hirokiky/plt.ipynb

## plt.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106cb47b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot([1,3,2,4])\n",
    "\n",
    "# Importing matplotlib.pyplot and calling .show()\n",
    "# in the same cell won't render any graphs.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1085c5748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calling .show() again will work properly.\n",
    "plt.plot([1,3,2,4])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

## plt_ok.ipynb

      
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              plt_ok.ipynb
            
          
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## requirements.txt
appnope==0.1.0
attrs==17.3.0
bleach==2.1.2
cycler==0.10.0
decorator==4.1.2
entrypoints==0.2.3
html5lib==1.0.1
ipykernel==4.8.0.dev0
ipython==6.2.1
ipython-genutils==0.2.0
ipywidgets==7.0.5
jedi==0.11.1
Jinja2==2.10
jsonschema==2.6.0
jupyter==1.0.0
jupyter-client==5.2.0
jupyter-console==5.2.0
jupyter-core==4.4.0
MarkupSafe==1.0
matplotlib==2.1.1
mistune==0.8.3
nbconvert==5.3.1
nbformat==4.4.0
notebook==5.2.2
numpy==1.13.3
pandocfilters==1.4.2
parso==0.1.1
pexpect==4.3.1
pickleshare==0.7.4
pluggy==0.6.0
prompt-toolkit==1.0.15
ptyprocess==0.5.2
py==1.5.2
Pygments==2.2.0
pyparsing==2.2.0
pytest==3.3.1
python-dateutil==2.6.1
pytz==2017.3
pyzmq==16.0.3
qtconsole==4.3.1
simplegeneric==0.8.1
six==1.11.0
terminado==0.8.1
testpath==0.3.1
tornado==4.5.2
traitlets==4.3.2
wcwidth==0.1.7
webencodings==0.5.1
widgetsnbextension==3.0.8
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<matplotlib.figure.Figure at 0x106cb47b8>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"import matplotlib.pyplot as plt\n",
	"plt.plot([1,3,2,4])\n",
	"\n",
	"# Importing matplotlib.pyplot and calling .show()\n",
	"# in the same cell won't render any graphs.\n",
	"plt.show()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x1085c5748>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"# Calling .show() again will work properly.\n",
	"plt.plot([1,3,2,4])\n",
	"plt.show()"
	]
	}
	],
	"metadata": {
	"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": "3.6.3"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}
	appnope==0.1.0
	attrs==17.3.0
	bleach==2.1.2
	cycler==0.10.0
	decorator==4.1.2
	entrypoints==0.2.3
	html5lib==1.0.1
	ipykernel==4.8.0.dev0
	ipython==6.2.1
	ipython-genutils==0.2.0
	ipywidgets==7.0.5
	jedi==0.11.1
	Jinja2==2.10
	jsonschema==2.6.0
	jupyter==1.0.0
	jupyter-client==5.2.0
	jupyter-console==5.2.0
	jupyter-core==4.4.0
	MarkupSafe==1.0
	matplotlib==2.1.1
	mistune==0.8.3
	nbconvert==5.3.1
	nbformat==4.4.0
	notebook==5.2.2
	numpy==1.13.3
	pandocfilters==1.4.2
	parso==0.1.1
	pexpect==4.3.1
	pickleshare==0.7.4
	pluggy==0.6.0
	prompt-toolkit==1.0.15
	ptyprocess==0.5.2
	py==1.5.2
	Pygments==2.2.0
	pyparsing==2.2.0
	pytest==3.3.1
	python-dateutil==2.6.1
	pytz==2017.3
	pyzmq==16.0.3
	qtconsole==4.3.1
	simplegeneric==0.8.1
	six==1.11.0
	terminado==0.8.1
	testpath==0.3.1
	tornado==4.5.2
	traitlets==4.3.2
	wcwidth==0.1.7
	webencodings==0.5.1
	widgetsnbextension==3.0.8