Compare commits
5 Commits
Author | SHA1 | Date | |
---|---|---|---|
![]() |
d506707eb8 | ||
![]() |
adb6adf1ba | ||
![]() |
92dca8796c | ||
![]() |
c37b42a4f4 | ||
![]() |
24f3ab5175 |
@ -15,7 +15,40 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2\n",
|
||||
"Hello Lumenn\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "AttributeError",
|
||||
"evalue": "module 'math' has no attribute 'average'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[19], line 6\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(a)\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHello Lumenn\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mmath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maverage\u001b[49m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m10\u001b[39m)))\n",
|
||||
"\u001b[1;31mAttributeError\u001b[0m: module 'math' has no attribute 'average'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import math\n",
|
||||
"\n",
|
||||
"a = 2\n",
|
||||
"print(a)\n",
|
||||
"print('Hello Lumenn')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
@ -58,22 +91,34 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 40,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"AlicjaBartosz 123\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"user1 = \"Alicja\"\n",
|
||||
"user2 = \"Bartosz\"\n",
|
||||
"user3 = \"Cecylia\""
|
||||
"import typing\n",
|
||||
"\n",
|
||||
"user1: str = \"Alicja\"\n",
|
||||
"user2: str = \"Bartosz\"\n",
|
||||
"user3: int = 123\n",
|
||||
"\n",
|
||||
"print(user1 + user2, user3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 24,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
@ -114,7 +159,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 26,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
@ -135,7 +180,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 36,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
@ -672,6 +717,9 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
},
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
@ -2897,7 +2945,7 @@
|
||||
"metadata": {
|
||||
"celltoolbar": "Slideshow",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@ -2911,9 +2959,9 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.3"
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
@ -5,3 +5,8 @@
|
||||
* oblicz pole koła i przypisz wynik do zmniennej `pole`. P = pi * r ** 2
|
||||
* wyświetl wynik na ekran.
|
||||
"""
|
||||
|
||||
pi = 3.14
|
||||
promien = 12
|
||||
|
||||
print(pi * promien ** 2)
|
@ -9,3 +9,6 @@ a = "12"
|
||||
b = "35.5"
|
||||
c = True
|
||||
|
||||
wynik = int(a) + float(b) + int(c)
|
||||
|
||||
print(wynik)
|
||||
|
@ -5,8 +5,8 @@
|
||||
* Wyświetl sumaryczną długość zmiennych firstname i surname.
|
||||
"""
|
||||
|
||||
firstname = "Tomasz"
|
||||
surname = "Dwojak"
|
||||
firstname = "Lumenn"
|
||||
surname = "Silme"
|
||||
|
||||
print(f"Nazywam się {firstname} {surname}.")
|
||||
|
||||
@ -14,4 +14,6 @@ print(firstname.lower())
|
||||
|
||||
print("Nazywam się %s %s" % (firstname, surname))
|
||||
|
||||
fullname = f'{firstname} {surname}'
|
||||
print(fullname, len(fullname))
|
||||
|
||||
|
@ -12,3 +12,15 @@ Poniżej znajduje się lista `websites`.
|
||||
websites = ['google.com', 'facebook.com', 'twitter.com', 'pinterest.com', 'python.org']
|
||||
|
||||
polish_websites = ['onet.pl', 'interia.pl', 'wp.pl']
|
||||
|
||||
|
||||
print(websites.index('pinterest.com'))
|
||||
websites[4] = 'yahoo.com'
|
||||
print(websites)
|
||||
websites.append('bing.com')
|
||||
|
||||
social_networks = websites[1:3]
|
||||
print(social_networks)
|
||||
|
||||
websites.extend(polish_websites)
|
||||
print(websites, len(websites))
|
@ -7,4 +7,9 @@ Korzystając z listy numbers:
|
||||
"""
|
||||
|
||||
|
||||
numbers = [1, 8, 6, 6, 6, 7, 2, 0, 3, 0, 2, 3, 7, 0, 7, 2, 0, 3, 9, 4]
|
||||
numbers = [1, 8, 6, 6, 6, 7, 2, 0, 3, 0, 2, 3, 7, 0, 7, 2, 0, 3, 9, 4]
|
||||
|
||||
print(numbers[1])
|
||||
print(numbers.count(7))
|
||||
print(len(numbers))
|
||||
print(max(numbers))
|
||||
|
@ -14,3 +14,15 @@ iris_setosa = [
|
||||
[4.9, 3, 1.4, 0.2],
|
||||
[4.7, 3.2, 1.3, 0.2],
|
||||
]
|
||||
|
||||
sum = 0
|
||||
for set in iris_setosa:
|
||||
sum += set[1]
|
||||
|
||||
print(sum/len(iris_setosa))
|
||||
|
||||
iris_setosa.append(
|
||||
[5.4, 3.9, 1.7, 0.4]
|
||||
)
|
||||
|
||||
print(iris_setosa)
|
@ -23,3 +23,15 @@ cecylia_data = {
|
||||
'name': 'Cecylia',
|
||||
'surname': 'Szymanowska'
|
||||
}
|
||||
|
||||
|
||||
print(data['place of birth'])
|
||||
print(data['year of death'] - data['year of birth'])
|
||||
data['place of death'] = 'Istanbul'
|
||||
print(data)
|
||||
data['place of birth'] = 'Zaosie'
|
||||
print(data)
|
||||
data['spouse'] = cecylia_data
|
||||
print(data)
|
||||
print(len(data['occupation']))
|
||||
print(data['spouse']['name'])
|
@ -3,4 +3,11 @@ Sprawdź czy tekst 'aAaAaA' znajduje się w tablicy passwords.
|
||||
W zależności czy znajduje się czy też nie, wyświetl na ekranie odpowiedni komunikat.
|
||||
"""
|
||||
|
||||
passwords = ['aaAaa', 'aAAAaa', 'aaaaaaA', 'aaaAAAAA', 'aaAAAaa', 'aAaAaA', 'aAaAaAA']
|
||||
passwords = ['aaAaa', 'aAAAaa', 'aaaaaaA', 'aaaAAAAA', 'aaAAAaa', 'aAaAaA', 'aAaAaAA']
|
||||
|
||||
|
||||
|
||||
if 'aAaAaA' in passwords:
|
||||
print('Exists')
|
||||
else:
|
||||
print('Not exits')
|
@ -11,4 +11,18 @@ Zmienna `points` zawiera liczbę uzyskanych punktów przez studenta.
|
||||
Napisz instrukcję warunką, która wyświetli ocenę studenta w zależności od liczby punktów.
|
||||
"""
|
||||
|
||||
points = 85
|
||||
points = 40
|
||||
|
||||
if points >= 90:
|
||||
print(5.0)
|
||||
elif points >= 80:
|
||||
print(4.5)
|
||||
elif points >= 70:
|
||||
print(4.0)
|
||||
elif points >= 60:
|
||||
print(3.5)
|
||||
elif points >= 50:
|
||||
print(3.0)
|
||||
else:
|
||||
print(2.0)
|
||||
|
||||
|
@ -2,3 +2,4 @@
|
||||
Oblicz sumę liczb od 1 do 678.
|
||||
"""
|
||||
|
||||
print(sum(range(1,678)))
|
@ -21,3 +21,8 @@ rozklad = {
|
||||
4: [],
|
||||
3: []
|
||||
}
|
||||
|
||||
for k in oceny:
|
||||
rozklad[oceny[k]].append(k)
|
||||
|
||||
print(rozklad)
|
@ -31,3 +31,12 @@ occasionals = {
|
||||
'October': 53596,
|
||||
'November': 10516,
|
||||
}
|
||||
|
||||
allrides = {}
|
||||
|
||||
for month in members:
|
||||
allrides[month] = members[month] + occasionals[month]
|
||||
|
||||
print(allrides)
|
||||
print(sum(members.values()))
|
||||
print(allrides['August'] / sum(allrides.values()) * 100)
|
@ -21,3 +21,10 @@ tree_per_sqkm = {
|
||||
"Taiwan": 69593,
|
||||
"Turkey": 11126,
|
||||
}
|
||||
|
||||
for country in tree_per_sqkm:
|
||||
sqkm = tree_per_sqkm[country]
|
||||
if sqkm > 20_000:
|
||||
print(f'Over 20k: {country}')
|
||||
if sqkm > 10_000 and sqkm < 20_000:
|
||||
print(f'Between 10k and 20k: {country}')
|
@ -8,4 +8,10 @@ równa wartości zmniennej `number_of_o`. Jeśli argument jest mniejszy niż 5,
|
||||
Wyświetl ten napis na ekran.
