1. Mito

Mito allows the user to do data cleaning from a spreadsheet inside Python. You can call Mito into your Python environment, and each edit you make in the Mito spreadsheet will generate the equivalent Python in the code cell below.

Here is a demo video:

Install commands for Mito:

python -m pip install mitoinstaller
python -m mitoinstaller install

Then open Jupyter Lab and call the Mitosheet

import mitosheet
mitosheet.sheet()

The full instructions can be found on the Mito website under “docs.”

In terms of data cleaning functionality, Mito has everything that a user can expect from a spreadsheet. These features range from trimming strings, to summary stats, to deduplicating and filling nulls values. Each of these operations will generate the equivalent Python for you.

Here is what it looks like to use filters, pivot tables, and graphs in Mito.

Mito is focused on making data cleaning processes faster and more accessible. While Mito provides a visual interface for your cleaning, it still generates fully documented Python that can be used to automate or share your analysis.

2. Arrow

Arrow is a Python package all about helping the user handle, dates, time, and timestamps. These can be some of the most annoying pieces of data for a user to handle and clean — arrow solves this.

Here is a screenshot from their documentation with their full list of features:

To install arrow:

pip install -U arrow

Here is a demo video of the package:

Parsing time data and cleaning it is an important step in the data science workflow. Packages like Arrow obfuscate these processes to a much friendlier syntax that make them easier to complete. Not only is the syntax shorter, but it is also easier to remember, so the user does not have to go to Google or Stack Overflow as often.

3. Scrubadub

This package is all about removing personally identifiable data from datasets. As privacy laws become more and more stringent (as they should!), Python users and businesses at large are increasingly focused on removing personal data.

Here are examples of the PII that this packages removes (taken from the package documentation):

To install:

pip install scrubadub

Here is an overview of how the package works:

I hope you find these packages helpful :)

New Python Packages for Data Cleaning was originally published in trymito on Medium, where people are continuing the conversation by highlighting and responding to this story.

In an effort to make your EDA processes more efficient, here are 9 functions I use for quick EDA!

Note!!!! — These functions require Pandas and Numpy.

df.info()

For any data frame the .info() function will tell you how many entrees you have, the names of each column, the data type of each column, and how many non-null values you have in each column. You can compare the quantity of non-null values to the total number of entries to find which columns have null values.

Find Duplicates

print(df.duplicated().sum())

There are multiple ways to find duplicates rows in your dataset. This function above is the easies, as it will find all the duplicate entries and print how many there are. If it prints “0”, there are no duplicates and you are good to go!

Find Unique Values in a Column

df["column_name"].unique()

In much of you EDA, you are focused on a few key columns. This functions quickly prints all the unique values of that column, so you can understand the breadth and range of the values. Below is what the output looks like:

Find the Counts of Unique Values in a Column

df["column_name"].value_counts()

This function build upon the previous one by providing you the unique values in that column that have the largest and smallest frequencies. This is a great way to look for outliers.

Find all the Null Values in a Dataframe

df.isnull().sum()

This function combines .isnull() and .sum() and will return a list of each column in the data frame with the amount of null values in each column. Finding null values is an important part of EDA and data cleaning. Here is the output of the function call:

Fill Null Values with Zeros (or any filler)

df.replace(np.nan, "0", inplace = True)

This function will take your entire data frame and fill the null values with zeros, or whatever value you put in the second argument of the function. It is certainly the fastest way to get rid of your null values, putting your dataset in a place that will avoid more errors and dead-ends in your analysis. If you are not sure whether or not Null values will impact your analysis, I advise you to either fill them or delete the entries that hold the null values.

Filter Rows in your Dataframe

df2 = df[df["column_name"] > 100]

The line of code above creates a new data frame that hold all the rows, where “column_name” is greater than some value. You can, of course, filter on other conditionals such as “less than” or “equals to” and more complex conditionals, with multiple conditions.

Create a box-plot for any column

df.boxplot()

The function above will return box plots for all the numerical columns in dataset.

To specify that the box plot only be created for a certain column, use this function:

df["column_name"].boxplot()

Create a Correlation Matrix

df.corr()

This pandas function will only return correlations for pairs of numeric columns.

To see all 9 of these functions in action, here is a quick tutorial video:

9 Pandas Functions for Exploratory Data Analysis was originally published in trymito on Medium, where people are continuing the conversation by highlighting and responding to this story.

Mito is a free Python package that allows users to integrate a fully functioning spreadsheet into their Jupyter Lab environment. Like any good spreadsheet, you can interactively look at your data, and make all the standard transformations that you would in Excel. Things like filtering, pivoting, and graphing your data is all only a couple clicks away in Mito. And to ensure that you’re able to take the edits that you’ve made to your dataframe with you in the rest of your analysis, every time you make an edit in the Mito spreadsheet, Mito automatically generates the equivalent Python in the code cell below.

When doing data analysis in Python, you’re forced to constantly print out the dataframe to validate that the transformations you’ve made are what you expect! By integrating a spreadsheet into your Python analysis, you can have the power of Python with the intuitiveness and conectedness of a spreadsheet.

Mito is great for Python users who want to generate their syntax more quickly, without needing to go to Stack Overflow or google. It is also used by Excel users, who want to transition their skills to Python.

To install Mito, run these commands:

python -m pip install mitoinstaller
python -m mitoinstaller install

Then, run these commands that should appear in Jupyter:

import mitosheet
mitosheet.sheet()

Here are the full install instructions.

Rows

Rows is a new age spreadsheet built for teams. Just like any good spreadsheet, it has all the basic Excel functions built in, SUM, VLOOKUP, and SUMIF just to name a few. But where Rows really stands out, is its collaboration and integrations features.

Rows integrates with your data sources like Google Analytics, Salesforce, and LinkedIn search, so you can pull all of your data into your Rows spreadsheet without copying and pasting across multiple spreadsheets. Use their built in connectors to pull and refresh all of your data.

After you’ve created the basic structure of your spreadsheet, you can collaborate with your teammates in real time to finish constructing your dataset.

Clay

Clay was designed specifically for constructing datasets that relies on data from multiple, live updating data sources. Like, Rows, they use the computation of the spreadsheet to pull and refresh your data. They let you see your complete dataset and obscure all of the messy data connection logic behind the scenes.

To help you get started, they have a rich set of Clay templates that will help you connect to your data sources.

Regardless of your work, there is probably a new-age spreadsheet that is being designed specifically for you. Some other interesting projects to check out:

1. StackerHQ — Build custom software that empowers your partners, teammates and customers.
2. Smartsheet — Empower business users to collaboratively create business-driven solutions

3 Spreadsheets of the Future was originally published in trymito on Medium, where people are continuing the conversation by highlighting and responding to this story.

Mito is a spreadsheet front-end for Python. You can call Mito into your Jupyter Notebook and each edit you make in the front-end will generate the equivalent Python. With Mito, you don’t have to spend any time looking for syntax on Stack Overflow or Google. The code is generated for you, so you can ensure that you are creating correct, clean code.

Here is a video demo:

To install Mito, use these three commands in your terminal:

python -m pip install mitoinstaller
python -m mitoinstaller install

Then to open the Mitosheet interface:

import mitosheet
mitosheet.sheet()

Here is a link to the full install instructions.

You can configure a Mito pivot table by selecting the Pivot button from the toolbar and then choosing your rows, columns, values and aggregation types.

Each edit in Mito generates the equivalent Python in the code cell below. It is a much faster way of producing code than constantly heading to Stack Overflow to find the correct syntax.

The pivot table above generates this code and auto-comments it as well!

Mito does not just generate the code for pivot tables. In Mito, you can merge datasets, filter, sort, use functions, look at summary statistics, and more — and Mito will generate the equivalent Python for each of these edits..

To create a Plotly chart, all the user has to do is click the graph button and select their axes.

Here is Mito’s full documentation.

