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Plotly's Python library is free and open source! Get started by downloading the client and reading the primer.
You can set up Plotly to work in online or offline mode, or in jupyter notebooks.
We also have a quick-reference cheatsheet (new!) to help you get started!
The tutorial below imports NumPy, Pandas, SciPy and PeakUtils.
importplotly.plotlyaspyimportplotly.graph_objsasgoimportplotly.figure_factoryasffimportnumpyasnpimportpandasaspdimportscipyimportpeakutils
For our baseline detection example, we will import some data on milk production by month:
milk_data=pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/monthly-milk-production-pounds.csv') time_series=milk_data['Monthly milk production (pounds per cow)'] time_series=np.asarray(time_series) df=milk_data[0:15] table=ff.create_table(df) py.iplot(table, filename='milk-production-dataframe')
# calculate baseline y valuesbaseline_values=peakutils.baseline(time_series) trace=go.Scatter( x=[jforjinrange(len(time_series))], y=time_series, mode='lines', marker=dict( color='#B292EA', ), name='Original Plot' ) trace2=go.Scatter( x=[jforjinrange(len(time_series))], y=baseline_values, mode='markers', marker=dict( size=3, color='#EB55BF', symbol='circle-open' ), name='Baseline' ) data= [trace, trace2] py.iplot(data, filename='milk-production-plot-with-baseline')
fromIPython.displayimportdisplay, HTMLdisplay(HTML('<link href="//fonts.googleapis.com/css?family=Open+Sans:600,400,300,200|Inconsolata|Ubuntu+Mono:400,700" rel="stylesheet" type="text/css" />')) display(HTML('<link rel="stylesheet" type="text/css" href="http://help.plot.ly/documentation/all_static/css/ipython-notebook-custom.css">')) ! pipinstallgit+https://github.com/plotly/publisher.git--upgradeimportpublisherpublisher.publish( 'python-Baseline-Detection.ipynb', 'python/baseline-detection/', 'Baseline Detection | plotly', 'Learn how to detect baselines on data in Python.', title='Baseline Detection in Python | plotly', name='Baseline Detection', language='python', page_type='example_index', has_thumbnail='false', display_as='peak-analysis', order=1, ipynb='~notebook_demo/117')