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Peak Finding in Python

Learn how to find peaks and valleys on datasets in Python


If you're using Dash Enterprise's Data Science Workspaces, you can copy/paste any of these cells into a Workspace Jupyter notebook.
Alternatively, download this entire tutorial as a Jupyter notebook and import it into your Workspace.
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New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Imports

The tutorial below imports Pandas, and SciPy.

In [1]:
import pandas as pd
from scipy.signal import find_peaks

Import Data

To start detecting peaks, we will import some data on milk production by month:

In [2]:
import plotly.graph_objects as go
import pandas as pd

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)']

fig = go.Figure(data=go.Scatter(
    y = time_series,
    mode = 'lines'
))

fig.show()

Peak Detection

We need to find the x-axis indices for the peaks in order to determine where the peaks are located.

In [3]:
import plotly.graph_objects as go
import pandas as pd
from scipy.signal import find_peaks

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)']

indices = find_peaks(time_series)[0]

fig = go.Figure()
fig.add_trace(go.Scatter(
    y=time_series,
    mode='lines+markers',
    name='Original Plot'
))

fig.add_trace(go.Scatter(
    x=indices,
    y=[time_series[j] for j in indices],
    mode='markers',
    marker=dict(
        size=8,
        color='red',
        symbol='cross'
    ),
    name='Detected Peaks'
))

fig.show()

Only Highest Peaks

We can attempt to set our threshold so that we identify as many of the highest peaks that we can.

In [4]:
import plotly.graph_objects as go
import numpy as np
import pandas as pd
from scipy.signal import find_peaks

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)']

indices = find_peaks(time_series, threshold=20)[0]

fig = go.Figure()
fig.add_trace(go.Scatter(
    y=time_series,
    mode='lines+markers',
    name='Original Plot'
))

fig.add_trace(go.Scatter(
    x=indices,
    y=[time_series[j] for j in indices],
    mode='markers',
    marker=dict(
        size=8,
        color='red',
        symbol='cross'
    ),
    name='Detected Peaks'
))

fig.show()

What About Dash?

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter