Merge pull request #40 from KxSystems/release301

Addition of 3.0.1 changes to github

Author: cmccarthy1

README.md

@@ -93,14 +93,15 @@ KX only officially supports versions of PyKX built by KX, i.e. versions of PyKX
 
 PyKX depends on the following third-party Python packages:
 
-- `pandas>=1.2, < 2.0; python_version=='3.8'`
-- `pandas>=1.2, < 2.2.0; python_version>'3.8'`
+- `pandas>=1.2, <2.0; python_version=='3.8'`
+- `pandas>=1.2, <=2.2.3; python_version>'3.8'`
 - `numpy~=1.22, <2.0; python_version<'3.11'`
 - `numpy~=1.23, <2.0; python_version=='3.11'`
 - `numpy~=1.26, <2.0; python_version=='3.12'`
 - `pytz>=2022.1`
 - `toml~=0.10.2`
 - `dill>=0.2.0`
+- `requests>=2.25.0`
 
 They are installed automatically by `pip` when PyKX is installed.
 
@@ -206,4 +207,4 @@ If you have any issues or questions you can post them to [community.kx.com](http
 ## Customer Support
 
 * Inquires or feedback: [`pykx@kx.com`](mailto:pykx@kx.com)
-* Support for Licensed Subscribers: [support.kx.com](https://support.kx.com/support/home)
+* Support for Licensed Subscribers: [support.kx.com](https://client.support.kx.com/)

conda-recipe/meta.yaml

@@ -28,7 +28,7 @@ requirements:
   run:
     - python
     - numpy>=1.22,<2.0
-    - pandas>=1.2, <2.2.0 # [py>38]
+    - pandas>=1.2, <=2.2.3 # [py>38]
     - pandas<2.0 # [py==38]
     - pytz>=2022.1
     - toml>=0.10.2

docs/examples/charting.ipynb

@@ -183,7 +183,7 @@
    "id": "2e76c5d1-7dd3-482c-90cb-c263d31ad808",
    "metadata": {},
    "source": [
-    "To make the column values compatible with most Matplotlib charts, first convert them to NumPy objects using the `.np()` function."
+    "To make the column values compatible with most [Matplotlib charts](https://matplotlib.org/), first convert them to NumPy objects using the `.np()` function."
    ]
   },
   {
@@ -215,7 +215,7 @@
    "source": [
     "## Plotly\n",
     "\n",
-    "Plotly allows you to pass `vector` objects as the `color` argument. Set this parameter using the `sym` column to obtain the scatter chart below.\n"
+    "[Plotly](https://plotly.com/python/) allows you to pass `vector` objects as the `color` argument. Set this parameter using the `sym` column to obtain the scatter chart below.\n"
    ]
   },
   {
@@ -290,7 +290,7 @@
    "source": [
     "## Seaborn\n",
     "\n",
-    "Seaborn allows you to set `data` as a PyKX table name without conversions and then call the `x` and `y` parameters using only the column names of that table.\n",
+    "[Seaborn](https://seaborn.pydata.org/) allows you to set `data` as a PyKX table name without conversions and then call the `x` and `y` parameters using only the column names of that table.\n",
     "\n",
     "The bar chart below demonstrates this by setting the data as the table object and using the column names for all parameters, without any conversions."
    ]
@@ -314,6 +314,7 @@
    ],
    "source": [
     "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
     "sns.catplot(\n",
     "    kind='bar',\n",

docs/examples/interface-overview.ipynb

@@ -8,9 +8,9 @@
     "\n",
     "_The purpose of this notebook is to introduce you to PyKX capabilities and functionality._\n",
     "\n",
-    "For the best experience, visit [what is PyKX](https://code.kx.com/pykx/2.5/getting-started/what_is_pykx.html) and the [quickstart guide](https://code.kx.com/pykx/2.5/getting-started/quickstart.html) first.\n",
+    "For the best experience, visit [what is PyKX](../getting-started/what_is_pykx.html) and the [quickstart guide](../getting-started/quickstart.html) first.\n",
     "\n",
-    "To follow along, we recommend to <a href=\"./PyKX%20Introduction%20Notebook.ipynb\" download> download the notebook. </a>\n",
+    "To follow along, we recommend to <a href=\"./interface-overview.ipynb\" download> download the notebook. </a>\n",
     "\n",
     "Now let's go through the following sections:\n",
     "\n",

docs/examples/jupyter-integration.ipynb

@@ -446,7 +446,7 @@
    "id": "dec08871-a771-4848-8283-f88206f54785",
    "metadata": {},
    "source": [
-    "In this state, you can execute Python code as well, but those cells must include `%%python`."
+    "In this state, you can execute Python code as well, but those cells must include `%%py`."
    ]
   },
   {
@@ -456,7 +456,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%python\n",
+    "%%py\n",
     "for fruit in ['apple', 'orange', 'banana']:\n",
     "    print(fruit)"
    ]
@@ -476,7 +476,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%python\n",
+    "%%py\n",
     "kx.util.jupyter_qfirst_disable()"
    ]
   },

docs/examples/streaming/index.md

@@ -10,7 +10,7 @@ tags: PyKX, q, streaming, basic
 
 _This page outlines the steps taken and functionality shown in demonstrating your first PyKX streaming application_
 
-To run this example please download the [zip](./real-time-pykx.zip) file containing the notebook or visit our github repository [here](https://github.com/pykx/docs/examples/streaming) to view the code directly.
+To run this example please download the [zip](./real-time-pykx.zip) file containing the notebook or visit our github repository [here](https://github.com/KxSystems/pykx/tree/main/docs/examples/streaming) to view the code directly.
 
