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Wprowadzenie do przetwarzania danych z pakietami numpy i pandas

import numpy as np
import pandas as pd
# Wprowadzenie do numpy
lista=[4,6,7,6,5,43,12,3,7,11,3,6]
[x**2 for x in lista]
 
tablica=np.array(lista)
print(tablica,type(tablica), tablica.dtype)
 
tablica**2
[ 4  6  7  6  5 43 12  3  7 11  3  6] <class 'numpy.ndarray'> int64





array([  16,   36,   49,   36,   25, 1849,  144,    9,   49,  121,    9,
         36])
# Tablice wielowymiarowe
m1=np.array([[4,7,2],[3,5,8]])
m1
array([[4, 7, 2],
       [3, 5, 8]])
# TAblice specjalne
np.zeros((4,6))
array([[0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0.]])
# TAblice specjalne
np.ones((4,6))
array([[1., 1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1., 1.]])
# genowanie sekwencji jako tablicy
t1=np.arange(-8,8,0.01)
t1
array([-8.  , -7.99, -7.98, ...,  7.97,  7.98,  7.99])
t2=np.linspace(-1,1,10)
t2
array([-1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,
        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ])
# Indeksowanie tablicy
print(tablica[2])
print(tablica[2:5])
7
[7 6 5]
# zmiana kształtu tablicy
m1=tablica.reshape((3,4))
m1
array([[ 4,  6,  7,  6],
       [ 5, 43, 12,  3],
       [ 7, 11,  3,  6]])
m1**2
array([[  16,   36,   49,   36],
       [  25, 1849,  144,    9],
       [  49,  121,    9,   36]])
m1[0:2,0:2]
array([[ 4,  6],
       [ 5, 43]])
# Operacje na tablicach
tb1=np.array([5,3,9])
tb2=np.array([3,1,7])
 
print(tb1+tb2)
print(tb1*tb2)
print(tb1**2)
print(tb1>5)
[ 8  4 16]
[15  3 63]
[25  9 81]
[False False  True]
# Funkcje statystyczne na tablicach
print(tablica)
np.mean(tablica)
[ 4  6  7  6  5 43 12  3  7 11  3  6]





np.float64(9.416666666666666)
print(tablica)
print(tablica.mean())
print(tablica.std())
[ 4  6  7  6  5 43 12  3  7 11  3  6]
9.416666666666666
10.467874134173035
# Wygenerowanie tablicy losowej
np.random.seed(33)
m1=np.random.randint(0,10,(5,5))
m1
array([[4, 7, 8, 2, 2],
       [9, 9, 3, 6, 3],
       [3, 1, 7, 6, 0],
       [0, 6, 6, 0, 4],
       [8, 8, 3, 7, 9]])
# Statystyki z wierszy
print(m1.mean(axis=1))
print(m1.max(axis=1))
[4.6 6.  3.4 3.2 7. ]
[8 9 7 6 9]
# Statystyki z kolumn
print(m1.mean(axis=0))
print(m1.max(axis=0))
[4.8 6.2 5.4 4.2 3.6]
[9 9 8 7 9]

Wprowadzenie do pakietu pandas

import numpy as np
import pandas as pd
# Typ danych series
 
s1=pd.Series([5,7,3,2,8,9])
print(s1,type(s1))
0    5
1    7
2    3
3    2
4    8
5    9
dtype: int64 <class 'pandas.core.series.Series'>
print(s1[2])
print(s1[1:4])
3
1    7
2    3
3    2
dtype: int64
s1=pd.Series([5,7,3,2,8,9],index=['a','a','b','c','b','a'])
print(s1,type(s1))
a    5
a    7
b    3
c    2
b    8
a    9
dtype: int64 <class 'pandas.core.series.Series'>
s1['a']
a    5
a    7
a    9
dtype: int64
s1[1:4]
a    7
b    3
c    2
dtype: int64
# Operacje na sieriach
s1**2
a    25
a    49
b     9
c     4
b    64
a    81
dtype: int64
# Typ DataFrame
dane={
        'Imię':['Ola','Tola','Ula','Ala','Zula','Ela'],
        'Wiek':[23,18,34,27,17,32],
        'Miasto' : ['Warszawa','Opole','Opole','Sopot','Warszawa','Sopot'],
        'Pensja' : [6700,5432,8760,6450,5670,8245]
     }
 
tb=pd.DataFrame(dane)
tb.to_csv(".\Dane2.csv")
tb=pd.read_csv(".\Dane2.csv")
tb

Unnamed: 0

Imię

Wiek

Miasto

Pensja

0

0

Ola

23

Warszawa

6700

1

1

Tola

18

Opole

5432

2

2

Ula

34

Opole

8760

3

3

Ala

27

Sopot

6450

4

4

Zula

17

Warszawa

5670

5

5

Ela

32

Sopot

8245

#Wstępne badanie DataFrame
tb.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6 entries, 0 to 5
Data columns (total 5 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   Unnamed: 0  6 non-null      int64 
 1   Imię        6 non-null      object
 2   Wiek        6 non-null      int64 
 3   Miasto      6 non-null      object
 4   Pensja      6 non-null      int64 
dtypes: int64(3), object(2)
memory usage: 368.0+ bytes
tb.shape
(6, 5)
tb.describe()

Unnamed: 0

Wiek

Pensja

count

6.000000

6.000000

6.000000

mean

2.500000

25.166667

6876.166667

std

1.870829

7.082843

1354.669025

min

0.000000

17.000000

5432.000000

25%

1.250000

19.250000

5865.000000

50%

2.500000

25.000000

6575.000000

75%

3.750000

30.750000

7858.750000

max

5.000000

34.000000

8760.000000

#Filtrowanie danych
w=tb['Wiek']>20
tb[w]

