Python_basics
Python Basics¶
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3 + 2 + 1 – 5 + 4 % 2 – 1 / 4 + 6
st1 = ‘Anything “yes”‘
print(st1)
Anything “yes”
Tuples and Lists¶
tuple_months = (‘January’,’February’,’March’,’April’,’May’,’June’,\
‘July’,’August’,’September’,’October’,’November’,’December’)
tuple_months
(‘January’,
‘February’,
‘September’,
‘October’,
‘November’,
‘December’)
list_cats = [‘Tom’, ‘Snappy’, ‘Kitty’, ‘Jessie’, ‘Chester’]
[‘Tom’, ‘Snappy’, ‘Kitty’, ‘Jessie’, ‘Chester’]
print(list_cats[2])
list_cats.append(‘Catherine’)
[‘Tom’, ‘Snappy’, ‘Kitty’, ‘Jessie’, ‘Chester’, ‘Catherine’]
del list_cats[1]
[‘Tom’, ‘Kitty’, ‘Jessie’, ‘Chester’, ‘Catherine’]
list_cats[1:-2]
[‘Kitty’, ‘Jessie’]
list_cats[:-2]
[‘Tom’, ‘Kitty’, ‘Jessie’]
my_set = {1, 2, 3}
your_set = {4, 2, 5}
my_set | your_set
{1, 2, 3, 4, 5}
my_set – your_set
my_set & your_set
Dictionaries¶
CO2_by_year = {1799:1, 1800:70, 1801:74, 1802:82, 1902:215630, 2002:1733297}
# Look up the emissions for the given year
CO2_by_year[1801]
# Add another year to the dictionary
CO2_by_year[1950] = 734914
CO2_by_year
1902: 215630,
2002: 1733297,
1950: 734914}
CO2_by_year[2009] = 1000000
CO2_by_year[2000] = 100000
import numpy as np
CO2_by_year[2012] = np.nan
CO2_by_year
1902: 215630,
2002: 1733297,
1950: 734914,
2009: 1000000,
2000: 100000,
2012: nan}
1950 in CO2_by_year
1951 in CO2_by_year
len(CO2_by_year)
del CO2_by_year[1950]
len(CO2_by_year)
for key in CO2_by_year:
print(key)
for k in CO2_by_year.keys():
for v in CO2_by_year.values():
CO2_by_year.values()
dict_values([1, 70, 74, 82, 215630, 1733297, 1000000, 100000, nan])
for key, value in CO2_by_year.items():
print(key, value)
1902 215630
2002 1733297
2009 1000000
2000 100000
Control flow statements¶
range(0, 5)
list(range(5))
[0, 1, 2, 3, 4]
for n in range(5):
v.append(n**2)
[0, 1, 4, 9, 16]
Conditional statements
if val > 10:
print(“Large value”)
elif val >= 0:
print(“Small value”)
print(“Negative value”)
Small value
Functions¶
def convert_to_celsius(fahrenheit):
”’ (number) -> number
Return the celsius degrees equivalent to
fahrenheit degrees.
celsius = (fahrenheit – 32) * 5 / 9
return celsius
convert_to_celsius?
convert_to_celsius(32)
convert_to_celsius(212)
convert_to_celsius(-40)
def convert_to_kelvin(fahrenheit):
”’ (number) -> number
Return the number of kelvin degrees equivalent
to fahrenheit degrees.
kelvin = convert_to_celsius(fahrenheit) + 273.15
return kelvin
convert_to_kelvin(32)
import fibo
import fibo as fb
ar = fibo.fib2(4)
[1, 1, 2, 3]
List Comprehension¶
$S = \{x^2 : x ~ \mbox{in} ~ \{0 … 9\}\}$
range(0, 10)
list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
S = [x**2 for x in range(10)]
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
$V = (1, 2, 4, 8, \ldots , 2^{12})$
V = [2**i for i in range(13)]
[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]
$M = \{ x ~ | ~ x ~ {\rm in} ~ S ~ {\rm and} ~ x ~ {\rm even} \}$
M = [x for x in S if x % 2 == 0]
[0, 4, 16, 36, 64]
noprimes = [j for i in range(2, 8) for j in range(i*2, 50, i)]
primes = [x for x in range(2, 50) if x not in noprimes]
print(primes)
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]
words = ‘The quick brown fox jumps over the lazy dog’.split()
print(words)
[‘The’, ‘quick’, ‘brown’, ‘fox’, ‘jumps’, ‘over’, ‘the’, ‘lazy’, ‘dog’]
stuff = [[w.upper(), w.lower(), len(w)] for w in words]
[[‘THE’, ‘the’, 3],
[‘QUICK’, ‘quick’, 5],
[‘BROWN’, ‘brown’, 5],
[‘FOX’, ‘fox’, 3],
[‘JUMPS’, ‘jumps’, 5],
[‘OVER’, ‘over’, 4],
[‘THE’, ‘the’, 3],
[‘LAZY’, ‘lazy’, 4],
[‘DOG’, ‘dog’, 3]]
for i in stuff:
[‘THE’, ‘the’, 3]
[‘QUICK’, ‘quick’, 5]
[‘BROWN’, ‘brown’, 5]
[‘FOX’, ‘fox’, 3]
[‘JUMPS’, ‘jumps’, 5]
[‘OVER’, ‘over’, 4]
[‘THE’, ‘the’, 3]
[‘LAZY’, ‘lazy’, 4]
[‘DOG’, ‘dog’, 3]
stuff = map(lambda w: [w.upper(), w.lower(), len(w)], words)
for i in stuff:
[‘THE’, ‘the’, 3]
[‘QUICK’, ‘quick’, 5]
[‘BROWN’, ‘brown’, 5]
[‘FOX’, ‘fox’, 3]
[‘JUMPS’, ‘jumps’, 5]
[‘OVER’, ‘over’, 4]
[‘THE’, ‘the’, 3]
[‘LAZY’, ‘lazy’, 4]
[‘DOG’, ‘dog’, 3]
def ls_comp(ww):
return ([[w.upper(), w.lower(), len(w)] for w in ww])
stuff1 = ls_comp(words)
[[‘THE’, ‘the’, 3],
[‘QUICK’, ‘quick’, 5],
[‘BROWN’, ‘brown’, 5],
[‘FOX’, ‘fox’, 3],
[‘JUMPS’, ‘jumps’, 5],
[‘OVER’, ‘over’, 4],
[‘THE’, ‘the’, 3],
[‘LAZY’, ‘lazy’, 4],
[‘DOG’, ‘dog’, 3]]
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