{
"cells": [
{
"cell_type": "markdown",
"id": "4a87b5ef",
"metadata": {},
"source": [
"--- \n",
" Department of Data Science \n",
"Course: Tools and Techniques for Data Science \n",
"\n",
"---\n",
"Instructor: Muhammad Arif Butt, Ph.D.
"
]
},
{
"cell_type": "markdown",
"id": "ab0dc25c",
"metadata": {},
"source": [
"Lecture 3.20 (Pandas-12) "
]
},
{
"cell_type": "markdown",
"id": "e2e9e8c5",
"metadata": {},
"source": [
"\n"
]
}
],
"source": [
"dtg = datetime.datetime(2021,12,31)\n",
"print(dtg)\n",
"print(type(dtg))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "dd1869b6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2021-12-31 04:30:54.000678\n"
]
}
],
"source": [
"print(datetime.datetime(2021, 12, 31, 4, 30, 54, 678))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a46febb5",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "992d4a09",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "5b197019",
"metadata": {},
"source": [
"**(ii) The `time([hour[, minute[, second[, microsecond[, tzinfo]]]]]) ` methods returns a time object. All arguments are optional**"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "b4c51969",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10:15:00\n",
"\n"
]
}
],
"source": [
"t1 = datetime.time(10, 15)\n",
"print(t1)\n",
"print(type(t1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d1e210f4",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "f9e7146d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "b4ddbdda",
"metadata": {},
"source": [
"**(iii) You can explore some commonly used attributes related with the ``.**\n",
"- `dtg.year:` returns the year\n",
"- `dtg.month:` returns the month\n",
"- `dtg.day:` returns the date\n",
"- `dtg.hour:` returns the hour\n",
"- `dtg.minute:` returns the minutes\n",
"- `dtg.second:` returns the seconds"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "a9cac04f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2021-12-31 04:25:58\n",
"\n"
]
}
],
"source": [
"dtg = datetime.datetime(2021, 12, 31, 4, 25, 58)\n",
"print(dtg)\n",
"print(type(dtg))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "7fb3b193",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2021"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtg.year"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "0fe16a1d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"12"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtg.month"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "4c20a73c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"31"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtg.day"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "a5d9ffcb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtg.hour"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "99a1c2e7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"25"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtg.minute"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "50e73280",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"58"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtg.second"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2acd54e1",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "28946f80",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "db4c9e9f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "8adc7c68",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "65f05563",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "e63806ac",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "f76a57ac",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3ece3d07",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "344873f3",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "18438927",
"metadata": {},
"source": [
"### c. Time Zones:\n",
"\n",
"\n"
]
}
],
"source": [
"#YYYY-MM-DD\n",
"import pandas as pd\n",
"str_date = '2022-03-06 08:30:15'\n",
"print(str_date)\n",
"print(type(str_date))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "177a51a0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2022-03-06 08:30:15\n",
"\n"
]
}
],
"source": [
"ts = pd.to_datetime(str_date)\n",
"print(ts)\n",
"print(type(ts))"
]
},
{
"cell_type": "markdown",
"id": "817f6812",
"metadata": {},
"source": [
"**`pd.Timestamp Attributes`**\n",
"\n",
"`Series.dt.[ts.]second`: Returns seconds\n",
"\n",
"`ts.minute`: Returns year\n",
"\n",
"`ts.hour`: Returns hour\n",
"\n",
"`ts.day`: Returns day\n",
"\n",
"`ts.month`: Returns month as January=1, December=12\n",
"\n",
"`ts.year`: Returns the year of datetime object\n",
"\n",
"`Series.dt.day_name()`: Returns name of the day as string\n",
"\n",
"`Series.dt.month_name()`: Returns month as string\n",
"\n",
"For details Read: https://pandas.pydata.org/docs/reference/api/pandas.Series.dt.year.html"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "a863d8ce",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2022"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts.year"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "829ca9af",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts.month"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "85564ddb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts.day"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "eb6d073e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'March'"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts.month_name()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "55482bf8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts.hour"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "6490fee1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"30"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts.minute"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "95852744",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts.quarter"
]
},
{
"cell_type": "markdown",
"id": "57df2339",
"metadata": {},
"source": [
">You can pass a list of strings containing dates to `pd.to_datetime()`, which will return a `DatetimeIndex` object"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "34603f8a",
"metadata": {},
"outputs": [],
"source": [
"# If there is a invalid string that cannot be converted to a valid date, you will get an error\n",
"#pd.to_datetime(['2017-01-05', 'Jan 6, 2017', 'abc'])"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "531f0b9b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2017-01-05', '2017-01-06', 'NaT'], dtype='datetime64[ns]', freq=None)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Use `errors=coerce` to translate the remaining data and keep `NaT` for invalid string\n",
"pd.to_datetime(['2017-01-05', 'Jan 6, 2017', 'abc'], errors='coerce')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e0826324",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "d543b639",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "fedd9485",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "08878c89",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "6ca04171",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "ee9498dc",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "4b289460",
"metadata": {},
"source": [
"### b. Convert Pandas Series of Strings to Series of Timestamps"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "4efb47fb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2022-03-06 08:30\n",
"1 2022/03/06 08:30\n",
"2 6 March, 2022 08:30\n",
"3 Mar 06, 2022 08:30\n",
"4 202203060830\n",
"dtype: object"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# A pandas series having same date but in different formats\n",
"s1 = pd.Series(['2022-03-06 08:30', '2022/03/06 08:30', '6 March, 2022 08:30', 'Mar 06, 2022 08:30', '202203060830'])\n",
"type(s1)\n",
"s1"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "a6e6b90d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2022-03-06 08:30:00\n",
"1 2022-03-06 08:30:00\n",
"2 2022-03-06 08:30:00\n",
"3 2022-03-06 08:30:00\n",
"4 2022-03-06 08:30:00\n",
"dtype: datetime64[ns]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# to_datetime() function will convert all these different formats into a common format\n",
"s2 = pd.to_datetime(s1)\n",
"s2"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "56db6352",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(s2)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "5ec54b33",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas._libs.tslibs.timestamps.Timestamp"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(s2[0])"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "e1f2c22e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6, 3)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2[0].day, s2[0].month"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e8fe752d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b9b02eec",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b690b86a",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d30d249",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "54687d75",
