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NB_01_TimeSeries_Intro.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Time Series Basics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing Time Series Data from csv-Files"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [],
"source": [
"temp = pd.read_csv(\"temp.csv\", parse_dates = [\"datetime\"], index_col= \"datetime\")"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>LA</th>\n",
" <th>NY</th>\n",
" </tr>\n",
" <tr>\n",
" <th>datetime</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-01-01 00:00:00</th>\n",
" <td>11.7</td>\n",
" <td>-1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 01:00:00</th>\n",
" <td>10.7</td>\n",
" <td>-1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 02:00:00</th>\n",
" <td>9.9</td>\n",
" <td>-2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 03:00:00</th>\n",
" <td>9.3</td>\n",
" <td>-2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 04:00:00</th>\n",
" <td>8.8</td>\n",
" <td>-2.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" LA NY\n",
"datetime \n",
"2013-01-01 00:00:00 11.7 -1.1\n",
"2013-01-01 01:00:00 10.7 -1.7\n",
"2013-01-01 02:00:00 9.9 -2.0\n",
"2013-01-01 03:00:00 9.3 -2.1\n",
"2013-01-01 04:00:00 8.8 -2.3"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp.head()"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 35064 entries, 2013-01-01 00:00:00 to 2016-12-31 23:00:00\n",
"Data columns (total 2 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 LA 35062 non-null float64\n",
" 1 NY 35064 non-null float64\n",
"dtypes: float64(2)\n",
"memory usage: 821.8 KB\n"
]
}
],
"source": [
"temp.info()"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"numpy.float64"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(temp.iloc[0, 0])"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2013-01-01 00:00:00', '2013-01-01 01:00:00',\n",
" '2013-01-01 02:00:00', '2013-01-01 03:00:00',\n",
" '2013-01-01 04:00:00', '2013-01-01 05:00:00',\n",
" '2013-01-01 06:00:00', '2013-01-01 07:00:00',\n",
" '2013-01-01 08:00:00', '2013-01-01 09:00:00',\n",
" ...\n",
" '2016-12-31 14:00:00', '2016-12-31 15:00:00',\n",
" '2016-12-31 16:00:00', '2016-12-31 17:00:00',\n",
" '2016-12-31 18:00:00', '2016-12-31 19:00:00',\n",
" '2016-12-31 20:00:00', '2016-12-31 21:00:00',\n",
" '2016-12-31 22:00:00', '2016-12-31 23:00:00'],\n",
" dtype='datetime64[ns]', name='datetime', length=35064, freq=None)"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp.index"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2013-01-01 00:00:00')"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp.index[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Converting strings to datetime objects with pd.to_datetime()"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [],
"source": [
"temp = pd.read_csv(\"temp.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>datetime</th>\n",
" <th>LA</th>\n",
" <th>NY</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2013-01-01 00:00:00</td>\n",
" <td>11.7</td>\n",
" <td>-1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013-01-01 01:00:00</td>\n",
" <td>10.7</td>\n",
" <td>-1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2013-01-01 02:00:00</td>\n",
" <td>9.9</td>\n",
" <td>-2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013-01-01 03:00:00</td>\n",
" <td>9.3</td>\n",
" <td>-2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013-01-01 04:00:00</td>\n",
" <td>8.8</td>\n",
" <td>-2.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" datetime LA NY\n",
"0 2013-01-01 00:00:00 11.7 -1.1\n",
"1 2013-01-01 01:00:00 10.7 -1.7\n",
"2 2013-01-01 02:00:00 9.9 -2.0\n",
"3 2013-01-01 03:00:00 9.3 -2.1\n",
"4 2013-01-01 04:00:00 8.8 -2.3"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp.head()"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 35064 entries, 0 to 35063\n",
"Data columns (total 3 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 datetime 35064 non-null object \n",
" 1 LA 35062 non-null float64\n",
" 2 NY 35064 non-null float64\n",
"dtypes: float64(2), object(1)\n",
"memory usage: 821.9+ KB\n"
]
}
],
"source": [
"temp.info()"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"'2013-01-01 00:00:00'"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp.datetime[0]"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 2013-01-01 00:00:00\n",
"1 2013-01-01 01:00:00\n",
"2 2013-01-01 02:00:00\n",
"3 2013-01-01 03:00:00\n",
"4 2013-01-01 04:00:00\n",
" ... \n",
"35059 2016-12-31 19:00:00\n",
"35060 2016-12-31 20:00:00\n",
"35061 2016-12-31 21:00:00\n",
"35062 2016-12-31 22:00:00\n",
"35063 2016-12-31 23:00:00\n",
"Name: datetime, Length: 35064, dtype: datetime64[ns]"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.to_datetime(temp.datetime)"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"temp = temp.set_index(pd.to_datetime(temp.datetime)).drop(\"datetime\", axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <th>NY</th>\n",
" </tr>\n",
" <tr>\n",
" <th>datetime</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-01-01 00:00:00</th>\n",
" <td>11.7</td>\n",
" <td>-1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 01:00:00</th>\n",
" <td>10.7</td>\n",
" <td>-1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 02:00:00</th>\n",
" <td>9.9</td>\n",
" <td>-2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 03:00:00</th>\n",
" <td>9.3</td>\n",
" <td>-2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 04:00:00</th>\n",
" <td>8.8</td>\n",
" <td>-2.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"text/plain": [
" LA NY\n",
"datetime \n",
"2013-01-01 00:00:00 11.7 -1.1\n",
"2013-01-01 01:00:00 10.7 -1.7\n",
"2013-01-01 02:00:00 9.9 -2.0\n",
"2013-01-01 03:00:00 9.3 -2.1\n",
"2013-01-01 04:00:00 8.8 -2.3"
]
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp.head()"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 35064 entries, 2013-01-01 00:00:00 to 2016-12-31 23:00:00\n",
"Data columns (total 2 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 LA 35062 non-null float64\n",
" 1 NY 35064 non-null float64\n",
"dtypes: float64(2)\n",
"memory usage: 821.8 KB\n"
]
}
],
"source": [
"temp.info()"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2013-01-01 00:00:00')"
]
},
"execution_count": 132,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp.index[0]"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2015-05-20 10:30:20')"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.to_datetime(\"2015-05-20 10:30:20\")"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2015-05-20 00:00:00')"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.to_datetime(\"20150520\")"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2015-05-20 00:00:00')"
]
},
"execution_count": 135,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.to_datetime(\"2015/05/20\")"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2015-05-20 00:00:00')"
]
},
"execution_count": 136,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.to_datetime(\"2015 05 20\")"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"#pd.to_datetime(\"2015-20-05\")"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2015-05-20 00:00:00')"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.to_datetime(\"2015 May 20\")"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2015-05-20 00:00:00')"
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.to_datetime(\"May 2015 20\")"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2015-05-20 00:00:00')"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.to_datetime(\"2015 20th may\")"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2015-05-20', '2015-02-20'], dtype='datetime64[ns]', freq=None)"
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.to_datetime([\"2015-05-20\", \"Feb 20 2015\"])"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2015-05-20', '2015-02-20', 'NaT'], dtype='datetime64[ns]', freq=None)"
]
},