2. Streamlit

Streamlit is an open source Python package that lets you spin up data apps for ML and Data Science super simply.

The analysis part of Data Science is important, but you need to be able to communicate those findings. Interactive apps that are easy for end-users to engage with, are becoming increasingly popular.

To import Streamlit, run:

pip install streamlit
streamlit hello

The second command will open a demo environment in another window.

In Streamlit, you can do things like create a line chart:

chart_data = pd.DataFrame(
     np.random.randn(20, 3),
     columns=['a', 'b', 'c'])

st.line_chart(chart_data)

Or add interactive widgets:

if st.checkbox('Show dataframe'):
    chart_data = pd.DataFrame(
       np.random.randn(20, 3),
       columns=['a', 'b', 'c'])

    chart_data

Here is there full getting started documentation.

Here if a full course that goes over different apps you can build with Streamlit:

3. Plotly

Plotly is the graphing library of the future. It is the best package for quickly and easily making interactive charts and graphs. Packages like matplotlib and seaborn are certainly intuitive as well, but they lack the interactivity that makes Plotly so strong.

To install Plotly, run this command:

$ pip install plotly==5.2.1

In Plotly, there are a whole host of interactive charts to choose from. There are simpler charts, where changing the color of the bars is the interactivity.

import plotly.graph_objects as go
fig = go.Figure(data=go.Bar(y=[2, 3, 1]))
fig.show()

They also offer more advanced, dynamic charts:

I hope you find these packages helpful :)

3 Python Packages for Every Data Scientist was originally published in trymito on Medium, where people are continuing the conversation by highlighting and responding to this story.

Colloquially, Excel is the greatest piece of software ever written. There is an endless list of positive qualities about Excel, but its flexibility, ease of use, and open environment for scratch work are the most noteworthy to me. As powerful as Excel is, I find it increasingly lagging in its ability to handle large datasets and perform data science like tasks, which is required for more and more tasks.

In this blog, I will go over all the useful tooling I have found to help Excel users transition their workflows to and augment their workflows with Python. I will break this into three categories: Transitioning Excel Workflows to Python, Using Python to Augment Excel, and Resources for Excel Users Learning Python

Transition from Excel to Python

There are many Excel users who want to learn Python. Fortunately there are many tools that make the transition easy.

Mito

Mito is a spreadsheet that generates Python. You can call it into your Python environment and each edit you make in the spreadsheet will generate the equivalent Python. Here is a demo video of some data exploration:

To install Mito, run these commands in your terminal:

python -m pip install mitoinstaller
python -m mitoinstaller install
python -m jupyter lab

Here is the full Mito documentation.

Lux

One of the many reasons that Excel users come to Python for is for better visualizations. Python allows for more dynamic charts on larger datasets than Excel can access. Lux is great for new Excel users transitioning to Python because it requires very little coding. All you have to do is pass in your dataset and Lux will recommend charts for your data that can you select from.

In the image above, you can see that Lux will recommend different distribution relationships for columns in your data frame, and the user just has to click which one(s) they want to use.

To install Lux in your Python environment, just run:

**pip install lux-api**

Pandas Profiling

This package allows for user friendly Exploratory Data Analysis. To install, run these commands:

import sys
!{sys.executable} -m pip install -U pandas-profiling[notebook]
!jupyter nbextension enable --py widgetsnbextension

Pandas Profiling allows you to easily explore summary statistics, find Null Values, and see correlations etc., in a visual environment. The best part is that it is called right into your Python editor, fitting seamlessly into your workflow.

Using Python to Augment Excel

For those who do not want to start using Python as their primary data analysis tool, but do want to reap the advantages of Python, there are many tools worth trying out.

XLwings

XLwings is an open source Python package that allows you to automate spreadsheet work with Python. You can manipulate your spreadsheet data with the flexibility and power of Python. Handling hundreds of thousands of data entries is a much smoother process with a Python engine running it. You can also use the package to create Python macros, replacing VBA.

To import the package, run this command:

pip install xlwing

This is what sample code from the documentation website looks like:

XLwings is great for using Python to speed up your Excel processes.

Openpyxl

Openpyxl is a Python package that lets you easily read and write Excel files from a Python environment. You can create an Excel workbook from scratch, define cell values, merge cells, insert images etc. Imagine you are using Excel but all the buttons are Python commands that you write. There is more nuance to this description — this is just the high level. Here is the full documentation, if interested.

Resources for Excel Users Learning Python

Many creators are sharing great guides for Excel users to transition to Python. Here are a few I recommend you check out:

1. Practical Business Python — a great website with Python, tutorials, tips, and documentation from the perspective of a business user. It is a great forum for learning how to introduce Python into your business tasks.
2. Derrick Sherill — his youtube channel has many helpful Python tutorials, but also some great videos on automating spreadsheet processes using Python
3. python-excel.org — this site has a list of Python packages built for connecting to spreadsheets, including some mentioned in this article.

I hope these resources were useful. Please leave a comment, if you have any others that you recommend :)

Transition from Excel to Python was originally published in trymito on Medium, where people are continuing the conversation by highlighting and responding to this story.

1. Mito

Mito is a spreadsheet front-end for Python. You can call Mito into your Jupyter Notebook and each edit you make in the front-end will generate the equivalent Python.

Here is a video demo:

To install Mito, use these commands:

python -m pip install mitoinstaller
python -m mitoinstaller install

Then to open the Mitosheet interface:

import mitosheet
mitosheet.sheet()

Here is a link to the full install instructions.

You can configure a Mito pivot table by selecting the Pivot button from the toolbar and then choosing your rows, columns, values and aggregation types.

Each edit in Mito generates the equivalent Python in the code cell below. It is a much faster way of producing code than constantly heading to Stack Overflow to find the correct syntax.

The pivot table above generates this code and auto-comments it as well!

Mito does not just generate the code for pivot tables. In Mito, you can merge datasets, filter, sort, use functions, look at summary statistics, and more — and Mito will generate the equivalent Python for each of these edits..

To create a Plotly chart, all the user has to do is click the graph button and select their axes.

Here is Mito’s full documentation.

2. Plotly

Plotly is the graphing library of the future. It is the best package for quickly and easily making interactive charts and graphs. Packages like matplotlib and seaborn are certainly intuitive as well, but they lack the interactivity that makes Plotly so strong.

To install Plotly, run this command:

$ pip install plotly==5.2.1

In Plotly, there are a whole host of interactive charts to choose from. There are simpler charts, where changing the color of the bars is the interactivity.

import plotly.graph_objects as go
fig = go.Figure(data=go.Bar(y=[2, 3, 1]))
fig.show()

They also offer more advanced, dynamic charts:

3. Tensorflow

Tensorflow is an open source machine learning package that was originally developed by Google. It has made machine learning in Python much more accessible, and continues to do so as new updates come out.

To import the package, run these commands

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

Here is an example of a simple model you can run with Tensorflow:

class SimpleModule(tf.Module):
  def __init__(self, name=None):
    super().__init__(name=name)
    self.a_variable = tf.Variable(5.0, name="train_me")
    self.non_trainable_variable = tf.Variable(5.0, trainable=False, name="do_not_train_me")
  def __call__(self, x):
    return self.a_variable * x + self.non_trainable_variable

simple_module = SimpleModule(name="simple")

simple_module(tf.constant(5.0))

Here is the full documentation and datasets for this model.

Tensorflow allows you to easily build neural networks. Here is a screenshot from their neural networks tutorial.

Here is a link to the full guide.

4. Selenium

Web-scraping can be an integral part of certain Data Science workflows. Selenium is making this process much easier.

To install the package, run this command:

pip install selenium

Using selenium, you can select what page you want to scrape:

driver.get("URL")

From here, you can use different strategies that the package provides to scrape the data you want. Here is a link to the full documentation

I hope you found these packages helpful :)

4 Python Packages for Better data science was originally published in trymito on Medium, where people are continuing the conversation by highlighting and responding to this story.

