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Dynamic Line Or Scatter Chart Based On A Date Range Filter
Single Scatter Point Chart
Line Chart for Multiple Data Points
Let’s say that we would like a dynamic chart where we can visualize the number of users who started a game session over the course of a month as a line chart, but also have the functionality that when a user chooses a single date/datapoint (in a filter), it is displayed as a single scatter point.
In some cases, depending on the filter applied in the SQL query, either a single data point or multiple data points can be returned. If the preference is to mix chart types depending on the resulting data point/s, then we can leverage our Plot.ly integration to generate a dynamic chart. For this analysis, we will be using our publicly available demo dataset which entails user gameplays by platform (i.e. web, android, iOS) and date.
Our SQL output returns the following two columns:
1. Gameplay_Date: The dates determined by the date range filter applied in the SQL query
2. User_Count: The number of users that have started a gameplay session on that date
Sisense for Cloud Data Teams Tip:
When plotting datetimes with Plot.ly, please check that your SQL output returns the dates in sequential order, otherwise your line chart will look like this:
The following bare-bones Python 3.7 code generates a dynamic chart that will create a single scatter point if only one value is returned, and a line chart if multiple data points are returned.
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#Objective: Create a line chart with an date interval that's defined at the dashboard level i.e. [created_at:aggregation], but if only one datapoint is selected--only that single datapoint will appear instead of a line. #Dataset: This is a public demo dataset available on Sisense for Cloud Data Teams entailing user gameplays by platform and date. #Columns: # Primary Key, Numeric: ID --'id of the gameplay session' # Numeric: User_ID--'id of the user" # String: Platform --'the type of platform that the game is played on' # Datetime: Created_at --'the time and date that the game session started' #SQL output is imported as a dataframe variable called 'df' import pandas as pd import plotly.plotly as py import plotly.graph_objs as go #This line shows the Sisense data frame, and is primarily used to just visualize the SQL output. periscope.table(df) #This line is just a safeguard to ensure that this column is set as a datetime df['GAMEPLAY_DATE'] = pd.to_datetime(df['GAMEPLAY_DATE']) #If the dataframe is a single point (one return value, i.e. in this case a single date) then plot the single datapoint: if len(df.index) <= 1: data=[go.Scatter( x=df['GAMEPLAY_DATE'], y=df['USER_COUNT'],mode='markers')] #The mode is simply set to markers to mark a single datapoint #Otherwise, plot a line graph: else: data=[go.Scatter( x=df['GAMEPLAY_DATE'], y=df['USER_COUNT'],mode='lines+markers')] #The mode is set to markers and lines, but this can be adjusted to just lines if you prefer #Next, we will label the title, axes, and format the appearance of the dates #This is an optional date format, and isn't necessary for the chart--go.Layout(title='Number of Gameplays', xaxis = go.layout.XAxis(tickformat = '%d %B (%a)<br>%Y') layout = go.Layout(title='Number of Gameplays', xaxis = go.layout.XAxis( tickformat = '%d %B (%a)<br>%Y'), yaxis = dict(title = 'Number of Users Playing'), margin=dict( l=50, r=50, b=125, t=25 ),hovermode='closest') #Let's create the layout and plot out the data points. fig = go.Figure(data=data,layout=layout) #Now, we can visualize the final figure. periscope.plotly(fig)
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