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Commit 1f0d9753 by Patryk Czarnik
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jupyter koniec i analiza w Pycharm

parent 513fe57c
Showing with 1180 additions and 4 deletions
dzien9/dane/analiza_pliku_emps.py 0 → 100644
import pandas
# emps = pandas.read_csv('emps.csv', sep=';')
emps = pandas.read_csv('emps.csv', sep=';', index_col='employee_id', parse_dates=['hire_date'])
print('Dane wczytane')
print(emps)
print('-'*80)
print('Średnia pensja:', emps.salary.mean())
print('Średnia pensja programistów:', emps[emps.job_title == 'Programmer'].salary.mean())
print('='*80)
# bierzemy rekord o ID = 100
king = emps.loc[100]
print(king)
print()
print(king.first_name, king.last_name, 'zarabia', king.salary)
print(king["first_name"], king["last_name"], 'zarabia', king["salary"])
dzien9/dane/emps.csv 0 → 100644
employee_id;first_name;last_name;job_title;salary;hire_date;department_name;address;postal_code;city;country
100;Steven;King;President;24000;1997-06-17;Executive;2004 Charade Rd;98199;Seattle;United States of America
101;Neena;Kochhar;Administration Vice President;17000;1999-09-21;Executive;2004 Charade Rd;98199;Seattle;United States of America
102;Lex;De Haan;Administration Vice President;17000;2003-01-13;Executive;2004 Charade Rd;98199;Seattle;United States of America
103;Alexander;Hunold;Programmer;9000;2000-01-03;IT;2014 Jabberwocky Rd;26192;Southlake;United States of America
104;Bruce;Ernst;Programmer;6000;2001-05-21;IT;2014 Jabberwocky Rd;26192;Southlake;United States of America
105;David;Austin;Programmer;4800;2007-06-25;IT;2014 Jabberwocky Rd;26192;Southlake;United States of America
106;Valli;Pataballa;Programmer;4800;2008-02-05;IT;2014 Jabberwocky Rd;26192;Southlake;United States of America
107;Diana;Lorentz;Programmer;4200;2009-02-07;IT;2014 Jabberwocky Rd;26192;Southlake;United States of America
108;Nancy;Greenberg;Finance Manager;12000;2004-08-17;Finance;2004 Charade Rd;98199;Seattle;United States of America
109;Daniel;Faviet;Accountant;9000;2004-08-16;Finance;2004 Charade Rd;98199;Seattle;United States of America
110;John;Chen;Accountant;8200;2007-09-28;Finance;2004 Charade Rd;98199;Seattle;United States of America
111;Ismael;Sciarra;Accountant;7700;2007-09-30;Finance;2004 Charade Rd;98199;Seattle;United States of America
112;Jose Manuel;Urman;Accountant;7800;1998-03-07;Finance;2004 Charade Rd;98199;Seattle;United States of America
113;Luis;Popp;Accountant;6900;2009-12-07;Finance;2004 Charade Rd;98199;Seattle;United States of America
114;Den;Raphaely;Purchasing Manager;11000;2004-12-07;Purchasing;2004 Charade Rd;98199;Seattle;United States of America
115;Alexander;Khoo;Purchasing Clerk;3100;2005-05-18;Purchasing;2004 Charade Rd;98199;Seattle;United States of America
116;Shelli;Baida;Purchasing Clerk;2900;2007-12-24;Purchasing;2004 Charade Rd;98199;Seattle;United States of America
117;Sigal;Tobias;Purchasing Clerk;2800;2007-07-24;Purchasing;2004 Charade Rd;98199;Seattle;United States of America
118;Guy;Himuro;Purchasing Clerk;2600;2008-11-15;Purchasing;2004 Charade Rd;98199;Seattle;United States of America
119;Karen;Colmenares;Purchasing Clerk;2500;2009-08-10;Purchasing;2004 Charade Rd;98199;Seattle;United States of America
120;Matthew;Weiss;Stock Manager;8000;2006-07-18;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
121;Adam;Fripp;Stock Manager;8200;2007-04-10;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
122;Payam;Kaufling;Stock Manager;7900;2005-05-01;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
123;Shanta;Vollman;Stock Manager;6500;2007-10-10;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
124;Kevin;Mourgos;Stock Manager;5800;2009-11-16;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
125;Julia;Nayer;Stock Clerk;3200;2007-07-16;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
126;Irene;Mikkilineni;Stock Clerk;2700;2008-09-28;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
127;James;Landry;Stock Clerk;2400;2009-01-14;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
128;Steven;Markle;Stock Clerk;2200;2010-03-08;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
129;Laura;Bissot;Stock Clerk;3300;2007-08-20;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
130;Mozhe;Atkinson;Stock Clerk;2800;2007-10-30;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
131;James;Marlow;Stock Clerk;2500;2007-02-16;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
132;TJ;Olson;Stock Clerk;2100;2009-04-10;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
133;Jason;Mallin;Stock Clerk;3300;2006-06-14;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
