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Plotly Version 0.5.6 - Release notes - Pandas, NumPy, Datetimes
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Plotly Version 0.5.6 - Release notes - Pandas, NumPy, Datetimes
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{
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"source": [
"Plotly and IPython: What's new, plotly 0.5.6\n",
"===\n",
"Highlights from this release\n",
"---\n",
"\n",
"- Better NumPy Support\n",
"- Datetime Support\n",
"- py.open=True opens the response url automatically\n",
"- Pandas Series support"
]
},
{
"cell_type": "code",
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"input": [
"import plotly\n",
"p = plotly.plotly('IPython.Demo', '1fw3zw2o13')"
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"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"NumPy Support\n",
"---"
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{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np"
],
"language": "python",
"metadata": {},
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"prompt_number": 17
},
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"cell_type": "code",
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"input": [
"p.iplot([{'z': np.random.randn(80,100), 'type': 'heatmap'}])"
],
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"output_type": "stream",
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"\n",
"\n",
"\n"
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],
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"metadata": {},
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"output_type": "stream",
"stream": "stdout",
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"\n",
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{
"cell_type": "code",
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"input": [
"n = datetime.datetime.now()\n",
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"p.iplot(x,y)"
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"output_type": "stream",
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"text": [
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"y = [1,2,3]\n",
"p.iplot(x,y)"
],
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"p.open=True\n",
"===\n",
"With this option enabled, plots automatically open up in your browser. No need to copy n paste the urls anymore."
]
},
{
"cell_type": "code",
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"input": [
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"metadata": {},
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"input": [
"p.plot([1,2,3],[4,2,1], layout={'title': 'p.open=True'})"
],
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"metadata": {},
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"output_type": "stream",
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"\n",
"\n",
"\n"
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"prompt_number": 25,
"text": [
"{u'error': u'',\n",
" u'filename': u'plot from API (70) (1)',\n",
" u'message': u'',\n",
" u'url': u'https://plot.ly/~IPython.Demo/438',\n",
" u'warning': u''}"
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"prompt_number": 25
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas Series\n",
"==="
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df=pd.DataFrame({'A':np.random.rand(100), 'B':np.random.rand(100),'C':np.random.rand(100)} ,index= np.array(range(100)))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 100.000000</td>\n",
" <td> 100.000000</td>\n",
" <td> 100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 0.545290</td>\n",
" <td> 0.529573</td>\n",
" <td> 0.483429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 0.286990</td>\n",
" <td> 0.299712</td>\n",
" <td> 0.271988</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 0.011408</td>\n",
" <td> 0.001798</td>\n",
" <td> 0.001315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 0.311730</td>\n",
" <td> 0.245510</td>\n",
" <td> 0.268717</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 0.561711</td>\n",
" <td> 0.558073</td>\n",
" <td> 0.488095</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 0.796309</td>\n",
" <td> 0.803161</td>\n",
" <td> 0.716313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 0.989962</td>\n",
" <td> 0.988582</td>\n",
" <td> 0.982066</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
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"prompt_number": 28,
"text": [
" A B C\n",
"count 100.000000 100.000000 100.000000\n",
"mean 0.545290 0.529573 0.483429\n",
"std 0.286990 0.299712 0.271988\n",
"min 0.011408 0.001798 0.001315\n",
"25% 0.311730 0.245510 0.268717\n",
"50% 0.561711 0.558073 0.488095\n",
"75% 0.796309 0.803161 0.716313\n",
"max 0.989962 0.988582 0.982066"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = df.index.values\n",
"p.iplot(x, df.A, x, df.B, x, df.B)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
"\n"
]
},
{
"html": [
"<iframe height=\"650\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/439/600/600\" width=\"650\"></iframe>"
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"prompt_number": 29,
"text": [
"<IPython.core.display.HTML at 0x105f70150>"
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}