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class Sample:
    def __init__(self, x, t):
        self.location = x
        self.time = t

    def __repr__(self):
        return f"Sample({self.location}, {self.time})"


class AlphaBetaFilter:

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class Sample:
    def __init__(self, x, t):
        self.location = x
        self.time = t

    def __repr__(self):
        return f"Sample({self.location}, {self.time})"

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import numpy as np
pie_x = np.linspace(0, np.pi * 3, 50)
t_loc = np.sin(pie_x)
error_loc = t_loc * (np.random.randint(950, 1050, 50) / 1000.0)  # creates error in data
samples = [Sample(loc, t) for t, loc in enumerate(error_loc)]  # create samples list

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tracker = AlphaBetaFilter(samples[0], alpha=0.85, beta=0.5, velocity=0.5)  # initiate a tracker
for sample in samples[1:]:
    tracker.add_sample(sample)  # tracking in "real time"

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import sqlite3
import pandas as pd
with sqlite3.connect('example.db') as conn:
    cursor = conn.cursor()

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fields = "id, street, size, number_of_rooms, parking, price"
query = f"create TABLE HOME_PRICES ({fields})"
cursor.execute(query)

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houses = [
    (1, 'first', 110, 4, 1, 100000),
    (2, 'second', 90, 3, 0, 65000),
    (3, 'second', 90, 3, 1, 72000)
]
cursor.executemany('insert into HOME_PRICES values (?, ?, ?, ?, ?, ?)', houses)
#cursor.executemany receives an iterable for multiple executions

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with sqlite3.connect('example.db') as conn:
    cursor = conn.cursor()
    
    # Changed inside the Database
    
    conn.commit()

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with sqlite3.connect('example.db') as conn:
    df = pd.read_sql("select * from HOME_PRICES", conn)
    print(df)

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with sqlite3.connect('example.db') as conn:

    # creating a cursor
    cursor = conn.cursor()

    # reading your own data
    df = pd.read_csv("your_csv.csv")

    # inserting data
    rows = [row for name, row in df.iterrows()]