134;Michael;Rogers;Stock Clerk;2900;2008-08-26;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
135;Ki;Gee;Stock Clerk;2400;2009-12-12;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
136;Hazel;Philtanker;Stock Clerk;2200;2011-02-06;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
137;Renske;Ladwig;Stock Clerk;3600;2005-07-14;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
138;Stephen;Stiles;Stock Clerk;3200;2007-10-26;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
139;John;Seo;Stock Clerk;2700;2008-02-12;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
140;Joshua;Patel;Stock Clerk;2500;2008-04-06;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
141;Trenna;Rajs;Stock Clerk;3500;2005-10-17;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
142;Curtis;Davies;Stock Clerk;3100;2007-01-29;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
143;Randall;Matos;Stock Clerk;2600;2008-03-15;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
144;Peter;Vargas;Stock Clerk;2500;2008-07-09;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
145;John;Russell;Sales Manager;14000;2006-10-01;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
146;Karen;Partners;Sales Manager;13500;2007-01-05;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
147;Alberto;Errazuriz;Sales Manager;12000;2007-03-10;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
148;Gerald;Cambrault;Sales Manager;11000;2009-10-15;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
149;Eleni;Zlotkey;Sales Manager;10500;2000-01-29;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
150;Peter;Tucker;Sales Representative;10000;2007-01-30;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
151;David;Bernstein;Sales Representative;9500;2007-03-24;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
152;Peter;Hall;Sales Representative;9000;2007-08-20;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
153;Christopher;Olsen;Sales Representative;8000;2008-03-30;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
154;Nanette;Cambrault;Sales Representative;7500;2008-12-09;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
155;Oliver;Tuvault;Sales Representative;7000;2009-11-23;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
156;Janette;King;Sales Representative;10000;2006-01-30;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
157;Patrick;Sully;Sales Representative;9500;2006-03-04;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
158;Allan;McEwen;Sales Representative;9000;2006-08-01;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
159;Lindsey;Smith;Sales Representative;8000;2007-03-10;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
160;Louise;Doran;Sales Representative;7500;2007-12-15;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
161;Sarath;Sewall;Sales Representative;7000;2008-11-03;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
162;Clara;Vishney;Sales Representative;10500;2007-11-11;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
163;Danielle;Greene;Sales Representative;9500;2009-03-19;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
164;Mattea;Marvins;Sales Representative;7200;2010-01-24;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
165;David;Lee;Sales Representative;6800;2000-02-23;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
166;Sundar;Ande;Sales Representative;6400;2000-03-24;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
167;Amit;Banda;Sales Representative;6200;2000-04-21;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
168;Lisa;Ozer;Sales Representative;11500;2007-03-11;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
169;Harrison;Bloom;Sales Representative;10000;2008-03-23;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
170;Tayler;Fox;Sales Representative;9600;2008-01-24;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
171;William;Smith;Sales Representative;7400;2009-02-23;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
172;Elizabeth;Bates;Sales Representative;7300;2009-03-24;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
173;Sundita;Kumar;Sales Representative;6100;2010-04-21;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
174;Ellen;Abel;Sales Representative;11000;2006-05-11;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
175;Alyssa;Hutton;Sales Representative;8800;2007-03-19;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
176;Jonathon;Taylor;Sales Representative;8600;2008-03-24;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
177;Jack;Livingston;Sales Representative;8400;2008-04-23;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
178;Kimberely;Grant;Sales Representative;7000;2009-05-24;;;;;
