Every Mode report contains an integrated notebook-style environment where you can use either Python or R to further explore and visualize your query results. Use moveable code blocks and markdown cells for exploratory data analysis, visualization, and collaboration. Notebooks are pre-loaded with a variety of Python and R libraries. You can add output cells to shareable reports, or share a link to the Notebook itself.

Using the Notebook

To get started using the Notebook:

  1. Open an existing report or create a new report and run one or more SQL queries from the Query Editor.
  2. Click New Notebook. Your query results will automatically be loaded into a datasets object.
  3. On the right side panel, click the dropdown to select the language you want to work in, either Python or R.

Key elements of the Mode Notebook:

  • Toolbar - Where you can manipulate and run your Notebook, restart the session, export, and more.
  • Cells - Compose code and view results in a Code cell, or contextualize your work with a Markdown cell.
  • Resources Panel - The right side panel provides resources to help you including keyboard shortcuts, external documentation, and supported libraries/packages.
  • Status Indicator - Where you are notified about your Notebook session status.

Working with cells

There are two types of cells in the Notebook.

Markdown - Markdown cells allow you to add context to your analysis. Any markdown code will render right in your Notebook.

Code - Input Python or R code into the IN section of the cell. When this cell runs, any corresponding output (including visualizations) will be shown in in the OUT section.


  • When you run your notebook, cells are executed in the order they are displayed, starting from the top cell.
  • To select or change a cell’s type, go to the dropdown menu in the top toolbar and choose Code or Markdown.
  • To run a cell, select it and press Shift + Return. Or click Run Cell in the toolbar.
  • The number next to the cell label will increment by one every time code in the cell is successfully run.



  1. Restart Session - Stops any current computations running in the Notebook. Changes to the Notebook that are stored in memory will be reverted, but code in input cells will be available to re-run after the Notebook restarts.
  2. Run Notebook - Runs all input cells in the Notebook in sequence (from top to bottom)
  3. Run Cell - Runs code in the selected cell
  4. Add New Cell - Adds new input cell above or below the current cell
  5. Move Cell Up - Moves the current input or markdown cell up
  6. Move Cell Down - Moves the current cell down
  7. Delete Cell - Deletes the current cell
  8. Freeze Cell - Freezes the current input cell so that no changes are allowed; also prevents this cell from running
  9. Fold Cell - Folds (hides) the current cell
  10. Markdown/Code dropdown - Allows you to select the type for the current input cell (as code or markdown)
  11. Add to Report Builder - Adds the output of the selected cell to the Report Builder
  12. Export Notebook - Exports all markdown and input cells as a .py or .r file

Notebook Status

The status indicator, located in the bottom right corner of the browser window, will notify you if there is an issue with your session. It may prompt you to restart the kernel.

  • Setting up notebook - Displayed when opening up a new Notebook, or after re-starting your session.
  • Ready - Notebook is ready to go.
  • Running - Your code is executing.
  • Loading dataframes - This message may display for larger datasets while dataframe information is loaded into the Notebook.
  • Notebook has encountered an unexpected error - Your session has crashed and will need to be restarted.
  • There was a problem with your session - Your session has terminated and you need to click Restart to get things working again.
  • Cell is still running. Hang tight! - This can appear when code being run includes long-running, computationally intense functions. The Notebook is still online.
  • Notebook is having trouble, try running again - The Notebook is experiencing problems. Please try running your code again to fix the issue.

Accessing query results

The Notebook has access to the results of every query in your report. However, the way you access those results differs depending on the language you’re using. In each case, all query results are delivered to the Notebook as a custom object called datasets. datasets contains objects of the following type:

Python: pandas DataFrame
R: Data Frame

In your Notebook code, reference query result sets in the datasets list by query name, position, or token. For example:

To return results for: Python R
First query added to report datasets[0] datasets[[1]]
Second query added to report datasets[1] datasets[[2]]
Query named ‘Active Users’ datasets["Active Users"] datasets[["Active Users"]]
Query with token ‘6763b688fb54’ datasets["6763b688fb54"] datasets[["6763b688fb54"]]


  • The datasets object won’t update in the Notebook until after all queries in the report have run successfully.
  • Remember that R is 1-indexed and Python is 0-indexed.
  • If you refer to query results by the query name, remember to update your code if you rename the query in your report.
  • The order of the results in the datasets object is based on when the query was added to the report. Renaming a query may change the order it’s displayed in the report editor but will not affect its position in the datasets object.
How to find a query’s token

To find the query token starting from the Notebook or editor, click View in the header, then View details, and then click SQL for the query you wish to use. The URL for SQL contains the query token at the end:


Query token


Adding cell output to your report

Add contents of the OUT section of any Notebook cell to the Report Builder by clicking on the cell and then clicking Add to Report Builder in the toolbar. You can adjust the dimensions and placement of this cell in the Report Builder.

Add CSV export to a cell

You can add an export button to a Notebook output cell so viewers can export calculated results to a CSV. For example, to add an export button to an output cell that generated a CSV of the query results of a query named “Active Users”, add the following code to the preceding input cell:

Python R
import notebooksalamode as mode
mode.export_csv(datasets["Active Users"])
export_data(datasets[["Active Users"]])

Tip: The export button can reference and export any pandas DataFrame (Python) or R Data Frame ® available in the cell.