|
||||
"""
|
||||
|
||||
number_of_o = 6
|
||||
number_of_o = 4
|
||||
|
||||
if number_of_o > 5:
|
||||
o = 'O' * number_of_o
|
||||
print(f'N{o}!' )
|
||||
else:
|
||||
print('It\'s not a big \'No!\'')
|
@ -12,4 +12,13 @@
|
||||
text = "this is a string , which i will use for string testing"
|
||||
vocab = [',', 'this', 'is', 'a', 'which', 'for', 'will', 'i']
|
||||
|
||||
text = text.split(' ')
|
||||
|
||||
oov = []
|
||||
|
||||
for word in text:
|
||||
if word not in vocab and word not in oov:
|
||||
oov.append(word)
|
||||
|
||||
print(oov)
|
||||
|
||||
|
@ -7,3 +7,22 @@ Ciąg Fibonacciego:
|
||||
a[0] = 1, a[1] = 1, a[n] = a[n-1] + a[n-2] dla n>=2
|
||||
|
||||
"""
|
||||
|
||||
def fibonacciValue(limit, currentValue=1, previousValue=1):
|
||||
if currentValue < limit:
|
||||
print(currentValue)
|
||||
fibonacciValue(limit, currentValue + previousValue, currentValue)
|
||||
else:
|
||||
return
|
||||
|
||||
def fibonacciSteps(stepLimit, currentValue = 1, previousValue = 1, step = 0):
|
||||
if step < stepLimit:
|
||||
print(currentValue)
|
||||
step += 1
|
||||
fibonacciSteps(stepLimit, currentValue + previousValue, currentValue, step)
|
||||
else:
|
||||
return
|
||||
|
||||
fibonacciValue(100)
|
||||
|
||||
fibonacciSteps(10)
|
@ -7,7 +7,12 @@ przez 3 lub 5 mniejszych niż n.
|
||||
"""
|
||||
|
||||
def sum_div35(n):
|
||||
pass
|
||||
sum = 0
|
||||
for i in range(n):
|
||||
if i % 3 == 0 or i % 5 == 0:
|
||||
sum += i
|
||||
return sum
|
||||
|
||||
input = 100
|
||||
print(sum_div35(input))
|
||||
# dla n =100 poprawna odpowiedź to 2318
|
||||
|
@ -3,4 +3,20 @@ Otwórz plik `zen_of_python.txt` i zlicz liczbę linii i słów w tym pliku.
|
||||
Następnie przerób kod na funkcję, która jako argument będzie przyjmować ściężkę do pliku i będzie zwracać
|
||||
słownik z dwoma kluczami: `liczba_linii` i `liczba_slow`.
|
||||
"""
|
||||
import pathlib
|
||||
|
||||
|
||||
def read_metadata(path):
|
||||
f = open(path, 'r')
|
||||
file_content = f.read()
|
||||
|
||||
response = {
|
||||
'liczba_linii': file_content.count('\n'),
|
||||
'liczba_slow': len(file_content.split())
|
||||
}
|
||||
|
||||
return response
|
||||
|
||||
|
||||
|
||||
print(read_metadata(f'{pathlib.Path(__file__).parent.resolve()}\..\zen_of_python.txt'))
|
||||
|
@ -7,6 +7,8 @@ Zadania: Zaimportuj bibliotekę statistics, która zawiera funckje do obliczenia
|
||||
Każda z tych funkcji przyjmuje jeden argument: listę wartości.
|
||||
"""
|
||||
|
||||
import statistics
|
||||
|
||||
members = {
|
||||
'April': 211819,
|
||||
'May': 682758,
|
||||
@ -17,3 +19,8 @@ members = {
|
||||
'October': 444177,
|
||||
'November': 136791,
|
||||
}
|
||||
|
||||
print('Mean:', statistics.mean(members.values()))
|
||||
print('Median:', statistics.median(members.values()))
|
||||
print('Variance:', statistics.variance(members.values()))
|
||||
print('Stdev:', statistics.stdev(members.values()))
|
@ -8,3 +8,21 @@ Biblioteka math posiada funkcję hypot, która oblicza odległość punktu od ś
|
||||
* Oblicz stosunek liczby punktów, dla których odległość wynosiła mniej niż 1 do całkowitej liczby punktów. Pomnóż tę wartocść przez 4.
|
||||
* Podstaw za n wartości 100, 1000, 1000000. Do jakiej wartości zbiegają wartości?
|
||||
"""
|
||||
|
||||
import math
|
||||
import random
|
||||
|
||||
cntLessThanOne = 0
|
||||
repetitions = 10000
|
||||
|
||||
for i in range(0,repetitions):
|
||||
x = random.random()
|
||||
y = random.random()
|
||||
distance = math.hypot(x, y)
|
||||
print(x, y, distance)
|
||||
if distance < 1:
|
||||
cntLessThanOne += 1
|
||||
|
||||
|
||||
print(cntLessThanOne)
|
||||
print(cntLessThanOne/repetitions * 4)
|
@ -1,2 +1,2 @@
|
||||
Brazil,39542
|
||||
Bulgaria,24987
|
||||
Brazil,39542
|
||||
Bulgaria,24987
|
||||
|
60
zajecia2/customers.csv
Normal file
60
zajecia2/customers.csv
Normal file
@ -0,0 +1,60 @@
|
||||
,CustomerId,FirstName,LastName,Company,Address,City,State,Country,PostalCode,Phone,Fax,Email,SupportRepId
|
||||
0,1,Luís,Gonçalves,Embraer - Empresa Brasileira de Aeronáutica S.A.,"Av. Brigadeiro Faria Lima, 2170",São José dos Campos,SP,Brazil,12227-000,+55 (12) 3923-5555,+55 (12) 3923-5566,luisg@embraer.com.br,3
|
||||
1,2,Leonie,Köhler,,Theodor-Heuss-Straße 34,Stuttgart,,Germany,70174,+49 0711 2842222,,leonekohler@surfeu.de,5
|
||||
2,3,François,Tremblay,,1498 rue Bélanger,Montréal,QC,Canada,H2G 1A7,+1 (514) 721-4711,,ftremblay@gmail.com,3
|
||||
3,4,Bjørn,Hansen,,Ullevålsveien 14,Oslo,,Norway,0171,+47 22 44 22 22,,bjorn.hansen@yahoo.no,4
|
||||
4,5,František,Wichterlová,JetBrains s.r.o.,Klanova 9/506,Prague,,Czech Republic,14700,+420 2 4172 5555,+420 2 4172 5555,frantisekw@jetbrains.com,4
|
||||
5,6,Helena,Holý,,Rilská 3174/6,Prague,,Czech Republic,14300,+420 2 4177 0449,,hholy@gmail.com,5
|
||||
6,7,Astrid,Gruber,,"Rotenturmstraße 4, 1010 Innere Stadt",Vienne,,Austria,1010,+43 01 5134505,,astrid.gruber@apple.at,5
|
||||
7,8,Daan,Peeters,,Grétrystraat 63,Brussels,,Belgium,1000,+32 02 219 03 03,,daan_peeters@apple.be,4
|
||||
8,9,Kara,Nielsen,,Sønder Boulevard 51,Copenhagen,,Denmark,1720,+453 3331 9991,,kara.nielsen@jubii.dk,4
|
||||
9,10,Eduardo,Martins,Woodstock Discos,"Rua Dr. Falcão Filho, 155",São Paulo,SP,Brazil,01007-010,+55 (11) 3033-5446,+55 (11) 3033-4564,eduardo@woodstock.com.br,4
|
||||
10,11,Alexandre,Rocha,Banco do Brasil S.A.,"Av. Paulista, 2022",São Paulo,SP,Brazil,01310-200,+55 (11) 3055-3278,+55 (11) 3055-8131,alero@uol.com.br,5
|
||||
11,12,Roberto,Almeida,Riotur,"Praça Pio X, 119",Rio de Janeiro,RJ,Brazil,20040-020,+55 (21) 2271-7000,+55 (21) 2271-7070,roberto.almeida@riotur.gov.br,3
|
||||
12,13,Fernanda,Ramos,,Qe 7 Bloco G,Brasília,DF,Brazil,71020-677,+55 (61) 3363-5547,+55 (61) 3363-7855,fernadaramos4@uol.com.br,4
|
||||
13,14,Mark,Philips,Telus,8210 111 ST NW,Edmonton,AB,Canada,T6G 2C7,+1 (780) 434-4554,+1 (780) 434-5565,mphilips12@shaw.ca,5