 In this example we will generate a real-time and historical analysis system which completes the following actions:
 

docs/getting-started/installing.md

@@ -254,14 +254,15 @@ This command should display the installed version of PyKX.
 
 		PyKX depends on the following third-party Python packages:
 
-		- `numpy~=1.22, <2.0; python_version<'3.11'`
-		- `numpy~=1.23, <2.0; python_version=='3.11'`
-		- `numpy~=1.26, <2.0; python_version=='3.12'`
-		- `pandas>=1.2, < 2.0; python_version=='3.8'`
-		- `pandas>=1.2, < 2.2.0; python_version>'3.8'`
-		- `pytz>=2022.1`
-		- `toml~=0.10.2`
-		- `dill>=0.2.0`
+      - `pandas>=1.2, <2.0; python_version=='3.8'`
+      - `pandas>=1.2, <=2.2.3; python_version>'3.8'`
+      - `numpy~=1.22, <2.0; python_version<'3.11'`
+      - `numpy~=1.23, <2.0; python_version=='3.11'`
+      - `numpy~=1.26, <2.0; python_version=='3.12'`
+      - `pytz>=2022.1`
+      - `toml~=0.10.2`
+      - `dill>=0.2.0`
+      - `requests>=2.25.0`
 
 		**Note**: All are installed automatically by `#!bash pip` when you install PyKX.
 

docs/getting-started/quickstart.md

@@ -122,7 +122,7 @@ You can generate PyKX objects in three ways. Click on the tabs below to follow t
 
 ## 3. Interact with PyKX objects
 
-You can interact with PyKX objects in a variety of ways, for example, through [indexing using Pythonic syntax](../user-guide/fundamentals/indexing.md), passing [PyKX objects to q/NumPy](../user-guide/fundamentals/creating.md#converting-pykx-objects-to-pythonic-types) functions, [querying via SQL/qSQL](https://code.kx.com/pykx/user-guide/fundamentals/querying.html) syntax or by [using the q functionality](https://code.kx.com/pykx/user-guide/advanced/context_interface.html) via the context interface. Each way is described in more depth under the the User guide > Fundamentals section. For now, we recommend a few examples:
+You can interact with PyKX objects in a variety of ways, for example, through [indexing using Pythonic syntax](../user-guide/fundamentals/indexing.md), passing [PyKX objects to q/NumPy](../user-guide/fundamentals/creating.md#converting-pykx-objects-to-pythonic-types) functions, [querying via Python/SQL/qSQL](..//user-guide/fundamentals/query/index.md) syntax or by [using the q functionality](../user-guide/advanced/context_interface.md) via the context interface. Each way is described in more depth under the the User guide > Fundamentals section. For now, we recommend a few examples:
 
 * Create a PyKX list and interact with it using indexing and slices:
 

docs/help/issues.md

@@ -120,3 +120,44 @@ q)f[]
 q)f[::] /equivalent
 7
 ```
+
+## Pandas
+
+### Known issues
+#### Changes in `DataFrame.equals` behavior from Pandas 2.2.0
+
+In Pandas 2.2.0, a difference was introduced in how `DataFrame.equals` handles DataFrames with different `_mgr` types.
+
+**Example:**
+```python
+>>> import pandas as pd
+>>> import pykx as kx
+
+>>> df1 = pd.DataFrame({'cl': ['foo']})
+>>> df2 = kx.q('([] cl:enlist `foo)').pd()
+
+>>> df2.equals(df1)
+True
+
+>>> df1.equals(df2) # Prior to Pandas 2.2.0, this would also evaluate to True
+False
+```
+
+**Cause:**  
+Pandas now checks the type of the `_mgr` (dataframes manager) property. PyKX uses a custom `_mgr` implementation for performance optimization.
+
+```python
+>>> type(df1._mgr)
+<class 'pandas.core.internals.managers.BlockManager'>
+
+>>> type(df2._mgr)
+<class 'pykx.util.BlockManagerUnconsolidated'>
+```
+
+**Workaround:**
+Comparing the full contents of DataFrames irrespective of `_mgr` types works regardless of order of df1 and df2 in the comparison. To do so, use one of the following approaches:
+```python
+>>> assert (df2 == df1).all().all()  # Element-wise comparison
+>>> pd.testing.assert_frame_equal(df2, df1)  # Pandas' built-in test
+>>> assert df1.compare(df2).empty  # Check if there are no differences
+```

docs/help/support.md

@@ -19,4 +19,4 @@ If you have any issues or questions you can post them to the following locations
 ## Customer Support
 
 * Inquires or feedback: [`pykx@kx.com`](mailto:pykx@kx.com)
-* Support for Licensed Subscribers: [support.kx.com](https://support.kx.com/support/home)
+* Support for Licensed Subscribers: [support.kx.com](https://client.support.kx.com/)