"metadata": {},
"source": [
"### c. Handling Issues of DateTime Formats\n",
"From above examples, it appears that `pd.to_datetime()` works fine for all date formats. Let us try storing 6 March, 2022 as '06/03/2022' or '06-03-2022'"
]
},
{
"cell_type": "markdown",
"id": "f9562539",
"metadata": {},
"source": [
"**(i) Problem 1:**"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "55610b05",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2022-06-03 00:00:00')"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts = pd.to_datetime('06-03-2022')\n",
"ts"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "4155d90e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3, 6)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts.day, ts.month"
]
},
{
"cell_type": "markdown",
"id": "ea4f7153",
"metadata": {},
"source": [
"**Oops!**, Pandas `to_datetime()` method has converted the string to datetime, but interpreted it as 3 June 2022\n",
">The `pd.to_datetime()` by default, will parse string with month first (MM/DD, MM DD, or MM-DD) format"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "5c252a91",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/1t/g3ylw8h50cjdqmk5d6jh1qmm0000gn/T/ipykernel_11456/3152508908.py:2: UserWarning: Parsing '26-03-2022' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
" ts = pd.to_datetime('26-03-2022')\n"
]
},
{
"data": {
"text/plain": [
"(26, 3)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Intelligence\n",
"ts = pd.to_datetime('26-03-2022')\n",
"ts.day, ts.month"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "41faab35",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "2de68cd0",
"metadata": {},
"source": [
"**(ii) Problem 2:**"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "85a11bf7",
"metadata": {},
"outputs": [],
"source": [
"#ts = pd.to_datetime('2022-03-06 08-PM')"
]
},
{
"cell_type": "markdown",
"id": "2b6213ee",
"metadata": {},
"source": [
"**Oops again**!, Pandas `to_datetime()` method has raised an error saying `ParserError: Unknown string format: 2022-03-06 08-PM`\n",
">It seems that `pd.to_datetime()` expects the time to be in 24 hours clock and not if the time is mentioned using AM (Ante-Meridiem meaning before Midday) or PM (Post-Meridiem, meaning after midday)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8d49ed90",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "4295b54f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "9110939b",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "768fc152",
"metadata": {},
"source": [
"**(iii) Solution of above two Problems:**\n",
">Pass an appropriate `format string` to the `format` argument of the `pd.to_datetime()` method. The format string need to be prepared as per the string date format.\n",
"Visit this link to see for Format codes: https://pandas.pydata.org/docs/reference/api/pandas.Period.strftime.html"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "9edd954d",
"metadata": {},
"outputs": [],
"source": [
"# Passing appropriate format string will resolve above two problems\n",
"ts = pd.to_datetime('06-03-2022 08-PM', format = '%d-%m-%Y %I-%p')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "f1a596e0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2022-03-06 20:00:00')"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "2e121dd3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6, 3)"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts.day, ts.month"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "55ef2e7e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "a00c1a82",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "0f5a2246",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "82c1a490",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "2102f083",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "d6aca85e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3a61f282",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "dc8b9a70",
"metadata": {},
"source": [
"### d. Convert a Single Integer to Pandas Timestamp\n",
"- Pandas `pd.to_datetime()` method can also be used to convert the first argument passed as integer to Pandas `Timestamp` object. \n",
"- The `unit` argument tells about the unit of the `arg`, and it can be seconds, days or years\n",
"- The `origin` argument can be any reference point from where you want to start counting your units from. The default value of `origin` is the UNIX epoch.\n",
"```\n",
"pd.to_datetime(arg, format=None, unit=None, origin='unix')\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "2868bcdd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1645623934\r\n"
]
}
],
"source": [
"!date +%s"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "a6c7d1ae",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('1970-01-20 01:06:34.235000')"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts = pd.to_datetime(1645594235, unit='ms', origin='unix')\n",
"ts"
]
},
{
"cell_type": "markdown",
"id": "c2f45869",
"metadata": {},
"source": [
">You can mention the origin as some other reference point of your choice"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "ed64ab8f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2022-01-11 00:00:00')"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts = pd.to_datetime(10, unit='D', origin='2022-01-01')\n",
"ts"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a536c17e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "4744af7f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "c4118d99",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "e8021bec",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "072c792d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "d16d2b3f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "c9946871",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b0a3269f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "7cef1736",
"metadata": {},
"source": [
"## 4. Practicing with a Simple Dataset"
]
},
{
"cell_type": "markdown",
"id": "20aab3ac",
"metadata": {},
"source": [
"### a. Option 1: Read the Dataset as such and then convert the Column Datatype to Timestamp64"
]
},
{
"cell_type": "markdown",
"id": "ad199219",
"metadata": {},
"source": [
"**Example 1:** A dataset with datetime in a format as expected by `pd.to_datetime()`"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "87f58d21",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name,dob,address,gender\r",
"\r\n",
"Khurram,2022-03-06 21:10,Lahore,Male\r",
"\r\n",
"Fatima,2022/03/06 08:30,Islamabad,Female\r",
"\r\n",
"Huzaifa,2022-03-06 20:15,Karachi,Male\r",
"\r\n",
"Shaista,2022.03.06 18:05,Peshawer,Female"
]
}
],
"source": [
"# yyyy-mm-dd hr:min\n",
"! cat datasets/datetime1.csv"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3e62ef56",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 54,
"id": "cf30ab52",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" name \n",
" dob \n",
" address \n",
" gender \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Khurram \n",
" 2022-03-06 21:10 \n",
" Lahore \n",
" Male \n",
" \n",
" \n",
" 1 \n",
" Fatima \n",
" 2022/03/06 08:30 \n",
" Islamabad \n",
" Female \n",
" \n",
" \n",
" 2 \n",
" Huzaifa \n",
" 2022-03-06 20:15 \n",
" Karachi \n",
" Male \n",
" \n",
" \n",
" 3 \n",
" Shaista \n",
" 2022.03.06 18:05 \n",
" Peshawer \n",
" Female \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" name \n",
" dob \n",
" address \n",
" gender \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Khurram \n",
" 2022-03-06 21:10:00 \n",
" Lahore \n",
" Male \n",
" \n",
" \n",
" 1 \n",
" Fatima \n",
" 2022-03-06 08:30:00 \n",
" Islamabad \n",
" Female \n",
" \n",
" \n",
" 2 \n",
" Huzaifa \n",
" 2022-03-06 20:15:00 \n",
" Karachi \n",
" Male \n",
" \n",
" \n",
" 3 \n",
" Shaista \n",
" 2022-03-06 18:05:00 \n",
" Peshawer \n",
" Female \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" name \n",
" dob \n",
" address \n",
" gender \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Khurram \n",
" 02-07-1980 08-PM \n",
" Lahore \n",
" Male \n",
" \n",
" \n",
" 1 \n",
" Fatima \n",
" 15-06-2001 06-AM \n",
" Islamabad \n",
" Female \n",
" \n",
" \n",
" 2 \n",
" Huzaifa \n",
" 08-04-1999 05-PM \n",
" Karachi \n",
" Male \n",
" \n",
" \n",
" 3 \n",
" Shaista \n",
" 10-09-2005 02-AM \n",
" Peshawer \n",
" Female \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" City \n",
" Colors Reported \n",
" Shape Reported \n",
" State \n",
" Time \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Ithaca \n",
" NaN \n",
" TRIANGLE \n",
" NY \n",
" 6/1/1930 22:00 \n",
" \n",
" \n",
" 1 \n",
" Willingboro \n",
" NaN \n",
" OTHER \n",
" NJ \n",
" 6/30/1930 20:00 \n",
" \n",
" \n",
" 2 \n",
" Holyoke \n",
" NaN \n",
" OVAL \n",
" CO \n",
" 2/15/1931 14:00 \n",
" \n",
" \n",
" 3 \n",
" Abilene \n",
" NaN \n",
" DISK \n",
" KS \n",
" 6/1/1931 13:00 \n",
" \n",
" \n",
" 4 \n",
" New York Worlds Fair \n",
" NaN \n",
" LIGHT \n",
" NY \n",
" 4/18/1933 19:00 \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 18236 \n",
" Grant Park \n",
" NaN \n",
" TRIANGLE \n",
" IL \n",
" 12/31/2000 23:00 \n",
" \n",
" \n",
" 18237 \n",
" Spirit Lake \n",
" NaN \n",
" DISK \n",
" IA \n",
" 12/31/2000 23:00 \n",
" \n",
" \n",
" 18238 \n",
" Eagle River \n",
" NaN \n",
" NaN \n",
" WI \n",
" 12/31/2000 23:45 \n",
" \n",
" \n",
" 18239 \n",
" Eagle River \n",
" RED \n",
" LIGHT \n",
" WI \n",
" 12/31/2000 23:45 \n",
" \n",
" \n",
" 18240 \n",
" Ybor \n",
" NaN \n",
" OVAL \n",
" FL \n",
" 12/31/2000 23:59 \n",
" \n",
" \n",
"
\n",
"
18241 rows × 5 columns
\n",
"
\n",
"RangeIndex: 18241 entries, 0 to 18240\n",
"Data columns (total 5 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 City 18216 non-null object\n",
" 1 Colors Reported 2882 non-null object\n",
" 2 Shape Reported 15597 non-null object\n",
" 3 State 18241 non-null object\n",
" 4 Time 18241 non-null object\n",
"dtypes: object(5)\n",
"memory usage: 712.7+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "f6224595",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'6/1/1930 22:00'"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The Time column of the dataframe contains strings\n",
"df.loc[0,'Time']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "20bedc43",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "678a7106",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "ef32cb7d",
"metadata": {},
"source": [
">Let us pass this column/series to the `pd.to_datetime()` method to convert the datatype to `datetime64`"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "4897d804",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1930-06-01 22:00:00\n",
"1 1930-06-30 20:00:00\n",
"2 1931-02-15 14:00:00\n",
"3 1931-06-01 13:00:00\n",
"4 1933-04-18 19:00:00\n",
" ... \n",
"18236 2000-12-31 23:00:00\n",
"18237 2000-12-31 23:00:00\n",
"18238 2000-12-31 23:45:00\n",
"18239 2000-12-31 23:45:00\n",
"18240 2000-12-31 23:59:00\n",
"Name: Time, Length: 18241, dtype: datetime64[ns]"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.to_datetime(df.loc[:,'Time'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0f950d96",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "1b2e0e90",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 74,
"id": "33237b4a",
"metadata": {},
"outputs": [],
"source": [
"df['Time'] = pd.to_datetime(df.loc[:,'Time'])"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "6182e264",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"City object\n",
"Colors Reported object\n",
"Shape Reported object\n",
"State object\n",
"Time datetime64[ns]\n",
"dtype: object"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2a739d61",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "15cadce7",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "a180c728",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "68a438e3",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "47a167ee",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "da4053cd",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "310f2108",
"metadata": {},
"source": [
"**Suppose I want to display only those UFO sightings that has been seen after 28 October 2000**"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "81fd0bdd",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" City \n",
" Colors Reported \n",
" Shape Reported \n",
" State \n",
" Time \n",
" \n",
" \n",
" \n",
" \n",
" 17791 \n",
" Lake Worth \n",
" YELLOW \n",
" LIGHT \n",
" FL \n",
" 2000-10-28 00:00:00 \n",
" \n",
" \n",
" 17792 \n",
" Springfield \n",
" NaN \n",
" CIGAR \n",
" MO \n",
" 2000-10-28 00:00:00 \n",
" \n",
" \n",
" 17793 \n",
" Laurel \n",
" BLUE \n",
" SPHERE \n",
" MD \n",
" 2000-10-28 01:00:00 \n",
" \n",
" \n",
" 17794 \n",
" Portland \n",
" NaN \n",
" SPHERE \n",
" ME \n",
" 2000-10-28 10:30:00 \n",
" \n",
" \n",
" 17795 \n",
" Kintnersville \n",
" ORANGE \n",
" OTHER \n",
" PA \n",
" 2000-10-28 15:00:00 \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 18236 \n",
" Grant Park \n",
" NaN \n",
" TRIANGLE \n",
" IL \n",
" 2000-12-31 23:00:00 \n",
" \n",
" \n",
" 18237 \n",
" Spirit Lake \n",
" NaN \n",
" DISK \n",
" IA \n",
" 2000-12-31 23:00:00 \n",
" \n",
" \n",
" 18238 \n",
" Eagle River \n",
" NaN \n",
" NaN \n",
" WI \n",
" 2000-12-31 23:45:00 \n",
" \n",
" \n",
" 18239 \n",
" Eagle River \n",
" RED \n",
" LIGHT \n",
" WI \n",
" 2000-12-31 23:45:00 \n",
" \n",
" \n",
" 18240 \n",
" Ybor \n",
" NaN \n",
" OVAL \n",
" FL \n",
" 2000-12-31 23:59:00 \n",
" \n",
" \n",
"
\n",
"
450 rows × 5 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" City \n",
" Colors Reported \n",
" Shape Reported \n",
" State \n",
" Time \n",
" \n",
" \n",
" \n",
" \n",
" 17791 \n",
" Lake Worth \n",
" YELLOW \n",
" LIGHT \n",
" FL \n",
" 2000-10-28 00:00:00 \n",
" \n",
" \n",
" 17792 \n",
" Springfield \n",
" NaN \n",
" CIGAR \n",
" MO \n",
" 2000-10-28 00:00:00 \n",
" \n",
" \n",
" 17793 \n",
" Laurel \n",
" BLUE \n",
" SPHERE \n",
" MD \n",
" 2000-10-28 01:00:00 \n",
" \n",
" \n",
" 17794 \n",
" Portland \n",
" NaN \n",
" SPHERE \n",
" ME \n",
" 2000-10-28 10:30:00 \n",
" \n",
" \n",
" 17795 \n",
" Kintnersville \n",
" ORANGE \n",
" OTHER \n",
" PA \n",
" 2000-10-28 15:00:00 \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 18236 \n",
" Grant Park \n",
" NaN \n",
" TRIANGLE \n",
" IL \n",
" 2000-12-31 23:00:00 \n",
" \n",
" \n",
" 18237 \n",
" Spirit Lake \n",
" NaN \n",
" DISK \n",
" IA \n",
" 2000-12-31 23:00:00 \n",
" \n",
" \n",
" 18238 \n",
" Eagle River \n",
" NaN \n",
" NaN \n",
" WI \n",
" 2000-12-31 23:45:00 \n",
" \n",
" \n",
" 18239 \n",
" Eagle River \n",
" RED \n",
" LIGHT \n",
" WI \n",
" 2000-12-31 23:45:00 \n",
" \n",
" \n",
" 18240 \n",
" Ybor \n",
" NaN \n",
" OVAL \n",
" FL \n",
" 2000-12-31 23:59:00 \n",
" \n",
" \n",
"
\n",
"
450 rows × 5 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" City \n",
" Colors Reported \n",
" Shape Reported \n",
" State \n",
" Time \n",
" \n",
" \n",
" \n",
" \n",
" 7860 \n",
" Greenville \n",
" NaN \n",
" LIGHT \n",
" IL \n",
" 1995-03-01 21:00:00 \n",
" \n",
" \n",
" 7861 \n",
" Sedalia \n",
" NaN \n",
" NaN \n",
" MO \n",
" 1995-03-01 21:00:00 \n",
" \n",
" \n",
" 7862 \n",
" Redmond \n",
" RED \n",
" NaN \n",
" WA \n",
" 1995-03-02 22:30:00 \n",
" \n",
" \n",
" 7863 \n",
" Prescott Valley \n",
" NaN \n",
" OVAL \n",
" AZ \n",
" 1995-03-04 00:00:00 \n",
" \n",
" \n",
" 7864 \n",
" Folsom \n",
" NaN \n",
" NaN \n",
" NJ \n",
" 1995-03-04 16:32:00 \n",
" \n",
" \n",
" 7865 \n",
" Anaheim \n",
" NaN \n",
" OTHER \n",
" CA \n",
" 1995-03-05 12:00:00 \n",
" \n",
" \n",
" 7866 \n",
" Columbus \n",
" NaN \n",
" NaN \n",
" OH \n",
" 1995-03-06 00:55:00 \n",
" \n",
" \n",
" 7867 \n",
" Hilltop \n",
" NaN \n",
" NaN \n",
" NJ \n",
" 1995-03-06 19:00:00 \n",
" \n",
" \n",
" 7868 \n",
" Florence \n",
" NaN \n",
" NaN \n",
" OR \n",
" 1995-03-06 19:10:00 \n",
" \n",
" \n",
" 7869 \n",
" Mountain City \n",
" NaN \n",
" NaN \n",
" TN \n",
" 1995-03-06 19:45:00 \n",
" \n",
" \n",
" 7870 \n",
" Ft. Bragg \n",
" NaN \n",
" NaN \n",
" CA \n",
" 1995-03-06 22:09:00 \n",
" \n",
" \n",
" 7871 \n",
" Burley \n",
" NaN \n",
" NaN \n",
" WA \n",
" 1995-03-06 23:30:00 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" City \n",
" Colors Reported \n",
" Shape Reported \n",
" State \n",
" Time \n",
" \n",
" \n",
" \n",
" \n",
" 18240 \n",
" Ybor \n",
" NaN \n",
" OVAL \n",
" FL \n",
" 2000-12-31 23:59:00 \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" City \n",
" Colors Reported \n",
" Shape Reported \n",
" State \n",
" Time \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Ithaca \n",
" NaN \n",
" TRIANGLE \n",
" NY \n",
" 1930-06-01 22:00:00 \n",
" \n",
" \n",
"
\n",
"
\n"
]
}
],
"source": [
"td = df.Time.max() - df.Time.min()\n",
"print(td)\n",
"print(type(td))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "da0e5155",
"metadata": {},
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"source": [
"## 6. Practicing with Crypto-Currency Dataset\n",
"\n",
"\n",
"\n",
"
\n",
" \n",
" \n",
" Date \n",
" Symbol \n",
" Open \n",
" High \n",
" Low \n",
" Close \n",
" Volume \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 2020-03-13 08-PM \n",
" ETHUSD \n",
" 129.94 \n",
" 131.82 \n",
" 126.87 \n",
" 128.71 \n",
" 1940673.93 \n",
" \n",
" \n",
" 1 \n",
" 2020-03-13 07-PM \n",
" ETHUSD \n",
" 119.51 \n",
" 132.02 \n",
" 117.10 \n",
" 129.94 \n",
" 7579741.09 \n",
" \n",
" \n",
" 2 \n",
" 2020-03-13 06-PM \n",
" ETHUSD \n",
" 124.47 \n",
" 124.85 \n",
" 115.50 \n",
" 119.51 \n",
" 4898735.81 \n",
" \n",
" \n",
" 3 \n",
" 2020-03-13 05-PM \n",
" ETHUSD \n",
" 124.08 \n",
" 127.42 \n",
" 121.63 \n",
" 124.47 \n",
" 2753450.92 \n",
" \n",
" \n",
" 4 \n",
" 2020-03-13 04-PM \n",
" ETHUSD \n",
" 124.85 \n",
" 129.51 \n",
" 120.17 \n",
" 124.08 \n",
" 4461424.71 \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 23669 \n",
" 2017-07-01 03-PM \n",
" ETHUSD \n",
" 265.74 \n",
" 272.74 \n",
" 265.00 \n",
" 272.57 \n",
" 1500282.55 \n",
" \n",
" \n",
" 23670 \n",
" 2017-07-01 02-PM \n",
" ETHUSD \n",
" 268.79 \n",
" 269.90 \n",
" 265.00 \n",
" 265.74 \n",
" 1702536.85 \n",
" \n",
" \n",
" 23671 \n",
" 2017-07-01 01-PM \n",
" ETHUSD \n",
" 274.83 \n",
" 274.93 \n",
" 265.00 \n",
" 268.79 \n",
" 3010787.99 \n",
" \n",
" \n",
" 23672 \n",
" 2017-07-01 12-PM \n",
" ETHUSD \n",
" 275.01 \n",
" 275.01 \n",
" 271.00 \n",
" 274.83 \n",
" 824362.87 \n",
" \n",
" \n",
" 23673 \n",
" 2017-07-01 11-AM \n",
" ETHUSD \n",
" 279.98 \n",
" 279.99 \n",
" 272.10 \n",
" 275.01 \n",
" 679358.87 \n",
" \n",
" \n",
"
\n",
"
23674 rows × 7 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" Date \n",
" Symbol \n",
" Open \n",
" High \n",
" Low \n",
" Close \n",
" Volume \n",
" dayofweek \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 2020-03-13 20:00:00 \n",
" ETHUSD \n",
" 129.94 \n",
" 131.82 \n",
" 126.87 \n",
" 128.71 \n",
" 1940673.93 \n",
" Friday \n",
" \n",
" \n",
" 1 \n",
" 2020-03-13 19:00:00 \n",
" ETHUSD \n",
" 119.51 \n",
" 132.02 \n",
" 117.10 \n",
" 129.94 \n",
" 7579741.09 \n",
" Friday \n",
" \n",
" \n",
" 2 \n",
" 2020-03-13 18:00:00 \n",
" ETHUSD \n",
" 124.47 \n",
" 124.85 \n",
" 115.50 \n",
" 119.51 \n",
" 4898735.81 \n",
" Friday \n",
" \n",
" \n",
" 3 \n",
" 2020-03-13 17:00:00 \n",
" ETHUSD \n",
" 124.08 \n",
" 127.42 \n",
" 121.63 \n",
" 124.47 \n",
" 2753450.92 \n",
" Friday \n",
" \n",
" \n",
" 4 \n",
" 2020-03-13 16:00:00 \n",
" ETHUSD \n",
" 124.85 \n",
" 129.51 \n",
" 120.17 \n",
" 124.08 \n",
" 4461424.71 \n",
" Friday \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 23669 \n",
" 2017-07-01 15:00:00 \n",
" ETHUSD \n",
" 265.74 \n",
" 272.74 \n",
" 265.00 \n",
" 272.57 \n",
" 1500282.55 \n",
" Saturday \n",
" \n",
" \n",
" 23670 \n",
" 2017-07-01 14:00:00 \n",
" ETHUSD \n",
" 268.79 \n",
" 269.90 \n",
" 265.00 \n",
" 265.74 \n",
" 1702536.85 \n",
" Saturday \n",
" \n",
" \n",
" 23671 \n",
" 2017-07-01 13:00:00 \n",
" ETHUSD \n",
" 274.83 \n",
" 274.93 \n",
" 265.00 \n",
" 268.79 \n",
" 3010787.99 \n",
" Saturday \n",
" \n",
" \n",
" 23672 \n",
" 2017-07-01 12:00:00 \n",
" ETHUSD \n",
" 275.01 \n",
" 275.01 \n",
" 271.00 \n",
" 274.83 \n",
" 824362.87 \n",
" Saturday \n",
" \n",
" \n",
" 23673 \n",
" 2017-07-01 11:00:00 \n",
" ETHUSD \n",
" 279.98 \n",
" 279.99 \n",
" 272.10 \n",
" 275.01 \n",
" 679358.87 \n",
" Saturday \n",
" \n",
" \n",
"
\n",
"
23674 rows × 8 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" Date \n",
" Symbol \n",
" Open \n",
" High \n",
" Low \n",
" Close \n",
" Volume \n",
" dayofweek \n",
" \n",
" \n",
" \n",
" \n",
" 1028 \n",
" 2020-01-31 00:00:00 \n",
" ETHUSD \n",
" 184.55 \n",
" 185.68 \n",
" 183.48 \n",
" 183.82 \n",
" 1107068.24 \n",
" Friday \n",
" \n",
" \n",
" 1029 \n",
" 2020-01-30 23:00:00 \n",
" ETHUSD \n",
" 186.62 \n",
" 186.89 \n",
" 182.99 \n",
" 184.55 \n",
" 1262371.00 \n",
" Thursday \n",
" \n",
" \n",
" 1030 \n",
" 2020-01-30 22:00:00 \n",
" ETHUSD \n",
" 185.03 \n",
" 186.63 \n",
" 183.90 \n",
" 186.62 \n",
" 992325.34 \n",
" Thursday \n",
" \n",
" \n",
" 1031 \n",
" 2020-01-30 21:00:00 \n",
" ETHUSD \n",
" 184.40 \n",
" 185.03 \n",
" 183.19 \n",
" 185.03 \n",
" 701167.77 \n",
" Thursday \n",
" \n",
" \n",
" 1032 \n",
" 2020-01-30 20:00:00 \n",
" ETHUSD \n",
" 181.26 \n",
" 185.14 \n",
" 181.26 \n",
" 184.40 \n",
" 2180199.04 \n",
" Thursday \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 1744 \n",
" 2020-01-01 04:00:00 \n",
" ETHUSD \n",
" 129.57 \n",
" 130.00 \n",
" 129.50 \n",
" 129.56 \n",
" 702786.82 \n",
" Wednesday \n",
" \n",
" \n",
" 1745 \n",
" 2020-01-01 03:00:00 \n",
" ETHUSD \n",
" 130.37 \n",
" 130.44 \n",
" 129.38 \n",
" 129.57 \n",
" 496704.23 \n",
" Wednesday \n",
" \n",
" \n",
" 1746 \n",
" 2020-01-01 02:00:00 \n",
" ETHUSD \n",
" 130.14 \n",
" 130.50 \n",
" 129.91 \n",
" 130.37 \n",
" 396315.72 \n",
" Wednesday \n",
" \n",
" \n",
" 1747 \n",
" 2020-01-01 01:00:00 \n",
" ETHUSD \n",
" 128.34 \n",
" 130.14 \n",
" 128.32 \n",
" 130.14 \n",
" 635419.40 \n",
" Wednesday \n",
" \n",
" \n",
" 1748 \n",
" 2020-01-01 00:00:00 \n",
" ETHUSD \n",
" 128.54 \n",
" 128.54 \n",
" 128.12 \n",
" 128.34 \n",
" 245119.91 \n",
" Wednesday \n",
" \n",
" \n",
"
\n",
"
721 rows × 8 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" Symbol \n",
" Open \n",
" High \n",
" Low \n",
" Close \n",
" Volume \n",
" dayofweek \n",
" \n",
" \n",
" Date \n",
" \n",
" \n",
" \n",
" \n",
" 2020-03-13 20:00:00 \n",
" ETHUSD \n",
" 129.94 \n",
" 131.82 \n",
" 126.87 \n",
" 128.71 \n",
" 1940673.93 \n",
" Friday \n",
" \n",
" \n",
" 2020-03-13 19:00:00 \n",
" ETHUSD \n",
" 119.51 \n",
" 132.02 \n",
" 117.10 \n",
" 129.94 \n",
" 7579741.09 \n",
" Friday \n",
" \n",
" \n",
" 2020-03-13 18:00:00 \n",
" ETHUSD \n",
" 124.47 \n",
" 124.85 \n",
" 115.50 \n",
" 119.51 \n",
" 4898735.81 \n",
" Friday \n",
" \n",
" \n",
" 2020-03-13 17:00:00 \n",
" ETHUSD \n",
" 124.08 \n",
" 127.42 \n",
" 121.63 \n",
" 124.47 \n",
" 2753450.92 \n",
" Friday \n",
" \n",
" \n",
" 2020-03-13 16:00:00 \n",
" ETHUSD \n",
" 124.85 \n",
" 129.51 \n",
" 120.17 \n",
" 124.08 \n",
" 4461424.71 \n",
" Friday \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 2017-07-01 15:00:00 \n",
" ETHUSD \n",
" 265.74 \n",
" 272.74 \n",
" 265.00 \n",
" 272.57 \n",
" 1500282.55 \n",
" Saturday \n",
" \n",
" \n",
" 2017-07-01 14:00:00 \n",
" ETHUSD \n",
" 268.79 \n",
" 269.90 \n",
" 265.00 \n",
" 265.74 \n",
" 1702536.85 \n",
" Saturday \n",
" \n",
" \n",
" 2017-07-01 13:00:00 \n",
" ETHUSD \n",
" 274.83 \n",
" 274.93 \n",
" 265.00 \n",
" 268.79 \n",
" 3010787.99 \n",
" Saturday \n",
" \n",
" \n",
" 2017-07-01 12:00:00 \n",
" ETHUSD \n",
" 275.01 \n",
" 275.01 \n",
" 271.00 \n",
" 274.83 \n",
" 824362.87 \n",
" Saturday \n",
" \n",
" \n",
" 2017-07-01 11:00:00 \n",
" ETHUSD \n",
" 279.98 \n",
" 279.99 \n",
" 272.10 \n",
" 275.01 \n",
" 679358.87 \n",
" Saturday \n",
" \n",
" \n",
"
\n",
"
23674 rows × 7 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" Symbol \n",
" Open \n",
" High \n",
" Low \n",
" Close \n",
" Volume \n",
" dayofweek \n",
" \n",
" \n",
" Date \n",
" \n",
" \n",
" \n",
" \n",
" 2019-07-01 23:00:00 \n",
" ETHUSD \n",
" 289.24 \n",
" 295.93 \n",
" 289.24 \n",
" 293.70 \n",
" 2083299.02 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 22:00:00 \n",
" ETHUSD \n",
" 291.17 \n",
" 291.31 \n",
" 288.10 \n",
" 289.24 \n",
" 1865830.49 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 21:00:00 \n",
" ETHUSD \n",
" 288.82 \n",
" 292.61 \n",
" 288.82 \n",
" 291.17 \n",
" 1731914.78 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 20:00:00 \n",
" ETHUSD \n",
" 281.38 \n",
" 290.94 \n",
" 281.38 \n",
" 288.82 \n",
" 3483143.68 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 19:00:00 \n",
" ETHUSD \n",
" 284.73 \n",
" 284.97 \n",
" 278.75 \n",
" 281.38 \n",
" 3420031.90 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 18:00:00 \n",
" ETHUSD \n",
" 282.89 \n",
" 285.76 \n",
" 281.50 \n",
" 284.73 \n",
" 1841330.22 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 17:00:00 \n",
" ETHUSD \n",
" 283.39 \n",
" 285.39 \n",
" 280.51 \n",
" 282.89 \n",
" 2346140.58 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 16:00:00 \n",
" ETHUSD \n",
" 284.49 \n",
" 287.48 \n",
" 282.01 \n",
" 283.39 \n",
" 3244834.65 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 15:00:00 \n",
" ETHUSD \n",
" 280.76 \n",
" 286.74 \n",
" 278.65 \n",
" 284.49 \n",
" 6552331.53 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 14:00:00 \n",
" ETHUSD \n",
" 285.50 \n",
" 289.37 \n",
" 280.15 \n",
" 280.76 \n",
" 3386287.24 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 13:00:00 \n",
" ETHUSD \n",
" 288.79 \n",
" 289.98 \n",
" 285.50 \n",
" 285.50 \n",
" 2447312.95 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 12:00:00 \n",
" ETHUSD \n",
" 294.27 \n",
" 295.62 \n",
" 284.16 \n",
" 288.79 \n",
" 2967508.75 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 11:00:00 \n",
" ETHUSD \n",
" 294.18 \n",
" 294.83 \n",
" 292.23 \n",
" 294.27 \n",
" 697966.32 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 10:00:00 \n",
" ETHUSD \n",
" 296.52 \n",
" 297.50 \n",
" 292.07 \n",
" 294.18 \n",
" 1267176.41 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 09:00:00 \n",
" ETHUSD \n",
" 298.10 \n",
" 298.24 \n",
" 296.01 \n",
" 296.52 \n",
" 499072.13 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 08:00:00 \n",
" ETHUSD \n",
" 293.05 \n",
" 298.10 \n",
" 292.92 \n",
" 298.10 \n",
" 1192722.45 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 07:00:00 \n",
" ETHUSD \n",
" 292.90 \n",
" 296.01 \n",
" 290.48 \n",
" 293.05 \n",
" 1483583.90 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 06:00:00 \n",
" ETHUSD \n",
" 294.56 \n",
" 295.92 \n",
" 291.73 \n",
" 292.90 \n",
" 1068694.39 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 05:00:00 \n",
" ETHUSD \n",
" 297.97 \n",
" 298.24 \n",
" 293.95 \n",
" 294.56 \n",
" 1277630.93 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 04:00:00 \n",
" ETHUSD \n",
" 298.15 \n",
" 299.80 \n",
" 295.57 \n",
" 297.97 \n",
" 1036527.82 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 03:00:00 \n",
" ETHUSD \n",
" 296.99 \n",
" 301.32 \n",
" 295.81 \n",
" 298.15 \n",
" 1465814.16 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 02:00:00 \n",
" ETHUSD \n",
" 293.93 \n",
" 298.58 \n",
" 292.15 \n",
" 296.99 \n",
" 1367256.41 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 01:00:00 \n",
" ETHUSD \n",
" 295.11 \n",
" 298.70 \n",
" 293.53 \n",
" 293.93 \n",
" 2293925.50 \n",
" Monday \n",
" \n",
" \n",
" 2019-07-01 00:00:00 \n",
" ETHUSD \n",
" 290.07 \n",
" 295.25 \n",
" 286.69 \n",
" 295.11 \n",
" 4209855.92 \n",
" Monday \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" Symbol \n",
" Open \n",
" High \n",
" Low \n",
" Close \n",
" Volume \n",
" dayofweek \n",
" \n",
" \n",
" Date \n",
" \n",
" \n",
" \n",
" \n",
" 2020-02-29 23:00:00 \n",
" ETHUSD \n",
" 223.35 \n",
" 223.58 \n",
" 216.83 \n",
" 217.31 \n",
" 1927939.88 \n",
" Saturday \n",
" \n",
" \n",
" 2020-02-29 22:00:00 \n",
" ETHUSD \n",
" 223.48 \n",
" 223.59 \n",
" 222.14 \n",
" 223.35 \n",
" 535998.57 \n",
" Saturday \n",
" \n",
" \n",
" 2020-02-29 21:00:00 \n",
" ETHUSD \n",
" 224.63 \n",
" 225.14 \n",
" 222.74 \n",
" 223.48 \n",
" 561158.03 \n",
" Saturday \n",
" \n",
" \n",
" 2020-02-29 20:00:00 \n",
" ETHUSD \n",
" 225.31 \n",
" 225.33 \n",
" 223.50 \n",
" 224.63 \n",
" 511648.65 \n",
" Saturday \n",
" \n",
" \n",
" 2020-02-29 19:00:00 \n",
" ETHUSD \n",
" 225.09 \n",
" 225.85 \n",
" 223.87 \n",
" 225.31 \n",
" 1250856.20 \n",
" Saturday \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 2020-01-01 04:00:00 \n",
" ETHUSD \n",
" 129.57 \n",
" 130.00 \n",
" 129.50 \n",
" 129.56 \n",
" 702786.82 \n",
" Wednesday \n",
" \n",
" \n",
" 2020-01-01 03:00:00 \n",
" ETHUSD \n",
" 130.37 \n",
" 130.44 \n",
" 129.38 \n",
" 129.57 \n",
" 496704.23 \n",
" Wednesday \n",
" \n",
" \n",
" 2020-01-01 02:00:00 \n",
" ETHUSD \n",
" 130.14 \n",
" 130.50 \n",
" 129.91 \n",
" 130.37 \n",
" 396315.72 \n",
" Wednesday \n",
" \n",
" \n",
" 2020-01-01 01:00:00 \n",
" ETHUSD \n",
" 128.34 \n",
" 130.14 \n",
" 128.32 \n",
" 130.14 \n",
" 635419.40 \n",
" Wednesday \n",
" \n",
" \n",
" 2020-01-01 00:00:00 \n",
" ETHUSD \n",
" 128.54 \n",
" 128.54 \n",
" 128.12 \n",
" 128.34 \n",
" 245119.91 \n",
" Wednesday \n",
" \n",
" \n",
"
\n",
"
1440 rows × 7 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" Symbol \n",
" Open \n",
" High \n",
" Low \n",
" Close \n",
" Volume \n",
" dayofweek \n",
" \n",
" \n",
" Date \n",
" \n",
" \n",
" \n",
" \n",
" 2020-03-13 20:00:00 \n",
" ETHUSD \n",
" 129.94 \n",
" 131.82 \n",
" 126.87 \n",
" 128.71 \n",
" 1940673.93 \n",
" Friday \n",
" \n",
" \n",
" 2020-03-13 19:00:00 \n",
" ETHUSD \n",
" 119.51 \n",
" 132.02 \n",
" 117.10 \n",
" 129.94 \n",
" 7579741.09 \n",
" Friday \n",
" \n",
" \n",
" 2020-03-13 18:00:00 \n",
" ETHUSD \n",
" 124.47 \n",
" 124.85 \n",
" 115.50 \n",
" 119.51 \n",
" 4898735.81 \n",
" Friday \n",
" \n",
" \n",
" 2020-03-13 17:00:00 \n",
" ETHUSD \n",
" 124.08 \n",
" 127.42 \n",
" 121.63 \n",
" 124.47 \n",
" 2753450.92 \n",
" Friday \n",
" \n",
" \n",
" 2020-03-13 16:00:00 \n",
" ETHUSD \n",
" 124.85 \n",
" 129.51 \n",
" 120.17 \n",
" 124.08 \n",
" 4461424.71 \n",
" Friday \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 2017-07-01 15:00:00 \n",
" ETHUSD \n",
" 265.74 \n",
" 272.74 \n",
" 265.00 \n",
" 272.57 \n",
" 1500282.55 \n",
" Saturday \n",
" \n",
" \n",
" 2017-07-01 14:00:00 \n",
" ETHUSD \n",
" 268.79 \n",
" 269.90 \n",
" 265.00 \n",
" 265.74 \n",
" 1702536.85 \n",
" Saturday \n",
" \n",
" \n",
" 2017-07-01 13:00:00 \n",
" ETHUSD \n",
" 274.83 \n",
" 274.93 \n",
" 265.00 \n",
" 268.79 \n",
" 3010787.99 \n",
" Saturday \n",
" \n",
" \n",
" 2017-07-01 12:00:00 \n",
" ETHUSD \n",
" 275.01 \n",
" 275.01 \n",
" 271.00 \n",
" 274.83 \n",
" 824362.87 \n",
" Saturday \n",
" \n",
" \n",
" 2017-07-01 11:00:00 \n",
" ETHUSD \n",
" 279.98 \n",
" 279.99 \n",
" 272.10 \n",
" 275.01 \n",
" 679358.87 \n",
" Saturday \n",
" \n",
" \n",
"
\n",
"
23674 rows × 7 columns
\n",
"
"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"df.loc[:, 'Close'].resample('M').max().plot()"
]
},
{
"cell_type": "code",
"execution_count": 113,
"id": "15f47d53",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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wMkBErJO0GriT4pM/Z/qTO2Zm4zXroh8R36L9eXqA4zu8ZgWwYrbbNDOzwfiOXLOa+eKijZOLvtmIuEhbilz0zcxmqYnfH+yibzZh/BeGdeMvUTEzy4iLvplZRlz0zcwy4qJvZpYRF30zs4y46JuZZcRF38wsIy76ZmYZcdE3M8uIi76ZWUZc9M3MMuKib2aWERd9M7OMuOibmWXERd/MLCMu+mZmGXHRNzPLyNiLvqQTJa2XtEHSWePevplZzsZa9CXNAT4OvA44AjhV0hHjzMHMLGfjPtI/BtgQET+MiH8HLgWWjDkHM7NsKSLGtzHpLcCJEfHfyp9PA34tIt7VErccWF7++AJgfcuq5gI/7mGTqcfVue3U4+rcdupxdW479bg6t51a3HMjYt4urRExtgU4Gbio8vNpwN/MYj1rJyGuCTl6bNKLa0KOHpv04nYs4z69sxlYWPl5AfDAmHMwM8vWuIv+94BFkg6T9DRgKbBmzDmYmWVr93FuLCK2SXoXcBUwB/hURKybxapWTkhcndtOPa7ObaceV+e2U4+rc9upxwFjvpBrZmb18h25ZmYZcdE3M8uIi76ZWUbGeiF3NiSJ4k7e+UBQfMTzxmi5GDEpcU3I0X322KQQ14Qc6xybTpK+kCvpt4ELgLuB+8vmBcCvAH8QEVdPUlwTcnSfPTYpxDUhxzrHpqt+7uQa9wLcBUy1aT8MuGvS4pqQo/vssUkhrgk51jk23ZbUz+nvTnEXb6v7gT0mMK4JObrP44trQo4em/Tiukr9nP6ngO9JuhS4r2xbSHEn7ycnMK4JObrP44trQo4em/Tiukr6nD6Aivn230Rx4UIU73RrIuLOSYxrQo7us8cmhbgm5Fjn2HSSfNE3M7Mh6vXkfx0L8GzgXOAHwE/K5a6ybd9Ji2tCju6zxyaFuCbkWOfYdFtSv5C7GtgKHBcRB0TEAcCrgUeAz09gXBNydJ89NinENSHHOsems17fHepYgPW9PDcpcU3I0X322KQQ14Qc6xybbkvqR/r3Snq/pIN2NEg6SNIHeOrq9STFNSFH99ljk0JcE3Ksc2w6Sr3onwIcAHxD0lZJDwNfB/YH3jqBcU3I0X322KQQ14Qc6xybjvzpHTOzjKR+pP8kSUd3+3nS4pqQo/vssUkhrgk51jk2rRpT9IEzZvh50uLq3Lb7nF5cndtOPa7Obacetwuf3jEzy0jqc+8gpT1v9bDjmpCj++yxSSGuCTnWOTadJH2kr8TnrR52XBNydJ89NinENSHHOsemq14/0F/HQuLzVg87rgk5us8emxTimpBjnWPTbUn9Qm7q81Z7jvDB45qQo8cmvbgm5Fjn2HSU+jn91Oet9hzh7rPHxmOTWlxXSZ/TB1Di81YPO64JObrPHpsU4pqQY51j00nyRd/MzIao15P/dSwkPm/1sOOakKP77LFJIa4JOdY5Nt2W1C/kpj5vtecId589Nh6b1OK66/XdoY6FxOetHnZcE3J0nz02KcQ1Icc6x6bbkvqR/r1Ke97qYcc1IUf32WOTQlwTcqxzbDpKveifQtrzVg87rgk5us8emxTimpBjnWPTkT+9Y2aWkdSP9J+kxOetHnZcE3J0nz02KcQ1Icc6x6ZVY4o+6c9b7TnC3edRxtW57dTj6tx26nG78OkdM7OMpD73DlLa81YPO64JObrPHpsU4pqQY51j00nSR/pKfN7qYcc1IUf32WOTQlwTcqxzbLrq9QP9dSwkPm/1sOOakKP77LFJIa4JOdY5Nt2W1C/kpj5vtecIHzyuCTl6bNKLa0KOdY5NR6mf00993mrPEe4+e2w8NqnFdZX0OX0AJT5v9bDjmpCj++yxSSGuCTnWOTadJF/0zcxsiHo9+V/HQuLzVg87rgk5us8emxTimpBjnWPTbUn9Qm7q81Z7jnD32WPjsUktrrte3x3qWEh83uphxzUhR/fZY5NCXBNyrHNsui2pH+nfq7TnrR52XBNydJ89NinENSHHOsemo9SL/ik8NX/0w+ptnukmxzUhR/fZY5NCXBNyrHNsOvKnd8zMMpL6kb6ZmQ2Ri76ZWUZc9M3MMpL63DsdSXptRFzTb5ykZwMnsvN81FdFxCOz2e6w19fntn8DeDAi1kv6j8CxFLPtXZ5CXwZdX7t1NrUvs+1H2TbR+3nAsZmIvkjau9zuQmAbxfTJV0fEE8PcLjT7SL/XCYaejJP0duBm4DjgmcBeFDc33FQ+V+v6+tz2RyjuxPt7SR8EPgQ8A3ivpA/PYn0pjs1O6xzztuvazzvFZbKfZzs2E9EXSW8FrqMo+u+i+JKU04BbJf3qkLeb9qd3JK3p9BTwWxGxV59x64Ffa33XlrQfcENEHF7n+vrc9jrgKIoCcD8wPyJ+LmkP4JaIOKrOvvS6vj7XmXRfht2PMnYi9vOIxmYi+iLp+8Cx5X6dC3wmIk6Q9GLgExHxyn7WN5PUT+/8OvA24N9a2kXxbthvnCj+ZGv1RPlc3evrJzYiIiTt+PNvRx5PsPNfcKmPTT/rTL0vw+4HTM5+HsXYTEpfBPyifPwz4ECAiPi+pH1msb6uUi/61wM/j4hvtD5Rviv3G7cCuFnS1Tx1B9uhwGuBDyawvn5iL5f0T8DTgYuA1ZKuB34T+GYCfel1ff2sM/W+DLsfMDn7eRRjMyl9+QpwpaRvAK+jnEdH0v7s/GbTz9h0lPTpnVEo/1Q7gZ3no74qIramsL4+t/0KiiPB6yU9H/jPwCbgC71eAGpZ3ySNzST1xft5TNuuqy+SXg8cAdy244KspN2APSLi8aFuK7eib2aWtehxZrY6FuCFwBXA5cDzgYspphG9EXjRpMX1uc6FwKXAPwF/THFEsOO5L9fdlxH1Oau4SdrPIxqbpHPsI26o+3jGulp3YZ+h6H8T+E/AqcC9FN8FqbLt2kmL63Od1wC/D7wU+BvgO8AB5XO31N2XEfU5q7hJ2s/+vRnf/+WmF/1qhze0PHfzpMX1uc5bW557G7CO4gig9r6MqM9ZxU3Sfvbvzfj+L8+0pP7pnTmVx+e1PPe0CYzrJ3YPSU+PiMcAIuLTkn4EXEVxU8mocqyzz7nFweTsZ//edI4b9j7urtd3hzoW4PeAvdu0/wrwkUmL63Od7wV+s03cy4Br6u7LiPqcVdwk7Wf/3ozv//JMiz+9Y2aWkcbNvSPp5pzi6ty2+5xeXJ3bTj2uzm2nHlfVuKLPzneo5RBX57bd5/Ti6tx26nF1bjv1uCc1sej3NJ3sBMXVuW33Ob24Oredelyd20497kkTe05f0tyI+HGX5/cDtkXEozOsZ3+KW+C3DjvH1Ek6OiL6/vOxw7r2ARYBPxzmWHo/Dy71/TzTPi5jvJ971esV3zoW4GGKSaaOp3yD6hD3OmAj8C2KK97rgH+hmDfj+Ercc4BLgH8FtlPMX7IJ+HN2vgvuUIo75KYpvsxgA/BQ2TbVY+63Vx73dMfdKNZJ73cFHt2yvLwcv5cBR1fi3ll5vAC4FthKcUPJ4ZXnPg3MLR+fQDGB1Vcpbio52ft55vVN0n4e9j6epP087H0847Z7DaxjAdZTfKnAtynmEj+fYt7p1rhbgRcBrwB+siOmbKve3PA14Ljy8e8Af03xOdi/AFZW4r4LnALMqbTNobgD7vpK2+90WN4MTFfierrjbhTrpPe7Ap8o13FdZflF+e/XKnHV8VxN8TGy3SgmAauur/pL/R3K/1zAXIpJpbLdz72ub5L287D38STt52Hv4xnraq+BdSwtv3iHAu+n+GabHwL/u0Pcfa2/RJXHrcXmpsrjH1Qe390lp7srj39J8W77d22WR9vlUP7c9o67UayT3u8KfAvwDeD1lbaNM+yT1hyq21oH7FM+/hawW/W5nPdzr+ubpP087H08Sft52Pt4piX1O3KfvDIdEZsoviruQ5JeQPEut8Mjkn4P2AfYKum9FEcnr2HnLxyYlvQ2iiOENwP3AEgSO1/UvknSBcAqnppXeyGwDLilEvd94K8i4o5dEpdeU/mx1zvuRrHOnu7ii4gvSLoS+KCk3wXeR/svlFgg6aMU+2aepD0i4pc7cqrE/U/gOkkfpzi6+7yky4DfAq5s7Voljxz2c6/r62edqe/nYe9jmJz97DtyK+9e5/UYtxD4W+ATwMEUd7jdQXHuq3o+81CKX6A7KM5FHlK2HwC8uRL3NOAMil/a28v4K4A/APasxP06cGiHnBZXHvd0x