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haydarai/Sales statistics.ipynb

Created December 10, 2018 19:25
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Sales statistics.ipynb
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" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
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"}(window));"
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"from bokeh.plotting import figure, output_file, show\n",
"from bokeh.io import output_notebook\n",
"\n",
"output_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Summary"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AMOUNT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4.384728e+07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AMOUNT\n",
"0 4.384728e+07"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_average_month = pd.read_json('http://35.240.46.26:5000/sales/summary/month/avg')\n",
"df_average_month"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AMOUNT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4.384728e+07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AMOUNT\n",
"0 4.384728e+07"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_sum_month = pd.read_json('http://35.240.46.26:5000/sales/summary/month/sum')\n",
"df_sum_month"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AMOUNT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4.879302e+06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AMOUNT\n",
"0 4.879302e+06"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_average_year = pd.read_json('http://35.240.46.26:5000/sales/summary/year/avg')\n",
"df_average_year"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AMOUNT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4.384728e+07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AMOUNT\n",
"0 4.384728e+07"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_average_month = pd.read_json('http://35.240.46.26:5000/sales/summary/month/avg')\n",
"df_average_month"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sales Overall"
]
},
{
"cell_type": "code",
"execution_count": 74,
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{
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"\n",
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" var attempts = 0;\n",
" var timer = setInterval(function(root) {\n",
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" embed_document(root);\n",
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"source": [
"df_overall = pd.read_json('http://35.240.46.26:5000/sales/last/overall')\n",
"df_overall = df_overall[:-1] # remove 2018\n",
"\n",
"p_last_overall = figure(title=\"Sales overall (Every year)\", x_axis_label='Year', y_axis_label='Amount')\n",
"p_last_overall.line(df_overall.YEAR, df_overall.AMOUNT, line_width=2)\n",
"show(p_last_overall)"
]
},
{
"cell_type": "markdown",
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"source": [
"# Sales overall (This year)"
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},
{
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{
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" embed_document(root);\n",
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"})(window);"
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"source": [
"df_this_year_overall = pd.read_json('http://35.240.46.26:5000/sales/last/year')\n",
"df_this_year_overall = df_this_year_overall[:-1] # remove this month\n",
"\n",
"p_this_year_overall = figure(title=\"Sales overall (This year)\", x_axis_label='Month', y_axis_label='Amount')\n",
"p_this_year_overall.line(df_this_year_overall.MONTH, df_this_year_overall.AMOUNT, line_width=2)\n",
"show(p_this_year_overall)"
]
},
{
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"source": [
"# Sales overall (This month)"
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{
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" clearInterval(timer);\n",
" }\n",
" }, 10, root)\n",
" }\n",
"})(window);"
],
"application/vnd.bokehjs_exec.v0+json": ""
},
"metadata": {
"application/vnd.bokehjs_exec.v0+json": {
"id": "4415"
}
},
"output_type": "display_data"
}
],
"source": [
"df_this_month_overall = pd.read_json('http://35.240.46.26:5000/sales/last/month')\n",
"\n",
"p_this_month_overall = figure(title=\"Sales overall (This month)\", x_axis_label='Date', y_axis_label='Amount')\n",
"p_this_month_overall.line(df_this_month_overall.DATE, df_this_month_overall.AMOUNT, line_width=2)\n",
"show(p_this_month_overall)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sales overall (Today)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'DataFrame' object has no attribute 'TIME'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-77-1b3ba560a30f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mp_today_overall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Sales overall (This month)\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_axis_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_axis_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Amount'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mp_today_overall\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_today_overall\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTIME\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_today_overall\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAMOUNT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline_width\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp_today_overall\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda2/envs/data-mining/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 4374\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4375\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4376\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4378\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'TIME'"
]
}
],
"source": [
"df_today_overall = pd.read_json('http://35.240.46.26:5000/sales/last/day')\n",
"\n",
"p_today_overall = figure(title=\"Sales overall (This month)\", x_axis_label='Time', y_axis_label='Amount')\n",
"p_today_overall.line(df_today_overall.TIME, df_today_overall.AMOUNT, line_width=2)\n",
"show(p_today_overall)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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