# Welcome to DataMotto
Your AI-Powered Data Notebook
Imagine a space where you can add your data and be instantly equipped with a set of specialized AIs, each an expert in a specific task, all collaborating to streamline your data analysis journey. That's the essence of our AI Notebook. Here, AI isn't just a feature—it's the core of a transformative journey from your raw data to profound insights. \
\
Our AI Agents can smartly clean, organize, and analyze your data. They're not just passive tools; they think ahead, suggesting your next steps proactively. You'll see smart suggestions pop up, and with just a click, you can choose the best path forward. It's data analysis made simple, intuitive, and highly effective.
# AI Notebooks
## Introducing AI Notebook
AI Notebook is a brand new platform designed for working with your data effortlessly. It's a simple yet powerful tool where you just connect your data, and an AI Agent is automatically chosen to assist you in that specific Notebook. This platform is all about making your data work process smooth and efficient.
## Data Prep Agent
Before diving deep into analysis, the foundation of your data must be solid and reliable. That's where the Data Prep Agent comes in. This dedicated AI specializes in refining your raw data, ensuring it's primed and cleaned for detailed analysis. The Data Prep Agent meticulously cleans, integrates, and enhances the quality of your data. Think of it as the meticulous preparer of your dataset, setting the stage for insightful analysis by ensuring the data is accurate, consistent, and ready for the next steps.
## Analysis Agent
With your data fully prepped, the Analysis Agent takes over. This AI excels in extracting valuable insights from your clean, structured data. It's skilled in exploring, visualizing, and modeling your data, transforming complex datasets into understandable and actionable information. The Analysis Agent is your guide through the maze of data, helping you to understand the story behind the numbers.
## Collaboration and Sharing
AI Notebook goes beyond data analysis; it's a collaborative platform. Easily share your Notebooks with team members to foster collaborative analysis and decision-making. Additionally, you have the option to make your Notebooks public, sharing your insights and visualizations with a broader audience.
AI Notebook redefines your data journey, providing specialized AI Agents for each phase of the process. From the meticulous care of the Data Prep Agent to the insightful analysis of the Analysis Agent, coupled with seamless collaboration features, AI Notebook is your comprehensive solution for turning data into actionable insights.
# Collaboration & Share
A collaboration feature is a function that enables multiple people to work together on a common page.
# Collaborate with access level
Collaboration features empower teams to work seamlessly and productively by allowing them to exchange information and collaborate within a unified, integrated environment.
### Add or invite to Notebook
* Select share to add or invite new collaborator as a guest to your page. In the side menu, you can view the list of collaborators and their respective access levels for the page. To add more collaborators, simply click the `Invite` button.
* In the pop-up window, enter the email address of the person you want to add as a collaborator. Choose their role from the dropdown menu, and then click `Invite`. The new collaborator will receive an email containing a link to sign in and join your page.
### Invite a new collaborator
* In the pop-up window, enter the email address of the person you wish to invite. Click `Invite` in the dropdown menu. The invitee will receive an email containing a link to register and join your page.
### Access level
When inviting a user to collaborate on a page, simply select the appropriate access level :
* In the opened modal, type the email address of person you want add.
* Click `Edit` or `View` button.
* Determine access level and press `Add` .
{% hint style="info" %}
The user now have the new access level you assigned to them, and they'll be able to collaborate with you on your page accordingly.
{% endhint %}
Our collaboration and access level feature also allows users to change the access level of an invite directly in the invite modal.
* To change the access level of an invitee in the invite modal select person.
* Click `Edit` or `View` button, and change the access level.
### Delete a collaborator
To delete a collaborator directly from the invite modal, follow these steps:
* Select the person you want to delete.
* Click the `Access dropdown` button at the right side.
{% hint style="info" %}
Note that deleting a collaborator will immediately revoke their access to the page and they will no longer be able to view or edit its content."
{% endhint %}
# Data & Integrations
# Notion Data
How to analyze your Notion Databases in DataMotto
## Step 1: Download from Notion
* Go to your Notion and find the database you want to analyze. If your database is displayed as an inline view on a page, make sure to open it as a full page (refer to the screen below). Otherwise, you cannot download it.
* Go to the three dots "..." located in the top right corner and click '**Export**' to download it in CSV format.
* Go to the downloaded file on your computer and unzip it. Now, you will see your data in CSV format.
## Step 2: Upload to DataMotto
* Log in to [https://datamotto.com](https://datamotto.com/).
* Create a "New notebook." Then, select the **Data** and upload your Notion CSV data. You are all set.
{% hint style="success" %}
Congratulation! Your Notion data is now uploaded to DataMotto, and Klarence is ready to clean and analyze it for you. :tada:
{% endhint %}
# HubSpot Data
How to analyze your HubSpot Records in DataMotto
{% hint style="info" %}
**Want to analyze your HubSpot CRM data with DataMotto's AI?** You have two options:
1. **Upload** your HubSpot database as a CSV file to DataMotto. This method is easy and straightforward.
2. **Integrate** your HubSpot database to DataMotto. This method is automatic, which save time for future analysis, but require a bit more effort to set up.
Below, you'll find a step-by-step guide for each method.
{% endhint %}
## Method 1: Upload
### Step 1: Download from HubSpot
1. **Find Your Records:**
* **Calls:** Go to Contacts > Calls.
* **Contacts:** Go to Contacts > Contacts.
* **Companies:** Go to Contacts > Companies.
* **Deals:** Go to Sales > Deals.
* **Payments:** Go to Sales > Payments > Payments.
* **Tickets:** Go to Service > Tickets.
* **Custom Objects:** Go to Contacts > \[Custom object]. If more than one, select the one you want.
2. **Open the View You Want to Export:**
* In list view, click the view's **tab** or **+ Add view**, then select from the menu.
* In grid board view (deals, tickets, or custom objects), choose the pipeline and view from dropdown menus.
3. **Export the Records:**
* In list view, click **Export** at the top right.
* In grid board view, click **Board actions > Export view**.
* Choose **CSV** as the file format.
* Select to Include only properties in the view or include All properties. The column order in the export file depends on what you choose.
* Click on **Export** button to download your records.
### Step 2: Upload to DataMotto
* Log in to [https://datamotto.com](https://datamotto.com/).
* Create a "New notebook." Then, select the **HubSpot** card and upload your HubSpot CSV data. You are all set.
{% hint style="success" %}
Congratulation! Your HubSpot data is now uploaded to DataMotto, and Klarence is ready to analyze it for you. :tada:
{% endhint %}
### Modify your data?
In case you need to modify your HubSpot data, go to your dashboard and click on \
"**Resources & Integrations**" in the left sidebar panel. There, you can modify your data.
## Method 2: Integration
{% hint style="info" %}
Integrating HubSpot databases to DataMotto is a feature available in the [Business](https://datamotto.com/pricing/) Plan. It streamlines your workflow by connecting your HubSpot records and syncing with the latest version automatically. If you're interested in upgrading to the Business Plan for your team to take advantage of this integration and other advanced features, please [contact us here](https://datamotto.com/request-demo/).
{% endhint %}
# Stripe Data
How to analyze your Stripe data in DataMotto
{% hint style="info" %}
**Want to analyze your Stripe financial data with DataMotto's AI?** You have two options:
1. **Upload** your Stripe data as a CSV file to DataMotto. This method is easy and straightforward.
2. **Integrate** your Stripe data to DataMotto. This method is automatic, which save time for future analysis, but require a bit more effort to set up.
Below, you'll find a step-by-step guide for each method.
{% endhint %}
## Method 1: Upload
### Step 1: Download from Stripe
Exporting data from Stripe is a simple and straightforward process. Whether you want to download information related to payments, customers, or any other data category, you can do so by following these steps:
1. **Log in to your Stripe account.**
2. **Navigate to the Data Category:** Go to the top bar panel in your account, and select the data category you're interested in (e.g., Payments, Customers, etc.).
3. **Find the Export Button:** Within the selected data category, you'll see an 'Export' button typically located at the top right corner or within the settings menu.
4. **Click the Export Button:** By clicking this button, you'll have the option to change the Time , Date range, and exported columns. Then, click on the **Export** button to save the data as a CSV file in your system.
### Step 2: Upload to DataMotto
* Log in to [https://datamotto.com](https://datamotto.com/).
* Create a "New notebook." Then, select the **Stripe** card and upload your Stripe CSV data. You are all set.
{% hint style="success" %}
Congratulation! Your Stripe data is now uploaded to DataMotto, and Klarence is ready to analyze it for you. :tada:
{% endhint %}
### Modify your data?
In case you need to modify your Stripe data, go to your dashboard and click on \
"**Resources & Integrations**" in the left sidebar panel. There, you can modify your data.
