I719 Fundamentals of Python/lecture6
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()
