
"""
EXERCICE 1
---
- print() the last item from both the year and the pop list to see what the predicted population for the year 2100 is. Use two print() functions.
- Before you can start, you should import matplotlib.pyplot as plt. pyplot is a sub-package of matplotlib, hence the dot.
- Use plt.plot() to build a line plot. year should be mapped on the horizontal axis, pop on the vertical axis. Don't forget to finish off with the plt.show() function to actually display the plot.
"""
year = [1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100]
pop = [2.53, 2.57, 2.62, 2.67, 2.71, 2.76, 2.81, 2.86, 2.92, 2.97, 3.03, 3.08, 3.14, 3.2, 3.26, 3.33, 3.4, 3.47, 3.54, 3.62, 3.69, 3.77, 3.84, 3.92, 4.0, 4.07, 4.15, 4.22, 4.3, 4.37, 4.45, 4.53, 4.61, 4.69, 4.78, 4.86, 4.95, 5.05, 5.14, 5.23, 5.32, 5.41, 5.49, 5.58, 5.66, 5.74, 5.82, 5.9, 5.98, 6.05, 6.13, 6.2, 6.28, 6.36, 6.44, 6.51, 6.59, 6.67, 6.75, 6.83, 6.92, 7.0, 7.08, 7.16, 7.24, 7.32, 7.4, 7.48, 7.56, 7.64, 7.72, 7.79, 7.87, 7.94, 8.01, 8.08, 8.15, 8.22, 8.29, 8.36, 8.42, 8.49, 8.56, 8.62, 8.68, 8.74, 8.8, 8.86, 8.92, 8.98, 9.04, 9.09, 9.15, 9.2, 9.26, 9.31, 9.36, 9.41, 9.46, 9.5, 9.55, 9.6, 9.64, 9.68, 9.73, 9.77, 9.81, 9.85, 9.88, 9.92, 9.96, 9.99, 10.03, 10.06, 10.09, 10.13, 10.16, 10.19, 10.22, 10.25, 10.28, 10.31, 10.33, 10.36, 10.38, 10.41, 10.43, 10.46, 10.48, 10.5, 10.52, 10.55, 10.57, 10.59, 10.61, 10.63, 10.65, 10.66, 10.68, 10.7, 10.72, 10.73, 10.75, 10.77, 10.78, 10.79, 10.81, 10.82, 10.83, 10.84, 10.85]

# Print the last item from year and pop
print(year[-1])
print(pop[-1])

# Import matplotlib.pyplot as plt
import matplotlib.pyplot as plt

# Make a line plot: year on the x-axis, pop on the y-axis
plt.plot(year, pop)
plt.show()

2100
10.85


"""
EXERCICE 2
---
- Print the last item from both the list gdp_cap, and the list life_exp; it is information about Zimbabwe.
- Build a line chart, with gdp_cap on the x-axis, and life_exp on the y-axis. Does it make sense to plot this data on a line plot?
- Don't forget to finish off with a plt.show() command, to actually display the plot.
"""
gdp_cap = [974.5803384, 5937.029525999999, 6223.367465, 4797.231267, 12779.37964, 34435.367439999995, 36126.4927, 29796.04834, 1391.253792, 33692.60508, 1441.284873, 3822.137084, 7446.298803, 12569.85177, 9065.800825, 10680.79282, 1217.032994, 430.0706916, 1713.778686, 2042.09524, 36319.23501, 706.016537, 1704.063724, 13171.63885, 4959.114854, 7006.580419, 986.1478792, 277.5518587, 3632.557798, 9645.06142, 1544.750112, 14619.222719999998, 8948.102923, 22833.30851, 35278.41874, 2082.4815670000003, 6025.374752000001, 6873.262326000001, 5581.180998, 5728.353514, 12154.08975, 641.3695236000001, 690.8055759, 33207.0844, 30470.0167, 13206.48452, 752.7497265, 32170.37442, 1327.60891, 27538.41188, 5186.050003, 942.6542111, 579.2317429999999, 1201.637154, 3548.3308460000003, 39724.97867, 18008.94444, 36180.78919, 2452.210407, 3540.651564, 11605.71449, 4471.061906, 40675.99635, 25523.2771, 28569.7197, 7320.880262000001, 31656.06806, 4519.461171, 1463.249282, 1593.06548, 23348.139730000003, 47306.98978, 10461.05868, 1569.331442, 414.5073415, 12057.49928, 1044.770126, 759.3499101, 12451.6558, 1042.581557, 1803.151496, 10956.99112, 11977.57496, 3095.7722710000003, 9253.896111, 3820.17523, 823.6856205, 944.0, 4811.060429, 1091.359778, 36797.93332, 25185.00911, 2749.320965, 619.6768923999999, 2013.977305, 49357.19017, 22316.19287, 2605.94758, 9809.185636, 4172.838464, 7408.905561, 3190.481016, 15389.924680000002, 20509.64777, 19328.70901, 7670.122558, 10808.47561, 863.0884639000001, 1598.435089, 21654.83194, 1712.472136, 9786.534714, 862.5407561000001, 47143.17964, 18678.31435, 25768.25759, 926.1410683, 9269.657808, 28821.0637, 3970.095407, 2602.394995, 4513.480643, 33859.74835, 37506.41907, 4184.548089, 28718.27684, 1107.482182, 7458.396326999999, 882.9699437999999, 18008.50924, 7092.923025, 8458.276384, 1056.380121, 33203.26128, 42951.65309, 10611.46299, 11415.80569, 2441.576404, 3025.349798, 2280.769906, 1271.211593, 469.70929810000007]
life_exp = [43.828, 76.423, 72.301, 42.731, 75.32, 81.235, 79.829, 75.635, 64.062, 79.441, 56.728, 65.554, 74.852, 50.728, 72.39, 73.005, 52.295, 49.58, 59.723, 50.43, 80.653, 44.74100000000001, 50.651, 78.553, 72.961, 72.889, 65.152, 46.462, 55.322, 78.782, 48.328, 75.748, 78.273, 76.486, 78.332, 54.791, 72.235, 74.994, 71.33800000000001, 71.878, 51.57899999999999, 58.04, 52.947, 79.313, 80.657, 56.735, 59.448, 79.406, 60.022, 79.483, 70.259, 56.007, 46.388000000000005, 60.916, 70.19800000000001, 82.208, 73.33800000000001, 81.757, 64.69800000000001, 70.65, 70.964, 59.545, 78.885, 80.745, 80.546, 72.567, 82.603, 72.535, 54.11, 67.297, 78.623, 77.58800000000001, 71.993, 42.592, 45.678, 73.952, 59.443000000000005, 48.303, 74.241, 54.467, 64.164, 72.801, 76.195, 66.803, 74.543, 71.164, 42.082, 62.069, 52.906000000000006, 63.785, 79.762, 80.204, 72.899, 56.867, 46.859, 80.196, 75.64, 65.483, 75.53699999999999, 71.752, 71.421, 71.688, 75.563, 78.098, 78.74600000000001, 76.442, 72.476, 46.242, 65.528, 72.777, 63.062, 74.002, 42.568000000000005, 79.972, 74.663, 77.926, 48.159, 49.339, 80.941, 72.396, 58.556, 39.613, 80.884, 81.70100000000001, 74.143, 78.4, 52.517, 70.616, 58.42, 69.819, 73.923, 71.777, 51.542, 79.425, 78.242, 76.384, 73.747, 74.249, 73.422, 62.698, 42.38399999999999, 43.487]

