _set_ticks_props, _subplots
-
-
-def ed_hist_frame(ed_df, column=None, by=None, grid=True, xlabelsize=None,
-                  xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False,
-                  sharey=False, figsize=None, layout=None, bins=10, **kwds):
-    """
-    See :pandas_api_docs:`pandas.DataFrame.hist` for usage.
-
-    Notes
-    -----
-    Derived from ``pandas.plotting._core.hist_frame 0.24.2`` - TODO update to ``0.25.1``
-
-    Ideally, we'd call `hist_frame` directly with histogram data,
-    but weights are applied to ALL series. For example, we can
-    plot a histogram of pre-binned data via:
-
-    .. code-block:: python
-
-        counts, bins = np.histogram(data)
-        plt.hist(bins[:-1], bins, weights=counts)
-
-    However,
-
-    .. code-block:: python
-
-        ax.hist(data[col].dropna().values, bins=bins, **kwds)
-
-    is for ``[col]`` and weights are a single array.
-
-    Examples
-    --------
-    >>> df = ed.DataFrame('localhost', 'flights')
-    >>> hist = df.select_dtypes(include=[np.number]).hist(figsize=[10,10]) # doctest: +SKIP
-    """
-    # Start with empty pandas data frame derived from
-    ed_df_bins, ed_df_weights = ed_df._hist(num_bins=bins)
-
-    if by is not None:
-        raise NotImplementedError("TODO")
-
-    if column is not None:
-        if not isinstance(column, (list, np.ndarray, ABCIndexClass)):
-            column = [column]
-        ed_df_bins = ed_df_bins[column]
-        ed_df_weights = ed_df_weights[column]
-    naxes = len(ed_df_bins.columns)
-
-    fig, axes = _subplots(naxes=naxes, ax=ax, squeeze=False,
-                          sharex=sharex, sharey=sharey, figsize=figsize,
-                          layout=layout)
-    _axes = _flatten(axes)
-
-    for i, col in enumerate(com.try_sort(ed_df_bins.columns)):
-        ax = _axes[i]
-
-        # pandas code
-        # pandas / plotting / _core.py: 2410
-        # ax.hist(data[col].dropna().values, bins=bins, **kwds)
-
-        ax.hist(ed_df_bins[col][:-1], bins=ed_df_bins[col], weights=ed_df_weights[col], **kwds)
-        ax.set_title(col)
-        ax.grid(grid)
-
-    _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot,
-                     ylabelsize=ylabelsize, yrot=yrot)
-    fig.subplots_adjust(wspace=0.3, hspace=0.3)
-
-    return axes
-
-def ed_hist_series(ed_s, column=None, by=None, grid=True, xlabelsize=None,
-                  xrot=None, ylabelsize=None, yrot=None, ax=None,
-                  figsize=None, layout=None, bins=10, **kwds):
-    """
-    See :pandas_api_docs:`pandas.Series.hist` for usage.
-
-    Notes
-    -----
-    Derived from ``pandas.plotting._core.hist_frame 0.24.2`` - TODO update to ``0.25.1``
-
-
-    Examples
-    --------
-    >>> df = ed.DataFrame('localhost', 'ecommerce')
-    >>> hist = df['taxful_total_price'].hist(figsize=[10,10]) # doctest: +SKIP
-    """
-    # this is mostly the same code as above, it has been split out
-    # to a series specific method now so we can expand series plotting
-
-
-    # Start with empty pandas data frame derived from
-    ed_s_bins, ed_s_weights = ed_s._hist(num_bins=bins)
-
-    if by is not None:
-        raise NotImplementedError("TODO")
-
-    # raise error rather than warning when series is not plottable
-    if ed_s_bins.empty:
-        raise ValueError("{} has no meaningful histogram interval. All values 0."
-                        .format(ed_s.name))
-
-    naxes = len(ed_s_bins.columns)
-    fig, axes = _subplots(naxes=naxes, ax=ax, squeeze=False, figsize=figsize, layout=layout)
-    _axes = _flatten(axes)
-    for i, col in enumerate(com.try_sort(ed_s_bins.columns)):
-        ax = _axes[i]
-        ax.hist(ed_s_bins[col][:-1], bins=ed_s_bins[col], weights=ed_s_weights[col], **kwds)
-        ax.grid(grid)
-
-    _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot,
-                     ylabelsize=ylabelsize, yrot=yrot)
-    fig.subplots_adjust(wspace=0.3, hspace=0.3)
-
-    return axes

