Review exercise – growth rates¶
$$\text{FV} = \text{PV} (1 + i)^t$$
In [1]:
PV = 1e4 # Scientific notation for 10000
FV = 47123
t = 10
In [ ]:
NoneType and bool¶
NoneType¶
In [2]:
my_var = None
In [3]:
type(my_var)
Out[3]:
In [4]:
# Nothing is displayed!
my_var
In [5]:
# But it can be printed.
print(my_var)
Pay attention to what happens here.
In [6]:
strange = print(15)
In [7]:
strange
In [8]:
print(strange)
bool
In [9]:
# Can display multiple values in one line
True, False
Out[9]:
In [10]:
type(True), type(False)
Out[10]:
In [11]:
int(True), int(False)
Out[11]:
In [12]:
# Equivalent to 3 + 1 - 0
3 + True - False
Out[12]:
Keywords¶
In [13]:
# Doesn't work
3 = 4
In [14]:
# Similarly, doesn't work
True = 14
In [15]:
# Works, but don't do it!
# Don't uncomment this, because it will ruin the demos later on.
# bool = "breaking the rules"
# bool
Quick Check 1¶
In [16]:
# A = str('None') + str('00')
In [17]:
# B = True * 8 - float(False) * 15
In [18]:
# C = int(None) - 1
Comparisons¶
In [19]:
# is age at least age_limit?
age_limit = 21
age = 17
age >= age_limit
Out[19]:
In [20]:
# is password_guess equal to true_password?
true_password = 'qwerty1093x!'
password_guess = 'QWERTY1093x!'
password_guess == true_password
Out[20]:
In [21]:
3 == 3
Out[21]:
In [22]:
'hello' != 'howdy'
Out[22]:
In [23]:
-3 > -2
Out[23]:
In [24]:
-3 < -2
Out[24]:
In [25]:
'alpha' >= 'beta'
Out[25]:
In [26]:
x = 5 # set x equal to 5
In [27]:
x == 5 # is x equal to 5?
Out[27]:
In [28]:
y = x == 5
y
Out[28]:
Comparing different types¶
In [29]:
17 == '17'
Out[29]:
In [30]:
'zebra' != True
Out[30]:
In [31]:
True == 1.0
Out[31]:
In [32]:
5 > True
Out[32]:
In [33]:
'alpha' >= 'beta'
Out[33]:
In [34]:
'alpha' >= 5
Floating point issues, revisited¶
In [35]:
0.1 * 2 == 0.2
Out[35]:
In [36]:
0.1 * 6
Out[36]:
In [37]:
0.1 * 6 == 0.6
Out[37]:
In [38]:
abs(0.1 * 6 - 0.6) < 0.0001
Out[38]:
String containment¶
In [39]:
'berkeley' in 'uc berkeley'
Out[39]:
In [40]:
'stanford' in 'uc berkeley'
Out[40]:
In [41]:
'berkeley' in 'UC BERKELEY'
Out[41]:
Quick Check 2¶
In [ ]:
In [ ]:
Boolean operators¶
In [42]:
year = 'junior'
units = 125
In [43]:
year_check = year == 'senior'
year_check
Out[43]:
In [44]:
units_check = units >= 120
units_check
Out[44]:
In [45]:
ready_to_grad = year_check and units_check
almost_ready = year_check or units_check
In [46]:
ready_to_grad
Out[46]:
In [47]:
units_check
Out[47]:
In [48]:
n = 12
(n % 2 == 0) and (n % 4 == 0)
Out[48]:
In [49]:
(n % 2 == 0) and not (n % 5 == 0)
Out[49]:
In [50]:
(n % 3 != 0) and (n % 4 != 0)
Out[50]:
In [51]:
True and False and True and True
Out[51]:
In [52]:
True or False or True or True
Out[52]:
In [53]:
3 < 4 <= 5
Out[53]:
In [54]:
3 < 4 > 2 < 11 > -1
Out[54]:
In [55]:
3 < 4 < 2 > 11 > -1
Out[55]:
Quick Check 3¶
In [ ]:
In [ ]:
Demo¶
The first cell contains code that's mostly copied from last lecture. Ignore it once again!
In [56]:
from datascience import *
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import numpy as np
data = Table.read_table('data/countries.csv')
data = data.relabeled('Country(or dependent territory)', 'Country') \
.relabeled('% of world', '%') \
.relabeled('Source(official or UN)', 'Source')
data = data.with_columns(
'Country', data.apply(lambda s: s[:s.index('[')].lower() if '[' in s else s.lower(), 'Country'),
'Population', data.apply(lambda i: int(i.replace(',', '')), 'Population'),
'%', data.apply(lambda f: float(f.replace('%', '')), '%')
)
def first_letter(s):
return s[0]
def last_letter(s):
return s[-1]
In [57]:
data
Out[57]:
Below, assign first_or_last to a string containing a single lowercase letter.
We'll look at the distribution of populations of countries whose names either begin or end with first_or_last.
In [58]:
first_or_last = 'a'
In [59]:
relevant_countries = data.where(data.apply(
# Focus on this part!
lambda name: first_letter(name) == first_or_last or last_letter(name) == first_or_last
, 'Country')).sort('Population', descending = True)
In [60]:
relevant_countries
Out[60]:
In [61]:
# Ignore everything except the last line!
plt.figure(figsize = (10, 7))
names = relevant_countries.column('Country')
pops = relevant_countries.column('Population')
if relevant_countries.num_rows > 15:
names = names[:15]
pops = pops[:15]
sns.barplot(x = pops, y = names, orient = 'h')
# Focus on this part!
plt.title('Populations of countries starting or ending with ' + first_or_last);
