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= Lists and Arrays =
= Lists and Arrays =


== TASK 1 ==
== Part 1: List manipulation ==


Write a function that makes all characters in a list uppercase!
Please see, you should know how to do these things!<br />
https://wiki.itcollege.ee/index.php/I719_Fundamentals_of_Python/lists


call it with the following list, and print the result
<pre>['cat', 'dog']</pre>
=== Solution: Imperative ===
You must know how to do this solution
<source lang="python">def uppercase_list(l):
    result = []
    for i in l:
        result.append(i.upper())
    return result</source>
=== Solution: Functional ===
<source lang="python">def uppercase_list(l):
    return list(map(lambda i: i.upper(), l))</source>
=== Solution: Modern ===
<source lang="python">def uppercase_list(l):
    return [i.upper() for i in l]</source>
== 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.
<pre>{'cat': 'kass', 'dog': 'koer'}</pre>
=== Solution: Imperative ===
You need to understand how this solution works
<source lang="python">def uppercase_dict_values(d):
    result = {}
    for k, v in d.items():
        result[k] = v.upper()
    return result</source>
=== Solution: Modern ===
<source lang="python">def uppercase_dict_values(d):
    return {k: v.upper() for k, v in d.items()}</source>
== TASK 3 ==
write a function that filters out numbers divisible by 3 and 5<br />
call it with <code>range(0, 30)</code>
=== Solution: Imperative ===
<source lang="python">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</source>
=== Solution: Functional ===
<source lang="python">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)</source>
== Slicing ==
<source lang="python">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'</source>
== TASK 4 ==
write a function that sorts numbers in a list by the smallest digit of the integer.<br />
i.e.
<source lang="python">my_list = [11, 10, 9, 177, 199]
my_sort_function(my_list)
[10, 11, 177, 9, 199]</source>
=== Solution ===
<source lang="python">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)</source>
= Interlude: Packages and Requirements =
= Interlude: Packages and Requirements =


Line 122: Line 18:
and setup.py include information on depedencies<br />
and setup.py include information on depedencies<br />
see: https://github.com/pypa/sampleproject/blob/master/setup.py
see: https://github.com/pypa/sampleproject/blob/master/setup.py
=== NOTE ===
Numpy and Pandas will not be tested on and are not required to know to continue in the class. These packages will only be used in this lecture


= Numpy =
= Numpy =
Line 133: Line 33:
<code>import numpy as np</code>
<code>import numpy as np</code>


== Matrix multiplication ==
a 2x2 matrix by a 2x1 column vector
<pre>[[1, 2]  . [[1],  = [[1 * 1 + 2 * 2], = [[5],
[3, 4]]    [2]]    [3 * 1 + 4 * 2]]    [11]]</pre>
Results in a 2x1 column vector
in numpy:
<source lang="python">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]])</source>
== Solve system of linear equations ==
== Solve system of linear equations ==


<pre>x - y = 3
<pre>x - y = 3
7x - y = -3</pre>
7x - y = -3</pre>
becomes in matrix multiplaction
becomes in matrices<br />
(see how: https://www.khanacademy.org/math/precalculus/precalc-matrices/solving-equations-with-inverse-matrices/v/matrix-equations-systems)
 
<pre>[[1, -1]  . [[x],  = [[3],
[7, -1]]    [y]]    [-3]]</pre>
This can go back to the original equations with variables:
 
<pre>[[1, -1]  . [[x],  = [[1x + -1y], = [[3],
[7, -1]]    [y]]    [7x + -1y]]    [-3]]</pre>
=== Solving in numpy ===


<pre>A = [[1, -1]  . [[x],  = [[3],
    [7, -1]]    [y]]    [-3]]</pre>
<pre>In [68]: a = np.array([[1, -1],[7, -1]])
<pre>In [68]: a = np.array([[1, -1],[7, -1]])


Line 182: Line 107:


<source lang="python">import pandas as pd
<source lang="python">import pandas as pd
btc_price = pd.read_csv('~/Downloads/market-price.csv', names=['datetime', 'usd'], parse_dates=[0])</source>
btc_price = pd.read_csv('~/Downloads/market-price.csv', names=['date', 'usd'], parse_dates=[0])</source>
== TASKS ==
== TASKS ==


=== show only days where price is above 1000USD ===
=== show only days where price is above 1000USD ===


<source lang="python">In [4]: btc_price[btc_price.usd > 1000]
</source>
=== How many days was the price above 1000USD? ===
=== How many days was the price above 1000USD? ===


<source lang="python">len(btc_price[btc_price.usd > 1000])</source>
=== What was the price 6 months ago? ===
=== What was the price 6 months ago? ===


<source lang="python">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
</source>
=== What was the average price last year? ===
=== What was the average price last year? ===


<source lang="python">[10]: btc_price.usd.mean()</source>
=== What was the average price in august? ===
=== What was the average price in august? ===


<source lang="python">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</source>
=== Plot the days on a line graph ===
=== Plot the days on a line graph ===


Line 200: Line 161:
import pandas as pd
import pandas as pd


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






x = btc_price.datetime
x = btc_price.date
y = btc_price.usd
y = btc_price.usd
plt.plot(x, y)
plt.plot(x, y)
Line 213: Line 174:
plt.grid(True)
plt.grid(True)
plt.show()</source>
plt.show()</source>
== More information on Data Science in python ==
see https://github.com/jakevdp/PythonDataScienceHandbook/

Latest revision as of 19:33, 9 March 2017

Lists and Arrays

Part 1: List manipulation

Please see, you should know how to do these things!
https://wiki.itcollege.ee/index.php/I719_Fundamentals_of_Python/lists

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

NOTE

Numpy and Pandas will not be tested on and are not required to know to continue in the class. These packages will only be used in this lecture

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/

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