## Method 2: Integration
{% hint style="info" %}
Integrating Stripe data to DataMotto is a feature available in the [Business](https://datamotto.com/pricing/) Plan. It streamlines your workflow by connecting your Stripe data and syncing with the latest version automatically. If you're interested in upgrading to the Business Plan for your team to take advantage of this integration and other advanced features, please [contact us here](https://datamotto.com/request-demo/).
{% endhint %}
# Upload Your Data
You can easily upload your own generic files to the platform and use them in a variety of ways. To upload, follow these steps:
* Log in to [https://datamotto.com](https://datamotto.com/).
* Create a "New notebook." Then, select the **Upload data** card and upload your data. You are all set.
# Sample Datasets
We've curated a collection of datasets that you can use to supplement your pages and analyses. To access these datasets, simply browse through our selection and choose the ones that best fit your needs. Once you've found a dataset you want to use, simply import it into your page and start using it right away.
### Download a Dataset
To download a dataset, you can follow the steps below in **Datamoto's** dataset panel:
1. Open the Dataset modal.
2. Navigate to the **my data** or **curated data** tab.
3. Look for the `Download` button and click on it.
# Your Datasets
To view all of your uploaded Datasets in one place, simply navigate to the `Data Sources` section in the sidebar where you'll find a comprehensive list of all the Data you've connected. You can access the download or delete options for your files in the section.
# Coding Environment
The ability to work with multiple programming languages and pre-installed packages is crucial in data science. DataMotto offers a default environment that supports the most popular statistical languages - **Python**, **R**, and **SQL** - along with a collection of pre-installed packages that satisfy the needs of most users. However, if you require a specific package that is not already installed, you can manually install it in a reproducible environment to ensure consistency across different sessions. In this section, we will explore the default environment, pre-defined packages, and steps to install additional packages in R and Python.
## Default Environment:
DataMotto pages support the three most popular statistical languages - Python, R, and Julia - by default. This allows a page to execute code in multiple languages simultaneously, but in separate sessions. For instance, if a variable is created in Python, it cannot be accessed in the R session.
## Disk Space
Each page (notebook) in DataMotto has a capacity of 10GB for all the data added into its session environment. This includes any data you upload or generate within the page. If you need to work with larger datasets, we recommend using our data integration feature or connecting to external databases.
