# this usually comes from an internet resource
product = {
    "name": "MoccaMaster",
    "price": 139,
    "rating": 5,
    "country": "Netherlands",
    "manufacture_date": 2020
    }

# we usually don't print the whole dictionary
# because it looks a bit strange to users
# print(product)

# printing individual info
print(product['name'])
print(f"{product['price']} €")

actors = ["Elijah Wood", "Orlando Bloom", "Viggo Mortensen"]

# another example, a dictionary with a list
movie = {
    "name": "Lord of the rings: Two Towers",
    "year": 2002,
    "genre": "Fantasy",
    "length_minutes": 223,
    "actors": actors
    }

print(movie['name'])

# because movie is a dictionary, and actors is a list
# we need two sets of brackets
# example, 3rd actor in the list
print(movie['actors'][2])

# NEW FILE

# let's make a list of three dictionaries
p1 = {"name": "Coffee machine", "price": 119}
p2 = {"name": "Vacuum Cleaner", "price": 79}
p3 = {"name": "Tumbledryer", "price": 399}

all_products = [p1, p2, p3]

# get the price of the second product
# index 1 = 2nd
price_of_second = all_products[1]["price"]
print(price_of_second)
print()

# a basic loop that prints out the names and prices
# one by one, starting from first product
for p in all_products:
    print(p['name'])
    print(p['price'] * 1.24)
    print()

# NEW FILE, FUNCTIONS AND LAMBDAS

# function definitions are usually put in the top of the file
# or even better, in their own file
def triple_value(number):
    result = number * 3
    return result


def add_tax(price):
    result = price * 1.24
    result = round(result, 1)
    return result


# calling your own functions is like calling any other function in Python
tripled = triple_value(250)
print(tripled)

original_price = 356;
price_with_tax = add_tax(original_price)
print(price_with_tax)

# lambda is an anonymous function
# a shorthand for typical function
# useful for simple calculations
doubler = lambda x: x * 2
result = doubler(1500)

print(result)

even_test = lambda x : x % 2 == 0
print(even_test(444))

# NEW FILE

import numpy as np
import pandas as pd

# separate list for labels and values
labels = ["First", "Second", "Third", "Fourth"]
numbers = [10, 20, 40, 70]
numbers_np = np.array(numbers)

# make a series
ser1 = pd.Series(numbers_np, labels)

# you can also use a dictionary
measurements = {
    "Mon": 4,
    "Tue": 10,
    "Wed": -5,
    "Thu": -3,
    "Fri": 8,
    "Sat": 15,
    "Sun": -15
    }

# make a series
ser2 = pd.Series(measurements)

# some country codes

# if you have partially similar series, and you add them together
# unique values be NaN = not a number
country_names = ['Finland', 'Sweden', 'Norway']
country_numbers = [1, 2, 3]
#ser3 = pd.Series([1, 2, 3], ['Finland', 'Sweden', 'Norway'])
ser3 = pd.Series(country_numbers, country_names)

country_names = ['Finland', 'Sweden', 'Denmark']
country_numbers = [1, 2, 4]
ser4 = pd.Series(country_numbers, country_names)

#print(ser3['Sweden'])

combined = ser3 + ser4

# NEW FILE

import numpy as np
import pandas as pd
from numpy.random import randn

np.random.seed(123)

labels_1 = ['A', 'B', 'C', 'D']
labels_2 = ['X', 'Y', 'Z']

df = pd.DataFrame(randn(4, 3), labels_1, labels_2)

# this is the recommended way
print(df['Y'])

# this can create strange bugs if your
# column name overlaps with a function name
# print(df.Y)

# if you want more than column at the same time
# provide extra set of brackets
print(df[['Y', 'Z']])

# one of the most useful features in pandas
# you can create additional columns to help
# make new calculations!
# here we combine three columns and round to three decimals
df['total'] = round(df['X'] + df['Y'] + df['Z'], 3)

# the basic way to drop a column
#df = df.drop('Y', axis=1)
#df = df.drop('C')

# df.drop('Y', axis=1, inplace=True)
row = df.loc['B']
last_row = df.iloc[3]

sub_values = df[df>0]

# this is very useful with real datasets
filtered = df[df['Y'] > 0]

print()
print(filtered['total'])

# one of the most important features in pandas filtering
complex_filter = df[(df['Y'] > 0) | (df['Z'] > 0)]

# this is useful sometimes when you want to work
# with numeric indeces instead of text indeces
df = df.reset_index()

# you can do this as well, not very often used
# with real datasets though
newindeces = 'FI SE EN NO'.split()
df['Languages'] = newindeces
df= df.set_index('Languages')

# NEW FILE