refactor: code organization and formatting in Jupyter notebooks

Signed-off-by: sanjay7178 <[email protected]>

Author: sanjay7178

bkp/blogs/ibis-framework/index.ipynb

@@ -84,10 +84,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
     "import sqlite3\n",
-    "import ibis"
+    "\n",
+    "import ibis\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {

pages/blog/console-based-representation-in-astx/index.ipynb

@@ -74,9 +74,9 @@
    "outputs": [],
    "source": [
     "# import display for AST visualization\n",
-    "from IPython.display import display\n",
-    "from astx.viz import traverse_ast_ascii, graph_to_ascii\n",
-    "import astx"
+    "import astx\n",
+    "\n",
+    "from astx.viz import graph_to_ascii, traverse_ast_ascii"
    ]
   },
   {

pages/blog/console-based-representation-in-astx/index.md

@@ -54,9 +54,9 @@ The first thing to do is, in your Jupyter Notebook instance, import `display`, w
 
 ```python
 # import display for AST visualization
-from IPython.display import display
-from astx.viz import traverse_ast_ascii, graph_to_ascii
 import astx
+
+from astx.viz import graph_to_ascii, traverse_ast_ascii
 ```
 
 Then we create an instance of the Module class, and this instance will be the first node of the tree, or the root node. After that, we declare the variables and literal that will be part of the basic operation that we will parse into an AST.

pages/blog/scaling-machine-learning-projects-with-dask/index.ipynb

@@ -183,8 +183,8 @@
    "source": [
     "#Lazy Evalution with dask\n",
     "import dask.dataframe as dd\n",
-    "import pandas as pd\n",
     "import numpy as np\n",
+    "import pandas as pd\n",
     "\n",
     "# Creating a dummy dataset\n",
     "num_rows = 100  # Number of rows\n",
@@ -286,6 +286,7 @@
     "# Dynamic task scheduling with Dask\n",
     "import dask\n",
     "\n",
+    "\n",
     "@dask.delayed\n",
     "def square(x):\n",
     "    return x * x\n",

pages/blog/scaling-machine-learning-projects-with-dask/index.md

@@ -136,8 +136,8 @@ print(result.compute())  # Outputting the computed result
 ```python
 #Lazy Evalution with dask
 import dask.dataframe as dd
-import pandas as pd
 import numpy as np
+import pandas as pd
 
 # Creating a dummy dataset
 num_rows = 100  # Number of rows
@@ -230,6 +230,7 @@ print(y_dask.compute())
 # Dynamic task scheduling with Dask
 import dask
 
+
 @dask.delayed
 def square(x):
     return x * x

pages/blog/streamlining-project-automation-with-makim/index.ipynb

@@ -92,6 +92,7 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "\n",
     "os.environ[\"NO_COLOR\"] = \"1\""
    ]
   },

pages/blog/streamlining-project-automation-with-makim/index.md

@@ -61,6 +61,7 @@ For this tutorial, we will disable the output color feature provided by typer, t
 
 ```python
 import os
+
 os.environ["NO_COLOR"] = "1"
 ```
 

pages/blog/unlocking-the-power-of-multiple-dispatch-in-python-with-plum-dispatch/index.ipynb

@@ -72,6 +72,7 @@
    "source": [
     "from plum import dispatch\n",
     "\n",
+    "\n",
     "class Processor:\n",
     "    @dispatch\n",
     "    def process(self, data: int):\n",

pages/blog/unlocking-the-power-of-multiple-dispatch-in-python-with-plum-dispatch/index.md

@@ -41,6 +41,7 @@ To demonstrate the basic usage of `plum-dispatch`, let's start with a simple exa
 ```python
 from plum import dispatch
 
+
 class Processor:
     @dispatch
     def process(self, data: int):