90o1sks7uIr13EdLacayueWtSz7le0HUzliq2zjL4F/BP4vcCFwQu77udf1TdJ+HvY+nqT9PMp93G6Z2E/v2GDKo6VnRcRP687FRsf7OT/JF31JJwAnUXyTTQAPAJdFxJU9xH05Iq6azfq65PNnEfG/Rhk3YJ8bFzeEdTZuP49xbCYlbpd93M86O0llP4/i/1THHFMu+pI+AhxO8bGszWXzAuDtFBdg3jOKuBly2hQRh44qrq4+1znWue3nJoxN6nH9xnaSwn4e5+81kPw5/X/u0C52vuo+7LifdlgepbgBZCRxNfe5lrgc93NDxibpuEnaz6MYm25L9Qp3ih6TdEyb9v8APDbCuEeARRGxT8vyLGDLCOPq7HNdcXVu+xHq2c9NGJvU4/qJfYS09/Moxqaj1D+y+Q7gQknP4qk/ZxZSvKu+Y4RxlwDPBR5sk9NnRxg3ir6kHlfntuvaz73m109sbnH9xKa+n4cd11XS5/R3kHQwxYULAZsj4kfjiKtTXX2uc6xz289NGJvU4/qNrUNyv9e9ngdKZQH+PKe4JuToPntsUohrQo51js2OJfVz+u28KbO4OrftPqcXV+e2U4+rc9upxz2piUU/9S8r8BdDDB5X57ZTj6tz26nH1bnt1OOeekH5J0JjSNotIp7IJa4JObrP44trQo4em/TidnpNykVf0nnAFyPi2znElbH7U0xB+wDwSYr5tV8B3EUx58nWSYprQo4j6vOrKSYJWwhso5jn/aKI2ECLXmNzi2tCjkrwjtzUT++cBpwv6V5JH5L0sgmPg2LiqL0ovtziOopJp/6SYs7ziycwrgk5DjVO0rkUd1FeTzH97g8pvijk85JOrg5Mr7G5xTUhRxV30L6HYirrDwEfLh+/W9L5o4qbUb9Xfse5UM4fTfH1a39KMcf0D4Bz2PnbeyYiroy5tfxXwP3tnpukuCbkOIK46heP7A58u3y8H3BHy+t6is0trgk54jtyZyUAIuLuiPhgRBwJvBV4OvCVCYwD2E3F930uBPaWNAUg6QB2njN7UuKakOOw454oTwVB8ZV/cwCiOP3TemGu19jc4pqQY5J35Pb0zlDXQstXCU56XBl7KsWdfg9SnDP8KsXXqd0PLJ+0uCbkOIK4Uyi+7u5qiu90fUPZPg/4bMvY9BSbW1wTcqT4DuIbgDvL2Kspru/cALx8VHEzLalfyN07Ilq/LWdi4yrxcygusm+TtDvFd2feHxFbJjGuCTmOIG5/4HkUX3v3SOt4zCY2t7gG5eg7coexAC/MKa4JObrPHpsU4lLLEdijTdvcUcd1zLHXwNQWYFNOcU3I0X322KQQl0qOwKspJkabpjgVM1V57uZRxc20JD3LpoovZm77FLDvpMU1IUf3eXxxTcjRY9M1xw9RfFfwOklvAa6RdFpEXM/OF3yHHddV6uf0HwXeBzze5un/ExFzJymuCTm6z+OLa0KOHpuucbdFxEsq+R4JfAk4C/jTiDh6FHEz6vVPgjoW4GvAKzs8t3HS4pqQo/vssUkhrgk5AmuBg1ueXwDcCjw6qriZltSP9PcHHouIn+cQ14Qc3efxxTUhR49N17jXANMRcVtL+77AmRGxYhRxM0m66JuZ2ZD1+idBHQvwbOBciikLflIud5Vt+05aXBNydJ89NinENSHHOsem25L6NAyrga3AcRFxQEQcQPGxpa3A5ycwrgk5us8emxTimpBjnWPTWa/vDnUswPpenpuUuCbk6D57bFKIa0KOdY5NtyX1I/17Jb1f0kE7GiQdJOkDwH0TGNeEHN1nj00KcU3Isc6x6Sj1on8KcADwDUkPS3oY+DqwP8UslZMW14Qc3WePTQpxTcixzrHpyJ/eMTPLSOpH+kh6oaTjJe3V0n7iJMY1IUf3eXxxTcjRY5NeXFe9nvyvYwHeDawHvgzcAyypPHfzpMU1IUf32WOTQlwTcqxzbLottRf2rsnB7cDe5eMpituQ31P+fMukxTUhR/fZY5NCXBNyrHNsui1Jz7IJzInyy0ci4h5JxwFfkPRc2GlWuUmJa0KO7rPHJoW4JuRY59h0lPo5/R9JeumOH8oOvxGYC/zqBMY1IUf32WOTQlwTcqxzbDrr9U+COhaKGeQO7vDcqyYtrgk5us8emxTimpBjnWPTbfFHNs3MMpL66R0zMxsiF30zs4y46JtVSNou6VZJ6yTdJul/SOr6/0TSlKT/Mq4czQbhom+2s19ExEsj4kjgtcDrgXNmeM0U4KJvjeALuWYVkv4tIvau/Pw84HsUH4t7LvD3wI5b4N8VEd+RdD3wImAjsAr4KMUXWxwH7Al8PCL+dmydMOvCRd+sorXol21bgRcCjwJPRMRjkhYBn4uIxeVNMn8UEW8s45cDB0bEX0jaE/g2cHJEbBxnX8zaSf2OXLMU7LjbcQ/gY+UNMtuBwzvE/zbwYklvKX9+NrCI4i8Bs1q56Jt1UZ7e2Q48RHFu/0HgJRTXwx7r9DLgDyPiqrEkadYHX8g160DSPOATwMeiOA/6bGBLRDwBnAbMKUMfBZ5VeelVwBmS9ijXc3jrVLhmdfGRvtnOniHpVopTOdsoLtyeVz53AfBFSScD1wE/K9u/D2yTdBtwMXA+xSd6bpYkYBo4aTzpm3XnC7lmZhnx6R0zs4y46JuZZcRF38wsIy76ZmYZcdE3M8uIi76ZWUZc9M3MMvL/Acqx8h9dXOyUAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"df.loc[:, 'Close'].resample('M').max().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 114,
"id": "d371bff9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"df.loc[:, 'Close'].plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "96931b7b",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f885c0c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "397189a4",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "9382b4ed",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "48028673",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "4b5d3892",
"metadata": {},
"source": [
"# Bonus:"
]
},
{
"cell_type": "markdown",
"id": "ae8d0827",
"metadata": {},
"source": [
"## A. Creating a DatetimeIndex\n",
"- The `pd.date_range()` method returns a range of equally spaced time points as a DatetimeIndex, which is an immutable container for datetimes.\n",
"\n",
"```\n",
"pd.date_range(start=None, end=None, periods=None, freq=None)\n",
"```\n",
"\n",
"- Where,\n",
" - `start` is the left bound (str or datetime)\n",
" - `end` is the right bound (str or datetime)\n",
" - `periods` is the number of periods to generate\n",
" - `freq` can be `s`, `min`, `h`, `d`, `m`, `q`, `y` for seconds, minutes, ....\n",
"\n",
"\n",
"- Out of the four parameters: start, end, periods, and freq, exactly three must be specified"
]
},
{
"cell_type": "code",
"execution_count": 116,
"id": "58429ef0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2022-01-01 00:00:00', '2022-01-01 01:00:00',\n",
" '2022-01-01 02:00:00', '2022-01-01 03:00:00',\n",
" '2022-01-01 04:00:00', '2022-01-01 05:00:00',\n",
" '2022-01-01 06:00:00', '2022-01-01 07:00:00',\n",
" '2022-01-01 08:00:00', '2022-01-01 09:00:00'],\n",
" dtype='datetime64[ns]', freq='H')"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dti = pd.date_range(start='2022/1/1', periods=10, freq='h')\n",
"dti"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "487b7239",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 117,
"id": "fca10f33",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04',\n",
" '2022-01-05', '2022-01-06', '2022-01-07', '2022-01-08',\n",
" '2022-01-09', '2022-01-10', '2022-01-11', '2022-01-12',\n",
" '2022-01-13', '2022-01-14', '2022-01-15', '2022-01-16',\n",
" '2022-01-17', '2022-01-18', '2022-01-19', '2022-01-20',\n",
" '2022-01-21', '2022-01-22', '2022-01-23', '2022-01-24',\n",
" '2022-01-25', '2022-01-26', '2022-01-27', '2022-01-28',\n",
" '2022-01-29', '2022-01-30', '2022-01-31'],\n",
" dtype='datetime64[ns]', freq='D')"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dti = pd.date_range(start='2022/1/1', end='2022/1/31', freq='d')\n",
"dti"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "acbe75f3",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 118,
"id": "bd54b906",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2022-01-03', '2022-01-04', '2022-01-05', '2022-01-06',\n",
" '2022-01-07', '2022-01-10', '2022-01-11', '2022-01-12',\n",
" '2022-01-13', '2022-01-14', '2022-01-17', '2022-01-18',\n",
" '2022-01-19', '2022-01-20', '2022-01-21', '2022-01-24',\n",
" '2022-01-25', '2022-01-26', '2022-01-27', '2022-01-28',\n",
" '2022-01-31'],\n",
" dtype='datetime64[ns]', freq='B')"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# here freq=B mean include the business days and exclude the weekends\n",
"dti = pd.date_range(start='2022/1/1', end='2022/1/31', freq='B')\n",
"dti"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "db20feef",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 119,
"id": "f59ac394",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.indexes.datetimes.DatetimeIndex"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(dti)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8f876fbd",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "be0f1619",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 120,
"id": "f8d489eb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2022-01-01\n",
"1 2022-01-02\n",
"2 2022-01-03\n",
"3 2022-01-04\n",
"4 2022-01-05\n",
"5 2022-01-06\n",
"6 2022-01-07\n",
"7 2022-01-08\n",
"8 2022-01-09\n",
"9 2022-01-10\n",
"dtype: datetime64[ns]"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = pd.Series(pd.date_range(\"2022-01-01\", periods=10, freq=\"d\"))\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6639556d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 121,
"id": "61c272b0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 Saturday\n",
"1 Sunday\n",
"2 Monday\n",
"3 Tuesday\n",
"4 Wednesday\n",
"5 Thursday\n",
"6 Friday\n",
"7 Saturday\n",
"8 Sunday\n",
"9 Monday\n",
"dtype: object"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.dt.day_name()"
]
},
{
"cell_type": "code",
"execution_count": 122,
"id": "1d424b85",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas._libs.tslibs.timestamps.Timestamp"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(s[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "80fc43bd",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "098fef2c",
"metadata": {},
"source": [
"### b. A sample dataset w/o Datetime"
]
},
{
"cell_type": "code",
"execution_count": 123,
"id": "868675e9",
"metadata": {},
"outputs": [
{
"data": {
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" \n",
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" 2022-01-17 \n",
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" 28 \n",
" 54 \n",
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" 2022-01-18 \n",
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" 21 \n",
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" \n",
" \n",
" 2022-01-19 \n",
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" \n",
" \n",
"
\n",
"
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
![](\"https://upload.wikimedia.org/wikipedia/en/c/c8/University_of_the_Punjab_logo.png\"){kind=logo}
\n",
"\n",
"![](\"images/pandas-apps.png\")
\n",
"\n",
"## _Working with Time Series Data_\n",
"\n",
"**Read Documentation for details:** \n",
