Add scipy to requirements.txt and refactor data visualization in app.py

app.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 443,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 444,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,17 +109,18 @@
     "import pandas as pd\n",
     "import numpy as np\n",
     "import io\n",
+    "import helpers.utils as utils\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 445,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
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i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n          inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t  if (!reloading) {\n\t    throw e;\n\t  }\n\t}\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
       "application/vnd.holoviews_load.v0+json": ""
      },
      "metadata": {},
@@ -161,12 +162,12 @@
      "data": {
       "application/vnd.holoviews_exec.v0+json": "",
       "text/html": [
-       "<div id='b5fd0d4c-3636-402e-8326-0f10242d08f9'>\n",
-       "  <div id=\"c722f4ad-7a66-43bd-aa27-4bbc623a5c3b\" data-root-id=\"b5fd0d4c-3636-402e-8326-0f10242d08f9\" style=\"display: contents;\"></div>\n",
+       "<div id='1abf19e8-c829-4091-925a-9114dd4220aa'>\n",
+       "  <div id=\"ce3ddf3d-c57f-400b-acb4-f4d3c8688f2e\" data-root-id=\"1abf19e8-c829-4091-925a-9114dd4220aa\" style=\"display: contents;\"></div>\n",
        "</div>\n",
        "<script type=\"application/javascript\">(function(root) {\n",
-       "  var docs_json = {\"e30ff602-1ba7-4be0-bedb-9dd6b6301b42\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"b5fd0d4c-3636-402e-8326-0f10242d08f9\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"bdb2d655-80ca-4e44-8b26-cf407eb5094a\",\"attributes\":{\"plot_id\":\"b5fd0d4c-3636-402e-8326-0f10242d08f9\",\"comm_id\":\"f44bac24631040bbbad6afae06c136a1\",\"client_comm_id\":\"8409efc1cf814db885afe27289332e40\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
-       "  var render_items = [{\"docid\":\"e30ff602-1ba7-4be0-bedb-9dd6b6301b42\",\"roots\":{\"b5fd0d4c-3636-402e-8326-0f10242d08f9\":\"c722f4ad-7a66-43bd-aa27-4bbc623a5c3b\"},\"root_ids\":[\"b5fd0d4c-3636-402e-8326-0f10242d08f9\"]}];\n",
+       "  var docs_json = {\"213b43a3-1f08-4198-a068-6908f1b8f385\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"1abf19e8-c829-4091-925a-9114dd4220aa\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"0ce6c4ec-81ab-4d4a-9a0c-b4464e91c9e2\",\"attributes\":{\"plot_id\":\"1abf19e8-c829-4091-925a-9114dd4220aa\",\"comm_id\":\"6f1d04e503a1442c93729ca12d6088f8\",\"client_comm_id\":\"121fc28050f44e2b8fad4362e3d57201\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
+       "  var render_items = [{\"docid\":\"213b43a3-1f08-4198-a068-6908f1b8f385\",\"roots\":{\"1abf19e8-c829-4091-925a-9114dd4220aa\":\"ce3ddf3d-c57f-400b-acb4-f4d3c8688f2e\"},\"root_ids\":[\"1abf19e8-c829-4091-925a-9114dd4220aa\"]}];\n",
        "  var docs = Object.values(docs_json)\n",
        "  if (!docs) {\n",
        "    return\n",
@@ -230,7 +231,7 @@
      },
      "metadata": {
       "application/vnd.holoviews_exec.v0+json": {
-       "id": "b5fd0d4c-3636-402e-8326-0f10242d08f9"
+       "id": "1abf19e8-c829-4091-925a-9114dd4220aa"
       }
      },
      "output_type": "display_data"
@@ -254,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 446,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 447,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -292,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 448,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -306,23 +307,19 @@
     "        if file is None:\n",
     "            return \"No file selected\"\n",
     "        df = pd.read_csv(io.BytesIO(file))\n",
-    "        \n",
-    "    widget = pn.Column(\n",
-    "        pn.pane.Markdown(\n",
-    "            \"\"\"\n",
-    "            ## Data Preview\n",
-    "\n",
-    "            Here is a preview of the data.\n",
-    "            \"\"\"\n",
-    "        ),\n",
-    "        pn.pane.Markdown(f\"### Data Shape: {df.shape}\"),\n",
-    "        pn.pane.Markdown(f\"### Data Head\"),\n",
-    "        pn.pane.DataFrame(df.head(5)),\n",
-    "        pn.pane.Markdown(f\"### Describe\"),\n",
-    "        pn.pane.DataFrame(df.describe()),\n",
     "    \n",
+    "    visualizations = utils.Visualizations(df, pn)\n",
+    "    high_level_widget = visualizations.high_level_visualization()\n",
+    "    shape_widget = visualizations.data_shape_visualization()\n",
+    "    nature_widget = visualizations.nature_visualization()\n",
+    "    distribution_widget = visualizations.distribution_visualization()\n",
+    "    \n",
+    "    return pn.Column(\n",
+    "        high_level_widget,\n",
+    "        pn.Row(shape_widget,\n",
+    "        nature_widget,\n",
+    "        distribution_widget)\n",
     "    )\n",
-    "    return widget\n",
     "    \n",
     "\n",
     "bound_data = pn.bind(get_data, switch=switch, url=url, file=file)\n",
@@ -336,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 449,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -345,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 450,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -360,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 451,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [

app.py

@@ -9,6 +9,7 @@ import hvplot.pandas
 import pandas as pd
 import numpy as np
 import io
+import helpers.utils as utils
 
