{ "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", "#### Version Check\n", "Note: Table traces are available in version 2.2.1+.
\n", "Run `pip install plotly --upgrade` to update your Plotly version" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2.2.2'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly\n", "plotly.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Basic Table" ] }, { "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", "trace = go.Table(\n", " header=dict(values=['A Scores', 'B Scores']),\n", " cells=dict(values=[[100, 90, 80, 90],\n", " [95, 85, 75, 95]]))\n", "\n", "data = [trace] \n", "py.iplot(data, filename = 'basic_table')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Styled Table" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "trace = go.Table(\n", " header=dict(values=['A Scores', 'B Scores'],\n", " line = dict(color='#7D7F80'),\n", " fill = dict(color='#a1c3d1'),\n", " align = ['left'] * 5),\n", " cells=dict(values=[[100, 90, 80, 90],\n", " [95, 85, 75, 95]],\n", " line = dict(color='#7D7F80'),\n", " fill = dict(color='#EDFAFF'),\n", " align = ['left'] * 5))\n", "\n", "layout = dict(width=500, height=300)\n", "data = [trace]\n", "fig = dict(data=data, layout=layout)\n", "py.iplot(fig, filename = 'styled_table')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Use a Panda's Dataframe" ] }, { "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", "\n", "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv')\n", "\n", "trace = go.Table(\n", " header=dict(values=list(df.columns),\n", " fill = dict(color='#C2D4FF'),\n", " align = ['left'] * 5),\n", " cells=dict(values=[df.Rank, df.State, df.Postal, df.Population],\n", " fill = dict(color='#F5F8FF'),\n", " align = ['left'] * 5))\n", "\n", "data = [trace] \n", "py.iplot(data, filename = 'pandas_table')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Changing Row and Column Size" ] }, { "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", "values = [[['Salaries', 'Office', 'Merchandise', 'Legal', 'TOTAL
EXPENSES']],\n", "[[\"Lorem ipsum dolor sit amet, tollit discere inermis pri ut. Eos ea iusto timeam, an prima laboramus vim. Id usu aeterno adversarium, summo mollis timeam vel ad\",\n", " \"Lorem ipsum dolor sit amet, tollit discere inermis pri ut. Eos ea iusto timeam, an prima laboramus vim. Id usu aeterno adversarium, summo mollis timeam vel ad\",\n", " \"Lorem ipsum dolor sit amet, tollit discere inermis pri ut. Eos ea iusto timeam, an prima laboramus vim. Id usu aeterno adversarium, summo mollis timeam vel ad\",\n", " \"Lorem ipsum dolor sit amet, tollit discere inermis pri ut. Eos ea iusto timeam, an prima laboramus vim. Id usu aeterno adversarium, summo mollis timeam vel ad\",\n", " \"Lorem ipsum dolor sit amet, tollit discere inermis pri ut. Eos ea iusto timeam, an prima laboramus vim. Id usu aeterno adversarium, summo mollis timeam vel ad\"]]]\n", "\n", "\n", "trace0 = go.Table(\n", " columnorder = [1,2],\n", " columnwidth = [80,400],\n", " header = dict(\n", " values = [['EXPENSES
as of July 2017'],\n", " ['DESCRIPTION']],\n", " line = dict(color = '#506784'),\n", " fill = dict(color = '#119DFF'),\n", " align = ['left','center'],\n", " font = dict(color = 'white', size = 12),\n", " height = 40\n", " ),\n", " cells = dict(\n", " values = values,\n", " line = dict(color = '#506784'),\n", " fill = dict(color = ['#25FEFD', 'white']),\n", " align = ['left', 'center'],\n", " font = dict(color = '#506784', size = 12),\n", " height = 30\n", " ))\n", "\n", "data = [trace0]\n", "\n", "py.iplot(data, filename = \"Row and Column Size\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Alternating Row Colors " ] }, { "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", "headerColor = 'grey'\n", "rowEvenColor = 'lightgrey'\n", "rowOddColor = 'white'\n", "\n", "trace0 = go.Table(\n", " header = dict(\n", " values = [['EXPENSES'],\n", " ['Q1'],\n", " ['Q2'],\n", " ['Q3'],\n", " ['Q4']],\n", " line = dict(color = '#506784'),\n", " fill = dict(color = headerColor),\n", " align = ['left','center'],\n", " font = dict(color = 'white', size = 12)\n", " ),\n", " cells = dict(\n", " values = [\n", " [['Salaries', 'Office', 'Merchandise', 'Legal', 'TOTAL']],\n", " [[1200000, 20000, 80000, 2000, 12120000]],\n", " [[1300000, 20000, 70000, 2000, 130902000]],\n", " [[1300000, 20000, 120000, 2000, 131222000]],\n", " [[1400000, 20000, 90000, 2000, 14102000]]],\n", " line = dict(color = '#506784'),\n", " fill = dict(color = [rowOddColor,rowEvenColor,rowOddColor, rowEvenColor,rowOddColor]),\n", " align = ['left', 'center'],\n", " font = dict(color = '#506784', size = 11)\n", " ))\n", "\n", "data = [trace0]\n", "\n", "py.iplot(data, filename = \"alternating row colors\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Row Color Based on Variable" ] }, { "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", "import colorlover as cl\n", "\n", "colors = cl.scales['5']['seq']['Blues']\n", "data = {'Year' : [2010, 2011, 2012, 2013, 2014],\n", " 'Color' : colors}\n", "df = pd.DataFrame(data)\n", "\n", "\n", "trace0 = go.Table(\n", " header = dict(\n", " values = [\"Color\", \"YEAR\"],\n", " line = dict(color = 'white'),\n", " fill = dict(color = 'white'),\n", " align = ['center'],\n", " font = dict(color = 'black', size = 12)\n", " ),\n", " cells = dict(\n", " values = [df.Color, df.Year],\n", " line = dict(color = [df.Color]),\n", " fill = dict(color = [df.Color]),\n", " align = 'center',\n", " font = dict(color = 'black', size = 11)\n", " ))\n", "\n", "data = [trace0]\n", "\n", "py.iplot(data, filename = \"row variable color\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Cell Color Based on Variable" ] }, { "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 numpy as np\n", "import colorlover as cl\n", "\n", "colors = cl.scales['9']['seq']['Reds']\n", "a = np.random.randint(low=0, high=9, size=10)\n", "b = np.random.randint(low=0, high=9, size=10)\n", "c = np.random.randint(low=0, high=9, size=10)\n", "\n", "\n", "trace0 = go.Table(\n", " header = dict(\n", " values = ['Column A', 'Column B', 'Column C'],\n", " line = dict(color = 'white'),\n", " fill = dict(color = 'white'),\n", " align = 'center',\n", " font = dict(color = 'black', size = 12)\n", " ),\n", " cells = dict(\n", " values = [a,b,c],\n", " line = dict(color = [np.array(colors)[a],np.array(colors)[b],\n", " np.array(colors)[c]]),\n", " fill = dict(color = [np.array(colors)[a],np.array(colors)[b],\n", " np.array(colors)[c]]),\n", " align = 'center',\n", " font = dict(color = 'white', size = 11)\n", " ))\n", "\n", "data = [trace0]\n", "\n", "py.iplot(data, filename = \"cell variable color\")" ] }, { "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-tableplot) 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-tableplot/\", width=\"100%\", height=\"850px\", 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-tableplot/code\", width=\"100%\", height=500, frameBorder=\"0\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Reference\n", "For more information on tables and table attributes see: https://plotly.com/python/reference/#table." ] }, { "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-t0p8g8mf\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-i9ylj2up/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", "import publisher\n", "publisher.publish(\n", " 'table.ipynb', 'python/table/', 'Python Tables | plotly',\n", " 'How to make tables in Python with Plotly.',\n", " title = 'Tables | plotly',\n", " name = 'Tables',\n", " thumbnail='thumbnail/table.gif', language='python',\n", " has_thumbnail='true', display_as='basic', order=7,\n", " ipynb='~notebook_demo/197')" ] } ], "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": 1 }