# Print the last item of gdp_cap and life_exp
print(gdp_cap[-1])
print(life_exp[-1])

# Make a line plot, gdp_cap on the x-axis, life_exp on the y-axis
plt.plot(gdp_cap, life_exp)

# Display the plot
plt.show()

469.70929810000007
43.487


"""
EXERCICE 3
---
- Change the line plot that's coded in the script to a scatter plot.
- A correlation will become clear when you display the GDP per capita on a logarithmic scale. Add the line plt.xscale('log').
- Finish off your script with plt.show() to display the plot.
"""
# Change the line plot below to a scatter plot
plt.scatter(gdp_cap, life_exp)

# Put the x-axis on a logarithmic scale
plt.xscale('log')

# Show plot
plt.show()


"""
EXERCICE 4
---
- Build a scatter plot, where pop_2007 is mapped on the horizontal axis, and life_exp is mapped on the vertical axis.
- Finish the script with plt.show() to actually display the plot. Do you see a correlation?
"""
pop_2007 = [31.889923, 3.600523, 33.333216, 12.420476, 40.301927, 20.434176, 8.199783, 0.708573, 150.448339, 10.392226, 8.078314, 9.119152, 4.552198, 1.639131, 190.010647, 7.322858, 14.326203, 8.390505, 14.131858, 17.696293, 33.390141, 4.369038, 10.238807, 16.284741, 1318.683096, 44.22755, 0.71096, 64.606759, 3.80061, 4.133884, 18.013409, 4.493312, 11.416987, 10.228744, 5.46812, 0.496374, 9.319622, 13.75568, 80.264543, 6.939688, 0.551201, 4.906585, 76.511887, 5.23846, 61.083916, 1.454867, 1.688359, 82.400996, 22.873338, 10.70629, 12.572928, 9.947814, 1.472041, 8.502814, 7.483763, 6.980412, 9.956108, 0.301931, 1110.396331, 223.547, 69.45357, 27.499638, 4.109086, 6.426679, 58.147733, 2.780132, 127.467972, 6.053193, 35.610177, 23.301725, 49.04479, 2.505559, 3.921278, 2.012649, 3.193942, 6.036914, 19.167654, 13.327079, 24.821286, 12.031795, 3.270065, 1.250882, 108.700891, 2.874127, 0.684736, 33.757175, 19.951656, 47.76198, 2.05508, 28.90179, 16.570613, 4.115771, 5.675356, 12.894865, 135.031164, 4.627926, 3.204897, 169.270617, 3.242173, 6.667147, 28.674757, 91.077287, 38.518241, 10.642836, 3.942491, 0.798094, 22.276056, 8.860588, 0.199579, 27.601038, 12.267493, 10.150265, 6.144562, 4.553009, 5.447502, 2.009245, 9.118773, 43.997828, 40.448191, 20.378239, 42.292929, 1.133066, 9.031088, 7.554661, 19.314747, 23.174294, 38.13964, 65.068149, 5.701579, 1.056608, 10.276158, 71.158647, 29.170398, 60.776238, 301.139947, 3.447496, 26.084662, 85.262356, 4.018332, 22.211743, 11.746035, 12.311143]

# Build Scatter plot
plt.scatter(pop_2007, life_exp)

plt.xscale('log')

# Show plot
plt.show()


# Definition of countries and capital
countries = ['spain', 'france', 'germany', 'norway', 'italy']
capitals = ['madrid', 'paris', 'berlin', 'oslo', 'rome']

