Use a Chart-Level filter for your Y Axis -

Want to create a chart-level filter, allowing you to change your Y axis? Say hello to your new favorite library! Periscope's Python and R integration allows you to use (as well as a host of other libraries), some of which we will leverage in this example.

Here, in your SQL output, be sure that your fields for the X axis are preceded with an 'x' character, and all your fields for the Y axis options are preceded by the 'y' character. If the y axis should be formatted as a dollar or percent, ensure the prefix is  'y$' or 'y%'

Python 3.6 Code

# SQL output should have these columns:
#    x value prefixed with 'x'
#    y value(s) prefixed with 'y' -- if $ or %, prefix with 'y$' or 'y%'
#    series value(s) prefixed with 's'

import pandas as pd
import plotly.plotly as py
import plotly.graph_objs as go
import datetime
from datetime import timedelta
import numpy as np

community_post = ''
dummy_df = pd.DataFrame()
dummy_df['x_date'] = pd.date_range(start='1/1/2018', end='1/1/2019')
dummy_df['row_num'] = range(1, dummy_df.shape[0] + 1)
dummy_df['multiplier'] = np.random.randint(10,50, dummy_df.shape[0])
dummy_df['y$_revenue'] = dummy_df['row_num'] * dummy_df['multiplier']
dummy_df['y_purchases'] = np.random.randint(100, 1000, dummy_df.shape[0])

# Helper Function that removes underscores
def column_name(column):
  return column.split('_', 1)[1].replace('_',' ').title()

# Helper function that formats values as $ or %
def format(column):
  if column.startswith('Y$'):
    return '$.3s'
  elif column.startswith('Y%'):
    return '.0%'
    return '.3s'

# Helper function that returns unique column values
def unique_vals(df, column):
  return df.groupby(column).size().reset_index()[column]

# Get the x, y, and series columns
def get_columns(df):
  x_column = [c for c in df.columns if c.startswith('X')][0]
  y_columns = [c for c in df.columns if c.startswith('Y')]
  series_columns = [c for c in df.columns if c.startswith('S_')]
  unique_series = unique_vals(df, series_columns) if len(series_columns) > 0 else None
  return x_column, y_columns, series_columns, unique_series

def button(y_col, y_columns, unique_series = None):
  return {
    'label': column_name(y_col),
    'method': 'update',
    'args': [
        'visible': [c==y_col for c in y_columns for i in range(0, 1 if unique_series is None else len(unique_series))]
        'yaxis': {
          'tickformat': format(y_col),
          'hoverformat': format(y_col)

def style_link(text, link, **settings):
  style = ';'.join([f'{key.replace("_","-")}:{settings[key]}' for key in settings])
  return f'<a href="{link}" style="{style}">{text}</a>'

def plot(df, annotation=None):
  # Force consistent casing for columns
  df.columns = [c.upper() for c in df.columns]
  x_column, y_columns, series_columns, unique_series = get_columns(df)
  has_series = unique_series is not None
  showlegend = has_series

  data = []
  buttons = []

  for idx, y_col in enumerate(y_columns):
    buttons.append(button(y_col, y_columns, unique_series=unique_series))

    # if no series -- create the traces for each y value and only display the first one
    if not has_series:
      trace = go.Scatter(

    # if series -- create the traces for each series for each y value, still only displaying series for the first y value
      for idx_series, series in unique_series.iterrows():
        query = ' & '.join(f'{col} == "{series[{col}].iloc[0]}"' for col in series_columns)
        df_series = df.query(query)
        trace = go.Scatter(
          name=f'{", ".join([series[{col}].iloc[0] for col in series_columns])}',
          visible=(idx == 0)

  updatemenus = list([
      'active': 0,
      'buttons': buttons,
      'x': -.1,
      'y': 1.25,
      'xanchor': 'left',
      'yanchor': 'top',
      'bgcolor': '#FFFFFF'

  first_y = y_columns[0]
  xaxis = {'title': column_name(x_column)}

  # If x value is a date, then add the quick-filter options for dates
  if isinstance(df[x_column].iloc[0],
    duration = (df[x_column].max() - df[x_column].min()).days
    month_buttons = [dict(count=x, label=str(x)+'m', step='month', stepmode='backward') for x in [1,3,6] if x * 30 <= duration]

    xaxis['rangeselector'] = {
      'buttons': list(month_buttons + [{'step': 'all'}]) if len(month_buttons) > 0 else None,
      'xanchor': 'right',
      'yanchor': 'top',
      'x': 1,
      'y': 1.2

  layout = {
    'showlegend': showlegend,
    'yaxis': {
      'tickformat': format(first_y),
      'hoverformat': format(first_y)
    'xaxis': xaxis,
    'margin': {
      't': 20,
      'b': 50,
      'l': 60,
      'r': 10

  if annotation is not None:
    layout['annotations'] = [annotation]
  if len(y_columns) > 1:
    layout['updatemenus'] = updatemenus
    layout['yaxis']['title'] = column_name(y_columns[0])

  fig = dict(data=data, layout=layout)

  # Use Periscope to visualize a dataframe by passing the data to periscope.output()

except Exception as e:
    annotation = {
    'x': 0.5,
    'y': 0.5,
    'ax': 0,
    'ay': 0,
    'xref': 'paper',
    'yref': 'paper',
    'text': style_link('DUMMY<br><br><br><br>DATA<br><br><br><br>EXAMPLE', community_post, font_size='60px', font_weight='bold', color='rgba(0, 0, 0, .25)'),
    'showarrow': False,
    'textangle': -25
    plot(dummy_df, annotation=annotation)
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