"execution_count": 142,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.to_datetime([\"2015-05-20\", \"Feb 20 2015\", \"Elephant\"], errors=\"coerce\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Indexing and Slicing Time Series"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {},
"outputs": [],
"source": [
"temp = pd.read_csv(\"temp.csv\", parse_dates= [\"datetime\"], index_col= \"datetime\")"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {},
"outputs": [
{
"data": {
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" <th>2013-01-01 00:00:00</th>\n",
" <td>11.7</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2013-01-01 01:00:00</th>\n",
" <td>10.7</td>\n",
" <td>-1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 02:00:00</th>\n",
" <td>9.9</td>\n",
" <td>-2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 03:00:00</th>\n",
" <td>9.3</td>\n",
" <td>-2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 04:00:00</th>\n",
" <td>8.8</td>\n",
" <td>-2.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" LA NY\n",
"datetime \n",
"2013-01-01 00:00:00 11.7 -1.1\n",
"2013-01-01 01:00:00 10.7 -1.7\n",
"2013-01-01 02:00:00 9.9 -2.0\n",
"2013-01-01 03:00:00 9.3 -2.1\n",
"2013-01-01 04:00:00 8.8 -2.3"
]
},
"execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp.head()"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 35064 entries, 2013-01-01 00:00:00 to 2016-12-31 23:00:00\n",
"Data columns (total 2 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 LA 35062 non-null float64\n",
" 1 NY 35064 non-null float64\n",
"dtypes: float64(2)\n",
"memory usage: 821.8 KB\n"
]
}
],
"source": [
"temp.info()"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LA 10.7\n",
"NY -1.7\n",
"Name: 2013-01-01 01:00:00, dtype: float64"
]
},
"execution_count": 147,
"metadata": {},
"output_type": "execute_result"
}
],
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"temp.loc[\"2013-01-01 01:00:00\"]"
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" <th>2013-01-01 03:00:00</th>\n",
" <td>9.3</td>\n",
" <td>-2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 04:00:00</th>\n",
" <td>8.8</td>\n",
" <td>-2.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-05-20 19:00:00</th>\n",
" <td>17.7</td>\n",
" <td>18.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-05-20 20:00:00</th>\n",
" <td>18.4</td>\n",
" <td>17.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-05-20 21:00:00</th>\n",
" <td>18.0</td>\n",
" <td>17.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-05-20 22:00:00</th>\n",
" <td>19.1</td>\n",
" <td>14.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-05-20 23:00:00</th>\n",
" <td>19.1</td>\n",
" <td>14.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>20880 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" LA NY\n",
"datetime \n",
"2013-01-01 00:00:00 11.7 -1.1\n",
"2013-01-01 01:00:00 10.7 -1.7\n",
"2013-01-01 02:00:00 9.9 -2.0\n",
"2013-01-01 03:00:00 9.3 -2.1\n",
"2013-01-01 04:00:00 8.8 -2.3\n",
"... ... ...\n",
"2015-05-20 19:00:00 17.7 18.1\n",
"2015-05-20 20:00:00 18.4 17.8\n",
"2015-05-20 21:00:00 18.0 17.8\n",
"2015-05-20 22:00:00 19.1 14.2\n",
"2015-05-20 23:00:00 19.1 14.2\n",
"\n",
"[20880 rows x 2 columns]"
]
},
"execution_count": 157,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp.loc[:\"2015-05-20\"]"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>NY</th>\n",
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" <tr>\n",
" <th>datetime</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2015-02-20 00:00:00</th>\n",
" <td>16.4</td>\n",
" <td>-12.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 01:00:00</th>\n",
" <td>17.5</td>\n",
" <td>-12.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 02:00:00</th>\n",
" <td>14.6</td>\n",
" <td>-14.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 03:00:00</th>\n",
" <td>13.9</td>\n",
" <td>-14.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 04:00:00</th>\n",
" <td>10.3</td>\n",
" <td>-14.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 05:00:00</th>\n",
" <td>8.9</td>\n",
" <td>-15.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 06:00:00</th>\n",
" <td>9.0</td>\n",
" <td>-15.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 07:00:00</th>\n",
" <td>7.1</td>\n",
" <td>-15.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 08:00:00</th>\n",
" <td>6.6</td>\n",
" <td>-16.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 09:00:00</th>\n",
" <td>6.3</td>\n",
" <td>-16.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 10:00:00</th>\n",
" <td>5.8</td>\n",
" <td>-16.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 11:00:00</th>\n",
" <td>5.5</td>\n",
" <td>-17.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 12:00:00</th>\n",
" <td>5.8</td>\n",
" <td>-17.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 13:00:00</th>\n",
" <td>5.2</td>\n",
" <td>-17.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 14:00:00</th>\n",
" <td>5.3</td>\n",
" <td>-14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 15:00:00</th>\n",
" <td>10.5</td>\n",
" <td>-14.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 16:00:00</th>\n",
" <td>8.9</td>\n",
" <td>-14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 17:00:00</th>\n",
" <td>12.4</td>\n",
" <td>-10.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 18:00:00</th>\n",
" <td>12.0</td>\n",
" <td>-10.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 19:00:00</th>\n",
" <td>16.8</td>\n",
" <td>-9.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 20:00:00</th>\n",
" <td>17.7</td>\n",
" <td>-9.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 21:00:00</th>\n",
" <td>17.2</td>\n",
" <td>-9.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 22:00:00</th>\n",
" <td>18.5</td>\n",
" <td>-9.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-20 23:00:00</th>\n",
" <td>18.5</td>\n",
" <td>-14.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" LA NY\n",
"datetime \n",
"2015-02-20 00:00:00 16.4 -12.4\n",
"2015-02-20 01:00:00 17.5 -12.4\n",
"2015-02-20 02:00:00 14.6 -14.5\n",
"2015-02-20 03:00:00 13.9 -14.5\n",
"2015-02-20 04:00:00 10.3 -14.5\n",
"2015-02-20 05:00:00 8.9 -15.9\n",
"2015-02-20 06:00:00 9.0 -15.9\n",
"2015-02-20 07:00:00 7.1 -15.9\n",
"2015-02-20 08:00:00 6.6 -16.8\n",
"2015-02-20 09:00:00 6.3 -16.8\n",
"2015-02-20 10:00:00 5.8 -16.8\n",
"2015-02-20 11:00:00 5.5 -17.4\n",
"2015-02-20 12:00:00 5.8 -17.4\n",
"2015-02-20 13:00:00 5.2 -17.4\n",
"2015-02-20 14:00:00 5.3 -14.0\n",
"2015-02-20 15:00:00 10.5 -14.2\n",
"2015-02-20 16:00:00 8.9 -14.0\n",
"2015-02-20 17:00:00 12.4 -10.4\n",
"2015-02-20 18:00:00 12.0 -10.2\n",
"2015-02-20 19:00:00 16.8 -9.9\n",
"2015-02-20 20:00:00 17.7 -9.3\n",
"2015-02-20 21:00:00 17.2 -9.3\n",
"2015-02-20 22:00:00 18.5 -9.3\n",
"2015-02-20 23:00:00 18.5 -14.0"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp.loc[\"20FEBRUARY2015\"]"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"#temp.loc[[\"2015-05-20 10:00:00\", \"2015-05-20 12:00:00\"]]"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2015-05-20 10:00:00', '2015-05-20 12:00:00'], dtype='datetime64[ns]', freq=None)"
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"two_timestamps = pd.to_datetime([\"2015-05-20 10:00:00\", \"2015-05-20 12:00:00\"])\n",
"two_timestamps"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [
{
"data": {
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" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>2015-05-20 10:00:00</th>\n",
" <td>7.8</td>\n",
" <td>13.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-05-20 12:00:00</th>\n",
" <td>9.7</td>\n",
" <td>13.6</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" LA NY\n",
"2015-05-20 10:00:00 7.8 13.3\n",
"2015-05-20 12:00:00 9.7 13.6"
]
},
"execution_count": 161,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp.loc[two_timestamps]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Downsampling Time Series with resample()"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use(\"seaborn-v0_8\")"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Solarize_Light2',\n",
" '_classic_test_patch',\n",
" '_mpl-gallery',\n",
" '_mpl-gallery-nogrid',\n",
" 'bmh',\n",
" 'classic',\n",
" 'dark_background',\n",
" 'fast',\n",
" 'fivethirtyeight',\n",
" 'ggplot',\n",
" 'grayscale',\n",
" 'seaborn-v0_8',\n",
" 'seaborn-v0_8-bright',\n",
" 'seaborn-v0_8-colorblind',\n",
" 'seaborn-v0_8-dark',\n",
" 'seaborn-v0_8-dark-palette',\n",
" 'seaborn-v0_8-darkgrid',\n",
" 'seaborn-v0_8-deep',\n",
" 'seaborn-v0_8-muted',\n",
" 'seaborn-v0_8-notebook',\n",
" 'seaborn-v0_8-paper',\n",
" 'seaborn-v0_8-pastel',\n",
" 'seaborn-v0_8-poster',\n",
" 'seaborn-v0_8-talk',\n",
" 'seaborn-v0_8-ticks',\n",
" 'seaborn-v0_8-white',\n",
" 'seaborn-v0_8-whitegrid',\n",
" 'tableau-colorblind10']"
]
},
"execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.style.available"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {},
"outputs": [],
"source": [
"temp = pd.read_csv(\"temp.csv\", parse_dates= [\"datetime\"], index_col = \"datetime\")"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>LA</th>\n",
" <th>NY</th>\n",
" </tr>\n",
" <tr>\n",
" <th>datetime</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-01-01 00:00:00</th>\n",
" <td>11.7</td>\n",
" <td>-1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 01:00:00</th>\n",
" <td>10.7</td>\n",
" <td>-1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 02:00:00</th>\n",
" <td>9.9</td>\n",
" <td>-2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 03:00:00</th>\n",
" <td>9.3</td>\n",
" <td>-2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 04:00:00</th>\n",
" <td>8.8</td>\n",
" <td>-2.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" LA NY\n",
"datetime \n",
"2013-01-01 00:00:00 11.7 -1.1\n",
"2013-01-01 01:00:00 10.7 -1.7\n",
"2013-01-01 02:00:00 9.9 -2.0\n",
"2013-01-01 03:00:00 9.3 -2.1\n",
"2013-01-01 04:00:00 8.8 -2.3"
]
},
"execution_count": 165,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp.head()"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 35064 entries, 2013-01-01 00:00:00 to 2016-12-31 23:00:00\n",
"Data columns (total 2 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 LA 35062 non-null float64\n",
" 1 NY 35064 non-null float64\n",
"dtypes: float64(2)\n",
"memory usage: 821.8 KB\n"
]
}
],
"source": [
"temp.info()"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>NY</th>\n",
" </tr>\n",
" <tr>\n",
" <th>datetime</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-01-02 00:00:00</th>\n",
" <td>13.2</td>\n",
" <td>2.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 01:00:00</th>\n",
" <td>11.8</td>\n",
" <td>2.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 02:00:00</th>\n",
" <td>10.5</td>\n",
" <td>2.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 03:00:00</th>\n",
" <td>9.5</td>\n",
" <td>2.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 04:00:00</th>\n",
" <td>8.3</td>\n",
" <td>2.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 05:00:00</th>\n",
" <td>8.0</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 06:00:00</th>\n",
" <td>7.5</td>\n",
" <td>3.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 07:00:00</th>\n",
" <td>7.1</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 08:00:00</th>\n",
" <td>6.4</td>\n",
" <td>3.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 09:00:00</th>\n",
" <td>6.0</td>\n",
" <td>3.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 10:00:00</th>\n",
" <td>5.9</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 11:00:00</th>\n",
" <td>6.1</td>\n",
" <td>3.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 12:00:00</th>\n",
" <td>5.8</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 13:00:00</th>\n",
" <td>5.6</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 14:00:00</th>\n",
" <td>5.8</td>\n",
" <td>3.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 15:00:00</th>\n",
" <td>5.9</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 16:00:00</th>\n",
" <td>6.4</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 17:00:00</th>\n",
" <td>9.0</td>\n",
