It provides two methods with same API as pandas "plot()". The main aim of Cufflinks is to simplify data visualization by providing same API as that of pandas dataframe function "plot()" but generating interactive charts using Plotly. Cufflinks is built on top of another data visualization library named Plotly. What Can You Learn From This Article? ¶Īs a part of this tutorial, we have explained how to use Python library "cufflinks" to create interactive data visualizations. It let us generate interactive charts based on plotly directly from pandas dataframe with one line of code. To our surprise, there is a Python library named "cufflinks" that is designed with this aim in mind. It can be very helpful if we can generate interactive charts directly from the pandas dataframe. Python also have many data visualization libraries like Plotly, Bokeh, Holoviews, Bqplot, Altair, etc that can generate interactive charts. Almost everything is interactive nowadays (charts, apps, dashboards, etc). All the plots generated by matplotlib are static hence charts generated by pandas dataframe's '.plot()' API will be static as well.īut this is an era of interactivity. The pandas data visualization uses the matplotlib library behind the scene. We can create easily create charts like scatter charts, bar charts, line charts, etc with just a line of code (by calling "plot()" with necessary parameters). It let create charts from dataframe directly by simply calling plot() method on it. Pandas is the most preferred library nowadays by the majority of data scientists worldwide for working (loading, manipulating, etc) with structured datasets (tables).īesides data management and manipulation functionalities, it provides very convenient data visualization functionality. Cufflinks - How to create plotly charts from pandas dataframe with one line of code? ¶
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