# Exercise 01.1¶

Create a function that receives two inputs a and b, and returns the product of the a decimal of pi and the b decimal of pi.

i.e,
pi = 3.14159
if a = 2 and b = 4
result = 4 * 5
result = 20

Caveats:

• a and b are between 1 and 15
• decimals positions 1 and 2 are 1 and 4, respectively. (remember that python start indexing in 0)
In [ ]:
from math import pi
def mult_dec_pi(a, b):

result = ''
return result

In [ ]:
mult_dec_pi(a=2, b=4)
# 20.0

In [ ]:
mult_dec_pi(a=5, b=10)
# 45.0

In [ ]:
mult_dec_pi(a=14, b=1)
# 9.0

In [ ]:
mult_dec_pi(a=6, b=8)
# 10.0

In [ ]:
# Bonus
mult_dec_pi(a=16, b=4)
# 'Error'


# Exercise 01.2¶

Using the given dataset. Estimate a linear regression between Employed and GNP.

$$Employed = b_0 + b_1 * GNP$$

$$\hat b = (X^TX)^{-1}X^TY$$ $$Y = Employed$$ $$X = [1 \quad GNP]$$

In [13]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
# Import data
raw_data = """
Year,Employed,GNP
1947,60.323,234.289
1948,61.122,259.426
1949,60.171,258.054
1950,61.187,284.599
1951,63.221,328.975
1952,63.639,346.999
1953,64.989,365.385
1954,63.761,363.112
1955,66.019,397.469
1956,67.857,419.18
1957,68.169,442.769
1958,66.513,444.546
1959,68.655,482.704
1960,69.564,502.601
1961,69.331,518.173
1962,70.551,554.894"""

data = []
for line in raw_data.splitlines()[2:]:
words = line.split(',')
data.append(words)
data = np.array(data, dtype=np.float)
n_obs = data.shape[0]
plt.plot(data[:, 2], data[:, 1], 'bo')
plt.xlabel("GNP")
plt.ylabel("Employed")

Out[13]:
Text(0,0.5,'Employed')

# Exercise 01.3¶

Analyze the baby names dataset using pandas

In [7]:
import pandas as pd
import zipfile
with zipfile.ZipFile('../datasets/baby-names2.csv.zip', 'r') as z:
f = z.open('baby-names2.csv')

In [8]:
names.head()

Out[8]:
year name prop sex soundex
0 1880 John 0.081541 boy J500
1 1880 William 0.080511 boy W450
2 1880 James 0.050057 boy J520
3 1880 Charles 0.045167 boy C642
4 1880 George 0.043292 boy G620
In [9]:
names[names.year == 1993].head()

Out[9]:
year name prop sex soundex
113000 1993 Michael 0.024010 boy M240
113001 1993 Christopher 0.018572 boy C623
113002 1993 Matthew 0.017332 boy M300
113003 1993 Joshua 0.016268 boy J200
113004 1993 Tyler 0.014439 boy T460

### segment the data into boy and girl names¶

In [11]:
boys = names[names.sex == 'boy'].copy()
girls = names[names.sex == 'girl'].copy()


### Analyzing the popularity of a name over time¶

In [14]:
william = boys[boys['name']=='William']

plt.plot(range(william.shape[0]), william['prop'])
plt.xticks(range(william.shape[0])[::5], william['year'].values[::5], rotation='vertical')
plt.ylim([0, 0.1])
plt.show()

In [15]:
Daniel = boys[boys['name']=='Daniel']

plt.plot(range(Daniel.shape[0]), Daniel['prop'])
plt.xticks(range(Daniel.shape[0])[::5], Daniel['year'].values[::5], rotation='vertical')
plt.ylim([0, 0.1])
plt.show()


# Exercise 01.3¶

Which has been the most popular boy name every decade?

# Exercise 01.4¶

Which has been the most popular girl name?

# Exercise 01.5¶

What is the most popular new girl name? (new is a name that appears only in the 2000's)