 
 
@@ -61,23 +62,19 @@ def get_data(switch: pn.widgets.Switch, url: pn.widgets.TextAreaInput, file: pn.
         if file is None:
             return "No file selected"
         df = pd.read_csv(io.BytesIO(file))
-        
-    widget = pn.Column(
-        pn.pane.Markdown(
-            """
-            ## Data Preview
-
-            Here is a preview of the data.
-            """
-        ),
-        pn.pane.Markdown(f"### Data Shape: {df.shape}"),
-        pn.pane.Markdown(f"### Data Head"),
-        pn.pane.DataFrame(df.head(5)),
-        pn.pane.Markdown(f"### Describe"),
-        pn.pane.DataFrame(df.describe()),
     
+    visualizations = utils.Visualizations(df, pn)
+    high_level_widget = visualizations.high_level_visualization()
+    shape_widget = visualizations.data_shape_visualization()
+    nature_widget = visualizations.nature_visualization()
+    distribution_widget = visualizations.distribution_visualization()
+    
+    return pn.Column(
+        high_level_widget,
+        pn.Row(shape_widget,
+        nature_widget,
+        distribution_widget)
     )
-    return widget
     
 
 bound_data = pn.bind(get_data, switch=switch, url=url, file=file)

helpers/utils.py

@@ -0,0 +1,122 @@
+
+from math import e
+import panel as pn 
+import pandas as pd
+import hvplot.pandas
+
+# A class to process the dataframe and create visualizations according to the shape, nature, and distribution of the data.
+class Visualizations:
+    def __init__(self, df, pn: pn):
+        self.df = df
+        self.pn = pn
+
+    def high_level_visualization(self):
+        # Create a high-level visualization of the dataframe
+        # Check the number of rows and columns in the dataframe
+        rows, columns = self.df.shape
+        
+        if columns > 7:
+            widget = self.pn.Column(
+            self.pn.pane.Markdown(
+                """
+                # Data Preview
+
+                ### Here is a preview of the dataset.
+                """
+            ),
+            self.pn.Column(
+                self.pn.Column(
+                    self.pn.pane.Markdown("## Head"),
+                    self.pn.pane.DataFrame(self.df.head(5)),
+                ),
+                self.pn.Column(
+                    self.pn.pane.Markdown("## Describe"),
+                    self.pn.pane.DataFrame(self.df.describe(),)
+                ),
+            ),
+            
+            )
+            return widget
+        else:
+            widget = self.pn.Column(
+            self.pn.pane.Markdown(
+                """
+                # Data Preview
+
+                ### Here is a preview of the dataset.
+                """
+            ),
+            self.pn.Row(
+                self.pn.Column(
+                    self.pn.pane.Markdown("## Head"),
+                    self.pn.pane.DataFrame(self.df.head(5)),
+                ),
+                self.pn.Column(
+                    self.pn.pane.Markdown("## Describe"),
+                    self.pn.pane.DataFrame(self.df.describe(),)
+                ),
+            ),
+            
+            )
+            return widget
+    
+    def data_shape_visualization(self):
+        # Create a visualization of the shape of the dataframe
+        widget = self.pn.Column(
+            self.pn.pane.Markdown(
+                """
+                # Data Shape
+
+                ### Here is the shape of the dataset.
+                """
+            ),
+            self.pn.Column(
+                self.pn.Column(
+                    self.pn.pane.Markdown("## Shape"),
+                    self.pn.pane.DataFrame(pd.DataFrame({'Rows': [self.df.shape[0]], 'Columns': [self.df.shape[1]]}),   
+                )),
+            ),
+        )
+        return widget
+    
+    def nature_visualization(self):
+        # Create a visualization of the nature of the dataframe
+        widget = self.pn.Column(
+            self.pn.pane.Markdown(
+                """
+                # Data Nature
+
+                ### Here is the nature of the dataset.
+                """
+            ),
+            self.pn.Row(
+                self.pn.Column(
+                    self.pn.pane.Markdown("## Data Types"),
+                    self.pn.pane.DataFrame(self.df.dtypes.to_frame(),)
+                ),
+                self.pn.Column(
+                    self.pn.pane.Markdown("## Null Values"),
+                    self.pn.pane.DataFrame(self.df.isnull().sum().to_frame(),)
+                ),
+            ),
+        )
+        return widget
+    
+    def distribution_visualization(self):
+        # Create a visualization of the distribution of the dataframe
+        widget = self.pn.Column(
+            self.pn.pane.Markdown(
+                """
+                # Data Distribution
+
+                ### Here is the distribution of the dataset.
+                """
+            ),
+            self.pn.Column(
+                self.pn.Column(
+                    self.pn.pane.Markdown("## Distribution"),
+                    self.df.hvplot.density()
+                ),
+            ),
+        )
+        return widget

requirements.txt

@@ -4,3 +4,4 @@ transformers
 numpy
 aiohttp
 hvplot
+scipy