Unnamed: 0

Imię

Wiek

Miasto

Pensja

0

0

Ola

23

Warszawa

6700

2

2

Ula

34

Opole

8760

3

3

Ala

27

Sopot

6450

5

5

Ela

32

Sopot

8245

# Grupowanie danych
tb

Unnamed: 0

Imię

Wiek

Miasto

Pensja

0

0

Ola

23

Warszawa

6700

1

1

Tola

18

Opole

5432

2

2

Ula

34

Opole

8760

3

3

Ala

27

Sopot

6450

4

4

Zula

17

Warszawa

5670

5

5

Ela

32

Sopot

8245

tb.groupby('Miasto')['Pensja'].mean()
Miasto
Opole       7096.0
Sopot       7347.5
Warszawa    6185.0
Name: Pensja, dtype: float64
# Indeksowanie za pomocą metod iloc i loc
dane= {
    'Imię': ['Anna', 'Marek', 'Ewa', 'Piotr', 'Kasia'],
    'Wiek': [28, 35, 22, 45, 30],
    'Miasto': ['Warszawa', 'Kraków', 'Warszawa', 'Gdańsk', 'Kraków'],
    'Dochód': [3500, 4000, 2800, 5000, 4200]
}
tb1=pd.DataFrame(dane,index=['a','a','b','c','d'])
tb1

Imię

Wiek

Miasto

Dochód

a

Anna

28

Warszawa

3500

a

Marek

35

Kraków

4000

b

Ewa

22

Warszawa

2800

c

Piotr

45

Gdańsk

5000

d

Kasia

30

Kraków

4200

tb1.head(3)

Imię

Wiek

Miasto

Dochód

a

Anna

28

Warszawa

3500

a

Marek

35

Kraków

4000

b

Ewa

22

Warszawa

2800

tb1.tail(2)

Imię

Wiek

Miasto

Dochód

c

Piotr

45

Gdańsk

5000

d

Kasia

30

Kraków

4200

#Indeksowanie metodą loc
tb1.loc['a']

Imię

Wiek

Miasto

Dochód

a

Anna

28

Warszawa

3500

a

Marek

35

Kraków

4000

tb1['Wiek']
a    28
a    35
b    22
c    45
d    30
Name: Wiek, dtype: int64
tb1.loc[['a','c'],['Wiek','Miasto']]

Wiek

Miasto

a

28

Warszawa

a

35

Kraków

c

45

Gdańsk

tb1.loc['a':'c','Wiek':'Dochód']

Wiek

Miasto

Dochód

a

28

Warszawa

3500

a

35

Kraków

4000

b

22

Warszawa

2800

c

45

Gdańsk

5000

#Indeksowanie metod iloc
tb1.iloc[2]
Imię           Ewa
Wiek            22
Miasto    Warszawa
Dochód        2800
Name: b, dtype: object
tb1.iloc[2,3]
np.int64(2800)
tb1.iloc[1:3,0:2]

Imię

Wiek

a

Marek

35

b

Ewa

22

tb1.iloc[[0,2],[2,3]]

Miasto

Dochód

a

Warszawa

3500

b

Warszawa

2800

tb1['Wiek'].plot(color='red')
<Axes: >

png png

 
 

Spotkanie 3

Tutorial do wizualizacji danych

Autor: Chat GPT 4.5

Wykres liniowy

  1. Opis wykresu liniowego (Line plot) Wykres liniowy jest podstawowym i najczęściej wykorzystywanym rodzajem wykresu do przedstawiania danych ciągłych, które zmieniają się w czasie lub wzdłuż osi niezależnej. Pozwala w prosty i czytelny sposób pokazać trendy, wzrosty lub spadki oraz porównać kilka zbiorów danych jednocześnie.

Podstawowymi elementami wykresu liniowego są:

Oś pozioma (x) reprezentująca dane niezależne (np. czas, daty, numery indeksów). Oś pionowa (y) reprezentująca dane zależne. Punkty danych, które są połączone linią, pokazujące trend.

  1. Typowe zastosowania Analiza trendów – obserwacja zmian wartości w czasie (np. ceny akcji, zmiany temperatury). Porównywanie zbiorów danych – zestawienie wielu serii danych, np. wyniki eksperymentów lub porównanie różnych produktów. Prognozowanie – obserwacja dotychczasowego rozwoju oraz przewidywanie przyszłych wartości. Analiza danych finansowych – prezentowanie trendów cen, indeksów giełdowych, walut, kryptowalut. Monitorowanie procesów technicznych – np. wydajność systemu komputerowego lub procesów produkcyjnych.
  2. Maksymalny potencjał wykresu liniowego – przykład w Pythonie Poniżej kompleksowy przykład ukazujący pełnię możliwości wykresu liniowego, wykorzystujący bibliotekę Matplotlib:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
 
# Generowanie realistycznych danych
np.random.seed(42)
dni = pd.date_range(start='2024-01-01', periods=30)
 
# generowanie temperatur wokół zadanych średnich
temperatura_warszawa = np.random.normal(loc=2, scale=3, size=30)
temperatura_gdansk = np.random.normal(loc=0, scale=2, size=30)
temperatura_krakow = np.random.normal(loc=1, scale=4, size=30)
 
# Tworzenie DataFrame
df = pd.DataFrame({
    'Data': dni,
    'Warszawa': temperatura_warszawa,
    'Gdańsk': temperatura_gdansk,
    'Kraków': temperatura_krakow
})
 