eland/plotting/__init__.py

@@ -0,0 +1,28 @@
+#  Copyright 2019 Elasticsearch BV
+#
+#      Licensed under the Apache License, Version 2.0 (the "License");
+#      you may not use this file except in compliance with the License.
+#      You may obtain a copy of the License at
+#
+#          http://www.apache.org/licenses/LICENSE-2.0
+#
+#      Unless required by applicable law or agreed to in writing, software
+#      distributed under the License is distributed on an "AS IS" BASIS,
+#      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#      See the License for the specific language governing permissions and
+#      limitations under the License.
+"""
+Public plotting API
+
+Based from https://github.com/pandas-dev/pandas/blob/v0.25.3/pandas/plotting/__init__.py
+but only supporting a subset of plotting methods (for now).
+"""
+from eland.plotting._core import (
+    ed_hist_frame,
+    ed_hist_series,
+)
+
+__all___ = [
+    "ed_hist_frame",
+    "ed_hist_series",
+]

eland/plotting/_core.py

@@ -0,0 +1,127 @@
+#  Copyright 2019 Elasticsearch BV
+#
+#      Licensed under the Apache License, Version 2.0 (the "License");
+#      you may not use this file except in compliance with the License.
+#      You may obtain a copy of the License at
+#
+#          http://www.apache.org/licenses/LICENSE-2.0
+#
+#      Unless required by applicable law or agreed to in writing, software
+#      distributed under the License is distributed on an "AS IS" BASIS,
+#      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#      See the License for the specific language governing permissions and
+#      limitations under the License.
+from eland.plotting._matplotlib.hist import hist_series, hist_frame
+
+
+def ed_hist_series(
+        self,
+        by=None,
+        ax=None,
+        grid=True,
+        xlabelsize=None,
+        xrot=None,
+        ylabelsize=None,
+        yrot=None,
+        figsize=None,
+        bins=10,
+        **kwds
+):
+    """
+    Draw histogram of the input series using matplotlib.
+
+    See :pandas_api_docs:`pandas.Series.hist` for usage.
+
+    Notes
+    -----
+    Derived from ``pandas.plotting._core.hist_frame 0.25.3``
+
+    Examples
+    --------
+    >>> import matplotlib.pyplot as plt
+    >>> df = ed.DataFrame('localhost', 'flights')
+    >>> df[df.OriginWeather == 'Sunny']['FlightTimeMin'].hist(alpha=0.5, density=True) # doctest: +SKIP
+    >>> df[df.OriginWeather != 'Sunny']['FlightTimeMin'].hist(alpha=0.5, density=True) # doctest: +SKIP
+    >>> plt.show() # doctest: +SKIP
+
+    """
+    return hist_series(
+        self,
+        by=by,
+        ax=ax,
+        grid=grid,
+        xlabelsize=xlabelsize,
+        xrot=xrot,
+        ylabelsize=ylabelsize,
+        yrot=yrot,
+        figsize=figsize,
+        bins=bins,
+        **kwds
+    )
+
+
+def ed_hist_frame(
+        data,
+        column=None,
+        by=None,
+        grid=True,
+        xlabelsize=None,
+        xrot=None,
+        ylabelsize=None,
+        yrot=None,
+        ax=None,
+        sharex=False,
+        sharey=False,
+        figsize=None,
+        layout=None,
+        bins=10,
+        **kwds
+):
+    """
+    Make a histogram of the DataFrame's.
+
+    See :pandas_api_docs:`pandas.DataFrame.hist` for usage.
+
+    Notes
+    -----
+    Derived from ``pandas.plotting._core.hist_frame 0.25.3``
+
+    Ideally, we'd call the pandas method `hist_frame` directly
+    with histogram data, but weights are applied to ALL series.
+    For example, we can plot a histogram of pre-binned data via:
+
+    .. code-block:: python
+
+        counts, bins = np.histogram(data)
+        plt.hist(bins[:-1], bins, weights=counts)
+
+    However,
+
+    .. code-block:: python
+
+        ax.hist(data[col].dropna().values, bins=bins, **kwds)
+
+    is for ``[col]`` and weights are a single array.
+
+    Examples
+    --------
+    >>> df = ed.DataFrame('localhost', 'flights')
+    >>> hist = df.select_dtypes(include=[np.number]).hist(figsize=[10,10]) # doctest: +SKIP
+    """
+    return hist_frame(
+        data,
+        column=column,
+        by=by,
+        grid=grid,
+        xlabelsize=xlabelsize,
+        xrot=xrot,
+        ylabelsize=ylabelsize,
+        yrot=yrot,
+        ax=ax,
+        sharex=sharex,
+        sharey=sharey,
+        figsize=figsize,
+        layout=layout,
+        bins=bins,
+        **kwds
+    )