## User Storage
User storage refers to the total amount of data and resources a user has added to their workspace, which includes all of their pages. In DataMotto's free plan, users have access to a total storage limit of 5GB. For users with larger storage needs, we offer the Pro plan, which allows for unlimited data storage. However, it's important to note that each page has a disk space limit of 10GB, even in the Pro plan. Therefore, while users can store as much data as they need, they must ensure that they do not exceed the 10GB limit for each page.
##
## Pre-defined Packages:
We have installed a collection of pre-defined packages that cater to a wide range of user needs by default. As a beginner, you most likely won't need to install any new packages. However, advanced users can certainly install any packages they require.\
#### Predefined R Libraries:
The current list in Python session environemt is as follows, but to obtain the latest version, you may attempt `installed.packages().`
```
Package Version
abind 1.4-5
Amelia 1.8.0
animation 2.7
anytime 0.3.9
askpass 1.1
assertthat 0.2.1
backports 1.4.1
base 4.2.2
base64enc 0.1-3
BH 1.78.0-0
bigrquery 1.4.1
bit 4.0.5
bit64 4.0.5
bitops 1.0-7
blob 1.2.3
bmp 0.3
boot 1.3-28
brew 1.0-8
brio 1.1.3
broom 1.0.3
bslib 0.4.2
cachem 1.0.6
callr 3.7.3
caret 6.0-93
cellranger 1.1.0
checkmate 2.1.0
class 7.3-21
classInt 0.4-3
cli 3.6.0
clipr 0.8.0
clock 0.6.1
clue 0.3-61
cluster 2.1.3
codetools 0.2-19
collections 0.3.5
colorspace 2.1-0
commonmark 1.8.1
compiler 4.2.2
config 0.3.1
conflicted 1.2.0
coro 1.0.3
cpp11 0.4.3
crayon 1.5.2
credentials 1.3.2
crosstalk 1.2.0
crul 1.2.0
curl 4.3.3
cyclocomp 1.1.0
data.table 1.14.6
datasets 4.2.2
DBI 1.1.3
dbplyr 2.3.0
desc 1.4.2
devtools 2.4.5
DiagrammeR 1.0.9
dials 1.1.0
DiceDesign 1.9
dichromat 2.0-0.1
diffobj 0.3.5
digest 0.6.31
distributional 0.3.0
downlit 0.4.2
downloader 0.4
dplyr 1.1.0
dtplyr 1.2.2
dtw 1.22-3
dtwclust 5.5.11
e1071 1.7-13
ellipsis 0.3.2
evaluate 0.20
extrafont 0.18
extrafontdb 1.0
fable 0.3.2
fabletools 0.3.2
fansi 1.0.4
farver 2.1.1
fastmap 1.1.0
flexclust 1.4-1
fontawesome 0.5.0
forcats 1.0.0
foreach 1.5.2
forecast 8.20
foreign 0.8-82
formatR 1.12
fracdiff 1.5-2
fs 1.6.1
furrr 0.3.1
future 1.31.0
future.apply 1.10.0
gapminder 0.3.0
gargle 1.3.0
gdtools 0.3.0
generics 0.1.3
gert 1.5.0
gfonts 0.2.0
gganimate 1.0.8
ggforce 0.4.1
ggplot2 3.4.0
ggraph 2.1.0
ggrepel 0.9.1
ggthemes 4.2.4
gh 1.3.1
gistr 0.9.0
gitcreds 0.1.2
glmnet 4.1-4
globals 0.16.2
glue 1.6.2
googledrive 2.0.0
googlesheets4 1.0.1
gower 1.0.1
GPfit 1.0-8
graphics 4.2.2
graphlayouts 0.8.0
grDevices 4.2.2
grid 4.2.2
gridExtra 2.3
gridSVG 1.7-4
gtable 0.3.1
hardhat 1.2.0
haven 2.5.1
here 1.0.1
hexbin 1.28.2
highr 0.10
hms 1.1.2
hrbrthemes 0.8.0
htmltools 0.5.4
htmlwidgets 1.6.1
httpcode 0.3.0
httpuv 1.6.8
httr 1.4.4
ids 1.0.1
igraph 1.3.1
imager 0.42.18
infer 1.0.4
influenceR 0.1.0.1
ini 0.3.1
inline 0.3.19
ipred 0.9-13
IRdisplay 1.1
IRkernel 1.3.2
isoband 0.2.7
iterators 1.0.14
jpeg 0.1-9
jquerylib 0.1.4
jsonlite 1.8.4
keras 2.9.0
KernSmooth 2.23-20
knitr 1.42
labeling 0.4.2
languageserver 0.3.15
later 1.3.0
lattice 0.20-45
lava 1.7.1
lazyeval 0.2.2