# From string in countries and capitals, create dictionary europe
list(zip(countries, capitals))

[('spain', 'madrid'),
 ('france', 'paris'),
 ('germany', 'berlin'),
 ('norway', 'oslo'),
 ('italy', 'rome')]


zip(countries, capitals)

<zip at 0x7fed5fbe97c0>


dict(zip(countries, capitals))

{'spain': 'madrid', 'france': 'paris', 'germany': 'berlin', 'norway': 'oslo'}


# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin', 'norway':'oslo' }

# Print out the keys in europe
print(europe.keys())

dict_keys(['spain', 'france', 'germany', 'norway'])


# Print out the keys in europe
print(europe.values())

dict_values(['madrid', 'paris', 'berlin', 'oslo'])


print(europe.items())

dict_items([('spain', 'madrid'), ('france', 'paris'), ('germany', 'berlin'), ('norway', 'oslo')])


# Print out value that belongs to key 'norway'
print(europe['norway'])


europe = {'spain':'madrid', 'france':'paris', 'germany':'bonn', 
          'norway':'oslo', 'italy':'rome', 'poland':'warsaw', 
          'australia':'vienna' }

# Update capital of germany
europe['germany'] = 'berlin'

# Print europe
print(europe)

{'spain': 'madrid', 'france': 'paris', 'germany': 'berlin', 'norway': 'oslo', 'italy': 'rome', 'poland': 'warsaw', 'australia': 'vienna'}


europe['new_spain'] = europe['spain']
del europe['spain']


europe

{'france': 'paris',
 'germany': 'berlin',
 'norway': 'oslo',
 'italy': 'rome',
 'poland': 'warsaw',
 'australia': 'vienna',
 'new_spain': 'madrid'}


import pandas as pd


brics = pd.read_csv('./../02-intermediate/brics.csv')


brics


brics = pd.read_csv('./../02-intermediate/brics.csv', index_col=0)
brics


# Build cars DataFrame
names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']
dr =  [True, False, False, False, True, True, True]
cpc = [809, 731, 588, 18, 200, 70, 45]
dict = { 'country':names, 'drives_right':dr, 'cars_per_cap':cpc }
cars = pd.DataFrame.from_dict(dict)
print(cars)

         country  drives_right  cars_per_cap
0  United States          True           809
1      Australia         False           731
2          Japan         False           588
3          India         False            18
4         Russia          True           200
5        Morocco          True            70
6          Egypt          True            45


# Definition of row_labels
row_labels = ['US', 'AUS', 'JAP', 'IN', 'RU', 'MOR', 'EG']


list(cars.index)

[0, 1, 2, 3, 4, 5, 6]


# Specify row labels of cars
cars.index = row_labels


# Print cars again
print(cars)

           country  drives_right  cars_per_cap
US   United States          True           809
AUS      Australia         False           731
JAP          Japan         False           588
IN           India         False            18
RU          Russia          True           200
MOR        Morocco          True            70
EG           Egypt          True            45


# Import the cars.csv data: cars
cars = pd.read_csv('/Users/samiadrappeau/Documents/TBS/PYTHON-101/code/02-intermediate/cars.csv') # ADAPT THE PATH TO YOURS

# Print out cars
print(cars)

  Unnamed: 0  cars_per_cap        country  drives_right
0         US           809  United States          True
1        AUS           731      Australia         False
2        JPN           588          Japan         False
3         IN            18          India         False
4         RU           200         Russia          True
5        MOR            70        Morocco          True
6         EG            45          Egypt          True


# Import the cars.csv data: cars
cars = pd.read_csv('/Users/samiadrappeau/Documents/TBS/PYTHON-101/code/02-intermediate/cars.csv', index_col=0) # ADAPT THE PATH TO YOURS

# Print out cars
print(cars)

     cars_per_cap        country  drives_right
US            809  United States          True
AUS           731      Australia         False
JPN           588          Japan         False
IN             18          India         False
RU            200         Russia          True
MOR            70        Morocco          True
EG             45          Egypt          True


# Import the cars.csv data: cars
cars = pd.read_csv("./, index_col=0) # ADAPT THE PATH TO YOURS

# Print out cars
print(cars)


brics = pd.read_csv('../02-intermediate/brics.csv', index_col=0)


brics


brics.loc[:, "country"]

 BR          Brazil
 RU          Russia
 IN           India
 CH           China
 SA    South Africa
Name: country, dtype: object


type(brics.loc[:, "country"])

pandas.core.series.Series


brics[:, ["country", "capital"]]

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Input In [57], in <cell line: 1>()
----> 1 brics[:, ["country", "capital"]]

File ~/anaconda3/envs/datascience/lib/python3.8/site-packages/pandas/core/frame.py:3024, in DataFrame.__getitem__(self, key)
   3022 if self.columns.nlevels > 1:
   3023     return self._getitem_multilevel(key)
-> 3024 indexer = self.columns.get_loc(key)
   3025 if is_integer(indexer):
   3026     indexer = [indexer]

File ~/anaconda3/envs/datascience/lib/python3.8/site-packages/pandas/core/indexes/base.py:3080, in Index.get_loc(self, key, method, tolerance)
   3078 casted_key = self._maybe_cast_indexer(key)
   3079 try:
-> 3080     return self._engine.get_loc(casted_key)
   3081 except KeyError as err:
   3082     raise KeyError(key) from err

File pandas/_libs/index.pyx:70, in pandas._libs.index.IndexEngine.get_loc()

File pandas/_libs/index.pyx:75, in pandas._libs.index.IndexEngine.get_loc()