" <td>3.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 18:00:00</th>\n",
" <td>11.5</td>\n",
" <td>3.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 19:00:00</th>\n",
" <td>13.3</td>\n",
" <td>3.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 20:00:00</th>\n",
" <td>14.2</td>\n",
" <td>3.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 21:00:00</th>\n",
" <td>15.0</td>\n",
" <td>2.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 22:00:00</th>\n",
" <td>14.9</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 23:00:00</th>\n",
" <td>15.1</td>\n",
" <td>1.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" LA NY\n",
"datetime \n",
"2013-01-02 00:00:00 13.2 2.6\n",
"2013-01-02 01:00:00 11.8 2.7\n",
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" <th>2016-12-21</th>\n",
" <td>13.209524</td>\n",
" <td>-2.248810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-28</th>\n",
" <td>11.930357</td>\n",
" <td>4.688095</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-04</th>\n",
" <td>15.084722</td>\n",
" <td>1.573611</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>210 rows × 2 columns</p>\n",
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],
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" LA NY\n",
"datetime \n",
"2013-01-02 9.070833 1.402083\n",
"2013-01-09 11.033333 1.033929\n",
"2013-01-16 8.870238 6.001190\n",
"2013-01-23 14.678571 1.010714\n",
"2013-01-30 12.554762 -4.382738\n",
"... ... ...\n",
"2016-12-07 13.205357 5.964286\n",
"2016-12-14 14.490476 1.228571\n",
"2016-12-21 13.209524 -2.248810\n",
"2016-12-28 11.930357 4.688095\n",
"2017-01-04 15.084722 1.573611\n",
"\n",
"[210 rows x 2 columns]"
]
},
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"metadata": {},
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"temp.resample(\"W-Wed\").mean()"
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"datetime \n",
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" <td>21.032778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-01</th>\n",
" <td>16.137903</td>\n",
" <td>14.321640</td>\n",
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" <tr>\n",
" <th>2016-11-01</th>\n",
" <td>17.196111</td>\n",
" <td>8.539722</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
" <td>13.390457</td>\n",
" <td>2.327285</td>\n",
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"</table>\n",
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],
"text/plain": [
" LA NY\n",
"datetime \n",
"2013-01-01 11.596237 1.129570\n",
"2013-02-01 12.587202 0.617857\n",
"2013-03-01 15.069946 3.719220\n",
"2013-04-01 16.487361 10.699306\n",
"2013-05-01 19.005780 15.824328\n",
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"2013-07-01 22.093952 26.329704\n",
"2013-08-01 21.513172 22.480376\n",
"2013-09-01 22.404861 18.291806\n",
"2013-10-01 16.620430 14.335215\n",
"2013-11-01 15.107917 7.111944\n",
"2013-12-01 13.416935 2.776210\n",
"2014-01-01 16.247715 -2.210349\n",
"2014-02-01 14.326637 -1.596280\n",
"2014-03-01 15.836156 1.994758\n",
"2014-04-01 16.783472 10.128611\n",
"2014-05-01 20.041398 16.829167\n",
"2014-06-01 19.810556 21.784861\n",
"2014-07-01 23.056183 24.019624\n",
"2014-08-01 22.146102 21.848387\n",
"2014-09-01 21.620278 19.267222\n",
"2014-10-01 16.968280 14.265860\n",
"2014-11-01 11.322083 5.719167\n",
"2014-12-01 8.523925 3.513844\n",
"2015-01-01 9.527016 -4.179301\n",
"2015-02-01 11.587649 -7.239732\n",
"2015-03-01 14.465995 1.020161\n",
"2015-04-01 13.527917 10.180417\n",
"2015-05-01 14.540054 18.731586\n",
"2015-06-01 20.995000 20.347500\n",
"2015-07-01 22.899059 24.252151\n",
"2015-08-01 24.846909 24.046774\n",
"2015-09-01 24.586250 21.589861\n",
"2015-10-01 21.785753 12.656452\n",
"2015-11-01 15.255278 10.117500\n",
"2015-12-01 11.919758 8.662500\n",
"2016-01-01 12.509274 0.168952\n",
"2016-02-01 16.600431 2.069971\n",
"2016-03-01 15.686425 8.070430\n",
"2016-04-01 17.726111 10.535556\n",
"2016-05-01 17.375403 15.874462\n",
"2016-06-01 22.536111 21.462639\n",
"2016-07-01 24.541532 24.767608\n",
"2016-08-01 23.983199 25.351882\n",
