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Px.line(df_medals, x='nation', y='gold', title="Olympic Medals")īar charts can be used to visualize the result which varies in the time interval. Here we will be using the Medel for Olympic Short Track Speed Skating data set provided by Plotly as dals_wide() # Now we require a data set on which we can Plot the graph, luckily the Plotly library comes with some built-in custom data sets for example purposes, you can check out all the data set from the Plotly Data Package official website. We will start with importing the required libraries. When you execute it on the Jupyter notebook you will see the similar outputĪs you can see that Plotly give more option than the standard Python matplotlib library and its graph are more interactive. arr = np.random.randn(20,5) #random 2d arrayĭf = pd.DataFrame(arr, columns=) #data frame Then using the cufflinks API we can call the iplot() method on the DataFrame to plot the DataFrame values on Plotly. Now let’s first create a random numpy array of 20 rows and 5 columns and then convert it into a Pandas DataFrame. Import cufflinks as cf #to plot pd data frame Now launch your Jupyter notebook and start with importing the required modules. You can install all the below-mentioned libraries using the Python pip command on the terminal/Command prompt or directly use the Jupyter Notebook to install libraries.Ĭufflinks (API connect Pandas Data frame with Plotly) pip install cufflinks Plotly Basic So make sure that all those libraries are installed for your Python environment. Install Required Librariesįor this tutorial, we will be using some of Python’s most popular Data Science libraries along with Plotly.
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If Jupyter Notebook is not Installed in your System please check this article on How to Install Jupiter notebook for Python. When it comes to Data Science with Python we should always use the Jupyter Notebook to code, because it is specially designed for Data Science related work and Python Data Science libraries work very efficiently with Jupyter Notebook.