Available libraries


The Notebook comes pre-loaded with the following Python libraries:

Library Description
agate Data analysis library with human-readable code
basemap matplotlib toolkit to plot data on maps with coastlines, lakes, rivers, and political boundaries
beautiful soup Parsing HTML, JSON and XML data
cufflinks Bind Plotly directly to pandas dataframes
engarde Defensive data analysis
emcee An MIT MCMC library
folium Build Leaflet.js maps
gensim Unsupervised semantic modeling from plain text
geomap Generate maps of geolocation data
HDBSCAN Hierarchical Density-Based Spatial Clustering of Applications with Noise
jsonify Converts a .csv file to JSON
keras Neural networks API that can run on TensorFlow or Theano
lifelines Survival analysis
lifetimes User behavior analysis
matplotlib 2D plotting visualizations
networkx Complex network manipulation
nltk Natural language toolkit
numexpr Fast numerical array expression evaluator
numPy Various scientific computing functions
pandas Data structures and data analysis tools
pandaSQL Query pandas dataframes using SQL syntax
patsy Describing statistical models/building design matrices
plotly Data visualizations, dashboards & collaborative analysis
prettytable Easily display tabular data in ASCII table format
prophet Forecasting with time series data
pyzipcode Query zip codes and location data
requests Make HTTP requests*
scikit-image Image processing
scikit-learn Tools for data mining and analysis
sciPy Various advanced mathematics, science and engineering functions
seaborn Statistical graphics visualizations
sexMachine Predict gender from first names
statsmodels Estimating statistical models/performing statistical tests
symPy Symbolic mathematics
tabulate Pretty-print tabular data
tensorflow Numerical computation using data flow graphs
textblob Common NLP tasks
ua-Parser Fast and reliable user agent parser
wordcloud Wordcloud generator

IMPORTANT: We discourage accessing APIs that require authentication using personally identifiable credentials and information, as they will be visible to viewers of your report.


The Notebook comes pre-loaded with the following R packages:

Library Description
assertthat Easy pre- and post-assertions
blob S3 class to represent BLOBs
BTYD Buy-til-you-die (BTYD) models
BTYDplus Extends BTYD
caret Streamlines creation of predictive models
causalImpact Estimates causal effect of intervention on time series
cluster Cluster analysis extended Rousseeuw et al.
colorspace Color space manipulation
data.table Extends data.frame
dichromat Color schemes for dichromats
digest Create compact hash digests of R objects
DMwR Functions & data for Data Mining
dplyr A grammar of data manipulation
forcats Working with categorical variables (factors)
forecast Forecasting for time series & linear models
GGally Extension to ggplot2
ggcorplot Plots a correlation matrix
ggdendro Dendrograms & tree plots with ggplot2
ggplot2 System for creating graphics
ggpubr Publication-ready ggplot2 plots
ggridges Ridgeline plots in ggplot2
ggthemes Extra themes, scales, & geoms for ggplot2
glue Glue strings to data
gtable Arrange grobs in tables
hts Hierarchical & grouped time series
httr Tools for working with URLs & HTTP*
iterators Provides iterator construct
itertools Various tools for creating iterators
kernlab Kernel-based machine learning lab
kknn Weighted k-nearest neighbors
lars Least angle regression, lasso & forward stagewise
lattice Trellis graphics
lazyeval Lazy (non-standard) evaluation
lubridate Date and time manipulation
magrittr A forward-pipe operator
MASS Functions & datasets to support Venables & Ripley
modelr Modelling functions that work with the pipe
munsell Utilities for using Munsell colors
nnet Feed-forward neural networks & multinomial log-linear models
plotly Library for data visualization, dashboards & collaborative analysis
prophet Automatic forecasting procedure
proto Prototype object-based programming
purrr Tools for working with functional vectors
RColorBrewer ColorBrewer palettes
reshape2 Transform data between wide & long
rlang Functions for base types & core R & tidyverse features
scales Scale functions for visualizations
stringr Work with character strings & reg ex
tidyr Easily create tidy data
tm Text mining package
utf8 Fixes bugs in R’s UTF-8 handling
viridisLite Port of matplotlib color maps
xml2 Parse xml
zoo S3 infrastructure for regular & irregular time series

IMPORTANT: We discourage accessing APIs that require authentication using personally identifiable credentials and information, as they will be visible to viewers of your report.


How much memory is available to the Notebook?

Each Notebook session has the following resources available, depending on the version of your Mode organization:

Available Memory Run-time Limit
Mode Studio 4GB 15 minutes
Mode Business 12GB 15 minutes
Can I access data from an external site in the Notebook?

Mode supports the Requests library in Python, which allows you to make HTTP requests.

Important: We discourage accessing APIs that require authentication using personally identifiable credentials, as they will be visible in plain-text to viewers of the report.

Can I import custom libraries into the Notebook?

Not currently, but this is something we’re thinking about adding in a future release. If you would like to see this feature or know of open source libraries you’d like to see added, please let us know!

Last updated May 17, 2018