leaflet 2.1.1
leaflet.providers 1.9.0
lhs 1.1.6
lifecycle 1.0.3
lintr 2.0.1
listenv 0.9.0
lmtest 0.9-40
lobstr 1.1.1
loo 2.5.1
lubridate 1.9.1
magick 2.7.3
magrittr 2.0.3
maps 3.4.0
markdown 1.1
MASS 7.3-58.2
Matrix 1.5-3
matrixStats 0.62.0
mefa 3.2-8
memoise 2.0.1
methods 4.2.2
mgcv 1.8-41
mime 0.12
miniUI 0.1.1.1
modeldata 1.1.0
modelenv 0.1.0
ModelMetrics 1.2.2.2
modelr 0.1.10
modeltools 0.2-23
munsell 0.5.0
nlme 3.1-162
nnet 7.3-18
numDeriv 2016.8-1.1
nycflights13 1.0.2
openssl 2.0.5
oro.nifti 0.11.4
parallel 4.2.2
parallelly 1.34.0
parsnip 1.0.3
patchwork 1.1.2
pbdZMQ 0.3-9
pillar 1.8.1
pkgbuild 1.4.0
pkgconfig 2.0.3
pkgdown 2.0.7
pkgload 1.3.2
plogr 0.2.0
plotly 4.10.0
plyr 1.8.8
png 0.1-7
polyclip 1.10-0
praise 1.0.0
prettyunits 1.1.1
pROC 1.18.0
processx 3.8.0
prodlim 2019.11.13
profvis 0.3.7
progress 1.2.2
progressr 0.13.0
promises 1.2.0.1
proto 1.0.0
proxy 0.4-27
pryr 0.1.5
ps 1.7.2
purrr 1.0.1
quadprog 1.5-8
quantmod 0.4.20
R.cache 0.15.0
R.methodsS3 1.8.1
R.oo 1.24.0
R.utils 2.11.0
R6 2.5.1
ragg 1.2.2
randomForest 4.7-1.1
rapidjsonr 1.2.0
rappdirs 0.3.3
rARPACK 0.11-0
raster 3.5-15
rattle 5.5.1
rbokeh 0.5.2
rcmdcheck 1.4.0
RColorBrewer 1.1-3
Rcpp 1.0.10
RcppArmadillo 0.11.4.3.1
RcppEigen 0.3.3.9.2
RcppParallel 5.1.5
RcppThread 2.1.3
RcppTOML 0.1.7
RCurl 1.98-1.10
readbitmap 0.1.5
readr 2.1.3
readxl 1.4.1
recipes 1.0.4
rematch 1.0.1
rematch2 2.1.2
remotes 2.4.2
renv 0.15.5
repr 1.1.6
reprex 2.0.2
reshape2 1.4.4
reticulate 1.25
rex 1.2.1
rgdal 1.5-32
rgeos 0.6-1
rlang 1.0.6
rmarkdown 2.20
rmutil 1.1.9
RNifti 1.4.0
RODBC 1.3-20
roxygen2 7.2.3
rpart 4.1.19
rpart.plot 3.1.1
rprojroot 2.0.3
rsample 1.1.1
RSpectra 0.16-1
RSQLite 2.2.20
rstan 2.21.5
rstudioapi 0.14
rTorch 0.4.2
Rttf2pt1 1.3.10
rversions 2.1.2
rvest 1.0.3
s2 1.0.7
sass 0.4.5
scales 1.2.1
selectr 0.4-2
sessioninfo 1.2.2
sf 1.0-7
shape 1.4.6
shiny 1.7.4
shinyjs 2.1.0
slider 0.3.0
sourcetools 0.1.7-1
sp 1.4-7
spatial 7.3-15
splines 4.2.2
SQUAREM 2021.1
StanHeaders 2.21.0-7
stats 4.2.2
stats4 4.2.2
stringi 1.7.12
stringr 1.5.0
styler 1.9.0
survival 3.5-0
sys 3.4.1
systemfonts 1.0.4
tcltk 4.2.2
tensorflow 2.9.0
terra 1.5-21
testthat 3.1.6
textshaping 0.3.6
tfautograph 0.3.2
tfruns 1.5.0
tibble 3.1.8
tidygraph 1.2.3
tidymodels 1.0.0
tidyr 1.3.0
tidyselect 1.2.0
tidyverse 1.3.2
tiff 0.1-11
timechange 0.2.0
timeDate 4022.108
timereg 2.0.2
tinytex 0.44
tools 4.2.2
torch 0.9.0
triebeard 0.3.0
tseries 0.10-53
tsibble 1.1.3
TTR 0.24.3
tune 1.0.1
tweenr 2.0.2
tzdb 0.3.0
udunits2 0.13.2.1
units 0.8-0
urca 1.3-3
urlchecker 1.0.1
urltools 1.7.3
usethis 2.1.6
utf8 1.2.3
utils 4.2.2
uuid 1.1-0
vctrs 0.5.2
viridis 0.6.2
viridisLite 0.4.1
visNetwork 2.1.0
vroom 1.6.1
waldo 0.4.0
warp 0.2.0
whisker 0.4.1
withr 2.5.0
wk 0.6.0
workflows 1.1.2
workflowsets 1.0.0
xfun 0.37
xgboost 1.6.0.1
XML 3.99-0.9
xml2 1.3.3
xmlparsedata 1.0.5
xopen 1.0.0
xtable 1.8-4
xts 0.12.2
yaml 2.3.7
yardstick 1.1.0
zeallot 0.1.0
zip 2.2.2
zoo 1.8-11
```
\
\
The current list in Python session environemt is as follows, but to obtain the latest version, you may attempt `!pip list`.
