{
"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 dowloading 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/), and [scipy](https://www.scipy.org/)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import plotly.plotly as py\n",
"import plotly.graph_objs as go\n",
"from plotly.tools import FigureFactory as FF\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import scipy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Make the Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are generating a 1D dataset from a `Weibull Distribution` which has the distrubution\n",
"\n",
"$$\n",
"\\begin{align*}\n",
"X = \\log(U)^{\\frac{1}{a}}\n",
"\\end{align*}\n",
"$$\n",
"\n",
"where $U$ is drawn from the `Uniform Distribution`."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.86317076 0.79217698 2.07432654 0.70721605 0.24102326 1.44261213\n",
" 0.85526797 1.0158948 1.19976016 1.78112064]\n"
]
}
],
"source": [
"x=np.random.weibull(1.25, size=1000)\n",
"print(x[:10])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Histogram"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By using a histogram, we can properly divide a 1D dataset into bins with a particular size or width, so as to form a discrete probability distribution"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trace = go.Histogram(x=x, xbins=dict(start=np.min(x), size=0.25, end=np.max(x)),\n",
" marker=dict(color='rgb(0, 0, 100)'))\n",
"\n",
"layout = go.Layout(\n",
" title=\"Histogram Frequency Counts\"\n",
")\n",
"\n",
"fig = go.Figure(data=go.Data([trace]), layout=layout)\n",
"py.iplot(fig, filename='histogram-freq-counts')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Larger Bins"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can experiment with our bin size and the histogram by grouping the data into larger intervals"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trace = go.Histogram(x=x, xbins=dict(start=np.min(x), size=0.75, end=np.max(x)),\n",
" marker=dict(color='rgb(0, 0, 100)'))\n",
"\n",
"layout = go.Layout(\n",
" title=\"Histogram Frequency Counts\"\n",
")\n",
"\n",
"fig = go.Figure(data=go.Data([trace]), layout=layout)\n",
"py.iplot(fig, filename='histogram-freq-counts-larger-bins')"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"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 /var/folders/ld/6cl3s_l50wd40tdjq2b03jxh0000gp/T/pip-ctBFME-build\n",
"Installing collected packages: publisher\n",
" Found existing installation: publisher 0.10\n",
" Uninstalling publisher-0.10:\n",
" Successfully uninstalled publisher-0.10\n",
" Running setup.py install for publisher ... \u001b[?25l-\b \b\\\b \bdone\n",
"\u001b[?25hSuccessfully installed publisher-0.10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/brandendunbar/Desktop/test/venv/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from nbconvert instead.\n",
" \"You should import from nbconvert instead.\", ShimWarning)\n",
"/Users/brandendunbar/Desktop/test/venv/lib/python2.7/site-packages/publisher/publisher.py:53: UserWarning: Did you \"Save\" this notebook before running this command? Remember to save, always save.\n",
" warnings.warn('Did you \"Save\" this notebook before running this command? '\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-Frequency-Counts.ipynb', 'python/frequency-counts/', 'Frequency Counts | plotly',\n",
" 'Learn how to perform frequency counts using Python.',\n",
" title='Frequency Counts in Python. | plotly',\n",
" name='Frequency Counts',\n",
" language='python',\n",
" page_type='example_index', has_thumbnail='false', display_as='statistics', order=2,\n",
" ipynb= '~notebook_demo/111')"
]
},
{
"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": "2.7.10"
}
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"nbformat": 4,
"nbformat_minor": 0
}