Pandas GUI — a graphical interface for Pandas

Pandas GUI abstracts pandas into a really user friendly environment that makes exploratory data analysis and data wrangling much more visual for the user.

Here is a demo video:

To install, use this line of code:

pip install pandasgui

Within Pandas GUI, you can filter datasets, generate summary statistics and more!

Mito — A Spreadsheet Extension for Jupyter Lab that Generates Code

Mito is an editable spreadsheet that you can call into your Python environment. Each edit you make in the Mitosheet will generate the equivalent Python in the code cell below. Mito is powerful for data scientists who want to get their analysis done faster, or Excel-familiar users, who want to generate their Python in a similar environment.

Here is a demo video for Mito:

Within Mito, you can:

• Create Visualizations
• Look at Summary Statistics
• Merge Dataset Together
• Generate Pivot Tables
• Edit Specific Cell Values
• Filter and Sort Datasets
• Save and Replay Analyses (macros)
• See Your Edit Steps
• and more…

Each edit generates the equivalent Python.

To install Mito, run these commands:

python -m pip install mitoinstaller
python -m mitoinstaller install

Then open Jupyter Lab and render a Mitosheet:

import mitosheet
mitosheet.sheet()

Here are the full install instructions.

Lux — Automated Visualization Suggestions and Generation

Lux will take any data frame and automatically suggest visualizations that the user can click on and create in an instant. Getting the syntax correct for visualizations can be time consuming and Lux reduces this time to only a few seconds.

To install lux:

In the above gif, you can see how the user can pass in any dataframe and Lux will bring up a menu of charts to choose from. The focus of these charts range from data exploration to presentation. The user can also download these charts to share further.

Lux also offer an intent feature, where you can specify which attributes (columns) they are interested in. Lux will offer charts specific to those columns.

df.intent = ["Column1","Column2"]
df

Bokeh — Create Interactive Visualizations in Python

To start using Bokeh:

import numpy as np from bokeh.io import output_notebook, show
from bokeh.plotting import figure

Bokeh innovates on graphing packages like Matplotlib and Seaborn by creating plots that are interactive for end users.

Here are some of the options they offer:

Here is the full documentation

Interactive charts are becoming increasingly import in data science. Many packages, like Plotly and Streamlit, are working to give data scientist easy ways to make their work valuable to users outside the data science team. Bokeh seems to be working in that same direction.

Here is a demo video from the Data Professor that walks through great graphing packages like Bokeh:

I hope you find these tools helpful :)

4 Python Tools that Make you Code was originally published in trymito on Medium, where people are continuing the conversation by highlighting and responding to this story.

There is a new Python package called Mito, which allows you to edit a DataFrame, as if it were a spreadsheet. Mito a Python extension that you can call into your Jupyter environment. Each edit you make in Mito will generate the equivalent Python in the code cell below. Mito looks and feels like a spreadsheet — allowing the user to perform Python data science tasks without needing to write perfect code (or any code at all!)

Here is Mito in action:

Here are the commands to install Mito:

python -m pip install mitoinstaller
python -m mitoinstaller install

Then open Jupyter Lab and call the Mitosheet

import mitosheet
mitosheet.sheet()

The full instructions can be found on the Mito website under “docs.”

One of the great things about spreadsheets is that you can edit your data very quickly. In Mito, you can pick any data-point in the DataFrame and change the value by typing it in — just like a spreadsheet.

Before you start editing your values, you’ll want to import your data. Mito allows you to pass in any DataFrame, but you can also pass in any Excel or CSV file, directly from your local file system, which you can navigate from Mito. The tool can handle data sizes that a spreadsheet will not allow and process the data much faster than any spreadsheet.

After you perform your analysis, you can also export for your data as an Excel or CSV file by clicking the “export” button in the tool bar.

As mentioned above, each edit you make in Mito will generate the equivalent Python in the code cell below in your notebook. This is an incredibly fast way to generate Python scripts and perform data analysis and cleaning.

Mito has operations such as:

• Pivot Tables
• Graphing
• Merging
• Filtering
• Sorting
• Summary Statistics
• Spreadsheet Formulas
• Deduplicating
• and more!

Here is what Pivot tables and Graphing look like in Mito (each of these generates production ready Python for you):

One powerful feature of Mito from the list above is the ability to use spreadsheet formulas inside a Python environment. These formulas are simple to use and require much less tedious syntax than Pandas, yet they still generate the equivalent Pandas code in the cell below.

New Mito Features:

Mito recently introduced formatting features where, you can format your cells like you are using Excel.

You can also export your DataFrame to Excel or a CSV file, using the export interface:

New in Python: Format your DataFrame like Excel was originally published in trymito on Medium, where people are continuing the conversation by highlighting and responding to this story.

Mito is a spreadsheet front-end for Python. You can call Mito into your Jupyter Notebook and each edit you make in the front-end will generate the equivalent Python.

Here is a video demo:

To install Mito, use these commands:

python -m pip install mitoinstaller
python -m mitoinstaller install

Here is a link to the full install instructions.

Mito is a great package for slicing and dicing your data. Mito allows the users to create interactive pivot tables and graphs with just a few clicks.

Mito pivot tables are a great way to see relationships between different variables and group the data in a way that makes insights more apparent.

You can configure a Mito pivot table by selecting the Pivot button from the toolbar and then choosing your rows, columns, values and aggregation types.

Each edit in Mito generates the equivalent Python in the code cell below. It is a much faster way of producing code than constantly heading to Stack Overflow to find the correct syntax.

The pivot table above generates this code and auto-comments it as well!

Mito does not just generate the code for pivot tables. In Mito, you can merge datasets, filter, sort, use functions, look at summary statistics, and more — and Mito will generate the equivalent Python for each of these edits.

Mito also allows the users to generate dynamic Plotly charts without any coding required. Plotly is an amazing Python graphing package.

To create a Plotly chart, all the user has to do is click the graph button and select their axes.

Here is Mito’s full documentation.

2.Pandas Profiling

Pandas Profiling takes the df.describe() function from Pandas and elaborates on the functionality, providing amazing summary information for a dataframe quickly and efficiently.

Pandas Profiling is a great tool for exploratory data analysis.

You can install the package locally with these commands:

import sys
!{sys.executable} -m pip install -U pandas-profiling[notebook]
!jupyter nbextension enable --py widgetsnbextension

Pandas Profiling provides advanced summary statistics and information for a dataset with out having to write very much code at all.

Here it the full description of the Pandas Profiling functionality, as described on the documentation website:

Two really powerful features are the reports on Missing Values and Descriptive Statistics. When analyzing a new dataset, handling missing values can be a pain. Pandas Profiling makes this process much easier. The descriptive statistics are great for understanding the dataset more in depth before proceeding in your analysis.

3.Lux

Lux is a great package for visualizing data. The tedium of getting the code exactly correct to make the chart you want can be a large time sink. Lux recommends graphs for you, that you can select with a click of a button.

Lux can be applied to any dataframe and will automatically suggest graphs that the user can choose from.

The users can also use the intent function to pass in the columns they are interested in exploring and Lux will automatically suggest graphs.

Lux can be installed with a simple line of Python:

**pip install lux-api**

I hope these packages are helpful :)

3 Python Packages to Simplify Data Science was originally published in trymito on Medium, where people are continuing the conversation by highlighting and responding to this story.

Introduction

Analytics is increasingly infused throughout organizations to help leaders make data-driven decisions. All teams consume and create data that has the potential to add value back to the company. The challenge is that these decentralized teams likely have different analytic tools and capabilities. But, to be most effective, the teams must work together to produce actionable insights.

This article will explore how Mitosheet, a free Python package, facilitates collaboration between disparate analytic teams to help organizations make data-driven decisions.

