{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This is for making the power function" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sys.path.insert(0, '/Users/joel/src/thundr/tracker')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from tracker import db" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from datetime import datetime" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Engine(postgres://joel:***@localhost:5432/test)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.setup_session()\n", "db.Session.get_bind()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_data = [[0, 1], [1, 4], [3, 20], [8, 30]]" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.DataFrame(test_data).set_index(0)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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BoAiCDgBF/C+VTC+6oT/llQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot(kind='bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reindex along a monotonic index and interpolate, linear" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "df = df.reindex(range(0, 9))" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df['interpolated'] = df.interpolate(method='linear')" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "df2 = pd.DataFrame([(datetime(2019, 1, x+1), y) for x,y in test_data]).set_index(0)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "df2 = df2.reindex(pd.DatetimeIndex(freq='1d', start=df2.index[0], end=df2.index[-1]))" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "df2['interpolated'] = df2.interpolate(method='linear')" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df2.plot(kind='bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Applying to sql" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df3 = pd.read_sql_query(\n", " sql='''select track_scid, as_of, playback_count from sc_track_stats''', \n", " con=db.Session.get_bind()\n", ")" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "grouped = df3.groupby('track_scid')" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "def apply_power(df):\n", " df4 = df.set_index('as_of').reindex(pd.DatetimeIndex(freq='1d', start=df.min()['as_of'], end=df.max()['as_of']))\n", " x0, y0, xf, yf = df4.index[0], df4.playback_count[0], df4.index[-1], df4.playback_count[-1]\n", " slope = (yf - y0) / (xf - x0).days\n", " df4['x'] = df4.index\n", " df4['power'] = ((df4.x - df4.index[0]).apply(lambda td: td.days * slope + y0) - df4.playback_count)\n", " return [df4['power'].sum(), x0, xf]" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/joel/src/thundr/tracker/venv/lib/python3.6/site-packages/ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in long_scalars\n", " after removing the cwd from sys.path.\n" ] } ], "source": [ "df_power = grouped.apply(apply_power)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "df_power.to_pickle('/Users/joel/Desktop/df_power1.pkl')" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "df_power2 = pd.DataFrame(index=df_power.index, data=df_power.as_matrix(), columns=['arr'])" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [], "source": [ "v = df_power2.head().arr.values" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[0.0,\n", " Timestamp('2019-02-14 00:00:00', freq='D'),\n", " Timestamp('2019-02-14 00:00:00', freq='D')],\n", " [0.0,\n", " Timestamp('2019-02-14 00:00:00', freq='D'),\n", " Timestamp('2019-02-14 00:00:00', freq='D')],\n", " [0.0,\n", " Timestamp('2019-02-14 00:00:00', freq='D'),\n", " Timestamp('2019-02-14 00:00:00', freq='D')],\n", " [3813783.0000000019,\n", " Timestamp('2019-02-01 00:00:00', freq='D'),\n", " Timestamp('2019-02-14 00:00:00', freq='D')],\n", " [17592792.0,\n", " Timestamp('2019-01-24 00:00:00', freq='D'),\n", " Timestamp('2019-02-14 00:00:00', freq='D')]]" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v.tolist()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [], "source": [ "df_power3 = pd.DataFrame(index=df_power2.index, data=df_power2.arr.values.tolist(), columns=['power', 'start', 'end'])" ] }, { "cell_type": "code", "execution_count": 142, "metadata": {}, "outputs": [], "source": [ "df_sorted = df_power3.sort_values(by=['power'], ascending=False)" ] }, { "cell_type": "code", "execution_count": 143, "metadata": {}, "outputs": [], "source": [ "df_sorted = df_sorted[df_sorted.index > 100000]" ] }, { "cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [], "source": [ "df_sorted['days'] = (df_sorted.end - df_sorted.start).apply(lambda x: x.days + 1)" ] }, { "cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [], "source": [ "df_sorted['power_avg'] = df_sorted.power / df_sorted.days" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_sorted.sort_values(by=['power_avg'], ascending=False, inplace=True)" ] }, { "cell_type": "code", "execution_count": 151, "metadata": {}, "outputs": [], "source": [ "df_sorted.to_csv('/Users/joel/Desktop/power1.csv')" ] }, { "cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [], "source": [ "df_sorted.to_sql(con=db.Session.get_bind(), name='sc_power_1')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 190, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def reindex_and_fill(df):\n", " indexed = df.set_index('as_of')\n", " return (\n", " indexed.reindex(pd.DatetimeIndex(freq='1d', start=indexed.index.min(), end=indexed.index.max()))\n", " .interpolate()\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 191, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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playback_count
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1
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