Update to work with pandas 2.2 (#6259)

holoviz · Jan 23, 2024 · 075b5ae · 075b5ae
1 parent 21dfdb9
commit 075b5ae
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Showing 11 changed files with 96 additions and 87 deletions.
diff --git a/doc/how_to/param/uis.md b/doc/how_to/param/uis.md
@@ -80,7 +80,7 @@ class Example(BaseClass):
     color                   = param.Color(default='#FFFFFF')
     date                    = param.Date(default=dt.datetime(2017, 1, 1),
                                          bounds=(dt.datetime(2017, 1, 1), dt.datetime(2017, 2, 1)))
-    dataframe               = param.DataFrame(default=pd._testing.makeDataFrame().iloc[:3])
+    dataframe               = param.DataFrame(default=pd.DataFrame({'A': [1, 2, 3]}))
     select_string           = param.ObjectSelector(default="yellow", objects=["red", "yellow", "green"])
     select_fn               = param.ObjectSelector(default=list,objects=[list, set, dict])
     int_list                = param.ListSelector(default=[3, 5], objects=[1, 3, 5, 7, 9], precedence=0.5)

diff --git a/examples/reference/panes/DataFrame.ipynb b/examples/reference/panes/DataFrame.ipynb
@@ -60,7 +60,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd._testing.makeMixedDataFrame()\n",
+    "df = pd.DataFrame({\n",
+    "    'int': [1, 2, 3],\n",
+    "    'float': [3.14, 6.28, 9.42],\n",
+    "    'str': ['A', 'B', 'C'],\n",
+    "    'bool': [True, False, True],\n",
+    "}, index=[1, 2, 3])\n",
     "\n",
     "df_pane = pn.pane.DataFrame(df, width=400)\n",
     "\n",

diff --git a/examples/reference/widgets/DataFrame.ipynb b/examples/reference/widgets/DataFrame.ipynb
@@ -3,11 +3,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
+    "import datetime as dt\n",
     "import pandas as pd\n",
     "import panel as pn\n",
     "\n",
@@ -83,12 +82,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.DataFrame({'int': [1, 2, 3], 'float': [3.14, 6.28, 9.42], 'str': ['A', 'B', 'C'], 'bool': [True, False, True]}, index=[1, 2, 3])\n",
+    "df = pd.DataFrame({\n",
+    "    'int': [1, 2, 3],\n",
+    "    'float': [3.14, 6.28, 9.42],\n",
+    "    'str': ['A', 'B', 'C'],\n",
+    "    'bool': [True, False, True],\n",
+    "    'date': [dt.date(2019, 1, 1), dt.date(2020, 1, 1), dt.date(2020, 1, 10)],\n",
+    "    'datetime': [dt.datetime(2019, 1, 1, 10), dt.datetime(2020, 1, 1, 12), dt.datetime(2020, 1, 10, 13)]\n",
+    "}, index=[1, 2, 3])\n",
     "\n",
     "df_widget = pn.widgets.DataFrame(df, name='DataFrame')\n",
     "\n",
@@ -105,9 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from bokeh.models.widgets.tables import SelectEditor, NumberFormatter\n",
@@ -129,9 +131,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "table.selection = [0, 2]\n",
@@ -155,14 +155,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "custom_df = pd._testing.makeMixedDataFrame()\n",
-    "\n",
-    "pn.widgets.DataFrame(custom_df, autosize_mode='none', widths={'index': 50, 'A': 50, 'B': 50, 'C': 70, 'D': 130}, width=350)"
+    "pn.widgets.DataFrame(df, autosize_mode='none', widths={'index': 50, 'int': 50, 'float': 50, 'str': 70, 'bool': 130}, width=350)"
    ]
   },
   {
@@ -177,12 +173,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "pn.widgets.DataFrame(custom_df, autosize_mode='fit_columns', width=300)"
+    "pn.widgets.DataFrame(df, autosize_mode='fit_columns', width=300)"
    ]
   },
   {
@@ -197,12 +191,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "pn.widgets.DataFrame(custom_df, autosize_mode='fit_viewport')"
+    "pn.widgets.DataFrame(df, autosize_mode='fit_viewport')"
    ]
   },
   {
@@ -221,12 +213,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "date_df = pd._testing.makeTimeDataFrame()\n",
+    "date_df = df.set_index('datetime').iloc[:5, :2]\n",
     "\n",
     "pn.widgets.DataFrame(date_df, height=400, widths=150, frozen_columns=1, autosize_mode='none')"
    ]
@@ -243,9 +233,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "agg_df = pd.concat([date_df, date_df.median().to_frame('Median').T, date_df.mean().to_frame('Mean').T])\n",
@@ -268,9 +256,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from bokeh.sampledata.population import data as population_data \n",
@@ -292,9 +278,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -315,9 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def stream_data():\n",
@@ -339,9 +321,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "patch_table = pn.widgets.DataFrame(df[['int', 'float', 'str', 'bool']])\n",
@@ -368,9 +348,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "patch_table.patch({\n",

diff --git a/examples/reference/widgets/Tabulator.ipynb b/examples/reference/widgets/Tabulator.ipynb
@@ -277,7 +277,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "custom_df = pd._testing.makeMixedDataFrame().iloc[:3, :]\n",
+    "custom_df = df.iloc[:3, :]\n",
     "\n",
     "pn.widgets.Tabulator(custom_df, widths={'index': 70, 'A': 50, 'B': 50, 'C': 70, 'D': 130})"
    ]
@@ -632,9 +632,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "wide_df = pd._testing.makeCustomDataframe(3, 10, r_idx_names=['index'])\n",
-    "\n",
-    "pn.widgets.Tabulator(wide_df, frozen_columns=['index'], width=400)"
+    "pn.widgets.Tabulator(df, frozen_columns=['index'], width=400)"
    ]
   },
   {
@@ -652,7 +650,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "date_df = pd._testing.makeTimeDataFrame().iloc[:5, :2]\n",
+    "date_df = df.set_index('date').iloc[:5, :2]\n",
     "agg_df = pd.concat([date_df, date_df.median().to_frame('Median').T, date_df.mean().to_frame('Mean').T])\n",
     "agg_df.index= agg_df.index.map(str)\n",
     "\n",
@@ -788,7 +786,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "large_df = pd._testing.makeCustomDataframe(100000, 5)\n",
+    "large_df = pd.DataFrame({'A': np.random.rand(10000)})\n",
     "pn.widgets.Tabulator(large_df, pagination='remote', page_size=3)"
    ]
   },
@@ -805,7 +803,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "medium_df = pd._testing.makeCustomDataframe(1000, 5)\n",
+    "medium_df = pd.DataFrame({'A': np.random.rand(1000)})\n",
     "pn.widgets.Tabulator(medium_df, pagination='local', page_size=3)"
    ]
   },
@@ -834,7 +832,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "filter_table = pn.widgets.Tabulator(pd._testing.makeMixedDataFrame())\n",
+    "filter_table = pn.widgets.Tabulator(df)\n",
     "filter_table"
    ]
   },
@@ -851,7 +849,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "filter_table.add_filter((0, 3), 'A')"
+    "filter_table.add_filter((1, 2), 'int')"
    ]
   },
   {
@@ -868,7 +866,7 @@
    "outputs": [],
    "source": [
     "slider = pn.widgets.RangeSlider(start=0, end=3, name='A Filter')\n",
-    "filter_table.add_filter(slider, 'A')"
+    "filter_table.add_filter(slider, 'int')"
    ]
   },
   {
@@ -884,8 +882,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "select = pn.widgets.MultiSelect(options=['foo1', 'foo2', 'foo3', 'foo4', 'foo5'], name='C Filter')\n",
-    "filter_table.add_filter(select, 'C')"
+    "select = pn.widgets.MultiSelect(options=list('ABC'), name='str Filter')\n",
+    "filter_table.add_filter(select, 'str')"
    ]
   },
   {
@@ -920,7 +918,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "select.value = ['foo1', 'foo2']\n",
+    "select.value = ['A', 'B']\n",
     "filter_table.current_view"
    ]
   },
@@ -1033,9 +1031,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "movie_filters = {\n",

diff --git a/panel/tests/io/test_cache.py b/panel/tests/io/test_cache.py
@@ -131,13 +131,19 @@ def test_ndarray_hash():
     )
 
 def test_dataframe_hash():
-    df1, df2 = pd._testing.makeMixedDataFrame(), pd._testing.makeMixedDataFrame()
+    data = {
+        "A": [0.0, 1.0, 2.0, 3.0, 4.0],
+        "B": [0.0, 1.0, 0.0, 1.0, 0.0],
+        "C": ["foo1", "foo2", "foo3", "foo4", "foo5"],
+        "D": pd.bdate_range("1/1/2009", periods=5),
+    }
+    df1, df2 = pd.DataFrame(data), pd.DataFrame(data)
     assert hashes_equal(df1, df2)
     df2['A'] = df2['A'].values[::-1]
     assert not hashes_equal(df1, df2)
 
 def test_series_hash():
-    series1 = pd._testing.makeStringSeries()
+    series1 = pd.Series([0.0, 1.0, 2.0, 3.0, 4.0])
     series2 = series1.copy()
     assert hashes_equal(series1, series2)
     series2.iloc[0] = 3.14

diff --git a/panel/tests/pane/test_markup.py b/panel/tests/pane/test_markup.py
@@ -14,12 +14,12 @@ def test_get_markdown_pane_type():
     assert PaneBase.get_pane_type("**Markdown**") is Markdown
 
 def test_get_dataframe_pane_type():
-    df = pd._testing.makeDataFrame()
+    df = pd.DataFrame({"A": [1, 2, 3]})
     assert PaneBase.get_pane_type(df) is DataFrame
 
 def test_get_series_pane_type():
-    df = pd._testing.makeDataFrame()
-    assert PaneBase.get_pane_type(df.iloc[:, 0]) is DataFrame
+    ser = pd.Series([1, 2, 3])
+    assert PaneBase.get_pane_type(ser) is DataFrame
 
 @streamz_available
 def test_get_streamz_dataframe_pane_type():
@@ -167,7 +167,7 @@ def test_html_pane_sanitize_html(document, comm):
     assert model.text.endswith('&lt;h1&gt;&lt;strong&gt;HTML&lt;/h1&gt;&lt;/strong&gt;')
 
 def test_dataframe_pane_pandas(document, comm):
-    pane = DataFrame(pd._testing.makeDataFrame())
+    pane = DataFrame(pd.DataFrame({"A": [1, 2, 3]}))
 
     # Create pane
     model = pane.get_root(document, comm=comm)
@@ -176,7 +176,7 @@ def test_dataframe_pane_pandas(document, comm):
     orig_text = model.text
 
     # Replace Pane.object
-    pane.object = pd._testing.makeMixedDataFrame()
+    pane.object = pd.DataFrame({"B": [1, 2, 3]})
     assert pane._models[model.ref['id']][0] is model
     assert model.text.startswith('&lt;table')
     assert model.text != orig_text

diff --git a/panel/tests/ui/io/test_convert.py b/panel/tests/ui/io/test_convert.py
@@ -57,7 +57,7 @@
 tabulator_app = """
 import panel as pn
 import pandas as pd
-tabulator = pn.widgets.Tabulator(pd._testing.makeMixedDataFrame())
+tabulator = pn.widgets.Tabulator(pd.DataFrame({'a': [1, 2, 3]}))
 
 def on_click(e):
     tabulator.theme = 'fast'