TypeError: '(slice(None, None, None), ['country', 'capital'])' is an invalid key


brics[["country", "capital"]]


brics["country", "capital"]

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
File ~/anaconda3/envs/datascience/lib/python3.8/site-packages/pandas/core/indexes/base.py:3080, in Index.get_loc(self, key, method, tolerance)
   3079 try:
-> 3080     return self._engine.get_loc(casted_key)
   3081 except KeyError as err:

File pandas/_libs/index.pyx:70, in pandas._libs.index.IndexEngine.get_loc()

File pandas/_libs/index.pyx:101, in pandas._libs.index.IndexEngine.get_loc()

File pandas/_libs/hashtable_class_helper.pxi:4554, in pandas._libs.hashtable.PyObjectHashTable.get_item()

File pandas/_libs/hashtable_class_helper.pxi:4562, in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: ('country', 'capital')

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
Input In [59], in <cell line: 1>()
----> 1 brics["country", "capital"]

File ~/anaconda3/envs/datascience/lib/python3.8/site-packages/pandas/core/frame.py:3024, in DataFrame.__getitem__(self, key)
   3022 if self.columns.nlevels > 1:
   3023     return self._getitem_multilevel(key)
-> 3024 indexer = self.columns.get_loc(key)
   3025 if is_integer(indexer):
   3026     indexer = [indexer]

File ~/anaconda3/envs/datascience/lib/python3.8/site-packages/pandas/core/indexes/base.py:3082, in Index.get_loc(self, key, method, tolerance)
   3080         return self._engine.get_loc(casted_key)
   3081     except KeyError as err:
-> 3082         raise KeyError(key) from err
   3084 if tolerance is not None:
   3085     tolerance = self._convert_tolerance(tolerance, np.asarray(key))

KeyError: ('country', 'capital')


brics


brics.columns

Index(['country', 'capital', 'area', 'population'], dtype='object')


brics.index

Index([' BR', ' RU', ' IN', ' CH', ' SA'], dtype='object')


"pyscript" == "Pyscript"

False


import numpy as np
my_house = np.array([18.0, 20.0, 10.75, 9.50, 25.0])
your_house = np.array([14.0, 24.0, 14.25, 9.0])


print(len(my_house))
print(len(your_house))

5
4


my_house < your_house

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [67], in <cell line: 1>()
----> 1 my_house < your_house

ValueError: operands could not be broadcast together with shapes (5,) (4,)


16 < my_house < 20

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [68], in <cell line: 1>()
----> 1 16 < my_house < 20

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()


my_kitchen = 18.0
your_kitchen = 14.0


2* my_kitchen < 3*your_kitchen

True


# Create arrays
import numpy as np
my_house = np.array([18.0, 20.0, 10.75, 9.50])
your_house = np.array([14.0, 24.0, 14.25, 9.0])


# my_house greater than 18.5 or smaller than 10
np.logical_or(my_house > 18.5, my_house < 10)

array([False,  True, False,  True])


# Both my_house and your_house smaller than 11
my_house < 11 and your_house < 11

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [73], in <cell line: 2>()
      1 # Both my_house and your_house smaller than 11
----> 2 my_house < 11 and your_house < 11

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()


np.logical_and(my_house < 11, your_house < 11)

array([False, False, False,  True])


"""
EXERCICE 1
---
- Extract the drives_right column as a Pandas Series and store it as dr.
- Use dr, a boolean Series, to subset the cars DataFrame. Store the resulting selection in sel.
- Print sel, and assert that drives_right is True for all observations.
"""
# Import cars data
import pandas as pd
cars = pd.read_csv('/Users/samiadrappeau/Documents/TBS/PYTHON-101/code/02-intermediate/cars.csv', index_col = 0) # ADAPT THE PATH TO YOURS


# Extract drives_right column as Series: dr
dr = cars["drives_right"]
print(dr)

US      True
AUS    False
JPN    False
IN     False
RU      True
MOR     True
EG      True
Name: drives_right, dtype: bool


# Use dr to subset cars: sel
sel = cars[dr]

# Print sel
print(sel)

     cars_per_cap        country  drives_right
US            809  United States          True
RU            200         Russia          True
MOR            70        Morocco          True
EG             45          Egypt          True


print(cars[cars["drives_right"]])

     cars_per_cap        country  drives_right
US            809  United States          True
RU            200         Russia          True
MOR            70        Morocco          True
EG             45          Egypt          True


"""
EXERCICE 3
---
- Select the cars_per_cap column from cars as a Pandas Series and store it as cpc.
- Use cpc in combination with a comparison operator and 500. You want to end up with a boolean Series that's True if the corresponding country has a cars_per_cap of more than 500 and False otherwise. Store this boolean Series as many_cars.
- Use many_cars to subset cars, similar to what you did before. Store the result as car_maniac.
- Print out car_maniac to see if you got it right.
"""
# Select the cars_per_cap column
cpc = cars["cars_per_cap"]
print(cpc)

# Create car_maniac: observations that have a cars_per_cap over 500
car_maniac = cpc > 500 
print(car_maniac)

# Print car_maniac

US     809
AUS    731
JPN    588
IN      18
RU     200
MOR     70
EG      45
Name: cars_per_cap, dtype: int64
US      True
AUS     True
JPN     True
IN     False
RU     False
MOR    False
EG     False
Name: cars_per_cap, dtype: bool


"""
EXERCICE 4
---
- Use the code sample provided to create a DataFrame medium, that includes all the observations of cars that have a cars_per_cap between 100 and 500.
Print out medium.
"""
# Import numpy, you'll need this
import numpy as np

