{ "cells": [ { "cell_type": "code", "execution_count": 43, "id": "243e48f7-3e9e-41c8-b8ed-2f2e225a02f6", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from sqlalchemy import create_engine\n", "\n", "mysql_engine = create_engine('mysql+mysqldb://root:password@127.0.0.1/employees')" ] }, { "cell_type": "code", "execution_count": 44, "id": "7d65be4d-c337-47f8-9b30-7da0dfebc7d6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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emp_nobirth_datefirst_namelast_namegenderhire_date
0100011953-09-02GeorgiFacelloM1986-06-26
1100021964-06-02BezalelSimmelF1985-11-21
2100031959-12-03PartoBamfordM1986-08-28
3100041954-05-01ChirstianKoblickM1986-12-01
4100051955-01-21KyoichiMaliniakM1989-09-12
.....................
3000194999951958-09-24DekangLichtnerF1993-01-12
3000204999961953-03-07ZitoBaazM1990-09-27
3000214999971961-08-03BerhardLenartM1986-04-21
3000224999981956-09-05PatriciaBreugelM1993-10-13
3000234999991958-05-01SachinTsukudaM1997-11-30
\n", "

300024 rows × 6 columns

\n", "
" ], "text/plain": [ " emp_no birth_date first_name last_name gender hire_date\n", "0 10001 1953-09-02 Georgi Facello M 1986-06-26\n", "1 10002 1964-06-02 Bezalel Simmel F 1985-11-21\n", "2 10003 1959-12-03 Parto Bamford M 1986-08-28\n", "3 10004 1954-05-01 Chirstian Koblick M 1986-12-01\n", "4 10005 1955-01-21 Kyoichi Maliniak M 1989-09-12\n", "... ... ... ... ... ... ...\n", "300019 499995 1958-09-24 Dekang Lichtner F 1993-01-12\n", "300020 499996 1953-03-07 Zito Baaz M 1990-09-27\n", "300021 499997 1961-08-03 Berhard Lenart M 1986-04-21\n", "300022 499998 1956-09-05 Patricia Breugel M 1993-10-13\n", "300023 499999 1958-05-01 Sachin Tsukuda M 1997-11-30\n", "\n", "[300024 rows x 6 columns]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "emp = pd.read_sql('employees',mysql_engine)\n", "emp" ] }, { "cell_type": "code", "execution_count": 45, "id": "76cc729e-5bcf-4c1d-b3d1-fe90267e7878", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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emp_nosalaryfrom_dateto_date
010001601171986-06-261987-06-26
110001621021987-06-261988-06-25
210001660741988-06-251989-06-25
310001665961989-06-251990-06-25
410001669611990-06-251991-06-25
...............
2844042499999637071997-11-301998-11-30
2844043499999670431998-11-301999-11-30
2844044499999707451999-11-302000-11-29
2844045499999743272000-11-292001-11-29
2844046499999773032001-11-29NaT
\n", "

2844047 rows × 4 columns

\n", "
" ], "text/plain": [ " emp_no salary from_date to_date\n", "0 10001 60117 1986-06-26 1987-06-26\n", "1 10001 62102 1987-06-26 1988-06-25\n", "2 10001 66074 1988-06-25 1989-06-25\n", "3 10001 66596 1989-06-25 1990-06-25\n", "4 10001 66961 1990-06-25 1991-06-25\n", "... ... ... ... ...\n", "2844042 499999 63707 1997-11-30 1998-11-30\n", "2844043 499999 67043 1998-11-30 1999-11-30\n", "2844044 499999 70745 1999-11-30 2000-11-29\n", "2844045 499999 74327 2000-11-29 2001-11-29\n", "2844046 499999 77303 2001-11-29 NaT\n", "\n", "[2844047 rows x 4 columns]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sal=pd.read_sql('salaries',mysql_engine)\n", "sal" ] }, { "cell_type": "code", "execution_count": 46, "id": "81841338-ca0e-491a-8d8b-9e237bf2ed50", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
emp_nosalaryfrom_dateto_date
010001889582002-06-229999-01-01
110002725272001-08-029999-01-01
210003433112001-12-019999-01-01
310004740572001-11-279999-01-01
410005946922001-09-099999-01-01
...............
300019499995528682002-06-019999-01-01
300020499996695012002-05-129999-01-01
300021499997834412001-08-269999-01-01
300022499998550032001-12-259999-01-01
300023499999773032001-11-299999-01-01
\n", "

300024 rows × 4 columns

\n", "
" ], "text/plain": [ " emp_no salary from_date to_date\n", "0 10001 88958 2002-06-22 9999-01-01\n", "1 10002 72527 2001-08-02 9999-01-01\n", "2 10003 43311 2001-12-01 9999-01-01\n", "3 10004 74057 2001-11-27 9999-01-01\n", "4 10005 94692 2001-09-09 9999-01-01\n", "... ... ... ... ...\n", "300019 499995 52868 2002-06-01 9999-01-01\n", "300020 499996 69501 2002-05-12 9999-01-01\n", "300021 499997 83441 2001-08-26 9999-01-01\n", "300022 499998 55003 2001-12-25 9999-01-01\n", "300023 499999 77303 2001-11-29 9999-01-01\n", "\n", "[300024 rows x 4 columns]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_sql('''\n", "SELECT s1.*\n", "FROM salaries s1 LEFT JOIN salaries s2\n", " ON (s1.emp_no = s2.emp_no AND s1.from_date < s2.from_date)\n", " WHERE s2.from_date IS NULL\n", "''', mysql_engine)" ] }, { "cell_type": "code", "execution_count": 47, "id": "53d7d946-b6f7-4584-8d41-78cbb7941464", "metadata": {}, "outputs": [], "source": [ "mssql_engine = create_engine('mssql+pymssql://sa:Yukon900@127.0.0.1:1434/BikeStores')" ] }, { "cell_type": "code", "execution_count": 48, "id": "30580967-8a0e-4a45-8586-3a0fafbc036c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['brands', 'categories', 'products', 'stocks']" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sqlalchemy import inspect\n", "\n", "insp = inspect(mssql_engine)\n", "insp.get_table_names('production')" ] }, { "cell_type": "code", "execution_count": 49, "id": "38de0628-6c61-4dcc-a5f6-fd96568a331e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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product_idproduct_namebrand_idcategory_idmodel_yearlist_price
01Trek 820 - 2016962016379.99
12Ritchey Timberwolf Frameset - 2016562016749.99
23Surly Wednesday Frameset - 2016862016999.99
34Trek Fuel EX 8 29 - 20169620162899.99
45Heller Shagamaw Frame - 20163620161320.99
.....................
316317Trek Checkpoint ALR 5 - 20199720191999.99
317318Trek Checkpoint ALR 5 Women's - 20199720191999.99
318319Trek Checkpoint SL 5 Women's - 20199720192799.99
319320Trek Checkpoint SL 6 - 20199720193799.99
320321Trek Checkpoint ALR Frameset - 20199720193199.99
\n", "

321 rows × 6 columns

\n", "
" ], "text/plain": [ " product_id product_name brand_id category_id \\\n", "0 1 Trek 820 - 2016 9 6 \n", "1 2 Ritchey Timberwolf Frameset - 2016 5 6 \n", "2 3 Surly Wednesday Frameset - 2016 8 6 \n", "3 4 Trek Fuel EX 8 29 - 2016 9 6 \n", "4 5 Heller Shagamaw Frame - 2016 3 6 \n", ".. ... ... ... ... \n", "316 317 Trek Checkpoint ALR 5 - 2019 9 7 \n", "317 318 Trek Checkpoint ALR 5 Women's - 2019 9 7 \n", "318 319 Trek Checkpoint SL 5 Women's - 2019 9 7 \n", "319 320 Trek Checkpoint SL 6 - 2019 9 7 \n", "320 321 Trek Checkpoint ALR Frameset - 2019 9 7 \n", "\n", " model_year list_price \n", "0 2016 379.99 \n", "1 2016 749.99 \n", "2 2016 999.99 \n", "3 2016 2899.99 \n", "4 2016 1320.99 \n", ".. ... ... \n", "316 2019 1999.99 \n", "317 2019 1999.99 \n", "318 2019 2799.99 \n", "319 2019 3799.99 \n", "320 2019 3199.99 \n", "\n", "[321 rows x 6 columns]" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_sql_table('products',mssql_engine,schema='production')" ] }, { "cell_type": "code", "execution_count": 50, "id": "4db8cf20-98ce-4d16-ab12-c23881fcf465", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
product_idproduct_namebrand_idcategory_idmodel_yearlist_price
0316Trek Checkpoint ALR 4 Women's - 20199720191699.99
1317Trek Checkpoint ALR 5 - 20199720191999.99
2318Trek Checkpoint ALR 5 Women's - 20199720191999.99
3319Trek Checkpoint SL 5 Women's - 20199720192799.99
4320Trek Checkpoint SL 6 - 20199720193799.99
5321Trek Checkpoint ALR Frameset - 20199720193199.99
\n", "
" ], "text/plain": [ " product_id product_name brand_id category_id \\\n", "0 316 Trek Checkpoint ALR 4 Women's - 2019 9 7 \n", "1 317 Trek Checkpoint ALR 5 - 2019 9 7 \n", "2 318 Trek Checkpoint ALR 5 Women's - 2019 9 7 \n", "3 319 Trek Checkpoint SL 5 Women's - 2019 9 7 \n", "4 320 Trek Checkpoint SL 6 - 2019 9 7 \n", "5 321 Trek Checkpoint ALR Frameset - 2019 9 7 \n", "\n", " model_year list_price \n", "0 2019 1699.99 \n", "1 2019 1999.99 \n", "2 2019 1999.99 \n", "3 2019 2799.99 \n", "4 2019 3799.99 \n", "5 2019 3199.99 " ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_sql('select * from production.products where model_year=2019',mssql_engine)" ] }, { "cell_type": "code", "execution_count": 51, "id": "cf4971d9-c2a1-4723-95ff-9cde9135516b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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XYOBJECTIDSEGMENTPOINTLATDDLONDDSEGPTID
0-121.51440439.557588119574139.557583-121.514390195741
1-121.51490439.561860219574239.561855-121.514891195742
2-121.51712139.567641319574339.567636-121.517107195743
3-121.52306139.577200419574439.577194-121.523048195744
4-121.50763039.520372519610139.520367-121.507617196101
...........................
139-121.18828938.91648114062819338.916476-121.188276628193
140-121.21963338.98225514162977138.982249-121.219620629771
141-121.19680238.96956814262977338.969563-121.196789629773
142-121.17766338.96872214362977438.968717-121.177650629774
143-121.20501338.97500514462977238.975000-121.205000629772
\n", "

144 rows × 8 columns

\n", "
" ], "text/plain": [ " X Y OBJECTID SEGMENT POINT LATDD LONDD \\\n", "0 -121.514404 39.557588 1 19574 1 39.557583 -121.514390 \n", "1 -121.514904 39.561860 2 19574 2 39.561855 -121.514891 \n", "2 -121.517121 39.567641 3 19574 3 39.567636 -121.517107 \n", "3 -121.523061 39.577200 4 19574 4 39.577194 -121.523048 \n", "4 -121.507630 39.520372 5 19610 1 39.520367 -121.507617 \n", ".. ... ... ... ... ... ... ... \n", "139 -121.188289 38.916481 140 62819 3 38.916476 -121.188276 \n", "140 -121.219633 38.982255 141 62977 1 38.982249 -121.219620 \n", "141 -121.196802 38.969568 142 62977 3 38.969563 -121.196789 \n", "142 -121.177663 38.968722 143 62977 4 38.968717 -121.177650 \n", "143 -121.205013 38.975005 144 62977 2 38.975000 -121.205000 \n", "\n", " SEGPTID \n", "0 195741 \n", "1 195742 \n", "2 195743 \n", "3 195744 \n", "4 196101 \n", ".. ... \n", "139 628193 \n", "140 629771 \n", "141 629773 \n", "142 629774 \n", "143 629772 \n", "\n", "[144 rows x 8 columns]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('https://data-cdfw.opendata.arcgis.com/datasets/e1b1ff8f4872409eb43447175105e8ce_0.csv')\n", "df" ] }, { "cell_type": "code", "execution_count": 52, "id": "267281c2-2a0a-4f79-bf96-585d9cda6581", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LocationIDLocationTypeLocationNameAreaType
04000HUB.H.INTERNAL_HUBINTERNAL
14001LOAD ZONE.Z.MAINEINTERNAL
24002LOAD ZONE.Z.NEWHAMPSHIREINTERNAL
34003LOAD ZONE.Z.VERMONTINTERNAL
44004LOAD ZONE.Z.CONNECTICUTINTERNAL
54005LOAD ZONE.Z.RHODEISLANDINTERNAL
64006LOAD ZONE.Z.SEMASSINTERNAL
74007LOAD ZONE.Z.WCMASSINTERNAL
84008LOAD ZONE.Z.NEMASSBOSTINTERNAL
94010EXT. NODE.I.SALBRYNB345 1EXTERNAL
104011EXT. NODE.I.ROSETON 345 1EXTERNAL
114012EXT. NODE.I.HQ_P1_P2345 5EXTERNAL
124013EXT. NODE.I.HQHIGATE120 2EXTERNAL
134014EXT. NODE.I.SHOREHAM138 99EXTERNAL
144017EXT. NODE.I.NRTHPORT138 5EXTERNAL
157000SYSTEMROSINTERNAL
167001RESERVE ZONESWCTINTERNAL
177002RESERVE ZONECTINTERNAL
187003RESERVE ZONENEMABSTNINTERNAL
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" ], "text/plain": [ " LocationID LocationType LocationName AreaType\n", "0 4000 HUB .H.INTERNAL_HUB INTERNAL\n", "1 4001 LOAD ZONE .Z.MAINE INTERNAL\n", "2 4002 LOAD ZONE .Z.NEWHAMPSHIRE INTERNAL\n", "3 4003 LOAD ZONE .Z.VERMONT INTERNAL\n", "4 4004 LOAD ZONE .Z.CONNECTICUT INTERNAL\n", "5 4005 LOAD ZONE .Z.RHODEISLAND INTERNAL\n", "6 4006 LOAD ZONE .Z.SEMASS INTERNAL\n", "7 4007 LOAD ZONE .Z.WCMASS INTERNAL\n", "8 4008 LOAD ZONE .Z.NEMASSBOST INTERNAL\n", "9 4010 EXT. NODE .I.SALBRYNB345 1 EXTERNAL\n", "10 4011 EXT. NODE .I.ROSETON 345 1 EXTERNAL\n", "11 4012 EXT. NODE .I.HQ_P1_P2345 5 EXTERNAL\n", "12 4013 EXT. NODE .I.HQHIGATE120 2 EXTERNAL\n", "13 4014 EXT. NODE .I.SHOREHAM138 99 EXTERNAL\n", "14 4017 EXT. NODE .I.NRTHPORT138 5 EXTERNAL\n", "15 7000 SYSTEM ROS INTERNAL\n", "16 7001 RESERVE ZONE SWCT INTERNAL\n", "17 7002 RESERVE ZONE CT INTERNAL\n", "18 7003 RESERVE ZONE NEMABSTN INTERNAL" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import requests as rq\n", "\n", "#Basic Authentication\n", "with open('userpass.txt') as f:\n", " username,password = tuple(f.readline().split(','))\n", "response = rq.get('https://webservices.iso-ne.com/api/v1.1/locations',auth=(username,password))\n", "df = pd.read_xml(response.text)\n", "df\n", " " ] }, { "cell_type": "code", "execution_count": 53, "id": "c7131e5f-78ed-4f6b-bd94-69be357eb617", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LocalObservationDateTimeEpochTimeWeatherTextWeatherIconHasPrecipitationPrecipitationTypeIsDayTimeTemperatureMobileLinkLink
02022-09-14T12:51:00-04:001663174260Mostly sunny2FalseNaNTrue{'Metric': {'Value': 22.5, 'Unit': 'C', 'UnitT...http://www.accuweather.com/en/us/schuylkill-ha...http://www.accuweather.com/en/us/schuylkill-ha...
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" ], "text/plain": [ " LocalObservationDateTime EpochTime WeatherText WeatherIcon \\\n", "0 2022-09-14T12:51:00-04:00 1663174260 Mostly sunny 2 \n", "\n", " HasPrecipitation PrecipitationType IsDayTime \\\n", "0 False NaN True \n", "\n", " Temperature \\\n", "0 {'Metric': {'Value': 22.5, 'Unit': 'C', 'UnitT... \n", "\n", " MobileLink \\\n", "0 http://www.accuweather.com/en/us/schuylkill-ha... \n", "\n", " Link \n", "0 http://www.accuweather.com/en/us/schuylkill-ha... " ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p = {\n", " 'apikey':'VdYJWBZQXvxFhj1IqImanA3grAqCiEfV',\n", "}\n", "resp = rq.get('http://dataservice.accuweather.com/currentconditions/v1/2098339',params=p)\n", "pd.read_json(resp.text)" ] }, { "cell_type": "code", "execution_count": 54, "id": "33bafd95-0aab-40ec-a15a-7b682ce848bc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs = pd.read_html('https://www.w3schools.com/html/html_tables.asp')\n", "len(dfs)" ] }, { "cell_type": "code", "execution_count": 55, "id": "c68673bb-792a-470a-baa0-ec35a42573eb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CompanyContactCountry
0Alfreds FutterkisteMaria AndersGermany
1Centro comercial MoctezumaFrancisco ChangMexico
2Ernst HandelRoland MendelAustria
3Island TradingHelen BennettUK
4Laughing Bacchus WinecellarsYoshi TannamuriCanada
5Magazzini Alimentari RiunitiGiovanni RovelliItaly
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" ], "text/plain": [ " Company Contact Country\n", "0 Alfreds Futterkiste Maria Anders Germany\n", "1 Centro comercial Moctezuma Francisco Chang Mexico\n", "2 Ernst Handel Roland Mendel Austria\n", "3 Island Trading Helen Bennett UK\n", "4 Laughing Bacchus Winecellars Yoshi Tannamuri Canada\n", "5 Magazzini Alimentari Riuniti Giovanni Rovelli Italy" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs[0]" ] }, { "cell_type": "code", "execution_count": 56, "id": "c9dc1344-ef71-4ce1-ac7c-424fdd1206b5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TagDescription
0<table>Defines a table
1<th>Defines a header cell in a table
2<tr>Defines a row in a table
3<td>Defines a cell in a table
4<caption>Defines a table caption
5<colgroup>Specifies a group of one or more columns in a ...
6<col>Specifies column properties for each column wi...
7<thead>Groups the header content in a table
8<tbody>Groups the body content in a table
9<tfoot>Groups the footer content in a table
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" ], "text/plain": [ " Tag Description\n", "0 Defines a table\n", "1 Defines a row in a table\n", "3 Specifies a group of one or more columns in a ...\n", "6 Specifies column properties for each column wi...\n", "7 Groups the header content in a table\n", "8 Groups the body content in a table\n", "9 Groups the footer content in a table" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs[1]" ] }, { "cell_type": "code", "execution_count": 57, "id": "2402954b-8560-4417-a6e1-513c6dcb251f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "
Defines a header cell in a table\n", "2
Defines a cell in a table\n", "4
Defines a table caption\n", "5
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countrycontinentyearlifeExppopgdpPercapiso_alphaiso_num
0AfghanistanAsia195228.8018425333779.445314AFG4
1AfghanistanAsia195730.3329240934820.853030AFG4
2AfghanistanAsia196231.99710267083853.100710AFG4
3AfghanistanAsia196734.02011537966836.197138AFG4
4AfghanistanAsia197236.08813079460739.981106AFG4
...........................
1699ZimbabweAfrica198762.3519216418706.157306ZWE716
1700ZimbabweAfrica199260.37710704340693.420786ZWE716
1701ZimbabweAfrica199746.80911404948792.449960ZWE716
1702ZimbabweAfrica200239.98911926563672.038623ZWE716
1703ZimbabweAfrica200743.48712311143469.709298ZWE716
\n", "

1704 rows × 8 columns

\n", "" ], "text/plain": [ " country continent year lifeExp pop gdpPercap iso_alpha \\\n", "0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG \n", "1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG \n", "2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG \n", "3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG \n", "4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG \n", "... ... ... ... ... ... ... ... \n", "1699 Zimbabwe Africa 1987 62.351 9216418 706.157306 ZWE \n", "1700 Zimbabwe Africa 1992 60.377 10704340 693.420786 ZWE \n", "1701 Zimbabwe Africa 1997 46.809 11404948 792.449960 ZWE \n", "1702 Zimbabwe Africa 2002 39.989 11926563 672.038623 ZWE \n", "1703 Zimbabwe Africa 2007 43.487 12311143 469.709298 ZWE \n", "\n", " iso_num \n", "0 4 \n", "1 4 \n", "2 4 \n", "3 4 \n", "4 4 \n", "... ... \n", "1699 716 \n", "1700 716 \n", "1701 716 \n", "1702 716 \n", "1703 716 \n", "\n", "[1704 rows x 8 columns]" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.express as px\n", "\n", "df = px.data.gapminder()\n", "df" ] }, { "cell_type": "code", "execution_count": 58, "id": "57ec06ff-daa0-4808-bc56-416c8148f6c4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrycontinentyearlifeExppopgdpPercapiso_alphaiso_numfixed_pop
0AfghanistanAsia195228.8018425333779.445314AFG48425333.00
1AfghanistanAsia195730.3329240934820.853030AFG49240934.00
2AfghanistanAsia196231.99710267083853.100710AFG410267083.00
3AfghanistanAsia196734.02011537966836.197138AFG411653345.66
4AfghanistanAsia197236.08813079460739.981106AFG413079460.00
..............................
1699ZimbabweAfrica198762.3519216418706.157306ZWE7169216418.00
1700ZimbabweAfrica199260.37710704340693.420786ZWE71610704340.00
1701ZimbabweAfrica199746.80911404948792.449960ZWE71611404948.00
1702ZimbabweAfrica200239.98911926563672.038623ZWE71611926563.00
1703ZimbabweAfrica200743.48712311143469.709298ZWE71612311143.00
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1704 rows × 9 columns

\n", "
" ], "text/plain": [ " country continent year lifeExp pop gdpPercap iso_alpha \\\n", "0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG \n", "1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG \n", "2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG \n", "3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG \n", "4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG \n", "... ... ... ... ... ... ... ... \n", "1699 Zimbabwe Africa 1987 62.351 9216418 706.157306 ZWE \n", "1700 Zimbabwe Africa 1992 60.377 10704340 693.420786 ZWE \n", "1701 Zimbabwe Africa 1997 46.809 11404948 792.449960 ZWE \n", "1702 Zimbabwe Africa 2002 39.989 11926563 672.038623 ZWE \n", "1703 Zimbabwe Africa 2007 43.487 12311143 469.709298 ZWE \n", "\n", " iso_num fixed_pop \n", "0 4 8425333.00 \n", "1 4 9240934.00 \n", "2 4 10267083.00 \n", "3 4 11653345.66 \n", "4 4 13079460.00 \n", "... ... ... \n", "1699 716 9216418.00 \n", "1700 716 10704340.00 \n", "1701 716 11404948.00 \n", "1702 716 11926563.00 \n", "1703 716 12311143.00 \n", "\n", "[1704 rows x 9 columns]" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def fixPop(x):\n", " return x['pop'] if x.year != 1967 else x['pop'] * 1.01\n", " \n", "df['fixed_pop'] = df.apply(fixPop,axis=1)\n", "df" ] }, { "cell_type": "code", "execution_count": 59, "id": "cb865b33-5fe2-4598-aa72-34a4cd7422f6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrycontinentyearlifeExppopgdpPercapiso_alphaiso_numfixed_popfixed_pop_lambda
0AfghanistanAsia195228.8018425333779.445314AFG48425333.008425333.00
1AfghanistanAsia195730.3329240934820.853030AFG49240934.009240934.00
2AfghanistanAsia196231.99710267083853.100710AFG410267083.0010267083.00
3AfghanistanAsia196734.02011537966836.197138AFG411653345.6611653345.66
4AfghanistanAsia197236.08813079460739.981106AFG413079460.0013079460.00
.................................
1699ZimbabweAfrica198762.3519216418706.157306ZWE7169216418.009216418.00
1700ZimbabweAfrica199260.37710704340693.420786ZWE71610704340.0010704340.00
1701ZimbabweAfrica199746.80911404948792.449960ZWE71611404948.0011404948.00
1702ZimbabweAfrica200239.98911926563672.038623ZWE71611926563.0011926563.00
1703ZimbabweAfrica200743.48712311143469.709298ZWE71612311143.0012311143.00
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1704 rows × 10 columns

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" ], "text/plain": [ " country continent year lifeExp pop gdpPercap iso_alpha \\\n", "0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG \n", "1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG \n", "2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG \n", "3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG \n", "4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG \n", "... ... ... ... ... ... ... ... \n", "1699 Zimbabwe Africa 1987 62.351 9216418 706.157306 ZWE \n", "1700 Zimbabwe Africa 1992 60.377 10704340 693.420786 ZWE \n", "1701 Zimbabwe Africa 1997 46.809 11404948 792.449960 ZWE \n", "1702 Zimbabwe Africa 2002 39.989 11926563 672.038623 ZWE \n", "1703 Zimbabwe Africa 2007 43.487 12311143 469.709298 ZWE \n", "\n", " iso_num fixed_pop fixed_pop_lambda \n", "0 4 8425333.00 8425333.00 \n", "1 4 9240934.00 9240934.00 \n", "2 4 10267083.00 10267083.00 \n", "3 4 11653345.66 11653345.66 \n", "4 4 13079460.00 13079460.00 \n", "... ... ... ... \n", "1699 716 9216418.00 9216418.00 \n", "1700 716 10704340.00 10704340.00 \n", "1701 716 11404948.00 11404948.00 \n", "1702 716 11926563.00 11926563.00 \n", "1703 716 12311143.00 12311143.00 \n", "\n", "[1704 rows x 10 columns]" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['fixed_pop_lambda'] = df.apply(lambda x: x['pop'] if x.year != 1967 else x['pop'] * 1.01,axis=1)\n", "df" ] }, { "cell_type": "code", "execution_count": 3, "id": "5391b072-4d4b-4204-a7bd-61c850692804", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " a b c d e f g \\\n", "0 8.944723 7.752738 6.684406 1.628755 0.399091 8.224990 4.021463 \n", "1 8.547211 9.398321 9.832756 8.292585 5.294127 1.635311 2.970403 \n", "2 0.655821 1.845909 8.460606 0.243788 5.464547 8.448926 7.438373 \n", "3 5.139514 3.411863 4.670033 2.936253 9.161014 9.334777 0.753321 \n", "4 7.358384 6.122101 2.156183 3.576593 3.991799 4.764575 1.297737 \n", "5 7.957199 3.931405 5.045519 0.116129 1.014867 2.218519 5.475246 \n", "6 0.232114 9.184459 3.938581 4.889738 9.435942 3.485889 5.579237 \n", "7 1.490956 9.458082 4.851028 3.765671 9.094899 7.327259 6.860747 \n", "8 3.807027 6.279791 4.867807 3.523232 6.877953 5.187374 1.391900 \n", "9 6.170356 6.708828 0.765444 9.968166 1.953046 6.412687 7.841132 \n", "\n", " h i j \n", "0 4.800138 2.997566 3.121875 \n", "1 6.824628 6.046200 4.079104 \n", "2 8.421051 6.644092 7.161966 \n", "3 1.143391 2.944671 1.215092 \n", "4 1.535719 9.684969 7.122543 \n", "5 4.774364 7.760366 4.782608 \n", "6 3.991281 8.912471 2.259759 \n", "7 0.557580 3.546149 3.147287 \n", "8 2.930733 7.811736 3.494604 \n", "9 3.441464 4.029287 6.993599 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "df = pd.DataFrame(np.random.rand(10,10),columns=list('abcdefghij')) * 10\n", "df" ] }, { "cell_type": "code", "execution_count": 2, "id": "f40b27f7-ec25-4928-ae0c-72bbedd94789", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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abcdefghij
01.8285120.360523-5.4521074.071351-5.0669463.9989403.5470800.243179-8.5358474.919476
13.804510-9.902955-6.0901463.4924094.139086-9.459874-5.402557-8.799132-9.7100731.627214
21.2913074.2009052.102707-9.9138612.0704701.5921541.687186-7.945810-6.603336-6.994897
34.064082-9.621563-7.5119212.0016991.010653-8.9644392.716315-6.3016160.974689-5.946650
4-5.644566-5.2314371.479682-8.134370-7.3971742.710740-5.8325390.8823794.693876-8.805219
52.2961150.489777-5.241339-9.0774631.064415-6.214227-5.309441-9.8315071.287065-6.288077
6-6.4743220.4899041.9940512.2236821.1249652.4994340.5119000.425484-9.015851-6.122449
7-7.9665733.8427801.6864614.5875882.391019-5.293809-5.6454394.718691-8.8518754.363987
8-9.760431-5.6140294.119470-8.812897-8.4692523.0417620.132648-7.542054-7.1540214.135862
9-8.6048324.6094704.2506490.3367423.543592-7.227278-5.327075-5.427247-6.9568451.166581
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countrycontinentyearlifeExppopgdpPercapiso_alphaiso_num
0AfghanistanAsia195228.8018425333779.445314AFG4
1AfghanistanAsia195730.3329240934820.853030AFG4
2AfghanistanAsia196231.99710267083853.100710AFG4
3AfghanistanAsia196734.02011537966836.197138AFG4
4AfghanistanAsia197236.08813079460739.981106AFG4
...........................
1699ZimbabweAfrica198762.3519216418706.157306ZWE716
1700ZimbabweAfrica199260.37710704340693.420786ZWE716
1701ZimbabweAfrica199746.80911404948792.449960ZWE716
1702ZimbabweAfrica200239.98911926563672.038623ZWE716
1703ZimbabweAfrica200743.48712311143469.709298ZWE716
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1704 rows × 8 columns

\n", "
" ], "text/plain": [ " country continent year lifeExp pop gdpPercap iso_alpha \\\n", "0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG \n", "1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG \n", "2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG \n", "3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG \n", "4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG \n", "... ... ... ... ... ... ... ... \n", "1699 Zimbabwe Africa 1987 62.351 9216418 706.157306 ZWE \n", "1700 Zimbabwe Africa 1992 60.377 10704340 693.420786 ZWE \n", "1701 Zimbabwe Africa 1997 46.809 11404948 792.449960 ZWE \n", "1702 Zimbabwe Africa 2002 39.989 11926563 672.038623 ZWE \n", "1703 Zimbabwe Africa 2007 43.487 12311143 469.709298 ZWE \n", "\n", " iso_num \n", "0 4 \n", "1 4 \n", "2 4 \n", "3 4 \n", "4 4 \n", "... ... \n", "1699 716 \n", "1700 716 \n", "1701 716 \n", "1702 716 \n", "1703 716 \n", "\n", "[1704 rows x 8 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.express as px\n", "df = px.data.gapminder()\n", "df" ] }, { "cell_type": "code", "execution_count": 64, "id": "39b39c8b-996c-44f4-8cf8-4554c526d232", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrycontinentgdpPercap
0AfghaASIA779.45
1AfghaASIA820.85
2AfghaASIA853.10
3AfghaASIA836.20
4AfghaASIA739.98
............
1699ZimbaAFRICA706.16
1700ZimbaAFRICA693.42
1701ZimbaAFRICA792.45
1702ZimbaAFRICA672.04
1703ZimbaAFRICA469.71
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1704 rows × 3 columns

\n", "
" ], "text/plain": [ " country continent gdpPercap\n", "0 Afgha ASIA 779.45\n", "1 Afgha ASIA 820.85\n", "2 Afgha ASIA 853.10\n", "3 Afgha ASIA 836.20\n", "4 Afgha ASIA 739.98\n", "... ... ... ...\n", "1699 Zimba AFRICA 706.16\n", "1700 Zimba AFRICA 693.42\n", "1701 Zimba AFRICA 792.45\n", "1702 Zimba AFRICA 672.04\n", "1703 Zimba AFRICA 469.71\n", "\n", "[1704 rows x 3 columns]" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.transform({\n", " 'country':lambda x:x[:5],\n", " 'continent':lambda x:x.upper(),\n", " 'gdpPercap':lambda x:round(x,2)\n", "})" ] }, { "cell_type": "code", "execution_count": 65, "id": "67125b58-f4c3-44f0-ad91-f563f85b3c67", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 28.801\n", "1 30.332\n", "2 31.997\n", "3 34.020\n", "4 36.088\n", " ... \n", "1699 62.351\n", "1700 60.377\n", "1701 46.809\n", "1702 39.989\n", "1703 43.487\n", "Name: lifeExp, Length: 1704, dtype: float64" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = df['lifeExp']\n", "s" ] }, { "cell_type": "code", "execution_count": 66, "id": "a9f6c213-563c-499e-b0d6-06f125149acb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 35.000\n", "1 35.000\n", "2 35.000\n", "3 35.000\n", "4 36.088\n", " ... \n", "1699 62.351\n", "1700 60.377\n", "1701 46.809\n", "1702 39.989\n", "1703 43.487\n", "Name: lifeExp, Length: 1704, dtype: float64" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.apply(lambda x:x if x > 35 else 35)" ] }, { "cell_type": "code", "execution_count": 67, "id": "47de2cab-06a0-4fee-883c-feb1395bbe35", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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012
028.80157.602288.01
130.33260.664303.32
231.99763.994319.97
334.02068.040340.20
436.08872.176360.88
............
169962.351124.702623.51
170060.377120.754603.77
170146.80993.618468.09
170239.98979.978399.89
170343.48786.974434.87
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1704 rows × 3 columns

