{ "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!\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Time Series Plot with `datetime` Objects ###" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "import pandas as pd\n", "from datetime import datetime\n", "\n", "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')\n", "\n", "data = [go.Scatter(x=df.Date, y=df['AAPL.High'])]\n", "\n", "py.iplot(data, filename = 'time-series-simple')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Date Strings" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "import pandas as pd\n", "\n", "df = pd.read_csv(\"https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv\")\n", "\n", "data = [go.Scatter(\n", " x=df.Date,\n", " y=df['AAPL.Close'])]\n", "\n", "py.iplot(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Time Series Plot with Custom Date Range " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "import datetime\n", "\n", "def to_unix_time(dt):\n", " epoch = datetime.datetime.utcfromtimestamp(0)\n", " return (dt - epoch).total_seconds() * 1000\n", "\n", "x = [datetime.datetime(year=2013, month=10, day=04),\n", " datetime.datetime(year=2013, month=11, day=05),\n", " datetime.datetime(year=2013, month=12, day=06)]\n", "data = [go.Scatter(\n", " x=x,\n", " y=[1, 3, 6])]\n", "\n", "layout = go.Layout(xaxis = dict(\n", " range = [to_unix_time(datetime.datetime(2013, 10, 17)),\n", " to_unix_time(datetime.datetime(2013, 11, 20))]\n", " ))\n", "\n", "fig = go.Figure(data = data, layout = layout)\n", "py.iplot(fig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Manually Set Range" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "import pandas as pd\n", "\n", "df = pd.read_csv(\"https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv\")\n", "\n", "trace_high = go.Scatter(\n", " x=df.Date,\n", " y=df['AAPL.High'],\n", " name = \"AAPL High\",\n", " line = dict(color = '#17BECF'),\n", " opacity = 0.8)\n", "\n", "trace_low = go.Scatter(\n", " x=df.Date,\n", " y=df['AAPL.Low'],\n", " name = \"AAPL Low\",\n", " line = dict(color = '#7F7F7F'),\n", " opacity = 0.8)\n", "\n", "data = [trace_high,trace_low]\n", "\n", "layout = dict(\n", " title = \"Manually Set Date Range\",\n", " xaxis = dict(\n", " range = ['2016-07-01','2016-12-31'])\n", ")\n", "\n", "fig = dict(data=data, layout=layout)\n", "py.iplot(fig, filename = \"Manually Set Range\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Time Series With Rangeslider" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "import pandas as pd\n", "\n", "df = pd.read_csv(\"https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv\")\n", "\n", "trace_high = go.Scatter(\n", " x=df.Date,\n", " y=df['AAPL.High'],\n", " name = \"AAPL High\",\n", " line = dict(color = '#17BECF'),\n", " opacity = 0.8)\n", "\n", "trace_low = go.Scatter(\n", " x=df.Date,\n", " y=df['AAPL.Low'],\n", " name = \"AAPL Low\",\n", " line = dict(color = '#7F7F7F'),\n", " opacity = 0.8)\n", "\n", "data = [trace_high,trace_low]\n", "\n", "layout = dict(\n", " title='Time Series with Rangeslider',\n", " xaxis=dict(\n", " rangeselector=dict(\n", " buttons=list([\n", " dict(count=1,\n", " label='1m',\n", " step='month',\n", " stepmode='backward'),\n", " dict(count=6,\n", " label='6m',\n", " step='month',\n", " stepmode='backward'),\n", " dict(step='all')\n", " ])\n", " ),\n", " rangeslider=dict(\n", " visible = True\n", " ),\n", " type='date'\n", " )\n", ")\n", "\n", "fig = dict(data=data, layout=layout)\n", "py.iplot(fig, filename = \"Time Series with Rangeslider\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dash Example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Dash](https://plotly.com/products/dash/) is an Open Source Python library which can help you convert plotly figures into a reactive, web-based application. Below is a simple example of a dashboard created using Dash. Its [source code](https://github.com/plotly/simple-example-chart-apps/tree/master/dash-timeseriesplot) can easily be deployed to a PaaS." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import IFrame\n", "IFrame(src= \"https://dash-simple-apps.plotly.host/dash-timeseriesplot/\", width=\"100%\", height=\"750px\", frameBorder=\"0\")" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import IFrame\n", "IFrame(src= \"https://dash-simple-apps.plotly.host/dash-timeseriesplot/code\", width=\"100%\", height=500, frameBorder=\"0\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Reference\n", "See https://plotly.com/python/reference/#layout-xaxis-rangeslider and
https://plotly.com/python/reference/#layout-xaxis-rangeselector for more information and chart attribute options!" ] }, { "cell_type": "code", "execution_count": 3, "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/s5/vjqn03zs7nn8zs_fwzcf14r40000gn/T/pip-req-build-mtlz90v1\n", "Building wheels for collected packages: publisher\n", " Building wheel for publisher (setup.py) ... \u001b[?25ldone\n", "\u001b[?25h Stored in directory: /private/var/folders/s5/vjqn03zs7nn8zs_fwzcf14r40000gn/T/pip-ephem-wheel-cache-mkvkhtzs/wheels/99/3e/a0/fbd22ba24cca72bdbaba53dbc23c1768755fb17b3af0f33966\n", "Successfully built publisher\n", "Installing collected packages: publisher\n", " Found existing installation: publisher 0.13\n", " Uninstalling publisher-0.13:\n", " Successfully uninstalled publisher-0.13\n", "Successfully installed publisher-0.13\n", "\u001b[33mYou are using pip version 19.0.3, however version 19.1.1 is available.\n", "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\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", "\n", "import publisher\n", "publisher.publish(\n", " 'time-series.ipynb', 'python/time-series/', 'Python Time Series | Examples | Plotly',\n", " 'How to plot date and time in python. ',\n", " title= 'Time Series Plots | plotly',\n", " name = 'Time Series',\n", " has_thumbnail='true', thumbnail='thumbnail/time-series.jpg', \n", " language='python', page_type='example_index', \n", " display_as='financial', order=0,\n", " ipynb='~notebook_demo/213')" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }