This repository was archived by the owner on Aug 29, 2025. It is now read-only.
This repository was archived by the owner on Aug 29, 2025. It is now read-only.

Create more table in one page #95

Description

@elianalien

Hey guys, i would like to show 2 dataframe table, one is the dataset and the other one is statistical calculation of the dataframe (pandas describe()) of the dataframe. but I couldn't manage to show it. here is my code:

# Define App Layout
app.layout = html.Div([
# Title
html.H2('Analytics Dashboard'),
# Dropdown to choose the dataset
html.Div([
html.Label('Choose Your Data'),
dcc.Dropdown(
options = [{'label':i, 'value':i} for i in onlyfiles], value = " ",
id="field-dropdown"
), ], style = {'width':'25%', 'display':'inline-block'}
),
# Visualize Dataframe using Dash's Data Table Experiments
dt.DataTable(
# initialize rows
rows = [{}],
# selection feature in dash table
row_selectable = True,
# filter feature in dash table
filterable = True,
# sort feature in dash table
sortable = True,
# initialize row selection array
selected_row_indices = [],
# data table id (for referencing)
id = 'datatable'
), dt.DataTable(
rows = [{}],
row_selectable = False,
filterable = False,
sortable = False,
id = 'statisticalTable'
), # Hidden div inside the app that stores the intermediate value
# Use for sharing data between callback
html.Div(id='intermediate-value', style={'display':'none'})
])
# Callback to read state change in dropdown
@app.callback(Output('intermediate-value','children'),[Input('field-dropdown', 'value')])
def updateTable(field_dropdown): # update function in dropdown change
# pandas dataframe construct
df = pd.read_csv(datapath+field_dropdown)
return df.to_json(date_format='iso', orient='split')
@app.callback(Output('datatable','rows'),[Input('intermediate-value', 'children')])
def updateTable(jsonified_clean_data):
print('update 1st table')
dff = pd.read_json(jsonified_clean_data, orient='split')
return dff.to_dict('record')
@app.callback(Output('statisticalTable','rows'),[Input('intermediate-value', 'children')])
def updateStatTable(jsonified_clean_data):
print('update 2nd table')
dff = pd.read_json(jsonified_clean_data, orient='split')
stat = dff.describe()
print(stat)
return stat.to_dict('record')
# server main if __name__ == '__main__':
app.run_server(debug=True)

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      , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all
       blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
      }
      } catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
      })();
      (function(){
      try {
      var __m = "github.com";
      var __re = new RegExp('^' + "github\\.com" + '
      
      Skip to content
      This repository was archived by the owner on Aug 29, 2025. It is now read-only.
      This repository was archived by the owner on Aug 29, 2025. It is now read-only.

      Create more table in one page #95

      Description

      @elianalien

      Hey guys, i would like to show 2 dataframe table, one is the dataset and the other one is statistical calculation of the dataframe (pandas describe()) of the dataframe. but I couldn't manage to show it. here is my code:

      # Define App Layout
      app.layout = html.Div([
      # Title
      html.H2('Analytics Dashboard'),
      # Dropdown to choose the dataset
      html.Div([
      html.Label('Choose Your Data'),
      dcc.Dropdown(
      options = [{'label':i, 'value':i} for i in onlyfiles], value = " ",
      id="field-dropdown"
      ), ], style = {'width':'25%', 'display':'inline-block'}
      ),
      # Visualize Dataframe using Dash's Data Table Experiments
      dt.DataTable(
      # initialize rows
      rows = [{}],
      # selection feature in dash table
      row_selectable = True,
      # filter feature in dash table
      filterable = True,
      # sort feature in dash table
      sortable = True,
      # initialize row selection array
      selected_row_indices = [],
      # data table id (for referencing)
      id = 'datatable'
      ), dt.DataTable(
      rows = [{}],
      row_selectable = False,
      filterable = False,
      sortable = False,
      id = 'statisticalTable'
      ), # Hidden div inside the app that stores the intermediate value
      # Use for sharing data between callback
      html.Div(id='intermediate-value', style={'display':'none'})
      ])
      # Callback to read state change in dropdown
      @app.callback(Output('intermediate-value','children'),[Input('field-dropdown', 'value')])
      def updateTable(field_dropdown): # update function in dropdown change
      # pandas dataframe construct
      df = pd.read_csv(datapath+field_dropdown)
      return df.to_json(date_format='iso', orient='split')
      @app.callback(Output('datatable','rows'),[Input('intermediate-value', 'children')])
      def updateTable(jsonified_clean_data):
      print('update 1st table')
      dff = pd.read_json(jsonified_clean_data, orient='split')
      return dff.to_dict('record')
      @app.callback(Output('statisticalTable','rows'),[Input('intermediate-value', 'children')])
      def updateStatTable(jsonified_clean_data):
      print('update 2nd table')
      dff = pd.read_json(jsonified_clean_data, orient='split')
      stat = dff.describe()
      print(stat)
      return stat.to_dict('record')
      # server main if __name__ == '__main__':
      app.run_server(debug=True)
      

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          No branches or pull requests

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          , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
          Skip to content
          This repository was archived by the owner on Aug 29, 2025. It is now read-only.
          This repository was archived by the owner on Aug 29, 2025. It is now read-only.

          Create more table in one page #95

          Description

          @elianalien

          Hey guys, i would like to show 2 dataframe table, one is the dataset and the other one is statistical calculation of the dataframe (pandas describe()) of the dataframe. but I couldn't manage to show it. here is my code:

          # Define App Layout
          app.layout = html.Div([
          # Title
          html.H2('Analytics Dashboard'),
          # Dropdown to choose the dataset
          html.Div([
          html.Label('Choose Your Data'),
          dcc.Dropdown(
          options = [{'label':i, 'value':i} for i in onlyfiles], value = " ",
          id="field-dropdown"
          ), ], style = {'width':'25%', 'display':'inline-block'}
          ),
          # Visualize Dataframe using Dash's Data Table Experiments
          dt.DataTable(
          # initialize rows
          rows = [{}],
          # selection feature in dash table
          row_selectable = True,
          # filter feature in dash table
          filterable = True,
          # sort feature in dash table
          sortable = True,
          # initialize row selection array
          selected_row_indices = [],
          # data table id (for referencing)
          id = 'datatable'
          ), dt.DataTable(
          rows = [{}],
          row_selectable = False,
          filterable = False,
          sortable = False,
          id = 'statisticalTable'
          ), # Hidden div inside the app that stores the intermediate value
          # Use for sharing data between callback
          html.Div(id='intermediate-value', style={'display':'none'})
          ])
          # Callback to read state change in dropdown
          @app.callback(Output('intermediate-value','children'),[Input('field-dropdown', 'value')])
          def updateTable(field_dropdown): # update function in dropdown change
          # pandas dataframe construct
          df = pd.read_csv(datapath+field_dropdown)
          return df.to_json(date_format='iso', orient='split')
          @app.callback(Output('datatable','rows'),[Input('intermediate-value', 'children')])
          def updateTable(jsonified_clean_data):
          print('update 1st table')
          dff = pd.read_json(jsonified_clean_data, orient='split')
          return dff.to_dict('record')
          @app.callback(Output('statisticalTable','rows'),[Input('intermediate-value', 'children')])
          def updateStatTable(jsonified_clean_data):
          print('update 2nd table')
          dff = pd.read_json(jsonified_clean_data, orient='split')
          stat = dff.describe()
          print(stat)
          return stat.to_dict('record')
          # server main if __name__ == '__main__':
          app.run_server(debug=True)
          

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              , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
              Skip to content
              This repository was archived by the owner on Aug 29, 2025. It is now read-only.
              This repository was archived by the owner on Aug 29, 2025. It is now read-only.

              Create more table in one page #95

              Description

              @elianalien

              Hey guys, i would like to show 2 dataframe table, one is the dataset and the other one is statistical calculation of the dataframe (pandas describe()) of the dataframe. but I couldn't manage to show it. here is my code:

              # Define App Layout
              app.layout = html.Div([
              # Title
              html.H2('Analytics Dashboard'),
              # Dropdown to choose the dataset
              html.Div([
              html.Label('Choose Your Data'),
              dcc.Dropdown(
              options = [{'label':i, 'value':i} for i in onlyfiles], value = " ",
              id="field-dropdown"
              ), ], style = {'width':'25%', 'display':'inline-block'}
              ),
              # Visualize Dataframe using Dash's Data Table Experiments
              dt.DataTable(
              # initialize rows
              rows = [{}],
              # selection feature in dash table
              row_selectable = True,
              # filter feature in dash table
              filterable = True,
              # sort feature in dash table
              sortable = True,
              # initialize row selection array
              selected_row_indices = [],
              # data table id (for referencing)
              id = 'datatable'
              ), dt.DataTable(
              rows = [{}],
              row_selectable = False,
              filterable = False,
              sortable = False,
              id = 'statisticalTable'
              ), # Hidden div inside the app that stores the intermediate value
              # Use for sharing data between callback
              html.Div(id='intermediate-value', style={'display':'none'})
              ])
              # Callback to read state change in dropdown
              @app.callback(Output('intermediate-value','children'),[Input('field-dropdown', 'value')])
              def updateTable(field_dropdown): # update function in dropdown change
              # pandas dataframe construct
              df = pd.read_csv(datapath+field_dropdown)
              return df.to_json(date_format='iso', orient='split')
              @app.callback(Output('datatable','rows'),[Input('intermediate-value', 'children')])
              def updateTable(jsonified_clean_data):
              print('update 1st table')
              dff = pd.read_json(jsonified_clean_data, orient='split')
              return dff.to_dict('record')
              @app.callback(Output('statisticalTable','rows'),[Input('intermediate-value', 'children')])
              def updateStatTable(jsonified_clean_data):
              print('update 2nd table')
              dff = pd.read_json(jsonified_clean_data, orient='split')
              stat = dff.describe()
              print(stat)
              return stat.to_dict('record')
              # server main if __name__ == '__main__':
              app.run_server(debug=True)
              

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                  No branches or pull requests

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                  , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
                  Skip to content
                  This repository was archived by the owner on Aug 29, 2025. It is now read-only.
                  This repository was archived by the owner on Aug 29, 2025. It is now read-only.

                  Create more table in one page #95

                  Description

                  @elianalien

                  Hey guys, i would like to show 2 dataframe table, one is the dataset and the other one is statistical calculation of the dataframe (pandas describe()) of the dataframe. but I couldn't manage to show it. here is my code:

                  # Define App Layout
                  app.layout = html.Div([
                  # Title
                  html.H2('Analytics Dashboard'),
                  # Dropdown to choose the dataset
                  html.Div([
                  html.Label('Choose Your Data'),
                  dcc.Dropdown(
                  options = [{'label':i, 'value':i} for i in onlyfiles], value = " ",
                  id="field-dropdown"
                  ), ], style = {'width':'25%', 'display':'inline-block'}
                  ),
                  # Visualize Dataframe using Dash's Data Table Experiments
                  dt.DataTable(
                  # initialize rows
                  rows = [{}],
                  # selection feature in dash table
                  row_selectable = True,
                  # filter feature in dash table
                  filterable = True,
                  # sort feature in dash table
                  sortable = True,
                  # initialize row selection array
                  selected_row_indices = [],
                  # data table id (for referencing)
                  id = 'datatable'
                  ), dt.DataTable(
                  rows = [{}],
                  row_selectable = False,
                  filterable = False,
                  sortable = False,
                  id = 'statisticalTable'
                  ), # Hidden div inside the app that stores the intermediate value
                  # Use for sharing data between callback
                  html.Div(id='intermediate-value', style={'display':'none'})
                  ])
                  # Callback to read state change in dropdown
                  @app.callback(Output('intermediate-value','children'),[Input('field-dropdown', 'value')])
                  def updateTable(field_dropdown): # update function in dropdown change
                  # pandas dataframe construct
                  df = pd.read_csv(datapath+field_dropdown)
                  return df.to_json(date_format='iso', orient='split')
                  @app.callback(Output('datatable','rows'),[Input('intermediate-value', 'children')])
                  def updateTable(jsonified_clean_data):
                  print('update 1st table')
                  dff = pd.read_json(jsonified_clean_data, orient='split')
                  return dff.to_dict('record')
                  @app.callback(Output('statisticalTable','rows'),[Input('intermediate-value', 'children')])
                  def updateStatTable(jsonified_clean_data):
                  print('update 2nd table')
                  dff = pd.read_json(jsonified_clean_data, orient='split')
                  stat = dff.describe()
                  print(stat)
                  return stat.to_dict('record')
                  # server main if __name__ == '__main__':
                  app.run_server(debug=True)
                  

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                      , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
                      Skip to content
                      This repository was archived by the owner on Aug 29, 2025. It is now read-only.
                      This repository was archived by the owner on Aug 29, 2025. It is now read-only.

