I719 Fundamentals of Python/lecture6 - Revision history

Eroman at 17:33, 9 March 2017

2017-03-09T17:33:59Z

@@ Line 1: / Line 1: @@
 = 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 />
 = Interlude: Packages and Requirements =
@@ Line 122: / Line 18: @@
 and setup.py include information on depedencies<br />
 see: https://github.com/pypa/sampleproject/blob/master/setup.py
 = Numpy =

Eroman at 17:26, 9 March 2017

2017-03-09T17:26:51Z

@@ Line 133: / Line 133: @@
 <code>import numpy as np</code>
 == Solve system of linear equations ==
 <pre>x - y = 3
 x - y = -3</pre>
-becomes in matrix multiplaction
+becomes in matrices<br />
 <pre>In [68]: a = np.array([[1, -1],[7, -1]])
@@ Line 182: / Line 207: @@
 <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 ==
 === show only days where price is above 1000USD ===
 === How many days was the price above 1000USD? ===
 === What was the price 6 months ago? ===
 === What was the average price last year? ===
 === What was the average price in august? ===
 === Plot the days on a line graph ===
@@ Line 200: / Line 261: @@
 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
 plt.plot(x, y)
@@ Line 213: / Line 274: @@
 plt.grid(True)
 plt.show()</source>

Eroman: add solutions for part 1

2017-03-09T16:30:18Z

add solutions for part 1

@@ Line 8: / Line 8: @@
 <pre>['cat', 'dog']</pre>
 == TASK 2 ==
@@ Line 15: / Line 32: @@
 <pre>{'cat': 'kass', 'dog': 'koer'}</pre>
-== Review ==
+=== Solution: Imperative ===
-write a function that makes all characters in a list uppercase
+You need to understand how this solution works
-* using a loop
+<source lang="python">def uppercase_dict_values(d):
-* using map
+    result = {}
-* using a list comprehension
+    for k, v in d.items():
+        result[k] = v.upper()
-== Filtering ==
+    return result</source>
+=== Solution: Modern ===
 == TASK 3 ==
@@ Line 34: / Line 50: @@
 call it with <code>range(0, 30)</code>
 == Slicing ==
@@ Line 66: / Line 101: @@
 my_sort_function(my_list)
 [10, 11, 177, 9, 199]</source>
 = Interlude: Packages and Requirements =
@@ Line 88: / Line 132: @@
 <code>import numpy as np</code>
 == Solve system of linear equations ==

Eroman at 22:13, 8 March 2017

2017-03-08T22:13:58Z

← Older revision		Revision as of 01:13, 9 March 2017
Line 34:		Line 34:
	call it with <code>range(0, 30)</code>		call it with <code>range(0, 30)</code>

	= Slicing =		== Slicing ==

	<source lang="python">In [1]: a = "Hello World"		<source lang="python">In [1]: a = "Hello World"
Line 66:		Line 66:
	my_sort_function(my_list)		my_sort_function(my_list)
	[10, 11, 177, 9, 199]</source>		[10, 11, 177, 9, 199]</source>
	== Interlude: Packages and Requirements ==		= Interlude: Packages and Requirements =

	python application often include a <code>requirements.txt</code> that lists pip dependencies		python application often include a <code>requirements.txt</code> that lists pip dependencies
Line 79:		Line 79:
	see: https://github.com/pypa/sampleproject/blob/master/setup.py		see: https://github.com/pypa/sampleproject/blob/master/setup.py

	=~~= Why numpy =~~=		= Numpy =

	* it is faster		* it is faster

Eroman: add lecture 6

2017-03-08T22:12:54Z

add lecture 6

New page

= 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

<pre>['cat', 'dog']</pre>
== 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>
== Review ==

write a function that makes all characters in a list uppercase

* using a loop
* using map
* using a list comprehension

== Filtering ==

* using for loop and creating a new list
* using <code>filter</code> + callable (function or lambda)
* using a list comprehension

== TASK 3 ==

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

= 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>
== Interlude: Packages and Requirements ==

python application often include a <code>requirements.txt</code> that lists pip dependencies

python packages are formatted as

<pre>package_name/
package_name/
__init__.py
setup.py</pre>
and setup.py include information on depedencies<br />
see: https://github.com/pypa/sampleproject/blob/master/setup.py

== Why numpy ==

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

== Basics ==

<code>import numpy as np</code>

sqaure every element in list of integers<br />
square every element in array of integers

change shape of array

== Solve system of linear equations ==

<pre>x - y = 3
7x - y = -3</pre>
becomes in matrix multiplaction

<pre>A = [[1, -1] . [[x], = [[3],
[7, -1]] [y]] [-3]]</pre>
<pre>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.]])</pre>
== Task 3 ==

solve for x,y, and z

<pre>x + y + z = 6
2y + 5z = -4
2x + 5y - z = 27</pre>
== Pandas ==

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

download the csv

== Open the the csv with pandas ==

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

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

=== How many days was the price above 1000USD? ===

=== What was the price 6 months ago? ===

=== What was the average price last year? ===

=== What was the average price in august? ===

=== Plot the days on a line graph ===

<source lang="python">import matplotlib.pyplot as plt
import pandas as pd

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

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

plt.xlabel('Date')
plt.ylabel('USD')
plt.title('BTC to USD')
plt.grid(True)
plt.show()</source>