```
Package Version
----------------------------- -----------
absl-py 1.4.0
aiofiles 22.1.0
aiohttp 3.8.3
aiosignal 1.3.1
aiosqlite 0.18.0
alembic 1.9.3
altair 4.2.2
anyio 3.6.2
appdirs 1.4.4
argon2-cffi 21.3.0
argon2-cffi-bindings 21.2.0
arrow 1.2.3
astroid 2.14.1
asttokens 2.2.1
astunparse 1.6.3
async-generator 1.10
async-timeout 4.0.2
attrs 22.2.0
autopep8 1.6.0
Babel 2.11.0
backcall 0.2.0
backports.functools-lru-cache 1.6.4
beautifulsoup4 4.11.2
bleach 6.0.0
blinker 1.5
bokeh 2.4.3
Bottleneck 1.3.6
brotlipy 0.7.0
cached-property 1.5.2
cachetools 5.3.0
certifi 2022.12.7
certipy 0.1.3
cffi 1.15.1
charset-normalizer 2.1.1
chex 0.1.6
click 8.1.3
cloudpickle 2.2.1
cmake 3.25.2
colorama 0.4.6
comm 0.1.2
conda 22.11.1
conda-package-handling 2.0.2
conda_package_streaming 0.7.0
contourpy 1.0.7
cryptography 39.0.1
cycler 0.11.0
Cython 0.29.33
cytoolz 0.12.0
dask 2023.1.1
datasets 2.9.0
debugpy 1.6.6
decorator 5.1.1
defusedxml 0.7.1
dill 0.3.6
distributed 2023.1.1
dm-tree 0.1.8
docstring-to-markdown 0.11
entrypoints 0.4
et-xmlfile 1.1.0
executing 1.2.0
fastapi 0.91.0
fastjsonschema 2.16.2
ffmpy 0.3.0
filelock 3.9.0
flake8 6.0.0
flatbuffers 23.1.21
flit_core 3.8.0
fonttools 4.38.0
fqdn 1.5.1
frozenlist 1.3.3
fsspec 2023.1.0
gast 0.4.0
GDAL 3.6.2
gmpy2 2.1.2
google-auth 2.16.0
google-auth-oauthlib 0.4.6
google-pasta 0.2.0
gradio 3.18.0
greenlet 2.0.2
grpcio 1.51.1
h11 0.14.0
h5py 3.8.0
HeapDict 1.0.1
httpcore 0.16.3
httpx 0.23.3
huggingface-hub 0.12.0
idna 3.4
imagecodecs 2022.12.24
imageio 2.25.0
importlib-metadata 6.0.0
importlib-resources 5.10.2
ipykernel 6.21.1
ipympl 0.9.2
ipython 8.9.0
ipython-genutils 0.2.0
ipywidgets 8.0.4
isoduration 20.11.0
isort 5.12.0
jax 0.4.3
jaxlib 0.4.3
jedi 0.18.2
Jinja2 3.1.2
joblib 1.2.0
json5 0.9.5
jsonpointer 2.3
jsonschema 4.17.3
jupyter_client 8.0.2
jupyter_core 5.2.0
jupyter-events 0.5.0
jupyter-lsp 1.5.1
jupyter_server 2.2.1
jupyter_server_fileid 0.6.0
jupyter_server_terminals 0.4.4
jupyter_server_ydoc 0.6.1
jupyter-telemetry 0.1.0
jupyter-ydoc 0.2.2
jupyterhub 3.1.1
jupyterlab 3.6.1
jupyterlab-lsp 3.10.2
jupyterlab-pygments 0.2.2
jupyterlab_server 2.19.0
jupyterlab-widgets 3.0.5
keras 2.11.0
kiwisolver 1.4.4
lazy-object-proxy 1.9.0
libclang 15.0.6.1
libmambapy 1.3.0
lightning-utilities 0.6.0.post0
linkify-it-py 1.0.3
llvmlite 0.39.1
locket 1.0.0
lz4 4.3.2
Mako 1.2.4
mamba 1.3.0
Markdown 3.4.1
markdown-it-py 2.1.0
MarkupSafe 2.1.2
matplotlib 3.6.3
matplotlib-inline 0.1.6
mccabe 0.7.0
mdit-py-plugins 0.3.3
mdurl 0.1.2
mistune 2.0.5
mpmath 1.2.1
msgpack 1.0.4
multidict 6.0.4
multiprocess 0.70.14
munkres 1.1.4
nbclassic 0.5.1
nbclient 0.7.2
nbconvert 7.2.9
nbformat 5.7.3
nest-asyncio 1.5.6
networkx 3.0
nltk 3.8.1
notebook 6.5.2
notebook_shim 0.2.2
numba 0.56.4
numexpr 2.8.3
numpy 1.23.5
nvidia-cublas-cu11 11.10.3.66
