Plotly.Express allows creating several types of histograms from a dataset using a single function px.histogram(df, parameters). In this article, I’d like to explore all the parameters and how they influence the look and feel of the chart.
A histogram is a special kind of bar chart showing the distribution of a variable(s). Plotly.Express allows creating several types of histograms from a dataset using a single function
px.histogram(df, parameters). In this article, I’d like to explore all the parameters and how they influence the look and feel of the chart.
Plotly.Express is the higher-level API of the python Plotly library specially designed to work with the data frames. It creates interactive charts which you can zoom in and out, switch on and off parts of the graph and a tooltip with information appears when you hover over any element on the plot.
All the charts can be created using this notebook on the **[GitHub**](https://github.com/vaclavdekanovsky/data-analysis-in-examples/blob/master/Vizualizations/Plotly/Histogram/Histograms.ipynb). Feel free to download, play, and update it.
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](https://plotly.com/python/getting-started/#jupyterlab-support-python-35). In a regular notebook, I had to install
conda install -c anaconda nbformat)
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.
Data visualization is the graphical representation of data in a graph, chart or other visual formats. It shows relationships of the data with images.
🔥To access the slide deck used in this session for Free, click here: https://bit.ly/GetPDF_DataV_P 🔥 Great Learning brings you this live session on 'Data Vis...
Data exploration is a key aspect of data analysis and model building. Without spending significant time on understanding the data and its patterns one cannot expect to build efficient predictive models.
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.