"https://pandas.pydata.org/docs/user_guide/timeseries.html#overview"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b153b19a",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f2f60b8",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "7109496b",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "a9e20ddb",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ec33647f",
"metadata": {},
"outputs": [],
"source": [
"# To install this library in Jupyter notebook\n",
"#import sys\n",
"#!{sys.executable} -m pip install pandas"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "671d3985",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('1.4.1',\n",
" ['/Users/arif/opt/anaconda3/envs/python10/lib/python3.10/site-packages/pandas'])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"pd.__version__ , pd.__path__"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "24e3f49c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "ae69a7b9",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3daf1a87",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "8dcbafbd",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "12db95e1",
"metadata": {},
"source": [
"## Learning agenda of this notebook\n",
"1. Recap of Python's Built-in Time and Datetime Modules\n",
" - Python Time module\n",
" - Python Datetime module\n",
" - Time Zones\n",
"2. Overview of Pandas Time Series Data Structures\n",
"3. Converting Strings to Pandas DateTime64 type\n",
" - Convert a Scalar String to DateTime\n",
" - Convert Pandas Series to DateTime\n",
" - Handling Issues of DateTime Formats\n",
" - Convert a Single Integer to Pandas DateTime\n",
"4. Practicing with a Simple Dataset\n",
"5. Practicing with UFO Dataset\n",
"6. Practicing with Crypto-Currency Dataset\n",
"7. Bonus:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d3af656",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "c63ececf",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b2e8052c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "c8ce0dd8",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "d2f1c77f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "cf573669",
"metadata": {},
"source": [
"## Overview of Time Series Data\n",
"#### What is Time Series Data?\n",
"- Time series data, also referred to as time-stamped data, is a sequence of data recorded at specific intervals of time (can be monthly, daily, hourly, ....).\n",
"- These data points are analyzed to forecast the future.\n",
"- It is time dependent.\n",
"- Time series data is effected by four components:\n",
" - **Trend:** Increase or decrease in the series over a period of time. It persist over a long period of time. For Example, population growth of a country over years\n",
" - **Seasonality:** Regular patterns of up and down fluctuations, e.g., Sale of icecream increases in every summer\n",
" - **Cyclicity:** Variations that are caused at irregular intervals. Forexample, 5 years of economic growth, followed by 3 years of recession, followed by 7 years of economic growth, followed. by 1 year of recession\n",
" - **Irregularity:** It refers to variations which occur due to unpredictable factors and also do not repeat in particular patterns. For examples, fluctuations caused by earthquakes, floods, wars, etc\n",
" \n",
"#### What is time series Analysis?\n",
"- Time series analysis is the use of statistical methods to analyze time series data and extract meaningful statistics and characteristics about the data. Time series analysis helps identify trends, cycles, and seasonal variances to aid in the forecasting of a future event.\n",
"- Time series analysis can be useful to see how a given variable changes over time (while time itself, in time series data, is often the independent variable). Time series analysis can also be used to examine how the changes associated with the chosen data point compare to shifts in other variables over the same time period."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d2413820",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "93b17f62",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "e642f910",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "0a04df60",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "30308522",
"metadata": {},
"source": [
"## 1. Recap of Python Modules Related to Date and Time"
]
},
{
"cell_type": "markdown",
"id": "1a058d60",
"metadata": {},
"source": [
"## a. Python Time Module\n",
"- Python Time module is principally for working with UNIX time stamps; expressed as a floating point number taken to be seconds since the unix epoch (00:00:00 UTC on 1 January 1970)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "64a13665",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['_STRUCT_TM_ITEMS', '__doc__', '__loader__', '__name__', '__package__', '__spec__', 'altzone', 'asctime', 'ctime', 'daylight', 'get_clock_info', 'gmtime', 'localtime', 'mktime', 'monotonic', 'monotonic_ns', 'perf_counter', 'perf_counter_ns', 'process_time', 'process_time_ns', 'sleep', 'strftime', 'strptime', 'struct_time', 'time', 'time_ns', 'timezone', 'tzname', 'tzset']\n"
]
}
],
"source": [
"# Use `dir()` to get the list of methods in the Python `time` module\n",
"import time\n",
"print(dir(time))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6e181cdd",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "8459a6de",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "0da747cd",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "f704287d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7c7db69",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "911697e9",
"metadata": {},
"source": [
"**(i) The `time.time()` method returns the current time in seconds since UNIX Epoch (00:00:00 UTC on 1 January 1970)**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d0fa8a98",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1645623933.013344"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"seconds = time.time()\n",
"seconds"
]
},
{
"cell_type": "markdown",
"id": "9ee734d8",
"metadata": {},
"source": [
"> You can achieve the same using the system `date` command and passing it `+%s` command line arugment"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5cc8b863",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1645623933\r\n"
]
}
],
"source": [
"!date +%s"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "83d89815",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "48e7a924",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f1587c0",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "ef8bc3c0",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "34cc80ff",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "39132701",
"metadata": {},
"source": [
"**(ii) The `time.ctime()` method returns a date time string corresponding to the number of seconds passed to it since UNIX Epoch.**"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "70d0bb12",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Thu Jan 1 05:00:00 1970'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Showing `+5:00` hours time delta because of local time zone (PKT) differs from UTC with 5 hours\n",
"dtg1 = time.ctime(0)\n",
"dtg1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3e3bb56a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Wed Feb 23 18:45:33 2022'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#If you pass the current elapsed seconds since UNIX epoch to the `ctime()` method, it returns current datetime\n",
"seconds = time.time()\n",
"dtg2 = time.ctime(seconds)\n",
"dtg2"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6431482c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wed Feb 23 18:45:33 PKT 2022\r\n"
]
}
],
"source": [
"#Get time using shell command\n",
"!date"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "78a88a21",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "abe89e6c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "815d83fd",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "56b95f64",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "5e47340a",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "54006989",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "9445c4b5",
"metadata": {},
"source": [
"## b. Python Datetime Module\n",
"The `datetime` module can support many of the same operations as `time` module, but provides a more object oriented set of types, and also has some limited support for time zones as well."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "121ebc36",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['MAXYEAR', 'MINYEAR', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'date', 'datetime', 'datetime_CAPI', 'sys', 'time', 'timedelta', 'timezone', 'tzinfo']\n"
]
}
],
"source": [
"# use dir() to get the list of complete functions in datetime module\n",
"import datetime\n",
"print(dir(datetime))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f544498b",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "33aa7221",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "09c0b7b2",