|
||||
14,15,Jennifer,Peterson,Rogers Canada,700 W Pender Street,Vancouver,BC,Canada,V6C 1G8,+1 (604) 688-2255,+1 (604) 688-8756,jenniferp@rogers.ca,3
|
||||
15,16,Frank,Harris,Google Inc.,1600 Amphitheatre Parkway,Mountain View,CA,USA,94043-1351,+1 (650) 253-0000,+1 (650) 253-0000,fharris@google.com,4
|
||||
16,17,Jack,Smith,Microsoft Corporation,1 Microsoft Way,Redmond,WA,USA,98052-8300,+1 (425) 882-8080,+1 (425) 882-8081,jacksmith@microsoft.com,5
|
||||
17,18,Michelle,Brooks,,627 Broadway,New York,NY,USA,10012-2612,+1 (212) 221-3546,+1 (212) 221-4679,michelleb@aol.com,3
|
||||
18,19,Tim,Goyer,Apple Inc.,1 Infinite Loop,Cupertino,CA,USA,95014,+1 (408) 996-1010,+1 (408) 996-1011,tgoyer@apple.com,3
|
||||
19,20,Dan,Miller,,541 Del Medio Avenue,Mountain View,CA,USA,94040-111,+1 (650) 644-3358,,dmiller@comcast.com,4
|
||||
20,21,Kathy,Chase,,801 W 4th Street,Reno,NV,USA,89503,+1 (775) 223-7665,,kachase@hotmail.com,5
|
||||
21,22,Heather,Leacock,,120 S Orange Ave,Orlando,FL,USA,32801,+1 (407) 999-7788,,hleacock@gmail.com,4
|
||||
22,23,John,Gordon,,69 Salem Street,Boston,MA,USA,2113,+1 (617) 522-1333,,johngordon22@yahoo.com,4
|
||||
23,24,Frank,Ralston,,162 E Superior Street,Chicago,IL,USA,60611,+1 (312) 332-3232,,fralston@gmail.com,3
|
||||
24,25,Victor,Stevens,,319 N. Frances Street,Madison,WI,USA,53703,+1 (608) 257-0597,,vstevens@yahoo.com,5
|
||||
25,26,Richard,Cunningham,,2211 W Berry Street,Fort Worth,TX,USA,76110,+1 (817) 924-7272,,ricunningham@hotmail.com,4
|
||||
26,27,Patrick,Gray,,1033 N Park Ave,Tucson,AZ,USA,85719,+1 (520) 622-4200,,patrick.gray@aol.com,4
|
||||
27,28,Julia,Barnett,,302 S 700 E,Salt Lake City,UT,USA,84102,+1 (801) 531-7272,,jubarnett@gmail.com,5
|
||||
28,29,Robert,Brown,,796 Dundas Street West,Toronto,ON,Canada,M6J 1V1,+1 (416) 363-8888,,robbrown@shaw.ca,3
|
||||
29,30,Edward,Francis,,230 Elgin Street,Ottawa,ON,Canada,K2P 1L7,+1 (613) 234-3322,,edfrancis@yachoo.ca,3
|
||||
30,31,Martha,Silk,,194A Chain Lake Drive,Halifax,NS,Canada,B3S 1C5,+1 (902) 450-0450,,marthasilk@gmail.com,5
|
||||
31,32,Aaron,Mitchell,,696 Osborne Street,Winnipeg,MB,Canada,R3L 2B9,+1 (204) 452-6452,,aaronmitchell@yahoo.ca,4
|
||||
32,33,Ellie,Sullivan,,5112 48 Street,Yellowknife,NT,Canada,X1A 1N6,+1 (867) 920-2233,,ellie.sullivan@shaw.ca,3
|
||||
33,34,João,Fernandes,,Rua da Assunção 53,Lisbon,,Portugal,,+351 (213) 466-111,,jfernandes@yahoo.pt,4
|
||||
34,35,Madalena,Sampaio,,"Rua dos Campeões Europeus de Viena, 4350",Porto,,Portugal,,+351 (225) 022-448,,masampaio@sapo.pt,4
|
||||
35,36,Hannah,Schneider,,Tauentzienstraße 8,Berlin,,Germany,10789,+49 030 26550280,,hannah.schneider@yahoo.de,5
|
||||
36,37,Fynn,Zimmermann,,Berger Straße 10,Frankfurt,,Germany,60316,+49 069 40598889,,fzimmermann@yahoo.de,3
|
||||
37,38,Niklas,Schröder,,Barbarossastraße 19,Berlin,,Germany,10779,+49 030 2141444,,nschroder@surfeu.de,3
|
||||
38,39,Camille,Bernard,,"4, Rue Milton",Paris,,France,75009,+33 01 49 70 65 65,,camille.bernard@yahoo.fr,4
|
||||
39,40,Dominique,Lefebvre,,"8, Rue Hanovre",Paris,,France,75002,+33 01 47 42 71 71,,dominiquelefebvre@gmail.com,4
|
||||
40,41,Marc,Dubois,,"11, Place Bellecour",Lyon,,France,69002,+33 04 78 30 30 30,,marc.dubois@hotmail.com,5
|
||||
41,42,Wyatt,Girard,,"9, Place Louis Barthou",Bordeaux,,France,33000,+33 05 56 96 96 96,,wyatt.girard@yahoo.fr,3
|
||||
42,43,Isabelle,Mercier,,"68, Rue Jouvence",Dijon,,France,21000,+33 03 80 73 66 99,,isabelle_mercier@apple.fr,3
|
||||
43,44,Terhi,Hämäläinen,,Porthaninkatu 9,Helsinki,,Finland,00530,+358 09 870 2000,,terhi.hamalainen@apple.fi,3
|
||||
44,45,Ladislav,Kovács,,Erzsébet krt. 58.,Budapest,,Hungary,H-1073,,,ladislav_kovacs@apple.hu,3
|
||||
45,46,Hugh,O'Reilly,,3 Chatham Street,Dublin,Dublin,Ireland,,+353 01 6792424,,hughoreilly@apple.ie,3
|
||||
46,47,Lucas,Mancini,,"Via Degli Scipioni, 43",Rome,RM,Italy,00192,+39 06 39733434,,lucas.mancini@yahoo.it,5
|
||||
47,48,Johannes,Van der Berg,,Lijnbaansgracht 120bg,Amsterdam,VV,Netherlands,1016,+31 020 6223130,,johavanderberg@yahoo.nl,5
|
||||
48,49,Stanisław,Wójcik,,Ordynacka 10,Warsaw,,Poland,00-358,+48 22 828 37 39,,stanisław.wójcik@wp.pl,4
|
||||
49,50,Enrique,Muñoz,,C/ San Bernardo 85,Madrid,,Spain,28015,+34 914 454 454,,enrique_munoz@yahoo.es,5
|
||||
50,51,Joakim,Johansson,,Celsiusg. 9,Stockholm,,Sweden,11230,+46 08-651 52 52,,joakim.johansson@yahoo.se,5
|
||||
51,52,Emma,Jones,,202 Hoxton Street,London,,United Kingdom,N1 5LH,+44 020 7707 0707,,emma_jones@hotmail.com,3
|
||||
52,53,Phil,Hughes,,113 Lupus St,London,,United Kingdom,SW1V 3EN,+44 020 7976 5722,,phil.hughes@gmail.com,3
|
||||
53,54,Steve,Murray,,110 Raeburn Pl,Edinburgh ,,United Kingdom,EH4 1HH,+44 0131 315 3300,,steve.murray@yahoo.uk,5
|
||||
54,55,Mark,Taylor,,421 Bourke Street,Sidney,NSW,Australia,2010,+61 (02) 9332 3633,,mark.taylor@yahoo.au,4
|
||||
55,56,Diego,Gutiérrez,,307 Macacha Güemes,Buenos Aires,,Argentina,1106,+54 (0)11 4311 4333,,diego.gutierrez@yahoo.ar,4
|
||||
56,57,Luis,Rojas,,"Calle Lira, 198",Santiago,,Chile,,+56 (0)2 635 4444,,luisrojas@yahoo.cl,5
|
||||
57,58,Manoj,Pareek,,"12,Community Centre",Delhi,,India,110017,+91 0124 39883988,,manoj.pareek@rediff.com,3
|
||||
58,59,Puja,Srivastava,,"3,Raj Bhavan Road",Bangalore,,India,560001,+91 080 22289999,,puja_srivastava@yahoo.in,3
|
|
@ -478,14 +478,60 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0 0\n",
|
||||
"1 1\n",
|
||||
"2 2\n",
|
||||
"3 3\n",
|
||||
"4 4\n",
|
||||
"5 5\n",
|
||||
"6 6\n",
|
||||
"7 7\n",
|
||||
"8 8\n",
|
||||
"9 9\n",
|
||||
"10 10\n",
|
||||
"dtype: int64 0 0\n",
|
||||
"1 1\n",
|
||||
"2 4\n",
|
||||
"3 9\n",
|
||||
"4 16\n",
|
||||
"5 25\n",
|
||||
"6 36\n",
|
||||
"7 49\n",
|
||||
"8 64\n",
|
||||
"9 81\n",
|
||||
"10 100\n",
|
||||
"dtype: int64 0 0.0\n",
|
||||
"1 1.5\n",
|
||||
"2 4.0\n",
|
