Data visualization is becoming a more and more important part of data science and data analysis. We are seeing a trend of Python packages that are working to make Python analysis more relevant for those outside the data science team. One way to do this is through visualizations. Visualizations makes data analysis understandable to less technical audiences. Here are 3 packages that make data visualization easier and more accessible.
Mito is a free Python package that allows the users to call a spreadsheet interface into their Jupyter environment. Every edit you make in the spreadsheet will generate the equivalent Python in the code cell below.
Mito allows the Python users to generate their graphs in a point and click environment, but it also generates the equivalent code for each of these graphs. This is allows for further customization and the ability to put the visualizations into production.
Here is a demo video from the Data Professor:
To install Mito, run these commands:
python -m pip install mitoinstaller
python -m mitoinstaller install
Then open Jupyter Lab, and this code should appear:
Just run those commands to render the Mitosheet.
Here are the full install instructions.
Mito has lot’s of great functionality for exploratory data analysis, data cleaning, and data analysis, including:
- Generating graphs and the equivalent code
- Creating pivot tables
- Merging datasets together
- Using spreadsheet formulas
- Filtering and sorting datasets
- Looking at summary statistics
- Filling null values
- and much more!
Here is the full documentation.
Data visualization in Python is extremely popular for data scientists, through packages like Matplotlib and Seaborn. But these packages, though thorough, do not make charts that are visually stimulating or interactive. Plotly is making huge strides in these two areas.
Plotly offers the standard charts that every Python users would want to use. Here is a screenshot from their website:
But they also give access to advanced graphics, centered around ML and AI.
But the real innovations is Plotly’s focus on interactive charts. Instead of having to worry about adjusting the code anytime you want to change the view, Plotly gives you a GUI to do so.
This is the first example from the Plotly Graphing Package documenation:
In even this simple example, you can change the color of the graph using the UI dropdown on the right.
In terms of a combination of ease of set-up and flexibility of graph, Plotly is the best graphing package I have come across.
Lux is a great tool for data visualization and exploratory data analysis. Lux will take any data frame and automatically recommend visualizations that help you explore and share the data. You do not need to write any of the visualization code yourself.
To import lux:
import pandas as pd
Below you can see how Lux provides visualization options for any data frame. All you need to do is select the chart you want. No coding required. This is a huge time saver, as getting the exact correct syntax from a package from like Matplotlib or Seaborn can be time intensive.
Lux allows you to export your visualizations as well, making the sharing process smooth and simple. You can export the visualizations to HTML, or you can convert them into the equivalent Matplotlib code, so you can edit them further.
I hope you found these packages helpful for your data visualization tasks :)