eland/plotting/_matplotlib/__init__.py

@@ -0,0 +1,40 @@
+#  Copyright 2019 Elasticsearch BV
+#
+#      Licensed under the Apache License, Version 2.0 (the "License");
+#      you may not use this file except in compliance with the License.
+#      You may obtain a copy of the License at
+#
+#          http://www.apache.org/licenses/LICENSE-2.0
+#
+#      Unless required by applicable law or agreed to in writing, software
+#      distributed under the License is distributed on an "AS IS" BASIS,
+#      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#      See the License for the specific language governing permissions and
+#      limitations under the License.
+#
+#      Licensed under the Apache License, Version 2.0 (the "License");
+#      you may not use this file except in compliance with the License.
+#      You may obtain a copy of the License at
+#
+#          http://www.apache.org/licenses/LICENSE-2.0
+#
+#      Unless required by applicable law or agreed to in writing, software
+#      distributed under the License is distributed on an "AS IS" BASIS,
+#      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#      See the License for the specific language governing permissions and
+#      limitations under the License.
+"""
+Public plotting API
+
+Based from https://github.com/pandas-dev/pandas/blob/v0.25.3/pandas/plotting/__init__.py
+but only supporting a subset of plotting methods (for now).
+"""
+from eland.plotting._matplotlib.hist import (
+    hist_frame,
+    hist_series,
+)
+
+__all___ = [
+    "hist_frame",
+    "hist_series",
+]