||||
"3 7.5\n",
|
||||
"4 12.0\n",
|
||||
"5 17.5\n",
|
||||
"6 24.0\n",
|
||||
"7 31.5\n",
|
||||
"8 40.0\n",
|
||||
"9 49.5\n",
|
||||
"10 60.0\n",
|
||||
"dtype: float64\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"n = pd.Series(range(0, 11))\n",
|
||||
"n2 = n * n\n",
|
||||
"trojkatne = n + n2 / 2\n",
|
||||
"print(n, n2, trojkatne)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -522,7 +568,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
@ -581,7 +627,7 @@
|
||||
"July 779511 194316"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -607,7 +653,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
@ -666,7 +712,7 @@
|
||||
"2 779511 194316"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -696,7 +742,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
@ -780,7 +826,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
@ -975,7 +1021,7 @@
|
||||
"[175 rows x 8 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -988,7 +1034,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 18,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
@ -1144,7 +1190,7 @@
|
||||
"5 0 0 373450 8.0500 NaN S "
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -1167,7 +1213,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 19,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
@ -1175,103 +1221,27 @@
|
||||
},
|
||||
"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>start_date</th>\n",
|
||||
" <th>start_station_code</th>\n",
|
||||
" <th>end_date</th>\n",
|
||||
" <th>end_station_code</th>\n",
|
||||
" <th>duration_sec</th>\n",
|
||||
" <th>is_member</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2019-04-14 07:55:22</td>\n",
|
||||
" <td>6001</td>\n",
|
||||
" <td>2019-04-14 08:07:16</td>\n",
|
||||
" <td>6132</td>\n",
|
||||
" <td>713</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2019-04-14 07:59:31</td>\n",
|
||||
" <td>6411</td>\n",
|
||||
" <td>2019-04-14 08:09:18</td>\n",
|
||||
" <td>6411</td>\n",
|
||||
" <td>587</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2019-04-14 07:59:55</td>\n",
|
||||
" <td>6097</td>\n",
|
||||
" <td>2019-04-14 08:12:11</td>\n",
|
||||
" <td>6036</td>\n",
|
||||
" <td>736</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2019-04-14 07:59:57</td>\n",
|
||||
" <td>6310</td>\n",
|
||||
" <td>2019-04-14 08:27:58</td>\n",
|
||||
" <td>6345</td>\n",
|
||||
" <td>1680</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2019-04-14 08:00:37</td>\n",
|
||||
" <td>7029</td>\n",
|
||||
" <td>2019-04-14 08:14:12</td>\n",
|
||||
" <td>6250</td>\n",
|
||||
" <td>814</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" start_date start_station_code end_date \\\n",
|
||||
"0 2019-04-14 07:55:22 6001 2019-04-14 08:07:16 \n",
|
||||
"1 2019-04-14 07:59:31 6411 2019-04-14 08:09:18 \n",
|
||||
"2 2019-04-14 07:59:55 6097 2019-04-14 08:12:11 \n",
|
||||
"3 2019-04-14 07:59:57 6310 2019-04-14 08:27:58 \n",
|
||||
"4 2019-04-14 08:00:37 7029 2019-04-14 08:14:12 \n",
|
||||
"\n",
|
||||
" end_station_code duration_sec is_member \n",
|
||||
"0 6132 713 1 \n",
|
||||
"1 6411 587 1 \n",
|
||||
"2 6036 736 1 \n",
|
||||
"3 6345 1680 1 \n",
|
||||
"4 6250 814 0 "
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
"ename": "ImportError",
|
||||
"evalue": "Missing optional dependency 'openpyxl'. Use pip or conda to install openpyxl.",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\pandas\\compat\\_optional.py:142\u001b[0m, in \u001b[0;36mimport_optional_dependency\u001b[1;34m(name, extra, errors, min_version)\u001b[0m\n\u001b[0;32m 141\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 142\u001b[0m module \u001b[39m=\u001b[39m importlib\u001b[39m.\u001b[39;49mimport_module(name)\n\u001b[0;32m 143\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mImportError\u001b[39;00m:\n",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\importlib\\__init__.py:126\u001b[0m, in \u001b[0;36mimport_module\u001b[1;34m(name, package)\u001b[0m\n\u001b[0;32m 125\u001b[0m level \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m--> 126\u001b[0m \u001b[39mreturn\u001b[39;00m _bootstrap\u001b[39m.\u001b[39;49m_gcd_import(name[level:], package, level)\n",
|
||||
"File \u001b[1;32m<frozen importlib._bootstrap>:1050\u001b[0m, in \u001b[0;36m_gcd_import\u001b[1;34m(name, package, level)\u001b[0m\n",
|
||||
"File \u001b[1;32m<frozen importlib._bootstrap>:1027\u001b[0m, in \u001b[0;36m_find_and_load\u001b[1;34m(name, import_)\u001b[0m\n",
|
||||
"File \u001b[1;32m<frozen importlib._bootstrap>:1004\u001b[0m, in \u001b[0;36m_find_and_load_unlocked\u001b[1;34m(name, import_)\u001b[0m\n",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'openpyxl'",
|
||||
"\nDuring handling of the above exception, another exception occurred:\n",
|
||||
"\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32mj:\\Python\\2023-programowanie-w-pythonie\\zajecia2\\data_analysis.ipynb Cell 39\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/j%3A/Python/2023-programowanie-w-pythonie/zajecia2/data_analysis.ipynb#X53sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_excel(\u001b[39m'\u001b[39;49m\u001b[39m./bikes.xlsx\u001b[39;49m\u001b[39m'\u001b[39;49m, engine\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mopenpyxl\u001b[39;49m\u001b[39m'\u001b[39;49m, nrows\u001b[39m=\u001b[39;49m\u001b[39m5\u001b[39;49m)\n\u001b[0;32m <a href='vscode-notebook-cell:/j%3A/Python/2023-programowanie-w-pythonie/zajecia2/data_analysis.ipynb#X53sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m df\n",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\pandas\\io\\excel\\_base.py:478\u001b[0m, in \u001b[0;36mread_excel\u001b[1;34m(io, sheet_name, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, date_format, thousands, decimal, comment, skipfooter, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 476\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(io, ExcelFile):\n\u001b[0;32m 477\u001b[0m should_close \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m--> 478\u001b[0m io \u001b[39m=\u001b[39m ExcelFile(io, storage_options\u001b[39m=\u001b[39;49mstorage_options, engine\u001b[39m=\u001b[39;49mengine)\n\u001b[0;32m 479\u001b[0m \u001b[39melif\u001b[39;00m engine \u001b[39mand\u001b[39;00m engine \u001b[39m!