nvidia-cuda-nvrtc-cu11 11.7.99
nvidia-cuda-runtime-cu11 11.7.99
nvidia-cudnn-cu11 8.5.0.96
oauthlib 3.2.2
openpyxl 3.1.0
opt-einsum 3.3.0
optax 0.1.4
orjson 3.8.6
packaging 23.0
pamela 1.0.0
pandas 1.5.3
pandocfilters 1.5.0
parso 0.8.3
partd 1.3.0
patsy 0.5.3
pexpect 4.8.0
pickleshare 0.7.5
Pillow 9.4.0
pip 23.0
pkgutil_resolve_name 1.3.10
platformdirs 3.0.0
plotly 5.10.0
pluggy 1.0.0
pooch 1.6.0
prometheus-client 0.16.0
prompt-toolkit 3.0.36
protobuf 3.19.6
psutil 5.9.4
ptyprocess 0.7.0
pure-eval 0.2.2
pyarrow 11.0.0
pyasn1 0.4.8
pyasn1-modules 0.2.8
pycodestyle 2.10.0
pycosat 0.6.4
pycparser 2.21
pycryptodome 3.17
pycurl 7.45.1
pydantic 1.10.4
pydocstyle 6.2.3
pydub 0.25.1
pyflakes 3.0.1
Pygments 2.14.0
PyJWT 2.6.0
pylint 2.16.1
pyOpenSSL 23.0.0
pyparsing 3.0.9
pyrsistent 0.19.3
PySocks 1.7.1
python-dateutil 2.8.2
python-json-logger 2.0.4
python-lsp-jsonrpc 1.0.0
python-lsp-server 1.7.1
python-multipart 0.0.5
pytoolconfig 1.2.5
pytorch-lightning 1.9.1
pytz 2022.7.1
pytz-deprecation-shim 0.1.0.post0
PyWavelets 1.4.1
PyYAML 6.0
pyzmq 25.0.0
regex 2022.10.31
requests 2.28.2
requests-oauthlib 1.3.1
responses 0.18.0
rfc3339-validator 0.1.4
rfc3986 1.5.0
rfc3986-validator 0.1.1
rope 1.7.0
rpy2 3.5.8
rsa 4.9
ruamel.yaml 0.17.21
ruamel.yaml.clib 0.2.7
scikit-image 0.19.3
scikit-learn 1.2.1
scipy 1.10.0
seaborn 0.12.2
Send2Trash 1.8.0
sentencepiece 0.1.97
seqeval 1.2.2
setuptools 67.1.0
simplegeneric 0.8.1
six 1.16.0
sniffio 1.3.0
snowballstemmer 2.2.0
sortedcontainers 2.4.0
soupsieve 2.3.2.post1
SQLAlchemy 2.0.2
stack-data 0.6.2
starlette 0.24.0
statsmodels 0.13.5
sympy 1.11.1
tables 3.7.0
tblib 1.7.0
tenacity 8.2.1
tensorboard 2.11.2
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.1
tensorflow-cpu 2.11.0
tensorflow-estimator 2.11.0
tensorflow-io-gcs-filesystem 0.30.0
termcolor 2.2.0
terminado 0.17.1
threadpoolctl 3.1.0
tifffile 2022.10.10
tinycss2 1.2.1
tokenizers 0.13.2
toml 0.10.2
tomli 2.0.1
tomlkit 0.11.6
toolz 0.12.0
torch 1.13.1
torchaudio 0.13.1
torchmetrics 0.11.1
torchvision 0.14.1
tornado 6.2
tqdm 4.64.1
traitlets 5.9.0
transformers 4.26.1
typing_extensions 4.4.0
tzdata 2022.7
tzlocal 4.2
uc-micro-py 1.0.1
ujson 5.7.0
unicodedata2 15.0.0
uri-template 1.2.0
urllib3 1.26.14
uvicorn 0.20.0
wcwidth 0.2.6
webcolors 1.12
webencodings 0.5.1
websocket-client 1.5.1
websockets 10.4
Werkzeug 2.2.2
whatthepatch 1.0.4
wheel 0.38.4
widgetsnbextension 4.0.5
wrapt 1.14.1
xlrd 2.0.1
xxhash 3.2.0
y-py 0.5.5
yapf 0.32.0
yarl 1.8.2
ypy-websocket 0.8.2
zict 2.2.0
zipp 3.12.1
zstandard 0.19.0
```
## Reproducible Environment:
If the package you want to use is not already installed, you can manually install it. It is recommended that you install your package in a reproducible environment; otherwise, you will need to reinstall packages each time your page session is restarted.
###
### Install R Packages:
To create a page-local R dependency management, initialize the renv in R.
`renv::init(force = TRUE)`
You can now easily install packages using
`install.package('NAME')` or `renv::install('NAME')`.
If you wish to install a particular version, use
`renv::install('NAME@VERSION')`, such as `renv::install('dplyr@1.0.0')`.
For more information about the renv package and creating a reproducible environment, we recommend reading the documentation available [`here`](https://rstudio.github.io/renv/index.html).
###
### Install Python Packages:
You can install a permanent package in Python as shown below:
`!pip install --user YourPkgName`
Having trouble installing or need a package pre-installed? If you encounter issues while installing a package, please contact us via , and we'll assist you promptly.
# Pricing & Plans
# Upgrading & Downgrading Plans
DataMotto offers 4 plans to cater to various users and teams. Here's a quick guide on upgrading or downgrading your plan:
## Upgrade Plan:
Upgrading your plan allows you to access more features and resources to enhance your experience with DataMotto. Here's a detailed guide on how to upgrade your plan:
1. Log in to your DataMotto account and navigate to the wrokspace.
2. At the bottom of the sidebar, click on "`Settings & Members`."
3. Click on the "`Plans`" tab, where you can view the available plans.
4. Select the plan to which you'd like to upgrade.
### Upgrading from the Free Plan to a paid plan:
* A window will pop up asking for your payment information. DataMotto securely processes payments through [Stripe](https://stripe.com/) platform.
* Once your payment is processed, your account will be immediately upgraded to the chosen plan.
* An updated badge will appear near your name in the dashboard (refresh your browser if needed).
* If you encounter any issues with your payment, contact for assistance.
{% hint style="success" %}
Congratulations. You upgraded your account!
{% endhint %}
{% hint style="info" %}
**Upgrading to the Business Plan:**\
For teams looking to unleash the full potential of DataMotto and our AI for their business needs, our Business plan offers a range of advanced features and benefits. To ensure that it's the right fit, we recommend [requesting a demo](https://datamotto.com/request-demo/) with our customer success team. They can guide you through the upgrade process. For any questions or concerns, our support team is always available at .
{% endhint %}
## Downgrade Plan:
Downgrading your plan is a simple process that allows you to switch to a lower-tier subscription. Here's a detailed guide on how to downgrade your plan:
1. Log in to your DataMotto account and go to the dashboard.
2. At the bottom of the sidebar, click on "`Settings & Members`"
3. Click on the "`Plans`" tab, where you can view the available plans.
4. Select the lower-tier plan to which you'd like to downgrade.
### Downgrading from Growth plan to the Free Plan:
* Your current Growth plan will be cancelled at the end of your billing interval.
* You'll still have access to your Growth features until the end of the billing period.
* After the billing period ends, your account will be switched to the Free Plan and your access to paid features will be limited accordingly.
### Downgrading from the Business Plan to the Growth Plan:
* Your available features will be adjusted to the Growth Plan immediately.
* DataMotto will credit your account with a prorated amount based on the time remaining in your billing cycle and the price difference between the two plans.
* This credit can be applied to future billing cycles or any additional services you may need.
Please note that when you downgrade your plan, some features or services may become unavailable due to the reduced plan level. It's important to review the differences between the plans before making the switch to ensure you're aware of the changes that will occur.
# Resources
# Blog
If you're interested in exploring our collection of informative blog articles, we invite you to head over to our Blog page. There, you'll find a variety of topics and insights to dive into, enriching your knowledge and understanding about DataMotto, Data Science, and AI.
{% embed url="" %}
# Evolution of DataMotto
Visit our Roadmap of DataMotto to learn more about the history of our data science AI notebook, from its initial release to its current state, as well as our plans for future development. Click here to visit the page.
{% embed url="" %}