"metadata": {},
"source": [
"**(i) The `datetime.datetime(year, month, day[, hour[, minute[, second[, microsecond[,tzinfo]]]]])` method is used to create any random date, along with time**"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "929a2bc2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2021-12-31 00:00:00\n",
"![](\"images/tz.png\")
\n",
"\n",
"- Since noon happens at different times in different parts of the world, therefore, the world is divided in different time zones.\n",
"- On Mac, Linux, and Windows operating systems, the information about these time zones is kept in files.\n",
"- Let me show you the contents of these files on my Mac system"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "caa5d76b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Thu Jan 1 05:00:00 1970'"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The UNIX Epoch in system local time is five hours ahead of mid night 1st Jan 1970\n",
"# (Coordinated Universal Time a successor to Greenwich Mean Time)\n",
"dtg1 = time.ctime(0)\n",
"dtg1"
]
},
{
"cell_type": "markdown",
"id": "c8098a53",
"metadata": {},
"source": [
"> You may have noticed that above cell does not display the exact UNIX epoch, i.e., mid-night 1st January 1970 rather is 5 hours ahead. This is because my machine is configured as per the time zone of Pakistan having a `+5:00` timedelta from Cooridnated Universtal Time (UTC a successor to GMT)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "1333e296",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+VERSION \u001b[34mCanada\u001b[m\u001b[m GB Iran NZ-CHAT UCT\r\n",
"\u001b[34mAfrica\u001b[m\u001b[m \u001b[34mChile\u001b[m\u001b[m GB-Eire Israel Navajo \u001b[34mUS\u001b[m\u001b[m\r\n",
"\u001b[34mAmerica\u001b[m\u001b[m Cuba GMT Jamaica PRC UTC\r\n",
"\u001b[34mAntarctica\u001b[m\u001b[m EET GMT+0 Japan PST8PDT Universal\r\n",
"\u001b[34mArctic\u001b[m\u001b[m EST GMT-0 Kwajalein \u001b[34mPacific\u001b[m\u001b[m W-SU\r\n",
"\u001b[34mAsia\u001b[m\u001b[m EST5EDT GMT0 Libya Poland WET\r\n",
"\u001b[34mAtlantic\u001b[m\u001b[m Egypt Greenwich MET Portugal Zulu\r\n",
"\u001b[34mAustralia\u001b[m\u001b[m Eire HST MST ROC iso3166.tab\r\n",
"\u001b[34mBrazil\u001b[m\u001b[m \u001b[34mEtc\u001b[m\u001b[m Hongkong MST7MDT ROK leapseconds\r\n",
"CET \u001b[34mEurope\u001b[m\u001b[m Iceland \u001b[34mMexico\u001b[m\u001b[m Singapore posixrules\r\n",
"CST6CDT Factory \u001b[34mIndian\u001b[m\u001b[m NZ Turkey zone.tab\r\n"
]
}
],
"source": [
"!ls /usr/share/zoneinfo/"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "4a57581c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Aden Chongqing Jerusalem Novokuznetsk Tashkent\r\n",
"Almaty Chungking Kabul Novosibirsk Tbilisi\r\n",
"Amman Colombo Kamchatka Omsk Tehran\r\n",
"Anadyr Dacca Karachi Oral Tel_Aviv\r\n",
"Aqtau Damascus Kashgar Phnom_Penh Thimbu\r\n",
"Aqtobe Dhaka Kathmandu Pontianak Thimphu\r\n",
"Ashgabat Dili Katmandu Pyongyang Tokyo\r\n",
"Ashkhabad Dubai Khandyga Qatar Tomsk\r\n",
"Atyrau Dushanbe Kolkata Qostanay Ujung_Pandang\r\n",
"Baghdad Famagusta Krasnoyarsk Qyzylorda Ulaanbaatar\r\n",
"Bahrain Gaza Kuala_Lumpur Rangoon Ulan_Bator\r\n",
"Baku Harbin Kuching Riyadh Urumqi\r\n",
"Bangkok Hebron Kuwait Saigon Ust-Nera\r\n",
"Barnaul Ho_Chi_Minh Macao Sakhalin Vientiane\r\n",
"Beirut Hong_Kong Macau Samarkand Vladivostok\r\n",
"Bishkek Hovd Magadan Seoul Yakutsk\r\n",
"Brunei Irkutsk Makassar Shanghai Yangon\r\n",
"Calcutta Istanbul Manila Singapore Yekaterinburg\r\n",
"Chita Jakarta Muscat Srednekolymsk Yerevan\r\n",
"Choibalsan Jayapura Nicosia Taipei\r\n"
]
}
],
"source": [
"!ls /usr/share/zoneinfo/Asia"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0263a5c6",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "921fed43",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "9e127fbc",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "6086280e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "28b459e1",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "4718752b",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "9250cda6",
"metadata": {},
"source": [
">On all UNIX based systems (Mac, Linux), `TZ` is an environment variable that can be set to any of the above files to get the date of that appropriate zone. By default the system is configured to set it to the local time of the country"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "1fa0d04a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wed Feb 23 18:45:33 PKT 2022\r\n"
]
}
],
"source": [
"! date"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "0c065ca5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wed Feb 23 18:45:33 PKT 2022\r\n"
]
}
],
"source": [
"! TZ=Asia/Karachi date"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "60366a50",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wed Feb 23 19:15:33 IST 2022\r\n"
]
}
],
"source": [
"! TZ=Asia/Calcutta date"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "f1e5d015",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wed Feb 23 18:45:33 +05 2022\r\n"
]
}
],
"source": [
"! TZ=Asia/Tashkent date"
]
},
{
"cell_type": "markdown",
"id": "0190d588",
"metadata": {},
"source": [
">So you can observe if we run `date` command after setting the TZ variable to Karachi and Calcutta, their local date times are displayed. Being in different time zones Pakistan Standard Time is 30 minutes before India"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bd322c36",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "972b03ad",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "1f1b1c72",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "989d9343",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "024a390f",
"metadata": {},
"source": [
"## 2. Overview of Pandas Time Series Data Structures\n",
"- **Timestamp & DatetimeIndex:**\n",
" - A `Timestamp` refer to particular moment in time, e.g., 28 July, 1969 at 11:00 am\n",
" - It is a replacement of Python's built-in datetime object\n",
" - The `pd.to_datetime()` method is used to create a `Timestamp` object\n",
" - The `pd.date_range()` method is used to generate a `DatetimeIndex` object\n",
"- **Period & PeriodIndex:**\n",
" - A `Period` refer to length of time between a start and end point, with each interval of uniform length\n",
" - The `pd.to_period()` method is used to create a `Period` object\n",
" - The `pd.period_range()` method is used to create a `PeriodIndex`\n",
"- **Timedelta & TimedeltaIndex:**\n",
" - A `Timedelta` or duration refer to an exact length of time, e.g., a duration of 235.54 seconds\n",
" - A `Timedelta` is created when you subtract two dates, while a `TimedeltaIndex` is created when you subtract two Periods"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "19c35812",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "d4ca6ead",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "46b16d5a",
"metadata": {},
"source": [
"## 3. Converting Strings to Pandas Timestamp Object\n",
"- Pandas `pd.to_datetime()` method is used to convert its only required argument `arg` to a Timestamp object.\n",
"\n",
"```\n",
"pd.to_datetime(arg, format=None, errors='raise', unit=None, origin='unix')\n",
"```\n",
"- Where,\n",
" - `arg` can be a string, Series, int, datetime, list, tuple, 1-d array, DataFrame/dict-like object to convert\n",
" - `errors` {‘ignore’, ‘raise’, ‘coerce’}, default ‘raise’\n",
" - If `raise`, then invalid parsing will raise an exception.\n",
" - If `coerce`, then invalid parsing will be set as NaT.\n",
" - If `ignore`, then invalid parsing will return the input\n",
" - `format`: Used if the `arg` is not in the format as expected by the method\n",
" - `unit`: Used if the `arg` is integer and can be (D,s,ms,us,ns) passed since `origin` (default is `ns`)\n",
" - `origin`: is the reference point from where you want to start counting your units from. The default value of `origin` is the UNIX epoch."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c17e7df2",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "07747255",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "82b72df9",
"metadata": {},
"source": [
"### a. Convert a Scalar String to Timestamp"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "a0f45498",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2022-03-06 08:30:15\n",
"![](\"images/ufo.png\")
"
]
},
{
"cell_type": "markdown",
"id": "97b21c71",
"metadata": {},
"source": [
"### a. Understanding the Dataset"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "5f2c69fc",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"df = pd.read_csv(\"datasets/ufo.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "ecbb8eb6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"![](\"images/cryptocurrency.png\")
"
]
},
{
"cell_type": "code",
"execution_count": 84,
"id": "731aaae1",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"