=\u001b[39m io\u001b[39m.\u001b[39mengine:\n\u001b[0;32m 480\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 481\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mEngine should not be specified when passing \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 482\u001b[0m \u001b[39m\"\u001b[39m\u001b[39man ExcelFile - ExcelFile already has the engine set\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 483\u001b[0m )\n",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\pandas\\io\\excel\\_base.py:1513\u001b[0m, in \u001b[0;36mExcelFile.__init__\u001b[1;34m(self, path_or_buffer, engine, storage_options)\u001b[0m\n\u001b[0;32m 1510\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mengine \u001b[39m=\u001b[39m engine\n\u001b[0;32m 1511\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstorage_options \u001b[39m=\u001b[39m storage_options\n\u001b[1;32m-> 1513\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reader \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engines[engine](\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_io, storage_options\u001b[39m=\u001b[39;49mstorage_options)\n",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\pandas\\io\\excel\\_openpyxl.py:548\u001b[0m, in \u001b[0;36mOpenpyxlReader.__init__\u001b[1;34m(self, filepath_or_buffer, storage_options)\u001b[0m\n\u001b[0;32m 533\u001b[0m \u001b[39m@doc\u001b[39m(storage_options\u001b[39m=\u001b[39m_shared_docs[\u001b[39m\"\u001b[39m\u001b[39mstorage_options\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[0;32m 534\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\n\u001b[0;32m 535\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[0;32m 536\u001b[0m filepath_or_buffer: FilePath \u001b[39m|\u001b[39m ReadBuffer[\u001b[39mbytes\u001b[39m],\n\u001b[0;32m 537\u001b[0m storage_options: StorageOptions \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m 538\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 539\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 540\u001b[0m \u001b[39m Reader using openpyxl engine.\u001b[39;00m\n\u001b[0;32m 541\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 546\u001b[0m \u001b[39m {storage_options}\u001b[39;00m\n\u001b[0;32m 547\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 548\u001b[0m import_optional_dependency(\u001b[39m\"\u001b[39;49m\u001b[39mopenpyxl\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m 549\u001b[0m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39m\u001b[39m__init__\u001b[39m(filepath_or_buffer, storage_options\u001b[39m=\u001b[39mstorage_options)\n",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\pandas\\compat\\_optional.py:145\u001b[0m, in \u001b[0;36mimport_optional_dependency\u001b[1;34m(name, extra, errors, min_version)\u001b[0m\n\u001b[0;32m 143\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mImportError\u001b[39;00m:\n\u001b[0;32m 144\u001b[0m \u001b[39mif\u001b[39;00m errors \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mraise\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m--> 145\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mImportError\u001b[39;00m(msg)\n\u001b[0;32m 146\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m 148\u001b[0m \u001b[39m# Handle submodules: if we have submodule, grab parent module from sys.modules\u001b[39;00m\n",
|
||||
"\u001b[1;31mImportError\u001b[0m: Missing optional dependency 'openpyxl'. Use pip or conda to install openpyxl."
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@ -1293,7 +1263,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 20,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
@ -1409,7 +1379,7 @@
|
||||
"[347 rows x 2 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -1422,23 +1392,13 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 21,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2023-11-17 15:53:28,542 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1\n",
|
||||
"2023-11-17 15:53:28,543 INFO sqlalchemy.engine.base.Engine ()\n",
|
||||
"2023-11-17 15:53:28,544 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1\n",
|
||||
"2023-11-17 15:53:28,545 INFO sqlalchemy.engine.base.Engine ()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
@ -1548,7 +1508,7 @@
|
||||
"[347 rows x 2 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -1602,7 +1562,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
@ -1618,14 +1578,30 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 27,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ModuleNotFoundError",
|
||||