# Create medium: observations with cars_per_cap between 100 and 500
medium = np.logical_and(cars["cars_per_cap"] > 100, cars["cars_per_cap"] < 500)

# Print medium
print(medium)

cars[medium]

US     False
AUS    False
JPN    False
IN     False
RU      True
MOR    False
EG     False
Name: cars_per_cap, dtype: bool


cars[(cars["cars_per_cap"] > 100) & (cars["cars_per_cap"] < 500)]


# Initialize offset
offset = 8

# Code the while loop
while offset != 0:
    print("correcting...")
    offset = offset -1
    print(offset)

correcting...
7
correcting...
6
correcting...
5
correcting...
4
correcting...
3
correcting...
2
correcting...
1
correcting...
0


# Initialize offset
offset = -6

# Code the while loop
while offset != 0 :
    print("correcting...")
    if offset > 0 :
        
        offset = offset - 1
    else : 
        offset = offset +1    
    print(offset)

correcting...
-5
correcting...
-4
correcting...
-3
correcting...
-2
correcting...
-1
correcting...
0


# areas list
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Code the for loop
for x in areas:
    print(x)

11.25
18.0
20.0
10.75
9.5


# Code the for loop
for i, x in enumerate(areas):
    print(i, ": ", x)

0 :  11.25
1 :  18.0
2 :  20.0
3 :  10.75
4 :  9.5


for x in enumerate(areas):
    print(x)

(0, 11.25)
(1, 18.0)
(2, 20.0)
(3, 10.75)
(4, 9.5)


for index, area in enumerate(areas) :
    print("room " + str(index+1) + ": " + str(area))

room 1: 11.25
room 2: 18.0
room 3: 20.0
room 4: 10.75
room 5: 9.5


house = [["hallway", 11.25], 
         ["kitchen", 18.0], 
         ["living room", 20.0], 
         ["bedroom", 10.75], 
         ["bathroom", 9.50]]


for [x, y] in house:
    print(str(x) + " is " + str(y) + " sqm")
    print(f"{x} is {y} sqm")

hallway is 11.25 sqm
hallway is 11.25 sqm
kitchen is 18.0 sqm
kitchen is 18.0 sqm
living room is 20.0 sqm
living room is 20.0 sqm
bedroom is 10.75 sqm
bedroom is 10.75 sqm
bathroom is 9.5 sqm
bathroom is 9.5 sqm


for x, y in house:
    print(str(x) + " is " + str(y) + " sqm")
    print(f"{x} is {y} sqm")

hallway is 11.25 sqm
hallway is 11.25 sqm
kitchen is 18.0 sqm
kitchen is 18.0 sqm
living room is 20.0 sqm
living room is 20.0 sqm
bedroom is 10.75 sqm
bedroom is 10.75 sqm
bathroom is 9.5 sqm
bathroom is 9.5 sqm


# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'bonn', 
          'norway':'oslo', 'italy':'rome', 'poland':'warsaw', 'australia':'vienna' }
          
# Iterate over europe

for x, y in europe.items():
    print("the capital of " + x.capitalize() + " is " + y.capitalize())

the capital of Spain is Madrid
the capital of France is Paris
the capital of Germany is Bonn
the capital of Norway is Oslo
the capital of Italy is Rome
the capital of Poland is Warsaw
the capital of Australia is Vienna


"""
EXERCICE 2
---
- Import the numpy package under the local alias np.
- Write a for loop that iterates over all elements in np_height and prints out "x inches" for each element, where x is the value in the array.
- Write a for loop that visits every element of the np_baseball array and prints it out.
"""
# Import numpy as np
import numpy as np