eland/plotting/_matplotlib/hist.py

@@ -0,0 +1,131 @@
+#  Copyright 2019 Elasticsearch BV
+#
+#      Licensed under the Apache License, Version 2.0 (the "License");
+#      you may not use this file except in compliance with the License.
+#      You may obtain a copy of the License at
+#
+#          http://www.apache.org/licenses/LICENSE-2.0
+#
+#      Unless required by applicable law or agreed to in writing, software
+#      distributed under the License is distributed on an "AS IS" BASIS,
+#      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#      See the License for the specific language governing permissions and
+#      limitations under the License.
+
+import numpy as np
+import pandas.core.common as com
+from pandas.core.dtypes.generic import ABCIndexClass
+from pandas.plotting._matplotlib import converter
+from pandas.plotting._matplotlib.tools import _flatten, _set_ticks_props, _subplots
+
+
+def hist_series(
+        self,
+        by=None,
+        ax=None,
+        grid=True,
+        xlabelsize=None,
+        xrot=None,
+        ylabelsize=None,
+        yrot=None,
+        figsize=None,
+        bins=10,
+        **kwds
+):
+    import matplotlib.pyplot as plt
+
+    if by is None:
+        if kwds.get("layout", None) is not None:
+            raise ValueError(
+                "The 'layout' keyword is not supported when " "'by' is None"
+            )
+        # hack until the plotting interface is a bit more unified
+        fig = kwds.pop(
+            "figure", plt.gcf() if plt.get_fignums() else plt.figure(figsize=figsize)
+        )
+        if figsize is not None and tuple(figsize) != tuple(fig.get_size_inches()):
+            fig.set_size_inches(*figsize, forward=True)
+        if ax is None:
+            ax = fig.gca()
+        elif ax.get_figure() != fig:
+            raise AssertionError("passed axis not bound to passed figure")
+
+        self_bins, self_weights = self._hist(num_bins=bins)
+        # As this is a series, squeeze Series to arrays
+        self_bins = self_bins.squeeze()
+        self_weights = self_weights.squeeze()
+
+        ax.hist(self_bins[:-1], bins=self_bins, weights=self_weights, **kwds)
+        ax.grid(grid)
+        axes = np.array([ax])
+
+        _set_ticks_props(
+            axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot
+        )
+
+    else:
+        raise NotImplementedError("TODO")
+
+    if hasattr(axes, "ndim"):
+        if axes.ndim == 1 and len(axes) == 1:
+            return axes[0]
+    return axes
+
+
+def hist_frame(
+        data,
+        column=None,
+        by=None,
+        grid=True,
+        xlabelsize=None,
+        xrot=None,
+        ylabelsize=None,
+        yrot=None,
+        ax=None,
+        sharex=False,
+        sharey=False,
+        figsize=None,
+        layout=None,
+        bins=10,
+        **kwds
+):
+    # Start with empty pandas data frame derived from
+    ed_df_bins, ed_df_weights = data._hist(num_bins=bins)
+
+    converter._WARN = False  # no warning for pandas plots
+    if by is not None:
+        raise NotImplementedError("TODO")
+
+    if column is not None:
+        if not isinstance(column, (list, np.ndarray, ABCIndexClass)):
+            column = [column]
+        ed_df_bins = ed_df_bins[column]
+        ed_df_weights = ed_df_weights[column]
+    naxes = len(ed_df_bins.columns)
+
+    if naxes == 0:
+        raise ValueError("hist method requires numerical columns, " "nothing to plot.")
+
+    fig, axes = _subplots(
+        naxes=naxes,
+        ax=ax,
+        squeeze=False,
+        sharex=sharex,
+        sharey=sharey,
+        figsize=figsize,
+        layout=layout,
+    )
+    _axes = _flatten(axes)
+
+    for i, col in enumerate(com.try_sort(data.columns)):
+        ax = _axes[i]
+        ax.hist(ed_df_bins[col][:-1], bins=ed_df_bins[col], weights=ed_df_weights[col], **kwds)
+        ax.set_title(col)
+        ax.grid(grid)
+
+    _set_ticks_props(
+        axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot
+    )
+    fig.subplots_adjust(wspace=0.3, hspace=0.3)
+
+    return axes

eland/query.py

@@ -149,7 +149,6 @@ class Query:
         if interval != 0:
             self._aggs[name] = agg
 
-
     def to_search_body(self):
         if self._query.empty():
             if self._aggs:

eland/query_compiler.py

@@ -732,11 +732,13 @@ def elasticsearch_date_to_pandas_date(value: Union[int, str], date_format: str)
         # TODO investigate how we could generate this just once for a bulk read.
         return pd.to_datetime(value)
 
+
 class FieldMappingCache:
     """
     Very simple dict cache for field mappings. This improves performance > 3 times on large datasets as
     DataFrame access is slower than dict access.
     """
+
     def __init__(self, mappings):
         self._mappings = mappings
 
@@ -764,4 +766,3 @@ class FieldMappingCache:
         self._date_field_format[es_field_name] = es_date_field_format
 
         return es_date_field_format
-

eland/series.py

@@ -37,11 +37,11 @@ import numpy as np
 import pandas as pd
 from pandas.io.common import _expand_user, _stringify_path
 
+import eland.plotting
 from eland import NDFrame
 from eland.arithmetics import ArithmeticSeries, ArithmeticString, ArithmeticNumber
 from eland.common import DEFAULT_NUM_ROWS_DISPLAYED, docstring_parameter
 from eland.filter import NotFilter, Equal, Greater, Less, GreaterEqual, LessEqual, ScriptFilter, IsIn
-import eland.plotting as gfx
 
 
 def _get_method_name():
@@ -108,7 +108,7 @@ class Series(NDFrame):
             index_field=index_field,
             query_compiler=query_compiler)
 
-    hist = gfx.ed_hist_series
+    hist = eland.plotting.ed_hist_series
 
     @property
     def empty(self):

eland/tests/field_mappings/test_scripted_fields_pytest.py

@@ -19,7 +19,6 @@ import numpy as np
 
 import eland as ed
 from eland.tests import FLIGHTS_INDEX_NAME, ES_TEST_CLIENT
-
 from eland.tests.common import TestData
 