# Rysowanie wykresu
plt.figure(figsize=(12, 6),dpi=100,facecolor='lime')
 
# Linie z różnymi stylami
plt.plot(df['Data'], df['Warszawa'], label='Warszawa', linestyle='-', linewidth=2, marker='o',color='magenta')
plt.plot(df['Data'], df['Gdańsk'], label='Gdańsk', linestyle='--', linewidth=2, marker='s')
plt.plot(df['Data'], df['Kraków'], label='Kraków', linestyle='-.', linewidth=2, marker='^')
 
# Tytuł i opisy osi
plt.title('Temperatura w styczniu 2024 w wybranych miastach Polski', fontsize=16, fontweight='bold')
plt.xlabel('Data', fontsize=14)
plt.ylabel('Temperatura [°C]', fontsize=14)
 
# Siatka pomocnicza
plt.grid(True, linestyle='--', alpha=0.5)
 
# Adnotacje – oznacz najwyższą temperaturę w Warszawie i najniższą w Gdańsku
max_warszawa = df['Warszawa'].idxmax()
min_gdansk = df['Gdańsk'].idxmin()
 
plt.annotate(f"Max Warszawa ({df['Warszawa'].max():.1f}°C)",
             xy=(df['Data'][max_warszawa], df['Warszawa'][max_warszawa]),
             xytext=(df['Data'][max_warszawa], df['Warszawa'][max_warszawa]+4),
             arrowprops=dict(facecolor='green', arrowstyle='->'))
 
plt.annotate(f"Min Gdańsk ({df['Gdańsk'].min():.1f}°C)",
             xy=(df['Data'][min_gdansk], df['Gdańsk'][min_gdansk]),
             xytext=(df['Data'][min_gdansk], df['Gdańsk'][min_gdansk]-5),
             arrowprops=dict(facecolor='red', arrowstyle='->'))
 
# Poprawienie czytelności osi x
plt.xticks(rotation=45)
 
# Legenda
plt.legend(title='Miasta', fontsize=12)
 
# Dopasowanie layoutu
plt.tight_layout()
 
# Wyświetlenie wykresu
plt.show()

png png

  1. Omówienie uzyskanego efektu Kilka serii danych: Wyraźnie zaprezentowane trzy serie danych (temperatura w trzech miastach). Różne style linii: Rozróżnienie serii za pomocą koloru, stylu linii oraz markerów punktów. Adnotacje: Podkreślenie ważnych informacji (najwyższa/najniższa wartość). Czytelność: Wyraźnie oznaczone osie, legenda i siatka pomagają w interpretacji. Wizualizacja czasowa: Oś pozioma z datami ułatwia interpretację zmiany danych w czasie.

Wykresy słupkowe

.

📌 1. Opis wykresów słupkowych Wykres słupkowy (bar chart) pozwala porównać wartości danych dla różnych kategorii. Każdy słupek reprezentuje wartość liczbową przypisaną do określonej kategorii.

Wykres słupkowy skumulowany (stacked bar chart) umożliwia porównanie sumarycznych wartości dla kategorii, jednocześnie pokazując wkład poszczególnych składowych w te sumy.

Cechy charakterystyczne:

Wizualizacja danych kategorycznych Czytelne porównanie wartości Pokazanie udziałów części składowych (słupki skumulowane) 📌 2. Typowe zastosowania 🔹 Wykres słupkowy: Porównanie sprzedaży według kategorii produktów. Wyniki wyborów lub badań opinii publicznej. Porównywanie wyników finansowych firm. 🔹 Wykres słupkowy skumulowany: Struktura kosztów lub przychodów firmy. Podział populacji według wieku w różnych regionach. Prezentacja wyników ankiet wielokrotnego wyboru.

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
 
# Tworzenie przykładowych danych
produkty = ['Laptop', 'Smartfon', 'Tablet', 'Smartwatch']
sprzedaz_2023 = [1200, 2500, 900, 400]
sprzedaz_2024 = [1500, 2700, 1100, 600]
 
# Pozycja słupków na osi x
x = np.arange(len(produkty))
 
# Rysowanie wykresu
plt.figure(figsize=(10,6),facecolor='lightgray')
 
# Słupki dla 2023 i 2024 obok siebie
plt.bar(x - 0.2, sprzedaz_2023, width=0.4, label='2023', edgecolor='black')
plt.bar(x + 0.2, sprzedaz_2024, width=0.4, label='2024', edgecolor='black')
 
# Tytuł, etykiety, siatka
plt.title('Porównanie sprzedaży produktów (2023 vs 2024)', fontsize=16)
plt.xlabel('Kategoria produktu', fontsize=14)
plt.ylabel('Liczba sprzedanych sztuk', fontsize=14)
plt.xticks(x, produkty, fontsize=12)
plt.grid(axis='y', linestyle='--', alpha=0.7)
 
# Dodanie wartości liczbowych na słupkach
for i in x:
    plt.text(i - 0.2, sprzedaz_2023[i] + 50, sprzedaz_2023[i], ha='center', fontsize=11)
    plt.text(i + 0.2, sprzedaz_2024[i] + 50, sprzedaz_2024[i], ha='center', fontsize=11)
 
# Legenda
plt.legend(title='Rok')
 
# Wyświetlenie wykresu
plt.tight_layout()
plt.show()

png png

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
 
# Tworzenie przykładowych danych
miasta = ['Warszawa', 'Kraków', 'Gdańsk', 'Poznań']
zarobki_niskie = [25, 35, 30, 28]
zarobki_srednie = [50, 45, 48, 52]
zarobki_wysokie = [25, 20, 22, 20]
 