Mitosheet homepage

Install instructions

Documentation

Github

What is Mito?

Microsoft Excel may be the oldest and most prolific analytic software created. Most analysts are familiar with at least Excel’s primary functions of filtering, sorting, and pivoting data.

On the other hand, Python’s capabilities are superior to Excel, but Python is far from the same adoption rate, even though it is currently the most popular coding language. What if we could have the best of both worlds?

This is where Mitosheet comes in — it is a free Excel-style Python widget that empowers users to analyze big data effortlessly without coding. Mimicking Excel’s user experience, users can similarly interact with data at scale. Where Excel maxes out at ~1M rows, Mito has no limit. Plus, advanced analytics, such as machine learning, can easily be applied since the data remains in Python.

Below is a glimpse of interacting with a Python dataframe using Mitosheet like one would an Excel spreadsheet. You can find more examples at the end of this article.

How does Mito empower collaboration?

Collaboration is the key to actionable analytics as different teams can contribute different strengths. One group may have more sophisticated analytic skills, another may be subject matter experts, and another may be best suited to operationalize the analysis. These teams need to collaborate even if they have different analytic tools or capabilities.

Mito overcomes those hurdles to bridge the gap between teams.

First, Python and Mito are free! Not every team can afford the hefty price tag of Alteryx, Talend, or similar tools. Even cheaper tools like Tableau will likely require involvement from procurement, accounts payable, and IT departments. These hurdles can result in different teams using different tools. Since it’s a free Python package, Mito removes all barriers by not requiring budget dollars or likely involvement from other departments. Python and Mito are easily accessible to all!

Second, Mito offers accelerated time to value with a natural and commonplace user experience. Nearly no instruction is needed if the user has Excel or Google Sheets experience. Analysts will immediately feel right at home when using Mito!

Third, Mito produces insightfully marked-down Python code with each click. This code is easily accessible right below the interface. It can be copied and pasted, edited, and shared between collaborators.

Collaboration in practice with Mito

Let’s see how Mito could help a pricing department needing to generate $20M in gross profit by year-end. Unfortunately, their analytics are limited by having only Excel 2013. To achieve this goal, they will need far more data than they’re used to analyzing. The CFO asks an advanced analytics team to help out, but the teams don’t have the same tools or acumen.

Mito is perfect for bridging the gap between the two teams.

The advanced analytics team engineers the required data without concern for data size. Armed with Mito, the pricing team fearlessly dives into the data. They use Mito with little instruction since the Excel-style widget is a natural way for them to interact with data.

Without needing to know Python, the pricing team can effortlessly import and query the data despite it being millions of rows. Every detail needed to make the best pricing decisions possible is now at their fingertips. As pricing requests come in, the team can effortlessly pivot, filter, sort, and chart the data in near real-time.

Over time, the pricing team develops consistently helpful analyses. With each analysis, Mito produces shareable marked-down Python code. The pricing team can share the code with IT to operationalize the data refresh, analyses, and distribution. By sharing the code, IT saves development time and effort.

With this descriptive analysis in hand, the pricing team can now re-engage with the advanced analytics team. A machine learning Python model is needed to scale beyond manual ad hoc analyses.

The advanced analytics team can collaboratively model since the original analysis is in Python already. Although the pricing team never needed to code, they became familiar with Python since Mito produced the code below the interface. Now they have the foundation to learn more advanced machine learning coding techniques.

This is just one example of how Mito can empower collaboration across analytic teams with different analytical tools and acumen.

Mitosheet: Empowering Collaboration

Mito offers many capabilities to make analysts more productive. However, its ability to empower collaboration between teams may be its most valuable feature. Mito clears the hurdles of analytic teams working collaboratively. Mito allows all analytical groups to effectively collaborate so that the business can rapidly action comprehensive insights.

Mitosheet Functionality

Below are just a few examples of Mitosheet’s powerful, intuitive functionality.

Mitosheet: Enabling Collaboration was originally published in trymito on Medium, where people are continuing the conversation by highlighting and responding to this story.