179;Charles;Johnson;Sales Representative;6200;2011-01-04;Sales;Magdalen Centre, The Oxford Science Park;OX9 9ZB;Oxford;United Kingdom
180;Winston;Taylor;Shipping Clerk;3200;2008-01-24;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
181;Jean;Fleaur;Shipping Clerk;3100;2008-02-23;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
182;Martha;Sullivan;Shipping Clerk;2500;2009-06-21;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
183;Girard;Geoni;Shipping Clerk;2800;2000-02-03;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
184;Nandita;Sarchand;Shipping Clerk;4200;2006-01-27;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
185;Alexis;Bull;Shipping Clerk;4100;2007-02-20;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
186;Julia;Dellinger;Shipping Clerk;3400;2008-06-24;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
187;Anthony;Cabrio;Shipping Clerk;3000;2009-02-07;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
188;Kelly;Chung;Shipping Clerk;3800;2007-06-14;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
189;Jennifer;Dilly;Shipping Clerk;3600;2007-08-13;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
190;Timothy;Gates;Shipping Clerk;2900;2008-07-11;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
191;Randall;Perkins;Shipping Clerk;2500;2009-12-19;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
192;Sarah;Bell;Shipping Clerk;4000;2006-02-04;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
193;Britney;Everett;Shipping Clerk;3900;2007-03-03;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
194;Samuel;McCain;Shipping Clerk;3200;2008-07-01;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
195;Vance;Jones;Shipping Clerk;2800;2009-03-17;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
196;Alana;Walsh;Shipping Clerk;3100;2008-04-24;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
197;Kevin;Feeney;Shipping Clerk;3000;2008-05-23;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
198;Donald;OConnell;Shipping Clerk;2600;2009-06-21;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
199;Douglas;Grant;Shipping Clerk;2600;2010-01-13;Shipping;2011 Interiors Blvd;99236;South San Francisco;United States of America
200;Jennifer;Whalen;Administration Assistant;4400;1987-09-17;Administration;2004 Charade Rd;98199;Seattle;United States of America
201;Michael;Hartstein;Marketing Manager;13000;2006-02-17;Marketing;147 Spadina Ave;M5V 2L7;Toronto;Canada
202;Pat;Fay;Marketing Representative;6000;2007-08-17;Marketing;147 Spadina Ave;M5V 2L7;Toronto;Canada
203;Susan;Mavris;Human Resources Representative;6500;2004-06-07;Human Resources;8204 Arthur St;;London;United Kingdom
204;Hermann;Baer;Public Relations Representative;10000;2004-06-07;Public Relations;Schwanthalerstr. 7031;80925;Munich;Germany
205;Shelley;Higgins;Accounting Manager;12000;2004-06-07;Accounting;2004 Charade Rd;98199;Seattle;United States of America
206;William;Gietz;Public Accountant;8300;2004-06-07;Accounting;2004 Charade Rd;98199;Seattle;United States of America
dzien9/dane/pobierz_kursy_gieldowe.py 0 → 100644
# Zapytaj o kod spółki, początkową i końcową datę
# (format dla dat: YYYYMMDD)
# *dla chętnych: gdy user nie poda daty końcowej, przyjmij datę dzisiejszą
# Gdy już pobierzesz dane:
# - zapisz do pliku csv (nazwa pliku niech zawiera kod oraz daty)
# - podziel dane na miesiące (resample) i np. oblicz średni kurs zamknięcia, ew. dodatkowe pola jak robiliśmy w Jupyterze
# - wygeneruj wykres liniowy i zapisz do pliku
# → dla chętnych dwie wersje: wykres czterech kursów (otwarcie, zamknięcie, min, max) oraz oddzielnie wstega Belingera
import pandas as pd
from datetime import date
ticker = input('Podaj symbol, np. pkn albo usdpln: ')
pocz = input('Podaj datę początkową w formacie YYYYMMDD: ')
konc = input('Podaj datę końcową w formacie YYYYMMDD: ')
# domyślny koniec: dzisiejsza data
if not konc:
konc = date.today().strftime('%Y%m%d')
# domyślny początek: 1 stycznia z tego samego roku
if not pocz:
pocz = konc[0:4] + '0101'
adres = f'https://stooq.pl/q/d/l/?s={ticker}&d1={pocz}&d2={konc}&i=d'
df = pd.read_csv(adres, index_col='Data', parse_dates=['Data'])
print(f'Pobrano dane: {len(df)} rekordów')
# nazwy kolumn: Otwarcie Najwyzszy Najnizszy Zamkniecie
print('Najniższy kurs:', df["Najnizszy"].min())
print('Najwyższy kurs:', df["Najwyzszy"].max())
print('Średni kurs zamknięcia:', df["Zamkniecie"].mean())
# Dodamy nowe kolumny
df["Zmiana"] = df["Zamkniecie"].diff()
df["Zmiana %"] = df["Zamkniecie"].pct_change() * 100
df["Średnia 5"] = df["Zamkniecie"].rolling(window=5).mean()
# To poprawiało format daty, ale jednocześnie Excel gubił informację, że ta kolumna jest typu data i stawała się zwykłym tekstem.