"2016-09-01 22.379306 21.032778\n",
"2016-10-01 16.137903 14.321640\n",
"2016-11-01 17.196111 8.539722\n",
"2016-12-01 13.390457 2.327285"
]
},
"execution_count": 174,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp.resample(\"MS\").mean()"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/px/ly_rt42543v0l9p8tmtmmm8r0000gn/T/ipykernel_28941/1689322633.py:1: FutureWarning: 'loffset' in .resample() and in Grouper() is deprecated.\n",
"\n",
">>> df.resample(freq=\"3s\", loffset=\"8H\")\n",
"\n",
"becomes:\n",
"\n",
">>> from pandas.tseries.frequencies import to_offset\n",
">>> df = df.resample(freq=\"3s\").mean()\n",
">>> df.index = df.index.to_timestamp() + to_offset(\"8H\")\n",
"\n",
" temp.resample(\"MS\", loffset=\"14D\").mean()\n"
]
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"datetime \n",
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]
},
"execution_count": 175,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp.resample(\"MS\", loffset=\"14D\").mean()"
]
},
{
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"2016-06-30 19.192353 15.956639\n",
"2016-09-30 23.648324 23.746603\n",
"2016-12-31 15.557201 8.394656"
]
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"source": [
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" <th>2013-02-28</th>\n",
" <td>12.066525</td>\n",
" <td>0.886723</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-05-31</th>\n",
" <td>16.859973</td>\n",
" <td>10.074230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-08-31</th>\n",
" <td>21.184601</td>\n",
" <td>23.694384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-30</th>\n",
" <td>18.028755</td>\n",
" <td>13.258288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-02-28</th>\n",
" <td>14.675000</td>\n",
" <td>-0.301713</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-05-31</th>\n",
" <td>17.562047</td>\n",
" <td>9.645652</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-08-31</th>\n",
" <td>21.691168</td>\n",
" <td>22.559284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-30</th>\n",
" <td>16.640522</td>\n",
" <td>13.097070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-02-28</th>\n",
" <td>9.822593</td>\n",
" <td>-2.481574</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-05-31</th>\n",
" <td>14.185054</td>\n",
" <td>9.975181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-08-31</th>\n",
" <td>22.934511</td>\n",
" <td>22.909692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-30</th>\n",
" <td>20.556090</td>\n",
" <td>14.764515</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-29</th>\n",
" <td>13.612225</td>\n",
" <td>3.668178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-05-31</th>\n",
" <td>16.920652</td>\n",
" <td>11.503895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-08-31</th>\n",
" <td>23.699457</td>\n",
" <td>23.886775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-30</th>\n",
" <td>18.544368</td>\n",
" <td>14.627976</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-02-28</th>\n",
" <td>13.390457</td>\n",
" <td>2.327285</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" LA NY\n",
"datetime \n",
"2013-02-28 12.066525 0.886723\n",
"2013-05-31 16.859973 10.074230\n",
"2013-08-31 21.184601 23.694384\n",
"2013-11-30 18.028755 13.258288\n",
"2014-02-28 14.675000 -0.301713\n",
"2014-05-31 17.562047 9.645652\n",
"2014-08-31 21.691168 22.559284\n",
"2014-11-30 16.640522 13.097070\n",
"2015-02-28 9.822593 -2.481574\n",
"2015-05-31 14.185054 9.975181\n",
"2015-08-31 22.934511 22.909692\n",
"2015-11-30 20.556090 14.764515\n",
"2016-02-29 13.612225 3.668178\n",
"2016-05-31 16.920652 11.503895\n",
"2016-08-31 23.699457 23.886775\n",
"2016-11-30 18.544368 14.627976\n",
"2017-02-28 13.390457 2.327285"
]
},
"execution_count": 177,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp.resample(\"Q-Feb\").mean()"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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{
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