In this exercise, I’ll walk you through the process of drawing a thick colored line on the top of the shaded progress of the concurrent events.
Creating interactive graphs with python’s Plotly.Express from a data frame works like a charm. With a single line of code, you can explore the basic characteristics of your dataset. Adding a few more code-lines you can conjure up a really fancy but very narrative chart.
In this exercise, I’ll walk you through the process of drawing a thick colored line on the top of the shaded progress of the concurrent events. It has two enormous benefits:
You can create all the charts with me, using the notebook stored on the Github. In this article you will learn:
I’ll use two datasets. The first about the progress of tourism around the world (tourist arrivals, 215 countries from 1995 to 2018), and the second showing ice-hockey national teams ranking in the last 6 years.
Plotly.Express was introduced in the version 4.0.0 of the plotly library and you can easily install it using:
## pip pip install plotly ## anaconda conda install -c anaconda plotly
Plotly Express also requires pandas to be installed, otherwise, you will get this error when you try to import it.
[In]: import plotly.express as px [Out]: ImportError: Plotly express requires pandas to be installed.
There are additional requirements if you want to use the plotly in Jupyter notebooks. For Jupyter Lab you need jupyterlab-plotly. In a regular notebook, I had to install
conda install -c anaconda nbformat)
Build interactive data visualization in Jupyter Notebooks using Plotly. Python is great for data exploration and data analysis and it’s all thanks to the support of amazing libraries like numpy, pandas, matplotlib, and many others.
Data visualization is the graphical representation of data in a graph, chart or other visual formats. It shows relationships of the data with images.
Python is great for data exploration and data analysis and it’s all thanks to the support of amazing libraries like numpy, pandas, matplotlib, and many others. During our data exploration and data analysis phase it’s very important to understand the data we are dealing with, and for that visual representations of our data can be extremely important.
Many a time, I have seen beginners in data science skip exploratory data analysis (EDA) and jump straight into building a hypothesis function or model. In my opinion, this should not be the case.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.