 

eland/tests/plotting/test_dataframe_hist_pytest.py

@@ -39,6 +39,7 @@ def test_plot_hist(fig_test, fig_ref):
         ed_ax = fig_test.subplots()
         ed_flights.hist(ax=ed_ax)
 
+
 @check_figures_equal(extensions=['png'])
 def test_plot_filtered_hist(fig_test, fig_ref):
     test_data = TestData()
@@ -49,8 +50,6 @@ def test_plot_filtered_hist(fig_test, fig_ref):
     pd_flights = pd_flights[pd_flights.FlightDelayMin > 0]
     ed_flights = ed_flights[ed_flights.FlightDelayMin > 0]
 
-    print(ed_flights.head())
-
     # This throws a userwarning
     # (https://github.com/pandas-dev/pandas/blob/171c71611886aab8549a8620c5b0071a129ad685/pandas/plotting/_matplotlib/tools.py#L222)
     with pytest.warns(UserWarning):

eland/tests/plotting/test_series_hist_pytest.py

@@ -25,8 +25,32 @@ def test_plot_hist(fig_test, fig_ref):
     pd_flights = test_data.pd_flights()['FlightDelayMin']
     ed_flights = test_data.ed_flights()['FlightDelayMin']
 
-    pd_ax = fig_ref.subplots()
+    pd_flights.hist(figure=fig_ref)
-    ed_ax = fig_test.subplots()
+    ed_flights.hist(figure=fig_test)
 
-    pd_flights.hist(ax=pd_ax)
+
-    ed_flights.hist(ax=ed_ax)
+@check_figures_equal(extensions=['png'])
+def test_plot_multiple_hists(fig_test, fig_ref):
+    test_data = TestData()
+
+    pd_flights = test_data.pd_flights()
+    ed_flights = test_data.ed_flights()
+
+    pd_flights[pd_flights.AvgTicketPrice < 250]['FlightDelayMin'].hist(figure=fig_ref, alpha=0.5, density=True)
+    pd_flights[pd_flights.AvgTicketPrice > 250]['FlightDelayMin'].hist(figure=fig_ref, alpha=0.5, density=True)
+
+    ed_flights[ed_flights.AvgTicketPrice < 250]['FlightDelayMin'].hist(figure=fig_test, alpha=0.5, density=True)
+    ed_flights[ed_flights.AvgTicketPrice > 250]['FlightDelayMin'].hist(figure=fig_test, alpha=0.5, density=True)
+
+@check_figures_equal(extensions=['png'])
+def test_plot_multiple_hists_pretty(fig_test, fig_ref):
+    test_data = TestData()
+
+    pd_flights = test_data.pd_flights()
+    ed_flights = test_data.ed_flights()
+
+    pd_flights[pd_flights.OriginWeather == 'Sunny']['FlightTimeMin'].hist(figure=fig_ref, alpha=0.5, density=True)
+    pd_flights[pd_flights.OriginWeather != 'Sunny']['FlightTimeMin'].hist(figure=fig_ref, alpha=0.5, density=True)
+
+    ed_flights[ed_flights.OriginWeather == 'Sunny']['FlightTimeMin'].hist(figure=fig_test, alpha=0.5, density=True)
+    ed_flights[ed_flights.OriginWeather != 'Sunny']['FlightTimeMin'].hist(figure=fig_test, alpha=0.5, density=True)

eland/tests/series/test_hist_pytest.py

@@ -16,8 +16,8 @@
 
 import numpy as np
 import pandas as pd
-from pandas.util.testing import assert_almost_equal
 import pytest
+from pandas.util.testing import assert_almost_equal
 
 from eland.tests.common import TestData
 

setup.py

@@ -15,7 +15,7 @@
 from codecs import open
 from os import path
 
-from setuptools import setup
+from setuptools import setup, find_packages
 
 here = path.abspath(path.dirname(__file__))
 about = {}
@@ -183,7 +183,7 @@ setup(
     license='Apache 2.0',
     classifiers=CLASSIFIERS,
     keywords='elastic eland pandas python',
-    packages=['eland'],
+    packages=find_packages(include=["eland", "eland.*"]),
     install_requires=[
         'elasticsearch>=7.0.5',
         'pandas==0.25.3',