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squarecubeneg
081729-9
11664-4
21664-4
349343-7
436216-6
549343-7
6927-3
781729-9
848-2
925125-5
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" ], "text/plain": [ " square cube neg\n", "0 81 729 -9\n", "1 16 64 -4\n", "2 16 64 -4\n", "3 49 343 -7\n", "4 36 216 -6\n", "5 49 343 -7\n", "6 9 27 -3\n", "7 81 729 -9\n", "8 4 8 -2\n", "9 25 125 -5" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.transform({\n", " 'square':lambda x:x*x,\n", " 'cube':lambda x:x**3,\n", " 'neg':lambda x:-x\n", "})" ] }, { "cell_type": "code", "execution_count": 72, "id": "55e469d0-2c2b-4572-a66f-d4521f771e99", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrycontinentyearlifeExppopgdpPercapiso_alphaiso_numfixed_pop
0AfghanistanAsia195228.8018425333779.445314AFG48425333.00
1AfghanistanAsia195730.3329240934820.853030AFG49240934.00
2AfghanistanAsia196231.99710267083853.100710AFG410267083.00
3AfghanistanAsia196734.02011537966836.197138AFG411653345.66
4AfghanistanAsia197236.08813079460739.981106AFG413079460.00
..............................
1699ZimbabweAfrica198762.3519216418706.157306ZWE7169216418.00
1700ZimbabweAfrica199260.37710704340693.420786ZWE71610597296.60
1701ZimbabweAfrica199746.80911404948792.449960ZWE71611404948.00
1702ZimbabweAfrica200239.98911926563672.038623ZWE71612522891.15
1703ZimbabweAfrica200743.48712311143469.709298ZWE71612311143.00
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1704 rows × 9 columns

\n", "
" ], "text/plain": [ " country continent year lifeExp pop gdpPercap iso_alpha \\\n", "0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG \n", "1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG \n", "2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG \n", "3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG \n", "4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG \n", "... ... ... ... ... ... ... ... \n", "1699 Zimbabwe Africa 1987 62.351 9216418 706.157306 ZWE \n", "1700 Zimbabwe Africa 1992 60.377 10704340 693.420786 ZWE \n", "1701 Zimbabwe Africa 1997 46.809 11404948 792.449960 ZWE \n", "1702 Zimbabwe Africa 2002 39.989 11926563 672.038623 ZWE \n", "1703 Zimbabwe Africa 2007 43.487 12311143 469.709298 ZWE \n", "\n", " iso_num fixed_pop \n", "0 4 8425333.00 \n", "1 4 9240934.00 \n", "2 4 10267083.00 \n", "3 4 11653345.66 \n", "4 4 13079460.00 \n", "... ... ... \n", "1699 716 9216418.00 \n", "1700 716 10597296.60 \n", "1701 716 11404948.00 \n", "1702 716 12522891.15 \n", "1703 716 12311143.00 \n", "\n", "[1704 rows x 9 columns]" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def fixPop(x,**kwargs):\n", " return x['pop'] * kwargs.get(str(x.year),1)\n", "\n", "badyears = {\n", " '1967':1.01,\n", " '1992':0.99,\n", " '2002':1.05\n", "}\n", "df['fixed_pop'] = df.apply(fixPop,axis=1,**badyears)\n", "df" ] }, { "cell_type": "code", "execution_count": 73, "id": "7a6db6a8-1780-475d-bb8b-c3aa8dfe7d93", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "continent\n", "Africa 98\n", "Americas 1\n", "Asia 25\n", "Europe 0\n", "Oceania 0\n", "dtype: int64" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('continent').apply(lambda x:len(x[x['lifeExp'] < 40]))" ] }, { "cell_type": "code", "execution_count": 74, "id": "36ec3983-0dff-440c-96b8-1a48e40344cb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrycontinentyearlifeExppopgdpPercapiso_alphaiso_numfixed_pop
continent
Africa36AngolaAfrica195230.01542320953520.610273AGO244232095.00
37AngolaAfrica195731.99945613613827.940465AGO244561361.00
38AngolaAfrica196234.00048260154269.276742AGO244826015.00
39AngolaAfrica196735.98552474695522.776375AGO245299943.69
40AngolaAfrica197237.92858948585473.288005AGO245894858.00
.................................
Asia1668Yemen, Rep.Asia195232.5484963829781.717576YEM8874963829.00
1669Yemen, Rep.Asia195733.9705498090804.830455YEM8875498090.00
1670Yemen, Rep.Asia196235.1806120081825.623201YEM8876120081.00
1671Yemen, Rep.Asia196736.9846740785862.442146YEM8876808192.85
1672Yemen, Rep.Asia197239.84874070751265.047031YEM8877407075.00
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124 rows × 9 columns