# Tworzenie DataFrame
df = pd.DataFrame({
    'Miasto': miasta,
    'Niskie': zarobki_niskie,
    'Średnie': zarobki_srednie,
    'Wysokie': zarobki_wysokie
})
 
# Pozycja słupków na osi x
x = np.arange(len(df))
 
# Rysowanie wykresu
plt.figure(figsize=(10,6),facecolor='lightgreen')
 
# Skumulowane słupki
plt.bar(df['Miasto'], df['Niskie'], label='Niskie', edgecolor='black')
plt.bar(df['Miasto'], df['Średnie'], bottom=df['Niskie'], label='Średnie', edgecolor='black')
plt.bar(df['Miasto'], df['Wysokie'], bottom=df['Niskie'] + df['Średnie'], label='Wysokie', edgecolor='black')
 
# Tytuł, etykiety, siatka
plt.title('Rozkład wynagrodzeń w wybranych miastach [%]', fontsize=16)
plt.xlabel('Miasto', fontsize=14)
plt.ylabel('Udział procentowy [%]', fontsize=14)
plt.grid(axis='y', linestyle='--', alpha=0.7)
 
# Dodanie wartości liczbowych na słupkach
for i in range(len(df)):
    plt.text(x[i], zarobki_niskie[i]/2, f"{zarobki_niskie[i]}%", ha='center', va='center', fontsize=11, color='white')
    plt.text(x[i], zarobki_niskie[i] + zarobki_srednie[i]/2, f"{zarobki_srednie[i]}%", ha='center', va='center', fontsize=11, color='white')
    plt.text(x[i], zarobki_niskie[i] + zarobki_srednie[i] + zarobki_wysokie[i]/2, f"{zarobki_wysokie[i]}%", ha='center', va='center', fontsize=11, color='white')
 
# Legenda
plt.legend(title='Poziom zarobków')
 
# Wyświetlenie wykresu
plt.tight_layout()
plt.show()

png png

📌 4. Podsumowanie możliwości wykresów słupkowych:

Dzięki zaawansowanym opcjom wizualizacyjnym Python oferuje potężne narzędzia do prezentowania danych kategorycznych w przejrzysty i estetyczny sposób. Dodając szczegółowe opisy, dane liczbowe, różnorodne style graficzne oraz układy skumulowane, wykresy słupkowe stają się niezwykle skuteczne w komunikacji wizualnej, zwiększając ich informacyjny i analityczny potencjał.

Wykres kołowy

📌 1. Opis wykresu kołowego Wykres kołowy (ang. pie chart) jest wykresem używanym do prezentowania proporcji, czyli udziału poszczególnych kategorii w całości. Każdy fragment (wycinek koła) reprezentuje jedną kategorię, a rozmiar tego fragmentu odzwierciedla jego udział procentowy w całkowitej sumie wartości wszystkich kategorii.

Charakterystyczne cechy: Wizualizacja danych procentowych. Przedstawienie relacji między częścią a całością. Prosty, intuicyjny odbiór. 📌 2. Typowe zastosowania Wykresy kołowe są używane głównie do:

Prezentacji struktury przychodów lub kosztów firmy. Analizy wyników ankiet i badań rynkowych. Pokazania udziału różnych produktów w sprzedaży. Prezentacji udziałów rynkowych firm w sektorze. Ważne, by liczba kategorii była umiarkowana (najlepiej do 6-8), ponieważ duża liczba kategorii zmniejsza czytelność.

📌 3. Zaawansowany przykład wykresu kołowego w Pythonie (pełny potencjał) Poniższy przykład maksymalnie wykorzystuje możliwości biblioteki Matplotlib do stworzenia atrakcyjnego i czytelnego wykresu kołowego.

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
 
# Dane – udział firm na rynku smartfonów
udzialy = [28, 35, 17, 13, 10, 7]
firmy = ['Samsung', 'Apple', 'Xiaomi', 'OPPO', 'Vivo', 'Inne']
 
# Automatyczne wyróżnienie największego kawałka
explode = [0.1 if val == max(udzialy) else 0 for val in udzialy]
 
# Kolory (opcjonalnie)
colors = plt.cm.Paired.colors
 
# Tworzenie wykresu kołowego
plt.figure(figsize=(9, 9),facecolor='lightgray')
wedges, texts, autotexts = plt.pie(
    udzialy,
    labels=firmy,
    autopct='%1.1f%%',
    startangle=140,
    explode=explode,  # automatyczne wyróżnienie
    colors=colors,
    shadow=True,
    wedgeprops=dict(edgecolor='black', linewidth=1),
    textprops={'fontsize': 13}
)
 
# Tytuł wykresu
plt.title('Udziały producentów smartfonów na rynku (2024)', fontsize=16, fontweight='bold')
 
# Dodanie legendy
plt.legend(wedges, firmy, title="Firmy", loc="best", bbox_to_anchor=(1, 0, 0.5, 1), fontsize=11)
 
# Zapewnienie równego koła (nie elipsy)
plt.axis('equal')
 
# Wyświetlenie wykresu
plt.tight_layout()
plt.show()

png png

📌 4. Omówienie efektów tego wykresu: Powyższy wykres demonstruje, jak maksymalnie wykorzystać potencjał wykresu kołowego:

Eksplozja (explode) – wyróżnia szczególnie ważną kategorię. Wyświetlanie procentów – czytelna prezentacja udziałów każdej kategorii. Cienie i krawędzie – estetyczne dodatki poprawiające odbiór. Kolorystyka – wyraźne i różnorodne kolory zwiększają atrakcyjność wizualną. Legenda i opisy – ułatwiają szybkie odniesienie do kategorii danych. Dzięki temu wykres kołowy staje się czytelny, estetyczny, profesjonalny, a przede wszystkim bardzo skuteczny w przekazywaniu kluczowych informacji.

⚠️ Ważna wskazówka na koniec: Używaj wykresów kołowych z rozwagą – pamiętaj, że ich największą zaletą jest prezentacja niewielkiej liczby kategorii. Przy dużej liczbie kategorii rozważ inne wykresy (np. słupkowe poziome lub wykresy typu donut).

Wykres pudełkowy i skrzypcowy

📌 1. Opis wykresów 🟢 Wykres pudełkowy (Boxplot) Wykres pudełkowy (boxplot) wizualizuje rozkład danych za pomocą kilku kluczowych statystyk opisowych:

mediana (linia środkowa pudełka) pierwszy i trzeci kwartyl (Q1 i Q3 – brzegi pudełka) wąsy (rozstęp danych: min/max lub wartości odstające) wartości odstające, zaznaczane jako punkty poza pudełkiem. Dzięki temu umożliwia on szybkie porównanie rozkładów wielu grup danych oraz identyfikację wartości odstających.

🟢 Wykres violinowy (Violin plot) Wykres violinowy łączy zalety wykresu pudełkowego z wykresem gęstości (KDE). Pokazuje nie tylko podstawowe statystyki rozkładu (mediana, kwartyle), ale też szczegółowy kształt rozkładu, uwypuklając takie cech jak wielomodalność, asymetria czy zagęszczenie danych.

📌 2. Typowe zastosowania Oba typy wykresów są powszechnie stosowane do:

Analizy porównawczej kilku grup danych. Identyfikacji wartości odstających (boxplot). Badania rozkładów danych liczbowych (czas oczekiwania, wyniki testów, pensje). Wizualizacji rozkładów danych numerycznych dla poszczogólnych kategorii. Przykładowe obszary zastosowań:

Analizy statystyczne i badania naukowe. Wizualizacja danych w finansach (np. zmienność cen akcji). Badania satysfakcji, ocenianie wyników testów itp. 📌 3. Zaawansowany przykład realizacji wykresów w Pythonie

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
 
# Generowanie danych przykładowych
np.random.seed(0)
df = pd.DataFrame({
    'Grupa': np.repeat(['Grupa A', 'Grupa B', 'Grupa C'], 100),
    'Wynik': np.concatenate([
        np.random.normal(70, 10, 100),
        np.random.normal(75, 15, 100),
        np.random.normal(65, 20, 100)
    ])
})
 
# Rozmiar figur
plt.figure(figsize=(14, 6),facecolor='lightblue')
 
# --------------- Wykres pudełkowy ----------------
plt.subplot(1, 2, 1)
sns.boxplot(x='Grupa', y='Wynik', data=df, palette='Pastel1', linewidth=2)
 
# Dodanie punktów indywidualnych obserwacji
sns.stripplot(x='Grupa', y='Wynik', data=df, color='black', alpha=0.5, jitter=True)
 
# Tytuł i opisy
plt.title('Wykres pudełkowy (Boxplot) z punktami danych', fontsize=15, fontweight='bold')
plt.xlabel('Grupa', fontsize=13)
plt.ylabel('Wynik', fontsize=13)
plt.grid(axis='y', linestyle='--', alpha=0.6)
 
# --------------- Violin Plot ----------------
plt.subplot(1, 2, 2)
sns.violinplot(x='Grupa', y='Wynik', data=df, palette='Pastel2', inner='quartile', linewidth=2)
 
# Dodanie mediany
sns.pointplot(x='Grupa', y='Wynik', data=df, estimator=np.median, color='black', markers='D', scale=0.5)
 
plt.title('Rozkład wyników - Violin Plot', fontsize=15, fontweight='bold')
plt.xlabel('Grupa', fontsize=13)
plt.ylabel('Wynik', fontsize=13)
plt.grid(axis='y', linestyle='--', alpha=0.6)
 
# Poprawienie układu wykresów
plt.tight_layout()
 
# Wyświetlenie wykresów
plt.show()
/tmp/ipykernel_170226/2987352356.py:22: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Grupa', y='Wynik', data=df, palette='Pastel1', linewidth=2)
/tmp/ipykernel_170226/2987352356.py:35: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.violinplot(x='Grupa', y='Wynik', data=df, palette='Pastel2', inner='quartile', linewidth=2)
/tmp/ipykernel_170226/2987352356.py:38: UserWarning: 

The `scale` parameter is deprecated and will be removed in v0.15.0. You can now control the size of each plot element using matplotlib `Line2D` parameters (e.g., `linewidth`, `markersize`, etc.).

  sns.pointplot(x='Grupa', y='Wynik', data=df, estimator=np.median, color='black', markers='D', scale=0.5)

png png

Wykresy funkcji dwóch zmiennych

📌 Opis wykresu przestrzennego (3D Surface Plot) Wykres przestrzenny (ang. 3D surface plot) przedstawia zależność wartości jednej zmiennej od dwóch innych zmiennych. Dzięki trójwymiarowej reprezentacji łatwo można zaobserwować wzajemne powiązania oraz szybko zidentyfikować trendy, wzory lub ekstremalne wartości.

Wykresy powierzchniowe doskonale sprawdzają się przy analizie danych złożonych, które zmieniają się wraz z dwoma niezależnymi zmiennymi.

📌 Zastosowania wykresów przestrzennych Wizualizacja danych naukowych (np. fizyka, chemia, biologia). Analiza funkcji matematycznych (np. rozkłady prawdopodobieństwa). Badanie efektywności modeli predykcyjnych (ML, Deep Learning). Prezentacja danych meteorologicznych (np. temperatury, opady w terenie). Wizualizacja potencjałów lub ryzyka w analizach finansowych

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
#%matplotlib notebook
%matplotlib ipympl
# Generowanie danych
x = np.linspace(-5, 5, 50)
y = np.linspace(-5, 5, 50)
X, Y = np.meshgrid(x, y)
 
# Funkcja Z zależna od X i Y
Z = np.sin(np.sqrt(X**2 + Y**2))
 
# Tworzenie wykresu
fig = plt.figure(figsize=(10, 7))
ax = fig.add_subplot(111, projection='3d')
 
# Tworzenie powierzchni (surface plot)
surf = ax.plot_surface(X, Y, Z, cmap='viridis', edgecolor='none', alpha=0.9)
 
# Dodanie paska kolorów
fig.colorbar(surf, shrink=0.5, aspect=10)
 
# Tytuł oraz opisy osi
ax.set_title('Wykres funkcji przestrzennej Z = sin(√(X² + Y²))', fontsize=15, fontweight='bold')
ax.set_xlabel('Oś X', fontsize=12)
ax.set_ylabel('Oś Y', fontsize=12)
ax.set_zlabel('Oś Z', fontsize=12)
 
# Zmiana kąta widzenia
ax.view_init(elev=25, azim=15)
 
# Wyświetlenie wykresu
plt.tight_layout()
plt.show()
<div class="jupyter-widgets widget-label" style="text-align: center;">
    Figure
</div>
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' width=1000.0/>
 

Spotkanie 5

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
 
 
# Generowanie przykładowych danych
np.random.seed(6767)
X = np.linspace(0, 10, 200)
y = 2.5 * X + 3+np.random.normal(0, 2, size=X.shape)  # dane z szumem
X= X.reshape(-1, 1)
 
 
model = LinearRegression()
model.fit(X,y)
y_pred = model.predict(X)
print(model.coef_)
print(model.intercept_)
 
# Wizualizacja wyników
plt.figure(figsize=(10, 6))
plt.scatter(X, y, color='blue', alpha=0.6, label='Dane rzeczywiste')
plt.plot(X, y_pred, color='red', linewidth=2, label='Regresja liniowa')
plt.xlabel('X')
plt.ylabel('y')
plt.title('Regresja liniowa - dopasowanie modelu')
plt.legend()
plt.grid(True)
plt.show()
[2.54483697]
2.870468659238794

png png

# CZy widać ocieplenie klimatu
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sqlalchemy import create_engine
 
connection_string = (
        "mssql+pyodbc://student:[email protected],50221/Synop?driver=ODBC+Driver+17+for+SQL+Server"
          )
engine = create_engine(connection_string)
 
query = """
Select year(data) as Rok,
       round(avg(TemperaturaPowietrza),2) as [Średnia temperatura]
from Depesze
where stacja=12375 and data between'20000101' and '20241231'
       and godzina='12:00'
group by year(data)
order by 1
    """
 
dane=pd.read_sql(query, engine) 
X=dane['Rok'].values.reshape(-1, 1)
y=dane['Średnia temperatura']
model=LinearRegression().fit(X,y)
 
y_pred=model.predict(X)
 
# Wizualizacja danych wraz z linią regresji
plt.figure(figsize=(10, 6))
plt.scatter(dane['Rok'], dane['Średnia temperatura'], color='blue', alpha=0.6, label='Dane rzeczywiste')
plt.plot(dane['Rok'], y_pred, color='red', linewidth=2, label='Linia regresji')
plt.title('Średnia temperatura w Warszawie (2000-2023)')
plt.xlabel('Rok')
plt.ylabel('Średnia temperatura (°C)')
plt.legend()
plt.grid(True)
plt.show()

png png

 

Spotkanie 6

# Import bibliotek
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_wine
from sklearn.model_selection import  cross_val_score
 
# Algorytmy klasyfikacji
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
 
data = load_wine()
X = data.data
y = data.target
#print(X)
#print(y)
 
# Lista modeli do porównania
models = [
    ('Drzewo Decyzyjne', DecisionTreeClassifier()),
    ('k-NN', KNeighborsClassifier()),
    ('SVM', SVC()),
    ('Las Losowy', RandomForestClassifier())
]
 
# Walidacja krzyżowa i przechowywanie wyników
results = []
names = []
 
for name, model in models:
    cv_scores = cross_val_score(model, X, y, cv=10, scoring='accuracy')
    results.append(cv_scores)
    names.append(name)
    print(f'{name}: średnia dokładność = {cv_scores.mean():.3f} (+/- {cv_scores.std():.3f})')
 
# Wykres pudełkowy wyników walidacji krzyżowej
plt.figure(figsize=(10, 6))
plt.boxplot(results, tick_labels=names)
plt.title('Porównanie modeli klasyfikacji (walidacja krzyżowa)')
plt.ylabel('Dokładność')
plt.grid(True)
plt.show()
Drzewo Decyzyjne: średnia dokładność = 0.859 (+/- 0.095)
k-NN: średnia dokładność = 0.675 (+/- 0.070)
SVM: średnia dokładność = 0.681 (+/- 0.087)
Las Losowy: średnia dokładność = 0.977 (+/- 0.028)

png png

 

Spotkanie 8

import numpy as np
import matplotlib.pyplot as plt
 
from keras.layers import Dense, Flatten,Dropout,Conv2D,MaxPooling2D
from keras.models import Sequential
from keras.utils import to_categorical
from keras.datasets import mnist
 
(X_train, y_train), (X_test, y_test) = mnist.load_data()
obraz, obszary = plt.subplots(ncols=5, sharex=False,
                sharey=True, figsize=(10, 4))
 
for i in range(5):
    obszary[i].set_title(y_train[i])
    obszary[i].imshow(X_train[i], cmap='gray')
    obszary[i].get_xaxis().set_visible(False)
    obszary[i].get_yaxis().set_visible(False)
X_train.shape
(60000, 28, 28)
temp = []
for i in range(len(y_train)):
    temp.append(to_categorical(y_train[i], num_classes=10))
 
y_train = np.array(temp)
 
temp = []
for i in range(len(y_test)):
    temp.append(to_categorical(y_test[i], num_classes=10))
 
y_test = np.array(temp)
y_test
array([[0., 0., 0., ..., 1., 0., 0.],
       [0., 0., 1., ..., 0., 0., 0.],
       [0., 1., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]])
model=Sequential()
model.add(Flatten(input_shape=(28,28)))
model.add(Dense(30,activation='sigmoid'))
model.add(Dense(10,activation='softmax'))
C:\Users\user\anaconda3\envs\tf-env\lib\site-packages\keras\src\layers\reshaping\flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(**kwargs)
model.summary()
model.compile(loss='categorical_crossentropy', 
     optimizer='adam',
     metrics=['acc'])
model.fit(X_train, y_train, epochs=30, validation_data=(X_test,y_test))
Epoch 1/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.6591 - loss: 1.1976 - val_acc: 0.8627 - val_loss: 0.4906
Epoch 2/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.8586 - loss: 0.4965 - val_acc: 0.8737 - val_loss: 0.4288
Epoch 3/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.8715 - loss: 0.4334 - val_acc: 0.8783 - val_loss: 0.3984
Epoch 4/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 1ms/step - acc: 0.8731 - loss: 0.4181 - val_acc: 0.8837 - val_loss: 0.3994
Epoch 5/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.8804 - loss: 0.3992 - val_acc: 0.8867 - val_loss: 0.3678
Epoch 6/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 2ms/step - acc: 0.8810 - loss: 0.3967 - val_acc: 0.8823 - val_loss: 0.3897
Epoch 7/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 2ms/step - acc: 0.8860 - loss: 0.3771 - val_acc: 0.8923 - val_loss: 0.3606
Epoch 8/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.8867 - loss: 0.3791 - val_acc: 0.8945 - val_loss: 0.3498
Epoch 9/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.8910 - loss: 0.3633 - val_acc: 0.8944 - val_loss: 0.3601
Epoch 10/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.8910 - loss: 0.3698 - val_acc: 0.9007 - val_loss: 0.3241
Epoch 11/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.8986 - loss: 0.3333 - val_acc: 0.8960 - val_loss: 0.3465
Epoch 12/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.8965 - loss: 0.3349 - val_acc: 0.9051 - val_loss: 0.3203
Epoch 13/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.8987 - loss: 0.3334 - val_acc: 0.9017 - val_loss: 0.3273
Epoch 14/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.9017 - loss: 0.3230 - val_acc: 0.9012 - val_loss: 0.3284
Epoch 15/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.9048 - loss: 0.3170 - val_acc: 0.8981 - val_loss: 0.3411
Epoch 16/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.9016 - loss: 0.3239 - val_acc: 0.9087 - val_loss: 0.3094
Epoch 17/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 2ms/step - acc: 0.9080 - loss: 0.3062 - val_acc: 0.9084 - val_loss: 0.3149
Epoch 18/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.9085 - loss: 0.3071 - val_acc: 0.9080 - val_loss: 0.3014
Epoch 19/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.9118 - loss: 0.2924 - val_acc: 0.9091 - val_loss: 0.3022
Epoch 20/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.9124 - loss: 0.2905 - val_acc: 0.9110 - val_loss: 0.2999
Epoch 21/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.9065 - loss: 0.3072 - val_acc: 0.9124 - val_loss: 0.2897
Epoch 22/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.9131 - loss: 0.2855 - val_acc: 0.9063 - val_loss: 0.3060
Epoch 23/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.9117 - loss: 0.2928 - val_acc: 0.9152 - val_loss: 0.2822
Epoch 24/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.9166 - loss: 0.2763 - val_acc: 0.9173 - val_loss: 0.2861
Epoch 25/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 2ms/step - acc: 0.9142 - loss: 0.2826 - val_acc: 0.9085 - val_loss: 0.3033
Epoch 26/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 1ms/step - acc: 0.9140 - loss: 0.2870 - val_acc: 0.9129 - val_loss: 0.2918
Epoch 27/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 1ms/step - acc: 0.9162 - loss: 0.2756 - val_acc: 0.9175 - val_loss: 0.2661
Epoch 28/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 1ms/step - acc: 0.9210 - loss: 0.2651 - val_acc: 0.9173 - val_loss: 0.2832
Epoch 29/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 1ms/step - acc: 0.9164 - loss: 0.2777 - val_acc: 0.9134 - val_loss: 0.2857
Epoch 30/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 2ms/step - acc: 0.9212 - loss: 0.2649 - val_acc: 0.9152 - val_loss: 0.2803





<keras.src.callbacks.history.History at 0x246dbb0af40>
predictions = model.predict(X_test)
predictions = np.argmax(predictions, axis=1)
predictions
[1m313/313[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 977us/step





array([7, 2, 1, ..., 4, 5, 6])
obraz, obszary = plt.subplots(ncols=10,nrows=4, sharex=False,
             sharey=True, figsize=(20, 14))
for i in range(40):
    obraz.axes[i].set_title(predictions[i])
    obraz.axes[i].imshow(X_test[i], cmap='gray')
    obraz.axes[i].get_xaxis().set_visible(False)
    obraz.axes[i].get_yaxis().set_visible(False)
plt.show()

png png

model = Sequential([
 
    Conv2D(64, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    MaxPooling2D(pool_size=(2, 2)),
    Dropout(0.35),
    Flatten(),
    Dense(128, activation='relu'),
    Dropout(0.3),
    Dense(10, activation='softmax')])
C:\Users\user\anaconda3\envs\tf-env\lib\site-packages\keras\src\layers\convolutional\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
model.summary()
model.compile(loss='categorical_crossentropy', 
     optimizer='adam',
     metrics=['acc'])
model.fit(X_train, y_train, epochs=30, validation_data=(X_test,y_test))
Epoch 1/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.7554 - loss: 2.4505 - val_acc: 0.9637 - val_loss: 0.1204
Epoch 2/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 8ms/step - acc: 0.9412 - loss: 0.2183 - val_acc: 0.9777 - val_loss: 0.0829
Epoch 3/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 8ms/step - acc: 0.9584 - loss: 0.1487 - val_acc: 0.9740 - val_loss: 0.0873
Epoch 4/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 8ms/step - acc: 0.9639 - loss: 0.1251 - val_acc: 0.9797 - val_loss: 0.0889
Epoch 5/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 8ms/step - acc: 0.9683 - loss: 0.1132 - val_acc: 0.9800 - val_loss: 0.0841
Epoch 6/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 8ms/step - acc: 0.9723 - loss: 0.1024 - val_acc: 0.9757 - val_loss: 0.0832
Epoch 7/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 8ms/step - acc: 0.9732 - loss: 0.0952 - val_acc: 0.9822 - val_loss: 0.0841
Epoch 8/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 8ms/step - acc: 0.9768 - loss: 0.0841 - val_acc: 0.9804 - val_loss: 0.0834
Epoch 9/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m17s[0m 9ms/step - acc: 0.9760 - loss: 0.0881 - val_acc: 0.9817 - val_loss: 0.0753
Epoch 10/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 9ms/step - acc: 0.9788 - loss: 0.0755 - val_acc: 0.9834 - val_loss: 0.0765
Epoch 11/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 8ms/step - acc: 0.9796 - loss: 0.0740 - val_acc: 0.9806 - val_loss: 0.0878
Epoch 12/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9793 - loss: 0.0710 - val_acc: 0.9830 - val_loss: 0.0877
Epoch 13/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9795 - loss: 0.0760 - val_acc: 0.9823 - val_loss: 0.0970
Epoch 14/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9812 - loss: 0.0727 - val_acc: 0.9834 - val_loss: 0.0954
Epoch 15/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9816 - loss: 0.0699 - val_acc: 0.9826 - val_loss: 0.0867
Epoch 16/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9816 - loss: 0.0655 - val_acc: 0.9823 - val_loss: 0.0938
Epoch 17/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9815 - loss: 0.0727 - val_acc: 0.9823 - val_loss: 0.0904
Epoch 18/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9835 - loss: 0.0632 - val_acc: 0.9825 - val_loss: 0.1071
Epoch 19/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9828 - loss: 0.0639 - val_acc: 0.9817 - val_loss: 0.1091
Epoch 20/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9843 - loss: 0.0574 - val_acc: 0.9833 - val_loss: 0.1257
Epoch 21/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9844 - loss: 0.0643 - val_acc: 0.9828 - val_loss: 0.1115
Epoch 22/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9838 - loss: 0.0656 - val_acc: 0.9847 - val_loss: 0.0853
Epoch 23/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9837 - loss: 0.0650 - val_acc: 0.9836 - val_loss: 0.0992
Epoch 24/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9831 - loss: 0.0692 - val_acc: 0.9836 - val_loss: 0.1028
Epoch 25/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9851 - loss: 0.0630 - val_acc: 0.9851 - val_loss: 0.1152
Epoch 26/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9852 - loss: 0.0602 - val_acc: 0.9853 - val_loss: 0.1016
Epoch 27/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9850 - loss: 0.0641 - val_acc: 0.9832 - val_loss: 0.1014
Epoch 28/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9857 - loss: 0.0597 - val_acc: 0.9839 - val_loss: 0.1153
Epoch 29/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 9ms/step - acc: 0.9864 - loss: 0.0586 - val_acc: 0.9834 - val_loss: 0.1218
Epoch 30/30
[1m1875/1875[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 8ms/step - acc: 0.9865 - loss: 0.0571 - val_acc: 0.9829 - val_loss: 0.1139





<keras.src.callbacks.history.History at 0x246de80ac70>
predictions = model.predict(X_test)
predictions = np.argmax(predictions, axis=1)
predictions
[1m313/313[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step  





array([7, 2, 1, ..., 4, 5, 6])
obraz, obszary = plt.subplots(ncols=10,nrows=4, sharex=False,
             sharey=True, figsize=(20, 14))
for i in range(40):
    obraz.axes[i].set_title(predictions[i])
    obraz.axes[i].imshow(X_test[i], cmap='gray')
    obraz.axes[i].get_xaxis().set_visible(False)
    obraz.axes[i].get_yaxis().set_visible(False)
plt.show()

png png