# I719 Fundamentals of Python/lecture6

## Contents

# 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()
```