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traffic_dataproc/nb_010_playing-with-timeseries.ipynb
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# nb 010: Playing with Timeseries\n", | |
"\n", | |
"I need to map an irregular series of timestamps to a series of values and interpolate it with piecewise interpolation. I.e. with pairs `(9:01AM, \"O\"), (9:10AM, \"V\")`, the value `9:03AM` should map to `O`.\n", | |
"\n", | |
"This can be done by keeping a sorted list of timestamps and indexing it by a strictly increasing time-key (for $O(1)$ indexing) or indexing by an arbitrary time-key (for $O(log(n))$ indexing.)\n", | |
"\n", | |
"But Pandas might implement this already, which would probably be much faster than any solution I came up with. I need to find out how to do that. Once I do that, I need to make timelines, and then extract charging events from these.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import pandas as pd\n", | |
"\n", | |
"index = [pd.Timestamp(\"9:00\"),\n", | |
" pd.Timestamp(\"9:10\"),\n", | |
" pd.Timestamp(\"9:12\"),\n", | |
" pd.Timestamp(\"9:30\"),\n", | |
" pd.Timestamp(\"9:31\"),\n", | |
" pd.Timestamp(\"9:52\")]\n", | |
"\n", | |
"data = [3, 4, 10, 7, 2, 12]\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"ss = pd.Series(data=data, index=index)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"2020-02-26 09:00:00 3\n", | |
"2020-02-26 09:10:00 4\n", | |
"2020-02-26 09:12:00 10\n", | |
"2020-02-26 09:30:00 7\n", | |
"2020-02-26 09:31:00 2\n", | |
"2020-02-26 09:52:00 12\n", | |
"dtype: int64" | |
] | |
}, | |
"execution_count": 3, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"ss" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"3" | |
] | |
}, | |
"execution_count": 4, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"ss[\"9:00\"]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"2020-02-26 09:00:00 3.0\n", | |
"2020-02-26 09:05:00 NaN\n", | |
"2020-02-26 09:10:00 4.0\n", | |
"2020-02-26 09:12:00 10.0\n", | |
"2020-02-26 09:30:00 7.0\n", | |
"2020-02-26 09:31:00 2.0\n", | |
"2020-02-26 09:52:00 12.0\n", | |
"dtype: float64" | |
] | |
}, | |
"execution_count": 6, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"# Hacky Method 1 for indexing between samples\n", | |
"\n", | |
"tk = pd.Timestamp(\"9:05\")\n", | |
"ss[tk] = float(\"nan\")\n", | |
"ss = ss.sort_index()\n", | |
"ss" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"3.0" | |
] | |
}, | |
"execution_count": 7, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"ss = ss.interpolate(\"pad\")\n", | |
"ss[tk] #should be 3, not 12." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"assert ss[tk] == ss[\"9:00\"]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 16, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"3.0" | |
] | |
}, | |
"execution_count": 16, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"# Better method!\n", | |
"idx = ss.index.get_loc(pd.Timestamp(\"9:02\"), method=\"ffill\")\n", | |
"ss[idx]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 22, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"tk = pd.Timestamp(\"9:51\")\n", | |
"idx = ss.index.get_loc(pd.Timestamp(tk), method=\"ffill\")\n", | |
"val = ss[idx]\n", | |
"\n", | |
"assert val == 2.0\n", | |
"\n", | |
"# Awesome!! This works great." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"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": "3.7.5" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 4 | |
} |