# df.index = df.index.strftime('%Y-%m-%d')
# Specyfikacja jak agregować w formie słownika - poniżej przekazuję to do agg
agregaty = {
'Otwarcie': ['min', 'mean', 'max'],
'Zamkniecie': ['min', 'mean', 'max'],
'Najnizszy': ['min'],
'Najwyzszy': ['max'],
}
# Wolumen agreguję tylko, jesli występuje w danych źródłowych
if 'Wolumen' in df:
agregaty['Wolumen'] = ['sum']
miesiace = df.resample('M').agg(agregaty)
plik_excel = f'kursy_{ticker}_{pocz}_{konc}.xlsx'
with pd.ExcelWriter(plik_excel) as xl_writer:
df.to_excel(excel_writer=xl_writer, sheet_name='Dane')
miesiace.to_excel(excel_writer=xl_writer, sheet_name='Miesiecznie')
print(f'Zapisano plik {plik_excel}')
wykres = df[["Otwarcie", "Zamkniecie", "Najwyzszy", "Najnizszy"]].plot(figsize=(20,10))
plik_wykres = f'kursy_{ticker}_{pocz}_{konc}.png'
wykres.get_figure().savefig(plik_wykres)
print(f'Zapisano wykres w pliku {plik_wykres}')
# Generowanie "wstęgi Boelingera"
WINDOW = 10
KORYTARZ = 2
srednia_kroczaca = df.Zamkniecie.rolling(window=WINDOW).mean()
odchylenie = df.Zamkniecie.rolling(window=WINDOW).std()
wstega_gorna = srednia_kroczaca + KORYTARZ * odchylenie
wstega_dolna = srednia_kroczaca - KORYTARZ * odchylenie
wstega = pd.DataFrame({
'Bieżące': df['Zamkniecie'],
'Trend': srednia_kroczaca,
'Górna': wstega_gorna,
'Dolna': wstega_dolna,
})
wykres = wstega.plot(figsize=(20, 10), grid=True)
wykres.fill_between(wstega.index, wstega["Górna"], wstega["Dolna"], color="#AAEEFF", alpha=0.25)
plik_bolinger = f'bolinger_{ticker}_{pocz}_{konc}.png'
wykres.get_figure().savefig(plik_bolinger)
print(f'Zapisano wstęgę Bolingera w pliku {plik_bolinger}')
jupyter/.ipynb_checkpoints/02_numpy-checkpoint.ipynb 0 → 100644
This source diff could not be displayed because it is too large. You can view the blob instead.