height_in = [74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 73, 75, 78, 79, 76, 74, 76, 72, 71, 75, 77, 74, 73, 74, 78, 73, 75, 73, 75, 75, 74, 69, 71, 74, 73, 73, 76, 74, 74, 70, 72, 77, 74, 70, 73, 75, 76, 76, 78, 74, 74, 76, 77, 81, 78, 75, 77, 75, 76, 74, 72, 72, 75, 73, 73, 73, 70, 70, 70, 76, 68, 71, 72, 75, 75, 75, 75, 68, 74, 78, 71, 73, 76, 74, 74, 79, 75, 73, 76, 74, 74, 73, 72, 74, 73, 74, 72, 73, 69, 72, 73, 75, 75, 73, 72, 72, 76, 74, 72, 77, 74, 77, 75, 76, 80, 74, 74, 75, 78, 73, 73, 74, 75, 76, 71, 73, 74, 76, 76, 74, 73, 74, 70, 72, 73, 73, 73, 73, 71, 74, 74, 72, 74, 71, 74, 73, 75, 75, 79, 73, 75, 76, 74, 76, 78, 74, 76, 72, 74, 76, 74, 75, 78, 75, 72, 74, 72, 74, 70, 71, 70, 75, 71, 71, 73, 72, 71, 73, 72, 75, 74, 74, 75, 73, 77, 73, 76, 75, 74, 76, 75, 73, 71, 76, 75, 72, 71, 77, 73, 74, 71, 72, 74, 75, 73, 72, 75, 75, 74, 72, 74, 71, 70, 74, 77, 77, 75, 75, 78, 75, 76, 73, 75, 75, 79, 77, 76, 71, 75, 74, 69, 71, 76, 72, 72, 70, 72, 73, 71, 72, 71, 73, 72, 73, 74, 74, 72, 75, 74, 74, 77, 75, 73, 72, 71, 74, 77, 75, 75, 75, 78, 78, 74, 76, 78, 76, 70, 72, 80, 74, 74, 71, 70, 72, 71, 74, 71, 72, 71, 74, 69, 76, 75, 75, 76, 73, 76, 73, 77, 73, 72, 72, 77, 77, 71, 74, 74, 73, 78, 75, 73, 70, 74, 72, 73, 73, 75, 75, 74, 76, 73, 74, 75, 75, 72, 73, 73, 72, 74, 78, 76, 73, 74, 75, 70, 75, 71, 72, 78, 75, 73, 73, 71, 75, 77, 72, 69, 73, 74, 72, 70, 75, 70, 72, 72, 74, 73, 74, 76, 75, 80, 72, 75, 73, 74, 74, 73, 75, 75, 71, 73, 75, 74, 74, 72, 74, 74, 74, 73, 76, 75, 72, 73, 73, 73, 72, 72, 72, 72, 71, 75, 75, 74, 73, 75, 79, 74, 76, 73, 74, 74, 72, 74, 74, 75, 78, 74, 74, 74, 77, 70, 73, 74, 73, 71, 75, 71, 72, 77, 74, 70, 77, 73, 72, 76, 71, 76, 78, 75, 73, 78, 74, 79, 75, 76, 72, 75, 75, 70, 72, 70, 74, 71, 76, 73, 76, 71, 69, 72, 72, 69, 73, 69, 73, 74, 74, 72, 71, 72, 72, 76, 76, 76, 74, 76, 75, 71, 72, 71, 73, 75, 76, 75, 71, 75, 74, 72, 73, 73, 73, 73, 76, 72, 76, 73, 73, 73, 75, 75, 77, 73, 72, 75, 70, 74, 72, 80, 71, 71, 74, 74, 73, 75, 76, 73, 77, 72, 73, 77, 76, 71, 75, 73, 74, 77, 71, 72, 73, 69, 73, 70, 74, 76, 73, 73, 75, 73, 79, 74, 73, 74, 77, 75, 74, 73, 77, 73, 77, 74, 74, 73, 77, 74, 77, 75, 77, 75, 71, 74, 70, 79, 72, 72, 70, 74, 74, 72, 73, 72, 74, 74, 76, 82, 74, 74, 70, 73, 73, 74, 77, 72, 76, 73, 73, 72, 74, 74, 71, 72, 75, 74, 74, 77, 70, 71, 73, 76, 71, 75, 74, 72, 76, 79, 76, 73, 76, 78, 75, 76, 72, 72, 73, 73, 75, 71, 76, 70, 75, 74, 75, 73, 71, 71, 72, 73, 73, 72, 69, 73, 78, 71, 73, 75, 76, 70, 74, 77, 75, 79, 72, 77, 73, 75, 75, 75, 73, 73, 76, 77, 75, 70, 71, 71, 75, 74, 69, 70, 75, 72, 75, 73, 72, 72, 72, 76, 75, 74, 69, 73, 72, 72, 75, 77, 76, 80, 77, 76, 79, 71, 75, 73, 76, 77, 73, 76, 70, 75, 73, 75, 70, 69, 71, 72, 72, 73, 70, 70, 73, 76, 75, 72, 73, 79, 71, 72, 74, 74, 74, 72, 76, 76, 72, 72, 71, 72, 72, 70, 77, 74, 72, 76, 71, 76, 71, 73, 70, 73, 73, 72, 71, 71, 71, 72, 72, 74, 74, 74, 71, 72, 75, 72, 71, 72, 72, 72, 72, 74, 74, 77, 75, 73, 75, 73, 76, 72, 77, 75, 72, 71, 71, 75, 72, 73, 73, 71, 70, 75, 71, 76, 73, 68, 71, 72, 74, 77, 72, 76, 78, 81, 72, 73, 76, 72, 72, 74, 76, 73, 76, 75, 70, 71, 74, 72, 73, 76, 76, 73, 71, 68, 71, 71, 74, 77, 69, 72, 76, 75, 76, 75, 76, 72, 74, 76, 74, 72, 75, 78, 77, 70, 72, 79, 74, 71, 68, 77, 75, 71, 72, 70, 72, 72, 73, 72, 74, 72, 72, 75, 72, 73, 74, 72, 78, 75, 72, 74, 75, 75, 76, 74, 74, 73, 74, 71, 74, 75, 76, 74, 76, 76, 73, 75, 75, 74, 68, 72, 75, 71, 70, 72, 73, 72, 75, 74, 70, 76, 71, 82, 72, 73, 74, 71, 75, 77, 72, 74, 72, 73, 78, 77, 73, 73, 73, 73, 73, 76, 75, 70, 73, 72, 73, 75, 74, 73, 73, 76, 73, 75, 70, 77, 72, 77, 74, 75, 75, 75, 75, 72, 74, 71, 76, 71, 75, 76, 83, 75, 74, 76, 72, 72, 75, 75, 72, 77, 73, 72, 70, 74, 72, 74, 72, 71, 70, 71, 76, 74, 76, 74, 74, 74, 75, 75, 71, 71, 74, 77, 71, 74, 75, 77, 76, 74, 76, 72, 71, 72, 75, 73, 68, 72, 69, 73, 73, 75, 70, 70, 74, 75, 74, 74, 73, 74, 75, 77, 73, 74, 76, 74, 75, 73, 76, 78, 75, 73, 77, 74, 72, 74, 72, 71, 73, 75, 73, 67, 67, 76, 74, 73, 70, 75, 70, 72, 77, 79, 78, 74, 75, 75, 78, 76, 75, 69, 75, 72, 75, 73, 74, 75, 75, 73]
np_height = np.array(height_in)

