I719 Fundamentals of Python/lecture6

Lists and Arrays

TASK 1

Write a function that makes all characters in a list uppercase!

call it with the following list, and print the result

['cat', 'dog']

Solution: Imperative

You must know how to do this solution

def uppercase_list(l):
    result = []
    for i in l:
        result.append(i.upper())
    return result

Solution: Functional

def uppercase_list(l):
    return list(map(lambda i: i.upper(), l))

Solution: Modern

def uppercase_list(l):
    return [i.upper() for i in l]

TASK 2

Write a function that makes all character in the values of a dict uppercase!

call it with the following dict and print the results.

{'cat': 'kass', 'dog': 'koer'}

Solution: Imperative

You need to understand how this solution works

def uppercase_dict_values(d):
    result = {}
    for k, v in d.items():
        result[k] = v.upper()
    return result

Solution: Modern

def uppercase_dict_values(d):
    return {k: v.upper() for k, v in d.items()}

TASK 3

write a function that filters out numbers divisible by 3 and 5
call it with range(0, 30)

Solution: Imperative

def exclude_divisible_by_3_and_5(l):
    result = []
    for i in l:
        if not (i % 3 == 0 or i % 5 == 0):
            result.append(i)
    return result

Solution: Functional

def is_not_divisible_by_3_and_5(n):
    if not (n % 3 == 0 or n % 5 == 0):
        return True
    else:
        return False

def exclude_divisible_by_3_and_5(l):
    result = filter(is_not_divisible_by_3_and_5, l)
    return list(result)

Slicing

In [1]: a = "Hello World"

In [2]: a[:2]
Out[2]: 'He'

In [3]: a[2:]
Out[3]: 'llo World'

In [4]: a[-2:]
Out[4]: 'ld'

In [5]: a[:-2]
Out[5]: 'Hello Wor'

In [6]: a[:-2:2]
Out[6]: 'HloWr'

In [7]: a[::2]
Out[7]: 'HloWrd'

In [8]: a[::-1]
Out[8]: 'dlroW olleH'

TASK 4

write a function that sorts numbers in a list by the smallest digit of the integer.
i.e.

my_list = [11, 10, 9, 177, 199]
my_sort_function(my_list)
[10, 11, 177, 9, 199]

Solution

def reverse_digit_order(n):
    """Revere the order of the digits in a number"""
    return str(n)[::-1]


def sort_list_by_smallest_digit(l):
    return sorted(l, key=reverse_digit_order)

Interlude: Packages and Requirements

python application often include a requirements.txt that lists pip dependencies

python packages are formatted as

package_name/
    package_name/
        __init__.py
    setup.py

and setup.py include information on depedencies
see: https://github.com/pypa/sampleproject/blob/master/setup.py

Numpy

• it is faster
• it allows multidimensional arrays
• meant for math

Basics

import numpy as np

Matrix multiplication

a 2x2 matrix by a 2x1 column vector

[[1, 2]  . [[1],  = [[1 * 1 + 2 * 2], = [[5],
 [3, 4]]    [2]]     [3 * 1 + 4 * 2]]    [11]]

Results in a 2x1 column vector

in numpy:

In [2]: A = np.array([[1,2],[3,4]])

In [3]: V = np.array([[1],[2]])

In [4]: A.dot(V)
Out[4]:
array([[ 5],
       [11]])

Solve system of linear equations

x - y = 3
7x - y = -3

becomes in matrices
(see how: https://www.khanacademy.org/math/precalculus/precalc-matrices/solving-equations-with-inverse-matrices/v/matrix-equations-systems)

[[1, -1]  . [[x],  = [[3],
 [7, -1]]    [y]]     [-3]]

This can go back to the original equations with variables:

[[1, -1]  . [[x],  = [[1x + -1y], = [[3],
 [7, -1]]    [y]]     [7x + -1y]]    [-3]]

Solving in numpy

In [68]: a = np.array([[1, -1],[7, -1]])

In [69]: a
Out[69]:
array([[ 1, -1],
       [ 7, -1]])

In [70]: b = np.array([[3],[-3]])

In [71]: b
Out[71]:
array([[ 3],
       [-3]])

In [72]: a_inverse = np.linalg.inv(a)

In [73]: a_inverse
Out[73]:
array([[-0.16666667,  0.16666667],
       [-1.16666667,  0.16666667]])

In [74]: a_inverse.dot(b)
Out[74]:
array([[-1.],
       [-4.]])

Task 3

solve for x,y, and z

x + y + z = 6
2y + 5z = -4
2x + 5y - z = 27

Pandas

https://blockchain.info/charts/market-price?timespan=1year

download the csv

Open the the csv with pandas

import pandas as pd
btc_price = pd.read_csv('~/Downloads/market-price.csv', names=['date', 'usd'], parse_dates=[0])

TASKS

show only days where price is above 1000USD

In [4]: btc_price[btc_price.usd > 1000]

How many days was the price above 1000USD?

len(btc_price[btc_price.usd > 1000])

What was the price 6 months ago?

In [3]: import datetime as dt

In [4]: dt.datetime.utcnow() - dt.timedelta(days=365/2)
Out[4]: datetime.datetime(2016, 9, 8, 5, 12, 52, 204106)

In [5]: datetime_to_check = dt.datetime.utcnow() - dt.timedelta(days=365/2)

In [6]: date_to_check = datetime_to_check.date()

In [7]: date_to_check
Out[7]: datetime.date(2016, 9, 8)
...
In [9]: btc_price[btc_price.date == date_to_check]
Out[9]:
      datetime         usd
184 2016-09-08  627.777875

What was the average price last year?

[10]: btc_price.usd.mean()

What was the average price in august?

In [27]: august_end = dt.date(month=9, year=2016, day=1)

In [28]: august_start = dt.date(month=8, year=2016, day=1)

In [29]: btc_price[(august_start <= btc_price.date) & (btc_price.date < august_end)]
Out[29]:
          date         usd
146 2016-08-01  606.322343
...
176 2016-08-31  574.827937

In [30]: btc_price[(august_start <= btc_price.date) & (btc_price.date < august_end)].mean()
Out[30]:
usd    579.744405
dtype: float64

Plot the days on a line graph

import matplotlib.pyplot as plt
import pandas as pd

btc_price = pd.read_csv('~/Downloads/market-price.csv', names=['date', 'usd'], parse_dates=[0])



x = btc_price.date
y = btc_price.usd
plt.plot(x, y)

plt.xlabel('Date')
plt.ylabel('USD')
plt.title('BTC to USD')
plt.grid(True)
plt.show()

More information on Data Science in python

see https://github.com/jakevdp/PythonDataScienceHandbook/