jupyter/.ipynb_checkpoints/03_pandas_podstawy-checkpoint.ipynb 0 → 100644
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "d8a0074a",
"metadata": {},
"outputs": [],
"source": [
"import pandas"
]
},
{
"cell_type": "markdown",
"id": "a7661422",
"metadata": {},
"source": [
"Dwie najważniejsze struktury danych: `Series` i `DataFrame`.\n",
"\n",
"`Series` - seria danych tego samego typu, indeksowana za pomocą liczb od 0 (jak lista), ale opcjonalnie także za pomocą innego indeksu. Seria odpowiada pojedynczej kolumnie w Excelu lub bazie danych.\n",
"\n",
"W praktyce serii rzadko używa się samodzielnie. Najczęściej powstają one w wyniku odczytania kolumny z DataFrame lub w wyniku obliczeń."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b53feca6",
"metadata": {},
"outputs": [],
"source": [
"imiona = pandas.Series(['Ala', 'Ola', 'Jan', 'Andrzej'])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "00a0eba4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 Ala\n",
"1 Ola\n",
"2 Jan\n",
"3 Andrzej\n",
"dtype: object"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"imiona"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5e76f8e2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 20\n",
"1 30\n",
"2 40\n",
"3 50\n",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"liczby = pandas.Series(range(20, 60, 10))\n",
"liczby"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "17d4a896",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"35.0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"liczby.mean()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "48a880e9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 3\n",
"2 5\n",
"3 7\n",
"4 9\n",
" ... \n",
"4995 9991\n",
"4996 9993\n",
"4997 9995\n",
"4998 9997\n",
"4999 9999\n",
"Length: 5000, dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"duzo = pandas.Series(range(1, 10001, 2))\n",
"duzo"
]
},
{
"cell_type": "markdown",
"id": "5c1e7868",
"metadata": {},
"source": [
"Domyślnym indeksem dla serii są liczby całkowite od 0 (jak w Pythonowych listach).\n",
"Ale można też określić własny indeks - wtedy serii można będzie używać też jak słownika, który pod określonymi kluczami ma zapisane jakieś wartości."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d55a6195",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PL Polska\n",
"CZ Czechy\n",
"FR Francja\n",
"DE Niemcy\n",
"UA Ukraina\n",
"RU Rosja\n",
"dtype: object"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kraje = pandas.Series(['Polska', 'Czechy', 'Francja', 'Niemcy', 'Ukraina', 'Rosja'],\n",
" index=['PL', 'CZ', 'FR', 'DE', 'UA', 'RU'])\n",
"kraje"
]
},
{
"cell_type": "markdown",
"id": "1db17181",
"metadata": {},
"source": [
"Dostęp \"słownikowy\":"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "4c4ca0e2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Niemcy'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kraje['DE']"
]
},
{
"cell_type": "markdown",
"id": "2f6e2eab",
"metadata": {},
"source": [
"Nadal możliwy jest dostęp po numerycznym indeksie, jak w zwykłych listach:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "9c25aef5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Polska'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kraje[0]"
]
},
{
"cell_type": "markdown",
"id": "cad1ab9f",
"metadata": {},
"source": [
"A co gdyby `index`em były liczby całkowite? To ważniejszy będzie `index`, a już nie działa odczyt wg numeru pozycji."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "618f280c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 poniedziałek\n",
"2 wtorek\n",
"3 środa\n",
"4 czwartek\n",
"5 piątek\n",
"6 sobota\n",
"7 niedziela\n",
"dtype: object"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dni_tygodnia = pandas.Series(['poniedziałek', 'wtorek', 'środa', 'czwartek', 'piątek', 'sobota', 'niedziela'],\n",
" index=[1, 2, 3, 4, 5, 6, 7])\n",
