{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#### New to Plotly?\n", "Plotly's Python library is free and open source! [Get started](https://plotly.com/python/getting-started/) by downloading the client and [reading the primer](https://plotly.com/python/getting-started/).\n", "
You can set up Plotly to work in [online](https://plotly.com/python/getting-started/#initialization-for-online-plotting) or [offline](https://plotly.com/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plotly.com/python/getting-started/#start-plotting-online).\n", "
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Imports\n", "The tutorial below imports [NumPy](http://www.numpy.org/), [Pandas](https://plotly.com/pandas/intro-to-pandas-tutorial/), [SciPy](https://www.scipy.org/) and [PeakUtils](http://pythonhosted.org/PeakUtils/)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "import plotly.figure_factory as ff\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import scipy\n", "import peakutils" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import Data\n", "For our baseline detection example, we will import some data on milk production by month:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "milk_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/monthly-milk-production-pounds.csv')\n", "time_series = milk_data['Monthly milk production (pounds per cow)']\n", "time_series = np.asarray(time_series)\n", "\n", "df = milk_data[0:15]\n", "\n", "table = ff.create_table(df)\n", "py.iplot(table, filename='milk-production-dataframe')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plot with Baseline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# calculate baseline y values\n", "baseline_values = peakutils.baseline(time_series)\n", "\n", "trace = go.Scatter(\n", " x=[j for j in range(len(time_series))],\n", " y=time_series,\n", " mode='lines',\n", " marker=dict(\n", " color='#B292EA',\n", " ),\n", " name='Original Plot'\n", ")\n", "\n", "trace2 = go.Scatter(\n", " x=[j for j in range(len(time_series))],\n", " y=baseline_values,\n", " mode='markers',\n", " marker=dict(\n", " size=3,\n", " color='#EB55BF',\n", " symbol='circle-open'\n", " ),\n", " name='Baseline'\n", ")\n", "\n", "data = [trace, trace2]\n", "py.iplot(data, filename='milk-production-plot-with-baseline')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Collecting git+https://github.com/plotly/publisher.git\n", " Cloning https://github.com/plotly/publisher.git to /private/var/folders/tc/bs9g6vrd36q74m5t8h9cgphh0000gn/T/pip-req-build-AcA6we\n", "Building wheels for collected packages: publisher\n", " Running setup.py bdist_wheel for publisher ... \u001b[?25ldone\n", "\u001b[?25h Stored in directory: /private/var/folders/tc/bs9g6vrd36q74m5t8h9cgphh0000gn/T/pip-ephem-wheel-cache-4AjEk_/wheels/99/3e/a0/fbd22ba24cca72bdbaba53dbc23c1768755fb17b3af0f33966\n", "Successfully built publisher\n", "Installing collected packages: publisher\n", " Found existing installation: publisher 0.11\n", " Uninstalling publisher-0.11:\n", " Successfully uninstalled publisher-0.11\n", "Successfully installed publisher-0.11\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning:\n", "\n", "The `IPython.nbconvert` package has been deprecated since IPython 4.0. You should import from nbconvert instead.\n", "\n", "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/publisher/publisher.py:53: UserWarning:\n", "\n", "Did you \"Save\" this notebook before running this command? Remember to save, always save.\n", "\n" ] } ], "source": [ "from IPython.display import display, HTML\n", "\n", "display(HTML(''))\n", "display(HTML(''))\n", "\n", "! pip install git+https://github.com/plotly/publisher.git --upgrade\n", "import publisher\n", "publisher.publish(\n", " 'python-Baseline-Detection.ipynb', 'python/baseline-detection/', 'Baseline Detection | plotly',\n", " 'Learn how to detect baselines on data in Python.',\n", " title='Baseline Detection in Python | plotly',\n", " name='Baseline Detection',\n", " language='python',\n", " page_type='example_index', has_thumbnail='false', display_as='peak-analysis', order=1,\n", " ipynb= '~notebook_demo/117')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }