Python-first steps

"""
Sources: https://learn.udacity.com/courses/ud170/
"""


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
def construct_plot(dx = range(-10, 11)):
    print(f'dx: {dx}')
    dy = [10 ** i for i in dx]
    print(f'dy: {dy}')

    plt.scatter(dx, dy)
    plt.xlabel('axa X')
    plt.ylabel('axa Y')
    plt.title('plot demo')
    print('Afisare  plot in SciView')
    # plt.show()

    print('Salvare imagine in format .jpg')
    current_time = datetime.datetime.now().strftime("%Y-%m-%d_%H_%M_%S")
    print(f'Data curenta: {current_time}')
    plt.savefig('./grafice/' + current_time + '.jpg')

def dictionaries():
    dic={'a':['mare', 'soare', 4], 'b':[10, 30, 40]}
    print(f'Intregul dictionar: {dic}')
    list_Key =list(dic.keys())
    print(f"cheia de la indxul 0: {list_Key[0]}")
    print(f"valorile de la cheia a si de index 2 dictionar: {dic['a'][1]}")
    print("*********************************************")
    print("Adaugarea unei valori  la valorile cheii 'a'")
    dic['a'].append("new value")
    print(f'noul dictionar: {dic}')
    print("*********************************************")
    print("inserare pe o pozitie dorita in b: ")
    dic['b'].insert( 1, 4)
    v = list(dic.values())
    print(f'New values: {v[1]}')


def functii_numpy():
    list = [7, 1181, 5]
    print(list)
    print(f'Suma:{np.sum(list)}')
    print(f'length:{np.size(list)} ')
    print(f'Media: {np.mean(list)}')
    print(f'Deviatia standaard: {np.std(list)}')
    print(f'Min:{np.min(list)}')
    print(f'Max:{np.max(list)}')

def histograma():
    list=[20, 2, 67]
    plt.hist(list, bins=8)
    plt.show()

def numpy_function():
    array1 = np.array(['white','green', 'cyan', 'black', 'orange'])
    print(f'array generate from np.array: {array1}')
    print("******************* Accesare element ******************")
    print(f'[0:2]{array1[0:2]}')
    print(f'[:2]{array1[:2]}')
    print(f'[1:2]{array1[1:2]}')
    print(f'[2:]{array1[2:]}')
    print(f'[:]{array1[:]}')

def numpy_element_types():
    print(np.array(['sun', 'clouds']).dtype) # >U6
    print(np.array([1, 2]).dtype)  # int32
    print(np.array([1.87, 2.33]).dtype) # float64
    print(np.array([True, False]).dtype) # bool


def multiplying():
    print(f'multilying in Pyuhon: {[1, 2]*4}')
    print(f'multilying in NUMPY: {np.array([1, 2])*4}')

def add():
    print(f'Add in Python: {[1, 2]+[10, 20]}')
    print(f'Add in NUMPY: {np.array([1, 2])+np.array([10, 20])}')

def arithmetic_opperation():
    a = np.array([3, 5, 7])
    b = np.array([2, 4, 2])

    print(a+b)
    print(a-b)
    print(a/b)
    print(a*b)
    print(a**b)
def arithmetic_opperation_scalar():
    a = np.array([3, 5, 7])
    b = 2

    print(a+b)
    print(a-b)
    print(a/b)
    print(a*b)
    print(a**b)

def logic():
    a = np.array([1, 2, 3, 4, 5])
    b = np.array([5, 4, 3, 2, 1])

    print(a > b)
    print(a < b)
    print(a == b)
    print(a == b)
    print(a != b)
def logic2():
    a = np.array([1, 2, 3, 4])
    b = 2

    print(a > b)
    print(a < b)
    print(a == b)
    print(a == b)
    print(a != b)



def index_arrays():
    a = np.array([1, 2, 3, 4])
    b = np.array([True, True, False, False])

    print(a[b])
    print(a[a>3])

def comp():
    a = np.array([3, 5, 7])
    b = np.array([2, 4, 2])
    a+=b
    print(a)

    a = np.array([3, 5, 7])
    b = np.array([2, 4, 2])
    a= a+b
    print(a)


def tablou_bidimensional():
    vector_bi = np.array([
        [1, 2, 3, 4, 5],
        [10, 20, 30, 40, 50],
        [100, 200, 300, 400, 500],
        [800, 900, 1000, 1100, 1200]])
    print(f'Intregul tablou bidimendional{vector_bi}\n')
    print(f'Preluarea elemntului de pe linia 1, coloana 2: {vector_bi[1, 2]}\n')
    print(f'Toate elementele de pe linia 1: {vector_bi[1, :]}\n')
    print(f'Liniile 1-->3, elementele de pe indexul 1-->2-1:\n {vector_bi[1:3, 1:2]}\n')


def op_aritmetice_array_bidimensionale():
    a = np.array([
        [1, 2],
        [3, 4]
    ])

    b = np.array([
        [10, 20],
        [30, 40]
    ])

    print(f'Adunarea matricilor:\n {a+b} ')
    print(f'Scaderea matricilor:\n {a-b} ')
    print(f'Inmultirea:\n {a*b} ')
    print(f'Inmultirea matricilor:\n {np.matmul(a,b)} ')


def dataFrame_function():
    tabel=pd.DataFrame(
        {
            'an': [2002, 2000],
            'varsta': [21, 23]
        }
    )
    print(tabel)
    print(f'Accesare inregistrare cu index:1:\n {tabel.loc[1]}')
    print(f'Accesare toate val:\n {tabel.loc[:]}')

def dataFrame_sum():
    tabel=pd.DataFrame(
        {
            'an': [2002, 2000],
            'varsta': [21, 23]
        }
    )
    print(f'{tabel.sum()}')
    print(tabel.values.sum())
    print(tabel.applymap(tabel.apply(np.mean)))

def serii():
    a = np.array([

        [[11, 12], [13, 14]],
        [[21, 22], [23, 24]]

    ])
    print(a)


def dimensiune_array():
    zerodimensional = np.array(1)
    unidimensional = np.array([1, 2, 3, 4])
    bidimensional = np.array([
        [1, 2, 3, 4],
        [5, 6, 7, 8]
    ])
    tridimensional = np.array([
        [
            [1, 2, 3, 4],
            [5, 6, 7, 8]],
        [
            [10, 20, 30, 40],
            [50, 60, 70, 80]]
    ])

    print(f'dimensiune1: {zerodimensional.ndim}')
    print(f'dimensiune2: {unidimensional.ndim}')
    print(f'dimensiune3: {bidimensional.ndim}')
    print(f'dimensiune4: {tridimensional.ndim}')

def ndim_func():
    multidimensiional = np.array([1,2], ndmin=5)
    print(multidimensiional)

    print(multidimensiional.ndim)

def concatenare():
    l = np.append(np.array([17, -9]), np.array([8]))
    print(l)

def conversie():
    a = np.array(['1', '2'], dtype='i')
    print(a)

    b = np.array([2, 4], dtype='S')
    print(b)

def copy_view():
    old = np.array([1, 2])
    new = old.copy()
    new[0]=100
    print("Folosire: copy()")
    print(old)
    print(new)

    new_view = old.view()
    new_view[0]=23
    print("\nFolosire: view()")
    print(new_view)
    print(old)

def shape():
    a = np.array([[1, 2], [2, 4]])
    print(a.shape)
    ne = a.reshape(-1)
    print(ne)

def iterare():
    arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

    for i in np.nditer(arr):
        print(i)

def concatenare():
    a = np.array([1, 2])
    b = np.array([3, 4])
    r = np.concatenate((a, b))
    print(r)

    v = np.stack((a, b), axis=1)


def split_hsplit():
    a = np.array([[1, 2],[3, 4],[5, 6], [7, 8]])
    print(np.array_split(a, 2))
    print(np.hsplit(a, 2))

def search():
    a = np.array([1, 2, 2, 5, 8, 2])
    rez = np.where(a%2 == 0)
    print(rez)

def sort_func():
    a = np.array([1, 'z', 10, 2, 'a',-1])
    v = np.sort(a)
    print(v)

def serii():
    a = [2.3, 3.9, 98.8]
    new = pd.Series(a, index=["inaltime", "masa", "others"])
    print(new)
    print(f'\ninaltime: {new["inaltime"]}')

def pd_to_string():
    data = {"a":[1, 2],
            "b": [3, 4]
            }
    a = pd.DataFrame(data)
    print(a)

    print(a.to_string())

pd_to_string()