"dni_tygodnia"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "c7707bc0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'poniedziałek'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dni_tygodnia[1]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "3e5aad15",
"metadata": {},
"outputs": [],
"source": [
"# dni_tygodnia[0]\n",
"# KeyError"
]
},
{
"cell_type": "markdown",
"id": "6133a321",
"metadata": {},
"source": [
"`DataFrame` to jest tabela z danymi. Myślimy o niej jak o arkuszu Excela lub tabeli w bazie danych. `DataFrame` składa się z wielu kolumn-serii , które sa powiązane wspólnym indeksem.\n",
"\n",
"DataFrame można tworzyć na różne sposoby.\n",
"\n",
"Wierszowo - podajemy **listę** wierszy. Każdy wiersz jako sekwencję elementów: krotka lub lista."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e2ac4092",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Ala</td>\n",
" <td>20</td>\n",
" <td>k</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Ola</td>\n",
" <td>30</td>\n",
" <td>k</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Jan</td>\n",
" <td>40</td>\n",
" <td>m</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Andrzej</td>\n",
" <td>50</td>\n",
" <td>m</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2\n",
"0 Ala 20 k\n",
"1 Ola 30 k\n",
"2 Jan 40 m\n",
"3 Andrzej 50 m"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1 = pandas.DataFrame([('Ala', 20, 'k'),\n",
" ('Ola', 30, 'k'),\n",
" ('Jan', 40, 'm'),\n",
" ('Andrzej', 50, 'm')])\n",
"df1"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "cfe252c6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 20\n",
"1 30\n",
"2 40\n",
"3 50\n",
"Name: 1, dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1[1]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "43dab2a0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 Ola\n",
"1 30\n",
"2 k\n",
"Name: 1, dtype: object"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.loc[1]"
]
},
{
"cell_type": "markdown",
"id": "dc4c030e",
"metadata": {},
"source": [
"Dodatkowo można podać własny indeks i nazwy kolumn."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "2f1651c1",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>imię</th>\n",
" <th>wiek</th>\n",
" <th>płeć</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>Ala</td>\n",
" <td>20</td>\n",
" <td>k</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>Ola</td>\n",
" <td>30</td>\n",
" <td>k</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>Jan</td>\n",
" <td>40</td>\n",
" <td>m</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>Andrzej</td>\n",
" <td>50</td>\n",
" <td>m</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" imię wiek płeć\n",
"101 Ala 20 k\n",
"102 Ola 30 k\n",
"103 Jan 40 m\n",
"104 Andrzej 50 m"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = pandas.DataFrame([['Ala', 20, 'k'],\n",
" ['Ola', 30, 'k'],\n",
" ['Jan', 40, 'm'],\n",
" ['Andrzej', 50, 'm']],\n",
" columns=['imię', 'wiek', 'płeć'],\n",
" index=[101, 102, 103, 104])\n",
"df2"
]
},
{
"cell_type": "markdown",
"id": "210e4c21",
"metadata": {},
"source": [
"Podawanie danych kolumnowo. Przekazujemy **słownik**, gdzie kluczem jest nazwa kolumny, a wartością seria danych w tej kolumnie."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "b014269e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>imie</th>\n",
" <th>wiek</th>\n",
" <th>plec</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>Ala</td>\n",
" <td>20</td>\n",
" <td>k</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>Ola</td>\n",
" <td>30</td>\n",
" <td>k</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>Jan</td>\n",
" <td>40</td>\n",
" <td>m</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>Andrzej</td>\n",
" <td>50</td>\n",
" <td>m</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" imie wiek plec\n",