"evalue": "No module named 'openpyxl'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32mj:\\Python\\2023-programowanie-w-pythonie\\zajecia2\\data_analysis.ipynb Cell 47\u001b[0m line \u001b[0;36m4\n\u001b[0;32m <a href='vscode-notebook-cell:/j%3A/Python/2023-programowanie-w-pythonie/zajecia2/data_analysis.ipynb#X64sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m df\u001b[39m.\u001b[39mto_csv(\u001b[39m'\u001b[39m\u001b[39mtmp.csv\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m <a href='vscode-notebook-cell:/j%3A/Python/2023-programowanie-w-pythonie/zajecia2/data_analysis.ipynb#X64sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m# zapis do arkusza kalkulacyjnego \u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/j%3A/Python/2023-programowanie-w-pythonie/zajecia2/data_analysis.ipynb#X64sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m df\u001b[39m.\u001b[39;49mto_excel(\u001b[39m'\u001b[39;49m\u001b[39mtmp.xlsx\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\pandas\\core\\generic.py:2252\u001b[0m, in \u001b[0;36mNDFrame.to_excel\u001b[1;34m(self, excel_writer, sheet_name, na_rep, float_format, columns, header, index, index_label, startrow, startcol, engine, merge_cells, inf_rep, freeze_panes, storage_options)\u001b[0m\n\u001b[0;32m 2239\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mpandas\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mio\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mformats\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mexcel\u001b[39;00m \u001b[39mimport\u001b[39;00m ExcelFormatter\n\u001b[0;32m 2241\u001b[0m formatter \u001b[39m=\u001b[39m ExcelFormatter(\n\u001b[0;32m 2242\u001b[0m df,\n\u001b[0;32m 2243\u001b[0m na_rep\u001b[39m=\u001b[39mna_rep,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 2250\u001b[0m inf_rep\u001b[39m=\u001b[39minf_rep,\n\u001b[0;32m 2251\u001b[0m )\n\u001b[1;32m-> 2252\u001b[0m formatter\u001b[39m.\u001b[39;49mwrite(\n\u001b[0;32m 2253\u001b[0m excel_writer,\n\u001b[0;32m 2254\u001b[0m sheet_name\u001b[39m=\u001b[39;49msheet_name,\n\u001b[0;32m 2255\u001b[0m startrow\u001b[39m=\u001b[39;49mstartrow,\n\u001b[0;32m 2256\u001b[0m startcol\u001b[39m=\u001b[39;49mstartcol,\n\u001b[0;32m 2257\u001b[0m freeze_panes\u001b[39m=\u001b[39;49mfreeze_panes,\n\u001b[0;32m 2258\u001b[0m engine\u001b[39m=\u001b[39;49mengine,\n\u001b[0;32m 2259\u001b[0m storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[0;32m 2260\u001b[0m )\n",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\pandas\\io\\formats\\excel.py:934\u001b[0m, in \u001b[0;36mExcelFormatter.write\u001b[1;34m(self, writer, sheet_name, startrow, startcol, freeze_panes, engine, storage_options)\u001b[0m\n\u001b[0;32m 930\u001b[0m need_save \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m 931\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 932\u001b[0m \u001b[39m# error: Cannot instantiate abstract class 'ExcelWriter' with abstract\u001b[39;00m\n\u001b[0;32m 933\u001b[0m \u001b[39m# attributes 'engine', 'save', 'supported_extensions' and 'write_cells'\u001b[39;00m\n\u001b[1;32m--> 934\u001b[0m writer \u001b[39m=\u001b[39m ExcelWriter( \u001b[39m# type: ignore[abstract]\u001b[39;49;00m\n\u001b[0;32m 935\u001b[0m writer, engine\u001b[39m=\u001b[39;49mengine, storage_options\u001b[39m=\u001b[39;49mstorage_options\n\u001b[0;32m 936\u001b[0m )\n\u001b[0;32m 937\u001b[0m need_save \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m 939\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\pandas\\io\\excel\\_openpyxl.py:56\u001b[0m, in \u001b[0;36mOpenpyxlWriter.__init__\u001b[1;34m(self, path, engine, date_format, datetime_format, mode, storage_options, if_sheet_exists, engine_kwargs, **kwargs)\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\n\u001b[0;32m 44\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[0;32m 45\u001b[0m path: FilePath \u001b[39m|\u001b[39m WriteExcelBuffer \u001b[39m|\u001b[39m ExcelWriter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 54\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 55\u001b[0m \u001b[39m# Use the openpyxl module as the Excel writer.\u001b[39;00m\n\u001b[1;32m---> 56\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mopenpyxl\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mworkbook\u001b[39;00m \u001b[39mimport\u001b[39;00m Workbook\n\u001b[0;32m 58\u001b[0m engine_kwargs \u001b[39m=\u001b[39m combine_kwargs(engine_kwargs, kwargs)\n\u001b[0;32m 60\u001b[0m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39m\u001b[39m__init__\u001b[39m(\n\u001b[0;32m 61\u001b[0m path,\n\u001b[0;32m 62\u001b[0m mode\u001b[39m=\u001b[39mmode,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 65\u001b[0m engine_kwargs\u001b[39m=\u001b[39mengine_kwargs,\n\u001b[0;32m 66\u001b[0m )\n",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'openpyxl'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\n",
|
||||
"# zapis do formatu CSV\n",
|
||||
"df.to_csv('tmp.csv')\n",
|
||||
"# zapis do arkusza kalkulacyjnego \n",
|
||||
@ -1645,7 +1621,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 28,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
@ -1666,7 +1642,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": 29,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
@ -1698,7 +1674,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": 30,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
@ -1739,14 +1715,450 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 38,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{0: 'Germany',\n",
|
||||
" 1: 'Norway',\n",
|
||||
" 2: 'Belgium',\n",
|
||||
" 3: 'Canada',\n",
|
||||
" 4: 'USA',\n",
|
||||
" 5: 'Germany',\n",
|
||||
" 6: 'Germany',\n",
|
||||
" 7: 'France',\n",
|
||||
" 8: 'France',\n",
|
||||
" 9: 'Ireland',\n",
|
||||
" 10: 'United Kingdom',\n",
|
||||
" 11: 'Germany',\n",
|
||||
" 12: 'USA',\n",
|
||||
" 13: 'USA',\n",
|
||||
" 14: 'USA',\n",
|
||||
" 15: 'USA',\n",
|
||||
" 16: 'USA',\n",
|
||||
" 17: 'Canada',\n",
|
||||
" 18: 'France',\n",
|
||||
" 19: 'United Kingdom',\n",
|
||||
" 20: 'Australia',\n",
|
||||
" 21: 'Chile',\n",
|
||||
" 22: 'India',\n",
|
||||
" 23: 'Norway',\n",
|
||||
" 24: 'Brazil',\n",
|
||||
" 25: 'USA',\n",
|
||||
" 26: 'Canada',\n",
|
||||
" 27: 'Portugal',\n",
|
||||
" 28: 'Germany',\n",
|
||||
" 29: 'Germany',\n",
|
||||
" 30: 'France',\n",
|
||||
" 31: 'Netherlands',\n",
|
||||
" 32: 'Chile',\n",
|
||||
" 33: 'Brazil',\n",
|
||||
" 34: 'Brazil',\n",
|
||||
" 35: 'Canada',\n",
|
||||
" 36: 'USA',\n",
|
||||
" 37: 'USA',\n",
|
||||
" 38: 'USA',\n",
|
||||
" 39: 'Germany',\n",
