Summary Python courses programming for economists

Programming for economists – Python courses
Introduction to Python

Types of variables

Int = integer: gehele getallen
Float = floating point: kommagetallen
Str = string: tekst -> “”
Bool = boolean: True or False

Check the type: type(variable)
Convert values into any type: str(), int(), etc.


Pyhton Lists
List = [[a, “a”], [b, “b”], [c, “c”]]

Subsetting lists
List = [0,1,2,3,4,5]
list[-1] : last element of the index = [5]
list[3:5] -> [3,4]  [start=inclusive:end=exclusive]

Slicing
list[3:]  [3,4,5]
list[:3]  [0,1,2]
print(list[1] + list[3])  4

list2 = [[1,2,3],[3,4,5],[6,7,8]]
x[2][:2]  [6,7]

Adding and removing elements
list_ext = list + [6,7]
del(list_ext[0]) #note: all indexes change immediatly after
list_ext[1]= “a” this call, so ‘1’ is now [0] and ‘2’ is [1]
 [1,a,3,4,5,6,7]

If you want to make a copy of list ‘list_copy’ and prevent that changes
in this list also affect ‘list’  list_copy = list(list)

Functions and packages for Python Lists
max(), min(), mean(), median(), len(), type()
round(number, ndigits) e.g. round(7.27,1)  7.3
complex([real[,imag]])
sorted(iterable, reverse = True)
 return a new sorted list from the items in iterable
(reverse=True, big -> small)
list.index(), list.count(), list.append(), list.remove(),
list.reverse()  reverses the order of the elements in the list
str.capitalize(), str.replace(), str.upper()
Numpy (Numeric Python)

,Import numpy as np
! Numpy arrays: contain only one type (int,floats, etc.)
! If you call: numpy_array + numpy_array it sums the firts element of
the first list with the first element of the second list
e.g. list = [1,2,3] ; np_array = np.array([1,2,3])
* list + list  [1,2,3,1,2,3]
* np_array + np_array  [2,4,6]

Numpy subsetting
np_array = np.array([1,2,3,4,5])
np_array > 2  array([False, False, True, True, True])
np_array[np_array>2]  array([3,4,5])

2D Numpy Arrays
Multiple rows/columns: np_2d.shape=4,2 means 4 rows w/ 2 elements each

np_2d[0][2] or np_2d[0,2] selects the third element of the first row
np_2d[:,1:3] selects the second and third element of every row
np_2d[1,:] selects only the entire second row

Basic statistics
np.mean(np_array)
np.median(np_array)
np.std(np_array)
np.corrcoef(np_array)

set an index to a np_array:
e.g. np_positions[‘GK’, ‘M’, ‘A’]
np_heights[191,184,185,…,179]

* gk_heights = np_heights[np_positions == ‘GK’]
This call prints the heights of the goalkeepers (GK)

* gk_heights = np_heights[np_positions != ‘GK’]
This call prints the heights of all other players then GK

, Intermediate Python

Matplotlib
Import matplotlib.pyplot as plt

plt.plot(x,y) ; plt.scatter()
plt.show()

put the x-axis on a log scale: plt.xscale(‘log’)

Histogram

plt.hist(x, bins=#, range=None, normed=False/True, weights=None)
plt.show()

plt.clf(): cleans up so that you can start fresh -> after plt.show()

Customization

plt.xlabel(‘’)
plt.ylabel(‘’)
plt.title(‘’)
plt.yticks([0,2,4])
plt.xticks([])

Add more data: y = [ , , ] + y ; x = [ , , ] + x

plt.scatter(x,y, s= np.array(), c = list, alpha = )
s = size (array)
c = colour (list/array)
alpha = transparancy

plt.grid(True)  gridlines are shown in the plot

Dictionaries in Python

create a dictionary: dict = { ‘’:{‘’: ,‘’: ,‘’:} }
data sorted in alphabetical order
if data belongs together europe = {’spain’:’madrid’, ‘france’:’paris’}
add data to dictionary: europe[‘italy’] = ‘rome’
dict[‘key’] = ‘value’  {‘key’:’value’}
check if it’s added: print(‘italy’in europe)-> True
delete: del(europe[‘australia’])

Dictionary of dictionaries:
europe = {‘spain’:{‘cap’:’madrid’,’pop’:46.77}, ‘france’:
{‘cap’:’paris’,’pop’:66.03}}

Create sub-dict data: (1) data = {‘capital’:’rome’, ‘population’:59,85}
(2) europe[‘italy’] = data

Select elements: europe[‘spain’][‘pop’]  46.77