"101 Ala 20 k\n",
"102 Ola 30 k\n",
"103 Jan 40 m\n",
"104 Andrzej 50 m"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3 = pandas.DataFrame({\n",
" 'imie': ['Ala', 'Ola', 'Jan', 'Andrzej'],\n",
" 'wiek': [20, 30, 40, 50],\n",
" 'plec': ['k', 'k', 'm', 'm'],\n",
"}, index=[101, 102, 103, 104])\n",
"df3"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "1d698936",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>imie</th>\n",
" <th>wiek</th>\n",
" <th>plec</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Ala</td>\n",
" <td>20</td>\n",
" <td>k</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Ola</td>\n",
" <td>30</td>\n",
" <td>k</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Jan</td>\n",
" <td>40</td>\n",
" <td>m</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Andrzej</td>\n",
" <td>50</td>\n",
" <td>m</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" imie wiek plec\n",
"0 Ala 20 k\n",
"1 Ola 30 k\n",
"2 Jan 40 m\n",
"3 Andrzej 50 m"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lista = ['k', 'k', 'm', 'm']\n",
"df4 = pandas.DataFrame({\n",
" 'imie': imiona,\n",
" 'wiek': range(20, 60, 10),\n",
" 'plec': lista})\n",
"df4"
]
},
{
"cell_type": "markdown",
"id": "6c01a478",
"metadata": {},
"source": [
"Kolumna z `DataFrame` jest typu `Series`."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "648b89a0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 20\n",
"1 30\n",
"2 40\n",
"3 50\n",
"Name: wiek, dtype: int64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df4.wiek"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "913e271e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(df4.wiek)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fec0926d",
"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": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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...@@ -4899,7 +4899,7 @@ ...@@ -4899,7 +4899,7 @@
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10 11 12 13 14 15 16 17 18 19
10 9 8 7 6 5 4 3 2 1
0 0 0 0 0 0 0 0 0 0
1.1 1.25 1.44 1.75 2.0 1.66 1.5 1.33 1.2 1.0
5 4 6 3 7 2 8 1 9 0
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jupyter/students.alx.pl/~pczarnik/dane/iris.data 0 → 100644
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
4.8,3.4,1.6,0.2,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
4.3,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setosa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1.0,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5.0,3.0,1.6,0.2,Iris-setosa
5.0,3.4,1.6,0.4,Iris-setosa
5.2,3.5,1.5,0.2,Iris-setosa
5.2,3.4,1.4,0.2,Iris-setosa
4.7,3.2,1.6,0.2,Iris-setosa
4.8,3.1,1.6,0.2,Iris-setosa
5.4,3.4,1.5,0.4,Iris-setosa
5.2,4.1,1.5,0.1,Iris-setosa
5.5,4.2,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.0,3.2,1.2,0.2,Iris-setosa
5.5,3.5,1.3,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
4.4,3.0,1.3,0.2,Iris-setosa
5.1,3.4,1.5,0.2,Iris-setosa
5.0,3.5,1.3,0.3,Iris-setosa
4.5,2.3,1.3,0.3,Iris-setosa
4.4,3.2,1.3,0.2,Iris-setosa
5.0,3.5,1.6,0.6,Iris-setosa
5.1,3.8,1.9,0.4,Iris-setosa
4.8,3.0,1.4,0.3,Iris-setosa
5.1,3.8,1.6,0.2,Iris-setosa
4.6,3.2,1.4,0.2,Iris-setosa
5.3,3.7,1.5,0.2,Iris-setosa
5.0,3.3,1.4,0.2,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.9,3.1,4.9,1.5,Iris-versicolor
5.5,2.3,4.0,1.3,Iris-versicolor
6.5,2.8,4.6,1.5,Iris-versicolor
5.7,2.8,4.5,1.3,Iris-versicolor
6.3,3.3,4.7,1.6,Iris-versicolor
4.9,2.4,3.3,1.0,Iris-versicolor
6.6,2.9,4.6,1.3,Iris-versicolor
5.2,2.7,3.9,1.4,Iris-versicolor
5.0,2.0,3.5,1.0,Iris-versicolor
5.9,3.0,4.2,1.5,Iris-versicolor
6.0,2.2,4.0,1.0,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor
5.6,2.9,3.6,1.3,Iris-versicolor
6.7,3.1,4.4,1.4,Iris-versicolor
5.6,3.0,4.5,1.5,Iris-versicolor
5.8,2.7,4.1,1.0,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
5.6,2.5,3.9,1.1,Iris-versicolor