baseball = [[74, 180], [74, 215], [72, 210], [72, 210], [73, 188], [69, 176], [69, 209], [71, 200], [76, 231], [71, 180], [73, 188], [73, 180], [74, 185], [74, 160], [69, 180], [70, 185], [73, 189], [75, 185], [78, 219], [79, 230], [76, 205], [74, 230], [76, 195], [72, 180], [71, 192], [75, 225], [77, 203], [74, 195], [73, 182], [74, 188], [78, 200], [73, 180], [75, 200], [73, 200], [75, 245], [75, 240], [74, 215], [69, 185], [71, 175], [74, 199], [73, 200], [73, 215], [76, 200], [74, 205], [74, 206], [70, 186], [72, 188], [77, 220], [74, 210], [70, 195], [73, 200], [75, 200], [76, 212], [76, 224], [78, 210], [74, 205], [74, 220], [76, 195], [77, 200], [81, 260], [78, 228], [75, 270], [77, 200], [75, 210], [76, 190], [74, 220], [72, 180], [72, 205], [75, 210], [73, 220], [73, 211], [73, 200], [70, 180], [70, 190], [70, 170], [76, 230], [68, 155], [71, 185], [72, 185], [75, 200], [75, 225], [75, 225], [75, 220], [68, 160], [74, 205], [78, 235], [71, 250], [73, 210], [76, 190], [74, 160], [74, 200], [79, 205], [75, 222], [73, 195], [76, 205], [74, 220], [74, 220], [73, 170], [72, 185], [74, 195], [73, 220], [74, 230], [72, 180], [73, 220], [69, 180], [72, 180], [73, 170], [75, 210], [75, 215], [73, 200], [72, 213], [72, 180], [76, 192], [74, 235], [72, 185], [77, 235], [74, 210], [77, 222], [75, 210], [76, 230], [80, 220], [74, 180], [74, 190], [75, 200], [78, 210], [73, 194], [73, 180], [74, 190], [75, 240], [76, 200], [71, 198], [73, 200], [74, 195], [76, 210], [76, 220], [74, 190], [73, 210], [74, 225], [70, 180], [72, 185], [73, 170], [73, 185], [73, 185], [73, 180], [71, 178], [74, 175], [74, 200], [72, 204], [74, 211], [71, 190], [74, 210], [73, 190], [75, 190], [75, 185], [79, 290], [73, 175], [75, 185], [76, 200], [74, 220], [76, 170], [78, 220], [74, 190], [76, 220], [72, 205], [74, 200], [76, 250], [74, 225], [75, 215], [78, 210], [75, 215], [72, 195], [74, 200], [72, 194], [74, 220], [70, 180], [71, 180], [70, 170], [75, 195], [71, 180], [71, 170], [73, 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195], [71, 160], [72, 180], [72, 205], [72, 200], [72, 185], [74, 245], [74, 190], [77, 210], [75, 200], [73, 200], [75, 222], [73, 215], [76, 240], [72, 170], [77, 220], [75, 156], [72, 190], [71, 202], [71, 221], [75, 200], [72, 190], [73, 210], [73, 190], [71, 200], [70, 165], [75, 190], [71, 185], [76, 230], [73, 208], [68, 209], [71, 175], [72, 180], [74, 200], [77, 205], [72, 200], [76, 250], [78, 210], [81, 230], [72, 244], [73, 202], [76, 240], [72, 200], [72, 215], [74, 177], [76, 210], [73, 170], [76, 215], [75, 217], [70, 198], [71, 200], [74, 220], [72, 170], [73, 200], [76, 230], [76, 231], [73, 183], [71, 192], [68, 167], [71, 190], [71, 180], [74, 180], [77, 215], [69, 160], [72, 205], [76, 223], [75, 175], [76, 170], [75, 190], [76, 240], [72, 175], [74, 230], [76, 223], [74, 196], [72, 167], [75, 195], [78, 190], [77, 250], [70, 190], [72, 190], [79, 190], [74, 170], [71, 160], [68, 150], [77, 225], [75, 220], [71, 209], [72, 210], [70, 176], [72, 260], [72, 195], [73, 190], [72, 184], [74, 180], [72, 195], [72, 195], [75, 219], [72, 225], [73, 212], [74, 202], [72, 185], [78, 200], [75, 209], [72, 200], [74, 195], [75, 228], [75, 210], [76, 190], [74, 212], [74, 190], [73, 218], [74, 220], [71, 190], [74, 235], [75, 210], [76, 200], [74, 188], [76, 210], [76, 235], [73, 188], [75, 215], [75, 216], [74, 220], [68, 180], [72, 185], [75, 200], [71, 210], [70, 220], [72, 185], [73, 231], [72, 210], [75, 195], [74, 200], [70, 205], [76, 200], [71, 190], [82, 250], [72, 185], [73, 180], [74, 170], [71, 180], [75, 208], [77, 235], [72, 215], [74, 244], [72, 220], [73, 185], [78, 230], [77, 190], [73, 200], [73, 180], [73, 190], [73, 196], [73, 180], [76, 230], [75, 224], [70, 160], [73, 178], [72, 205], [73, 185], [75, 210], [74, 180], [73, 190], [73, 200], [76, 257], [73, 190], [75, 220], [70, 165], [77, 205], [72, 200], [77, 208], [74, 185], [75, 215], [75, 170], [75, 235], [75, 210], [72, 170], [74, 180], [71, 170], [76, 190], [71, 150], [75, 230], [76, 203], [83, 260], [75, 246], [74, 186], [76, 210], [72, 198], [72, 210], [75, 215], [75, 180], [72, 200], [77, 245], [73, 200], [72, 192], [70, 192], [74, 200], [72, 192], [74, 205], [72, 190], [71, 186], [70, 170], [71, 197], [76, 219], [74, 200], [76, 220], [74, 207], [74, 225], [74, 207], [75, 212], [75, 225], [71, 170], [71, 190], [74, 210], [77, 230], [71, 210], [74, 200], [75, 238], [77, 234], [76, 222], [74, 200], [76, 190], [72, 170], [71, 220], [72, 223], [75, 210], [73, 215], [68, 196], [72, 175], [69, 175], [73, 189], [73, 205], [75, 210], [70, 180], [70, 180], [74, 197], [75, 220], [74, 228], [74, 190], [73, 204], [74, 165], [75, 216], [77, 220], [73, 208], [74, 210], [76, 215], [74, 195], [75, 200], [73, 215], [76, 229], [78, 240], [75, 207], [73, 205], [77, 208], [74, 185], [72, 190], [74, 170], [72, 208], [71, 225], [73, 190], [75, 225], [73, 185], [67, 180], [67, 165], [76, 240], [74, 220], [73, 212], [70, 163], [75, 215], [70, 175], [72, 205], [77, 210], [79, 205], [78, 208], [74, 215], [75, 180], [75, 200], [78, 230], [76, 211], [75, 230], [69, 190], [75, 220], [72, 180], [75, 205], [73, 190], [74, 180], [75, 205], [75, 190], [73, 195]]
np_baseball = np.array(baseball)