|
||||
" 40: 'Spain',\n",
|
||||
" 41: 'Sweden',\n",
|
||||
" 42: 'United Kingdom',\n",
|
||||
" 43: 'Australia',\n",
|
||||
" 44: 'India',\n",
|
||||
" 45: 'Czech Republic',\n",
|
||||
" 46: 'Canada',\n",
|
||||
" 47: 'Canada',\n",
|
||||
" 48: 'Canada',\n",
|
||||
" 49: 'Canada',\n",
|
||||
" 50: 'Portugal',\n",
|
||||
" 51: 'Germany',\n",
|
||||
" 52: 'Finland',\n",
|
||||
" 53: 'United Kingdom',\n",
|
||||
" 54: 'Belgium',\n",
|
||||
" 55: 'Denmark',\n",
|
||||
" 56: 'Brazil',\n",
|
||||
" 57: 'Brazil',\n",
|
||||
" 58: 'USA',\n",
|
||||
" 59: 'USA',\n",
|
||||
" 60: 'Canada',\n",
|
||||
" 61: 'Ireland',\n",
|
||||
" 62: 'Italy',\n",
|
||||
" 63: 'Poland',\n",
|
||||
" 64: 'Sweden',\n",
|
||||
" 65: 'Australia',\n",
|
||||
" 66: 'Germany',\n",
|
||||
" 67: 'Brazil',\n",
|
||||
" 68: 'USA',\n",
|
||||
" 69: 'USA',\n",
|
||||
" 70: 'USA',\n",
|
||||
" 71: 'Canada',\n",
|
||||
" 72: 'Portugal',\n",
|
||||
" 73: 'France',\n",
|
||||
" 74: 'Poland',\n",
|
||||
" 75: 'Norway',\n",
|
||||
" 76: 'Czech Republic',\n",
|
||||
" 77: 'Austria',\n",
|
||||
" 78: 'Denmark',\n",
|
||||
" 79: 'Brazil',\n",
|
||||
" 80: 'USA',\n",
|
||||
" 81: 'USA',\n",
|
||||
" 82: 'France',\n",
|
||||
" 83: 'France',\n",
|
||||
" 84: 'Hungary',\n",
|
||||
" 85: 'Italy',\n",
|
||||
" 86: 'Sweden',\n",
|
||||
" 87: 'Chile',\n",
|
||||
" 88: 'Austria',\n",
|
||||
" 89: 'USA',\n",
|
||||
" 90: 'USA',\n",
|
||||
" 91: 'USA',\n",
|
||||
" 92: 'USA',\n",
|
||||
" 93: 'Canada',\n",
|
||||
" 94: 'Germany',\n",
|
||||
" 95: 'Hungary',\n",
|
||||
" 96: 'India',\n",
|
||||
" 97: 'Brazil',\n",
|
||||
" 98: 'Canada',\n",
|
||||
" 99: 'Czech Republic',\n",
|
||||
" 100: 'Denmark',\n",
|
||||
" 101: 'Canada',\n",
|
||||
" 102: 'USA',\n",
|
||||
" 103: 'Germany',\n",
|
||||
" 104: 'France',\n",
|
||||
" 105: 'France',\n",
|
||||
" 106: 'France',\n",
|
||||
" 107: 'Italy',\n",
|
||||
" 108: 'United Kingdom',\n",
|
||||
" 109: 'Canada',\n",
|
||||
" 110: 'USA',\n",
|
||||
" 111: 'USA',\n",
|
||||
" 112: 'USA',\n",
|
||||
" 113: 'USA',\n",
|
||||
" 114: 'USA',\n",
|
||||
" 115: 'Canada',\n",
|
||||
" 116: 'France',\n",
|
||||
" 117: 'Australia',\n",
|
||||
" 118: 'Argentina',\n",
|
||||
" 119: 'India',\n",
|
||||
" 120: 'Brazil',\n",
|
||||
" 121: 'Czech Republic',\n",
|
||||
" 122: 'Brazil',\n",
|
||||
" 123: 'USA',\n",
|
||||
" 124: 'Portugal',\n",
|
||||
" 125: 'Portugal',\n",
|
||||
" 126: 'Germany',\n",
|
||||
" 127: 'France',\n",
|
||||
" 128: 'France',\n",
|
||||
" 129: 'Poland',\n",
|
||||
" 130: 'India',\n",
|
||||
" 131: 'Brazil',\n",
|
||||
" 132: 'Canada',\n",
|
||||
" 133: 'USA',\n",
|
||||
" 134: 'USA',\n",
|
||||
" 135: 'USA',\n",
|
||||
" 136: 'USA',\n",
|
||||
" 137: 'Germany',\n",
|
||||
" 138: 'Sweden',\n",
|
||||
" 139: 'United Kingdom',\n",
|
||||
" 140: 'United Kingdom',\n",
|
||||
" 141: 'Argentina',\n",
|
||||
" 142: 'Brazil',\n",
|
||||
" 143: 'Austria',\n",
|
||||
" 144: 'USA',\n",
|
||||
" 145: 'Canada',\n",
|
||||
" 146: 'Canada',\n",
|
||||
" 147: 'Canada',\n",
|
||||
" 148: 'Portugal',\n",
|
||||
" 149: 'France',\n",
|
||||
" 150: 'Hungary',\n",
|
||||
" 151: 'United Kingdom',\n",
|
||||
" 152: 'Denmark',\n",
|
||||
" 153: 'Brazil',\n",
|
||||
" 154: 'Brazil',\n",
|
||||
" 155: 'Canada',\n",
|
||||
" 156: 'USA',\n",
|
||||
" 157: 'USA',\n",
|
||||
" 158: 'Canada',\n",
|
||||
" 159: 'Italy',\n",
|
||||
" 160: 'Netherlands',\n",
|
||||
" 161: 'Spain',\n",
|
||||
" 162: 'United Kingdom',\n",
|
||||
" 163: 'Argentina',\n",
|
||||
" 164: 'Canada',\n",
|
||||
" 165: 'Brazil',\n",
|
||||
" 166: 'USA',\n",
|
||||
" 167: 'USA',\n",
|
||||
" 168: 'Canada',\n",
|
||||
" 169: 'Canada',\n",
|
||||
" 170: 'Portugal',\n",
|
||||
" 171: 'France',\n",
|
||||
" 172: 'Spain',\n",
|
||||
" 173: 'Czech Republic',\n",
|
||||
" 174: 'Czech Republic',\n",
|
||||
" 175: 'Belgium',\n",
|
||||
" 176: 'Brazil',\n",
|
||||
" 177: 'Canada',\n",
|
||||
" 178: 'USA',\n",
|
||||
" 179: 'Canada',\n",
|
||||
" 180: 'France',\n",
|
||||
" 181: 'Finland',\n",
|
||||
" 182: 'Ireland',\n",
|
||||
" 183: 'Netherlands',\n",
|
||||
" 184: 'United Kingdom',\n",
|
||||
" 185: 'India',\n",
|
||||
" 186: 'Belgium',\n",
|
||||
" 187: 'USA',\n",
|
||||
" 188: 'USA',\n",
|
||||
" 189: 'USA',\n",
|
||||
" 190: 'USA',\n",
|
||||
" 191: 'Canada',\n",
|
||||
" 192: 'Germany',\n",
|
||||
" 193: 'Ireland',\n",
|
||||
" 194: 'Brazil',\n",
|
||||
" 195: 'Germany',\n",
|
||||
" 196: 'Norway',\n",
|
||||
" 197: 'Czech Republic',\n",
|
||||
" 198: 'Brazil',\n",
|
||||
" 199: 'USA',\n",
|
||||
" 200: 'USA',\n",
|
||||
" 201: 'France',\n",
|
||||
" 202: 'France',\n",
|
||||
" 203: 'France',\n",
|
||||
" 204: 'Finland',\n",
|
||||
" 205: 'Netherlands',\n",
|
||||
" 206: 'United Kingdom',\n",
|
||||
" 207: 'Norway',\n",
|
||||
" 208: 'USA',\n",
|
||||
" 209: 'USA',\n",
|
||||
" 210: 'USA',\n",
|
||||
" 211: 'USA',\n",
|
||||
" 212: 'USA',\n",
|
||||
" 213: 'Canada',\n",
|
||||
" 214: 'France',\n",
|
||||
" 215: 'Argentina',\n",
|
||||
" 216: 'Chile',\n",
|
||||
" 217: 'India',\n",
|
||||
" 218: 'Germany',\n",
|
||||
" 219: 'Czech Republic',\n",
|
||||
" 220: 'Brazil',\n",
|
||||
" 221: 'USA',\n",
|
||||
" 222: 'Portugal',\n",
|
||||
" 223: 'Germany',\n",
|
||||
" 224: 'Germany',\n",
|
||||
" 225: 'France',\n",
|
||||
" 226: 'Finland',\n",
|
||||
" 227: 'Spain',\n",
|
||||
" 228: 'India',\n",
|
||||
" 229: 'Canada',\n",
|
||||
" 230: 'Canada',\n",
|
||||
" 231: 'USA',\n",
|
||||
" 232: 'USA',\n",
|
||||
" 233: 'USA',\n",
|
||||
" 234: 'Canada',\n",
|
||||
" 235: 'Germany',\n",
|
||||
" 236: 'United Kingdom',\n",
|
||||
" 237: 'United Kingdom',\n",
|
||||
" 238: 'Australia',\n",
|
||||
" 239: 'Chile',\n",
|
||||
" 240: 'Germany',\n",
|
||||
" 241: 'Belgium',\n",
|
||||
" 242: 'USA',\n",
|
||||
" 243: 'Canada',\n",
|
||||
" 244: 'Canada',\n",
|
||||
" 245: 'Portugal',\n",
|
||||