5.9,3.2,4.8,1.8,Iris-versicolor
6.1,2.8,4.0,1.3,Iris-versicolor
6.3,2.5,4.9,1.5,Iris-versicolor
6.1,2.8,4.7,1.2,Iris-versicolor
6.4,2.9,4.3,1.3,Iris-versicolor
6.6,3.0,4.4,1.4,Iris-versicolor
6.8,2.8,4.8,1.4,Iris-versicolor
6.7,3.0,5.0,1.7,Iris-versicolor
6.0,2.9,4.5,1.5,Iris-versicolor
5.7,2.6,3.5,1.0,Iris-versicolor
5.5,2.4,3.8,1.1,Iris-versicolor
5.5,2.4,3.7,1.0,Iris-versicolor
5.8,2.7,3.9,1.2,Iris-versicolor
6.0,2.7,5.1,1.6,Iris-versicolor
5.4,3.0,4.5,1.5,Iris-versicolor
6.0,3.4,4.5,1.6,Iris-versicolor
6.7,3.1,4.7,1.5,Iris-versicolor
6.3,2.3,4.4,1.3,Iris-versicolor
5.6,3.0,4.1,1.3,Iris-versicolor
5.5,2.5,4.0,1.3,Iris-versicolor
5.5,2.6,4.4,1.2,Iris-versicolor
6.1,3.0,4.6,1.4,Iris-versicolor
5.8,2.6,4.0,1.2,Iris-versicolor
5.0,2.3,3.3,1.0,Iris-versicolor
5.6,2.7,4.2,1.3,Iris-versicolor
5.7,3.0,4.2,1.2,Iris-versicolor
5.7,2.9,4.2,1.3,Iris-versicolor
6.2,2.9,4.3,1.3,Iris-versicolor
5.1,2.5,3.0,1.1,Iris-versicolor
5.7,2.8,4.1,1.3,Iris-versicolor
6.3,3.3,6.0,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
7.1,3.0,5.9,2.1,Iris-virginica
6.3,2.9,5.6,1.8,Iris-virginica
6.5,3.0,5.8,2.2,Iris-virginica
7.6,3.0,6.6,2.1,Iris-virginica
4.9,2.5,4.5,1.7,Iris-virginica
7.3,2.9,6.3,1.8,Iris-virginica
6.7,2.5,5.8,1.8,Iris-virginica
7.2,3.6,6.1,2.5,Iris-virginica
6.5,3.2,5.1,2.0,Iris-virginica
6.4,2.7,5.3,1.9,Iris-virginica
6.8,3.0,5.5,2.1,Iris-virginica
5.7,2.5,5.0,2.0,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica
6.4,3.2,5.3,2.3,Iris-virginica
6.5,3.0,5.5,1.8,Iris-virginica
7.7,3.8,6.7,2.2,Iris-virginica
7.7,2.6,6.9,2.3,Iris-virginica
6.0,2.2,5.0,1.5,Iris-virginica
6.9,3.2,5.7,2.3,Iris-virginica
5.6,2.8,4.9,2.0,Iris-virginica
7.7,2.8,6.7,2.0,Iris-virginica
6.3,2.7,4.9,1.8,Iris-virginica
6.7,3.3,5.7,2.1,Iris-virginica
7.2,3.2,6.0,1.8,Iris-virginica
6.2,2.8,4.8,1.8,Iris-virginica
6.1,3.0,4.9,1.8,Iris-virginica
6.4,2.8,5.6,2.1,Iris-virginica
7.2,3.0,5.8,1.6,Iris-virginica
7.4,2.8,6.1,1.9,Iris-virginica
7.9,3.8,6.4,2.0,Iris-virginica
6.4,2.8,5.6,2.2,Iris-virginica
6.3,2.8,5.1,1.5,Iris-virginica
6.1,2.6,5.6,1.4,Iris-virginica
7.7,3.0,6.1,2.3,Iris-virginica
6.3,3.4,5.6,2.4,Iris-virginica
6.4,3.1,5.5,1.8,Iris-virginica
6.0,3.0,4.8,1.8,Iris-virginica
6.9,3.1,5.4,2.1,Iris-virginica
6.7,3.1,5.6,2.4,Iris-virginica
6.9,3.1,5.1,2.3,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
6.8,3.2,5.9,2.3,Iris-virginica
6.7,3.3,5.7,2.5,Iris-virginica
6.7,3.0,5.2,2.3,Iris-virginica
6.3,2.5,5.0,1.9,Iris-virginica
6.5,3.0,5.2,2.0,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
5.9,3.0,5.1,1.8,Iris-virginica
jupyter/zmienione_liczby.txt 0 → 100644
4.850000000000000000e+01 5.350000000000000000e+01 5.850000000000000000e+01 6.350000000000000000e+01 6.850000000000000000e+01 7.350000000000000000e+01 7.850000000000000000e+01 8.350000000000000000e+01 8.850000000000000000e+01 9.350000000000000000e+01
4.850000000000000000e+01 4.350000000000000000e+01 3.850000000000000000e+01 3.350000000000000000e+01 2.850000000000000000e+01 2.350000000000000000e+01 1.850000000000000000e+01 1.350000000000000000e+01 8.500000000000000000e+00 3.500000000000000000e+00
-1.500000000000000000e+00 -1.500000000000000000e+00 -1.500000000000000000e+00 -1.500000000000000000e+00 -1.500000000000000000e+00 -1.500000000000000000e+00 -1.500000000000000000e+00 -1.500000000000000000e+00 -1.500000000000000000e+00 -1.500000000000000000e+00
4.000000000000000000e+00 4.750000000000000000e+00 5.699999999999999289e+00 7.250000000000000000e+00 8.500000000000000000e+00 6.799999999999998934e+00 6.000000000000000000e+00 5.150000000000000355e+00 4.500000000000000000e+00 3.500000000000000000e+00
2.350000000000000000e+01 1.850000000000000000e+01 2.850000000000000000e+01 1.350000000000000000e+01 3.350000000000000000e+01 8.500000000000000000e+00 3.850000000000000000e+01 3.500000000000000000e+00 4.350000000000000000e+01 -1.500000000000000000e+00
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