                      Create more table in one page #95

                      Description

                      @elianalien

                      Hey guys, i would like to show 2 dataframe table, one is the dataset and the other one is statistical calculation of the dataframe (pandas describe()) of the dataframe. but I couldn't manage to show it. here is my code:

                      # Define App Layout
                      app.layout = html.Div([
                      # Title
                      html.H2('Analytics Dashboard'),
                      # Dropdown to choose the dataset
                      html.Div([
                      html.Label('Choose Your Data'),
                      dcc.Dropdown(
                      options = [{'label':i, 'value':i} for i in onlyfiles], value = " ",
                      id="field-dropdown"
                      ), ], style = {'width':'25%', 'display':'inline-block'}
                      ),
                      # Visualize Dataframe using Dash's Data Table Experiments
                      dt.DataTable(
                      # initialize rows
                      rows = [{}],
                      # selection feature in dash table
                      row_selectable = True,
                      # filter feature in dash table
                      filterable = True,
                      # sort feature in dash table
                      sortable = True,
                      # initialize row selection array
                      selected_row_indices = [],
                      # data table id (for referencing)
                      id = 'datatable'
                      ), dt.DataTable(
                      rows = [{}],
                      row_selectable = False,
                      filterable = False,
                      sortable = False,
                      id = 'statisticalTable'
                      ), # Hidden div inside the app that stores the intermediate value
                      # Use for sharing data between callback
                      html.Div(id='intermediate-value', style={'display':'none'})
                      ])
                      # Callback to read state change in dropdown
                      @app.callback(Output('intermediate-value','children'),[Input('field-dropdown', 'value')])
                      def updateTable(field_dropdown): # update function in dropdown change
                      # pandas dataframe construct
                      df = pd.read_csv(datapath+field_dropdown)
                      return df.to_json(date_format='iso', orient='split')
                      @app.callback(Output('datatable','rows'),[Input('intermediate-value', 'children')])
                      def updateTable(jsonified_clean_data):
                      print('update 1st table')
                      dff = pd.read_json(jsonified_clean_data, orient='split')
                      return dff.to_dict('record')
                      @app.callback(Output('statisticalTable','rows'),[Input('intermediate-value', 'children')])
                      def updateStatTable(jsonified_clean_data):
                      print('update 2nd table')
                      dff = pd.read_json(jsonified_clean_data, orient='split')
                      stat = dff.describe()
                      print(stat)
                      return stat.to_dict('record')
                      # server main if __name__ == '__main__':
                      app.run_server(debug=True)
                      

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                        No labels
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                        No projects

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                          No milestone

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                          None yet

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                          No branches or pull requests

                          Issue actions

                          , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
                          Skip to content
                          This repository was archived by the owner on Aug 29, 2025. It is now read-only.
                          This repository was archived by the owner on Aug 29, 2025. It is now read-only.

                          Create more table in one page #95

                          Description

                          @elianalien

                          Hey guys, i would like to show 2 dataframe table, one is the dataset and the other one is statistical calculation of the dataframe (pandas describe()) of the dataframe. but I couldn't manage to show it. here is my code:

                          # Define App Layout
                          app.layout = html.Div([
                          # Title
                          html.H2('Analytics Dashboard'),
                          # Dropdown to choose the dataset
                          html.Div([
                          html.Label('Choose Your Data'),
                          dcc.Dropdown(
                          options = [{'label':i, 'value':i} for i in onlyfiles], value = " ",
                          id="field-dropdown"
                          ), ], style = {'width':'25%', 'display':'inline-block'}
                          ),
                          # Visualize Dataframe using Dash's Data Table Experiments
                          dt.DataTable(
                          # initialize rows
                          rows = [{}],
                          # selection feature in dash table
                          row_selectable = True,
                          # filter feature in dash table
                          filterable = True,
                          # sort feature in dash table
                          sortable = True,
                          # initialize row selection array
                          selected_row_indices = [],
                          # data table id (for referencing)
                          id = 'datatable'
                          ), dt.DataTable(
                          rows = [{}],
                          row_selectable = False,
                          filterable = False,
                          sortable = False,
                          id = 'statisticalTable'
                          ), # Hidden div inside the app that stores the intermediate value
                          # Use for sharing data between callback
                          html.Div(id='intermediate-value', style={'display':'none'})
                          ])
                          # Callback to read state change in dropdown
                          @app.callback(Output('intermediate-value','children'),[Input('field-dropdown', 'value')])
                          def updateTable(field_dropdown): # update function in dropdown change
                          # pandas dataframe construct
                          df = pd.read_csv(datapath+field_dropdown)
                          return df.to_json(date_format='iso', orient='split')
                          @app.callback(Output('datatable','rows'),[Input('intermediate-value', 'children')])
                          def updateTable(jsonified_clean_data):
                          print('update 1st table')
                          dff = pd.read_json(jsonified_clean_data, orient='split')
                          return dff.to_dict('record')
                          @app.callback(Output('statisticalTable','rows'),[Input('intermediate-value', 'children')])
                          def updateStatTable(jsonified_clean_data):
                          print('update 2nd table')
                          dff = pd.read_json(jsonified_clean_data, orient='split')
                          stat = dff.describe()
                          print(stat)
                          return stat.to_dict('record')
                          # server main if __name__ == '__main__':
                          app.run_server(debug=True)
                          

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                              , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
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                              This repository was archived by the owner on Aug 29, 2025. It is now read-only.
                              This repository was archived by the owner on Aug 29, 2025. It is now read-only.

                              Create more table in one page #95

                              Description

                              @elianalien

                              Hey guys, i would like to show 2 dataframe table, one is the dataset and the other one is statistical calculation of the dataframe (pandas describe()) of the dataframe. but I couldn't manage to show it. here is my code:

                              # Define App Layout
                              app.layout = html.Div([
                              # Title
                              html.H2('Analytics Dashboard'),
                              # Dropdown to choose the dataset
                              html.Div([
                              html.Label('Choose Your Data'),
                              dcc.Dropdown(
                              options = [{'label':i, 'value':i} for i in onlyfiles], value = " ",
                              id="field-dropdown"
                              ), ], style = {'width':'25%', 'display':'inline-block'}
                              ),
                              # Visualize Dataframe using Dash's Data Table Experiments
                              dt.DataTable(
                              # initialize rows
                              rows = [{}],
                              # selection feature in dash table
                              row_selectable = True,
                              # filter feature in dash table
                              filterable = True,
                              # sort feature in dash table
                              sortable = True,
                              # initialize row selection array
                              selected_row_indices = [],
                              # data table id (for referencing)
                              id = 'datatable'
                              ), dt.DataTable(
                              rows = [{}],
                              row_selectable = False,
                              filterable = False,
                              sortable = False,
                              id = 'statisticalTable'
                              ), # Hidden div inside the app that stores the intermediate value
                              # Use for sharing data between callback
                              html.Div(id='intermediate-value', style={'display':'none'})
                              ])
                              # Callback to read state change in dropdown
                              @app.callback(Output('intermediate-value','children'),[Input('field-dropdown', 'value')])
                              def updateTable(field_dropdown): # update function in dropdown change
                              # pandas dataframe construct
                              df = pd.read_csv(datapath+field_dropdown)
                              return df.to_json(date_format='iso', orient='split')
                              @app.callback(Output('datatable','rows'),[Input('intermediate-value', 'children')])
                              def updateTable(jsonified_clean_data):
                              print('update 1st table')
                              dff = pd.read_json(jsonified_clean_data, orient='split')
                              return dff.to_dict('record')
                              @app.callback(Output('statisticalTable','rows'),[Input('intermediate-value', 'children')])
                              def updateStatTable(jsonified_clean_data):
                              print('update 2nd table')
                              dff = pd.read_json(jsonified_clean_data, orient='split')
                              stat = dff.describe()
                              print(stat)
                              return stat.to_dict('record')
                              # server main if __name__ == '__main__':
                              app.run_server(debug=True)
                              

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