" 246: 'Germany',\n",
|
||||
" 247: 'France',\n",
|
||||
" 248: 'Ireland',\n",
|
||||
" 249: 'Australia',\n",
|
||||
" 250: 'Brazil',\n",
|
||||
" 251: 'Brazil',\n",
|
||||
" 252: 'Brazil',\n",
|
||||
" 253: 'Canada',\n",
|
||||
" 254: 'USA',\n",
|
||||
" 255: 'USA',\n",
|
||||
" 256: 'Portugal',\n",
|
||||
" 257: 'Netherlands',\n",
|
||||
" 258: 'Poland',\n",
|
||||
" 259: 'Sweden',\n",
|
||||
" 260: 'United Kingdom',\n",
|
||||
" 261: 'Chile',\n",
|
||||
" 262: 'Norway',\n",
|
||||
" 263: 'Brazil',\n",
|
||||
" 264: 'USA',\n",
|
||||
" 265: 'USA',\n",
|
||||
" 266: 'Canada',\n",
|
||||
" 267: 'Canada',\n",
|
||||
" 268: 'Germany',\n",
|
||||
" 269: 'France',\n",
|
||||
" 270: 'Sweden',\n",
|
||||
" 271: 'Czech Republic',\n",
|
||||
" 272: 'Austria',\n",
|
||||
" 273: 'Denmark',\n",
|
||||
" 274: 'Brazil',\n",
|
||||
" 275: 'Canada',\n",
|
||||
" 276: 'USA',\n",
|
||||
" 277: 'Canada',\n",
|
||||
" 278: 'Finland',\n",
|
||||
" 279: 'Hungary',\n",
|
||||
" 280: 'Italy',\n",
|
||||
" 281: 'Poland',\n",
|
||||
" 282: 'United Kingdom',\n",
|
||||
" 283: 'India',\n",
|
||||
" 284: 'Denmark',\n",
|
||||
" 285: 'USA',\n",
|
||||
" 286: 'USA',\n",
|
||||
" 287: 'USA',\n",
|
||||
" 288: 'USA',\n",
|
||||
" 289: 'Canada',\n",
|
||||
" 290: 'Germany',\n",
|
||||
" 291: 'Italy',\n",
|
||||
" 292: 'Germany',\n",
|
||||
" 293: 'Canada',\n",
|
||||
" 294: 'Czech Republic',\n",
|
||||
" 295: 'Austria',\n",
|
||||
" 296: 'Brazil',\n",
|
||||
" 297: 'USA',\n",
|
||||
" 298: 'USA',\n",
|
||||
" 299: 'France',\n",
|
||||
" 300: 'France',\n",
|
||||
" 301: 'France',\n",
|
||||
" 302: 'Hungary',\n",
|
||||
" 303: 'Poland',\n",
|
||||
" 304: 'Australia',\n",
|
||||
" 305: 'Czech Republic',\n",
|
||||
" 306: 'USA',\n",
|
||||
" 307: 'USA',\n",
|
||||
" 308: 'USA',\n",
|
||||
" 309: 'USA',\n",
|
||||
" 310: 'USA',\n",
|
||||
" 311: 'Portugal',\n",
|
||||
" 312: 'France',\n",
|
||||
" 313: 'Chile',\n",
|
||||
" 314: 'India',\n",
|
||||
" 315: 'Brazil',\n",
|
||||
" 316: 'Canada',\n",
|
||||
" 317: 'Austria',\n",
|
||||
" 318: 'Brazil',\n",
|
||||
" 319: 'USA',\n",
|
||||
" 320: 'Germany',\n",
|
||||
" 321: 'Germany',\n",
|
||||
" 322: 'France',\n",
|
||||
" 323: 'France',\n",
|
||||
" 324: 'Hungary',\n",
|
||||
" 325: 'Sweden',\n",
|
||||
" 326: 'Brazil',\n",
|
||||
" 327: 'Canada',\n",
|
||||
" 328: 'USA',\n",
|
||||
" 329: 'USA',\n",
|
||||
" 330: 'USA',\n",
|
||||
" 331: 'USA',\n",
|
||||
" 332: 'Canada',\n",
|
||||
" 333: 'France',\n",
|
||||
" 334: 'United Kingdom',\n",
|
||||
" 335: 'United Kingdom',\n",
|
||||
" 336: 'Argentina',\n",
|
||||
" 337: 'India',\n",
|
||||
" 338: 'Canada',\n",
|
||||
" 339: 'Denmark',\n",
|
||||
" 340: 'USA',\n",
|
||||
" 341: 'Canada',\n",
|
||||
" 342: 'Canada',\n",
|
||||
" 343: 'Portugal',\n",
|
||||
" 344: 'Germany',\n",
|
||||
" 345: 'France',\n",
|
||||
" 346: 'Italy',\n",
|
||||
" 347: 'Argentina',\n",
|
||||
" 348: 'Brazil',\n",
|
||||
" 349: 'Brazil',\n",
|
||||
" 350: 'Canada',\n",
|
||||
" 351: 'USA',\n",
|
||||
" 352: 'USA',\n",
|
||||
" 353: 'USA',\n",
|
||||
" 354: 'Portugal',\n",
|
||||
" 355: 'Poland',\n",
|
||||
" 356: 'Spain',\n",
|
||||
" 357: 'United Kingdom',\n",
|
||||
" 358: 'United Kingdom',\n",
|
||||
" 359: 'India',\n",
|
||||
" 360: 'Czech Republic',\n",
|
||||
" 361: 'Canada',\n",
|
||||
" 362: 'USA',\n",
|
||||
" 363: 'Canada',\n",
|
||||
" 364: 'Canada',\n",
|
||||
" 365: 'Canada',\n",
|
||||
" 366: 'Germany',\n",
|
||||
" 367: 'France',\n",
|
||||
" 368: 'United Kingdom',\n",
|
||||
" 369: 'Austria',\n",
|
||||
" 370: 'Belgium',\n",
|
||||
" 371: 'Brazil',\n",
|
||||
" 372: 'Brazil',\n",
|
||||
" 373: 'USA',\n",
|
||||
" 374: 'USA',\n",
|
||||
" 375: 'Canada',\n",
|
||||
" 376: 'Hungary',\n",
|
||||
" 377: 'Ireland',\n",
|
||||
" 378: 'Netherlands',\n",
|
||||
" 379: 'Spain',\n",
|
||||
" 380: 'United Kingdom',\n",
|
||||
" 381: 'Brazil',\n",
|
||||
" 382: 'Brazil',\n",
|
||||
" 383: 'USA',\n",
|
||||
" 384: 'USA',\n",
|
||||
" 385: 'USA',\n",
|
||||
" 386: 'Canada',\n",
|
||||
" 387: 'Canada',\n",
|
||||
" 388: 'France',\n",
|
||||
" 389: 'Netherlands',\n",
|
||||
" 390: 'Canada',\n",
|
||||
" 391: 'Norway',\n",
|
||||
" 392: 'Czech Republic',\n",
|
||||
" 393: 'Belgium',\n",
|
||||
" 394: 'Brazil',\n",
|
||||
" 395: 'USA',\n",
|
||||
" 396: 'USA',\n",
|
||||
" 397: 'France',\n",
|
||||
" 398: 'France',\n",
|
||||
" 399: 'Finland',\n",
|
||||
" 400: 'Ireland',\n",
|
||||
" 401: 'Spain',\n",
|
||||
" 402: 'Argentina',\n",
|
||||
" 403: 'Czech Republic',\n",
|
||||
" 404: 'USA',\n",
|
||||
" 405: 'USA',\n",
|
||||
" 406: 'USA',\n",
|
||||
" 407: 'USA',\n",
|
||||
" 408: 'Canada',\n",
|
||||
" 409: 'Portugal',\n",
|
||||
" 410: 'Finland',\n",
|
||||
" 411: 'India'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import sqlalchemy\n",
|
||||
"\n",
|
||||
"engine = sqlalchemy.create_engine('sqlite:///Chinook.sqlite', echo=True)\n",
|
||||
"connection = engine.raw_connection()\n",
|
||||
"\n",
|
||||
"df = pd.read_sql('SELECT * FROM Customer', con='sqlite:///Chinook.sqlite')\n",
|
||||
"df.to_csv('customers.csv')\n",
|
||||
"\n",
|
||||
"df = pd.read_sql('SELECT * FROM Employee', con='sqlite:///Chinook.sqlite')\n",
|
||||
"# df['City'].drop_duplicates()\n",
|
||||
"\n",
|
||||
"df = pd.read_sql('SELECT * FROM Invoice', con='sqlite:///Chinook.sqlite')\n",
|
||||
"df['BillingCountry'].drop_duplicates().to_dict()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -8095,7 +8507,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.3"
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
@ -1,4 +1,4 @@
|
||||
,members,occasionals
|
||||
May,682758,147898
|
||||
June,737011,171494
|
||||
July,779511,194316
|
||||
,members,occasionals
|
||||
May,682758,147898
|
||||
June,737011,171494
|
||||
July,779511,194316
|
||||
|
|
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue
Block a user