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" ], "text/plain": [ " country continent year lifeExp pop gdpPercap \\\n", "continent \n", "Africa 36 Angola Africa 1952 30.015 4232095 3520.610273 \n", " 37 Angola Africa 1957 31.999 4561361 3827.940465 \n", " 38 Angola Africa 1962 34.000 4826015 4269.276742 \n", " 39 Angola Africa 1967 35.985 5247469 5522.776375 \n", " 40 Angola Africa 1972 37.928 5894858 5473.288005 \n", "... ... ... ... ... ... ... \n", "Asia 1668 Yemen, Rep. Asia 1952 32.548 4963829 781.717576 \n", " 1669 Yemen, Rep. Asia 1957 33.970 5498090 804.830455 \n", " 1670 Yemen, Rep. Asia 1962 35.180 6120081 825.623201 \n", " 1671 Yemen, Rep. Asia 1967 36.984 6740785 862.442146 \n", " 1672 Yemen, Rep. Asia 1972 39.848 7407075 1265.047031 \n", "\n", " iso_alpha iso_num fixed_pop \n", "continent \n", "Africa 36 AGO 24 4232095.00 \n", " 37 AGO 24 4561361.00 \n", " 38 AGO 24 4826015.00 \n", " 39 AGO 24 5299943.69 \n", " 40 AGO 24 5894858.00 \n", "... ... ... ... \n", "Asia 1668 YEM 887 4963829.00 \n", " 1669 YEM 887 5498090.00 \n", " 1670 YEM 887 6120081.00 \n", " 1671 YEM 887 6808192.85 \n", " 1672 YEM 887 7407075.00 \n", "\n", "[124 rows x 9 columns]" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('continent').apply(lambda x:x[x['lifeExp'] < 40])" ] }, { "cell_type": "code", "execution_count": 5, "id": "04ba0a71-ab22-4914-ad0c-d76174078a4b", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{'Asia': 1.05,\n", " 'Europe': 1.09,\n", " 'Africa': 1.01,\n", " 'Americas': 0.96,\n", " 'Oceania': 0.98}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "factors = {c:0.9 + round(np.random.rand()*0.2,2) for c in df['continent'].unique()}\n", "factors" ] }, { "cell_type": "code", "execution_count": 9, "id": "ec3724a1-00d8-4bea-bf9b-55a92f3932ca", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "24 9372320.25\n", "25 10373564.56\n", "26 11110957.48\n", "27 12888103.99\n", "28 14908394.87\n", " ... \n", "1099 3250822.68\n", "1100 3368920.52\n", "1101 3602663.26\n", "1102 3829876.26\n", "1103 4033455.58\n", "Length: 1704, dtype: float64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs = []\n", "for n,g in df.groupby('continent'):\n", " dfs.append(\n", " g.apply(\n", " lambda x,factor:x['pop'] * factor,\n", " axis=1,\n", " factor=factors[n] \n", " )\n", " )\n", "pd.concat(dfs)" ] }, { "cell_type": "code", "execution_count": 77, "id": "489ec0e8-fb7a-42ae-95d8-2d81a8019792", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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0123456789
2022-09-14 08:00:0099.10855999.78470199.30940699.68680599.068133100.056513100.17290599.078959100.290538100.912277
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2022-09-14 08:00:04100.00743799.547416100.04006499.535501100.83296899.31283099.307909100.152388100.37670599.512596
.................................
2022-09-14 08:59:5599.224361100.58512899.51479999.69954599.438537100.708057100.89365099.72149099.67845099.989257
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3600 rows × 10 columns

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100.069765 100.082021 \n", "2022-09-14 08:37:00 99.862031 99.965519 \n", "2022-09-14 08:38:00 100.045841 100.034803 \n", "2022-09-14 08:39:00 99.868741 99.927623 \n", "2022-09-14 08:40:00 99.905407 100.067703 \n", "2022-09-14 08:41:00 100.004697 99.973989 \n", "2022-09-14 08:42:00 99.984541 100.197412 \n", "2022-09-14 08:43:00 100.032396 100.049756 \n", "2022-09-14 08:44:00 100.004385 99.978388 \n", "2022-09-14 08:45:00 100.078715 99.922155 \n", "2022-09-14 08:46:00 100.039189 100.055495 \n", "2022-09-14 08:47:00 99.902926 99.959797 \n", "2022-09-14 08:48:00 100.077236 100.050511 \n", "2022-09-14 08:49:00 99.921554 99.944374 \n", "2022-09-14 08:50:00 100.053149 99.905221 \n", "2022-09-14 08:51:00 99.993152 100.048129 \n", "2022-09-14 08:52:00 100.015084 99.982291 \n", "2022-09-14 08:53:00 99.977473 100.017663 \n", "2022-09-14 08:54:00 99.936163 99.988514 \n", "2022-09-14 08:55:00 100.019703 100.038617 \n", "2022-09-14 08:56:00 100.095587 100.073809 \n", "2022-09-14 08:57:00 100.047703 100.029517 \n", "2022-09-14 08:58:00 99.931792 100.088646 \n", "2022-09-14 08:59:00 100.051076 99.930974 " ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs = []\n", "for df in pd.read_csv('testchunk.csv',index_col=0,parse_dates=True,chunksize=600):\n", " dfs.append(df.resample('T').mean())\n", "pd.concat(dfs)" ] }, { "cell_type": "code", "execution_count": 84, "id": "88158de5-493f-4e99-950c-0c322bc1c141", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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avg_salary
064287.63630
164372.31860
264577.51630
363692.90550
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......
28063633.74970
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285 rows × 1 columns

\n", "
" ], "text/plain": [ " avg_salary\n", "0 64287.63630\n", "1 64372.31860\n", "2 64577.51630\n", "3 63692.90550\n", "4 63534.80060\n", ".. ...\n", "280 63633.74970\n", "281 63688.98220\n", "282 63300.05640\n", "283 64307.34460\n", "284 63675.54979\n", "\n", "[285 rows x 1 columns]" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = []\n", "for chunk in pd.read_sql('salaries',mysql_engine,chunksize=10000):\n", " s.append(chunk['salary'].mean())\n", "pd.DataFrame(s,columns=['avg_salary'])" ] }, { "cell_type": "code", "execution_count": null, "id": "ebc9da70-e0ff-4fa4-bf9c-6367796622a9", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 5 }