# For loop over np_height
for x in np_height:
    print(f"{x} inches")

74 inches
74 inches
72 inches
72 inches
73 inches
69 inches
69 inches
71 inches
76 inches
71 inches
73 inches
73 inches
74 inches
74 inches
69 inches
70 inches
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76 inches
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76 inches
72 inches
71 inches
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73 inches
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78 inches
73 inches
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73 inches
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69 inches
71 inches
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76 inches
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70 inches
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74 inches
70 inches
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76 inches
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78 inches
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81 inches
78 inches
75 inches
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75 inches
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74 inches
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70 inches
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68 inches
71 inches
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68 inches
74 inches
78 inches
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72 inches
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69 inches
72 inches
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80 inches
74 inches
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78 inches
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70 inches
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78 inches
78 inches
74 inches
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74 inches
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71 inches
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69 inches
76 inches
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73 inches
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73 inches
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71 inches
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78 inches
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70 inches
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74 inches
76 inches
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72 inches
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72 inches
74 inches
78 inches
76 inches
73 inches
74 inches
75 inches
70 inches
75 inches
71 inches
72 inches
78 inches
75 inches
73 inches
73 inches
71 inches
75 inches
77 inches
72 inches
69 inches
73 inches
74 inches
72 inches
70 inches
75 inches
70 inches
72 inches
72 inches
74 inches
73 inches
74 inches
76 inches
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80 inches
72 inches
75 inches
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75 inches
75 inches
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72 inches
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72 inches
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74 inches
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72 inches
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76 inches
72 inches
76 inches
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74 inches
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80 inches
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76 inches
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71 inches
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74 inches
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71 inches
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72 inches
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76 inches
72 inches
77 inches
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72 inches
71 inches
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72 inches
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70 inches
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68 inches
71 inches
72 inches
74 inches
77 inches
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76 inches
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81 inches
72 inches
73 inches
76 inches
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72 inches
74 inches
76 inches
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76 inches
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70 inches
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72 inches
73 inches
76 inches
76 inches
73 inches
71 inches
68 inches
71 inches
71 inches
74 inches
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69 inches
72 inches
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76 inches
72 inches
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72 inches
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78 inches
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70 inches
72 inches
79 inches
74 inches
71 inches
68 inches
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72 inches
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76 inches
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76 inches
76 inches
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75 inches
74 inches
68 inches
72 inches
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72 inches
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82 inches
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76 inches
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70 inches
77 inches
72 inches
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72 inches
74 inches
71 inches
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83 inches
75 inches
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70 inches
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70 inches
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74 inches
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76 inches
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71 inches
72 inches
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73 inches
68 inches
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78 inches
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77 inches
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76 inches
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78 inches
76 inches
75 inches
69 inches
75 inches
72 inches
75 inches
73 inches
74 inches
75 inches
75 inches
73 inches


# For loop over np_baseball
for x in np.nditer(np_baseball):
    print(x)

74
180
74
215
72
210
72
210
73
188
69
176
69
209
71
200
76
231
71
180
73
188
73
180
74
185
74
160
69
180
70
185
73
189
75
185
78
219
79
230
76
205
74
230
76
195
72
180
71
192
75
225
77
203
74
195
73
182
74
188
78
200
73
180
75
200
73
200
75
245
75
240
74
215
69
185
71
175
74
199
73
200
73
215
76
200
74
205
74
206
70
186
72
188
77
220
74
210
70
195
73
200
75
200
76
212
76
224
78
210
74
205
74
220
76
195
77
200
81
260
78
228
75
270
77
200
75
210
76
190
74
220
72
180
72
205
75
210
73
220
73
211
73
200
70
180
70
190
70
170
76
230
68
155
71
185
72
185
75
200
75
225
75
225
75
220
68
160
74
205
78
235
71
250
73
210
76
190
74
160
74
200
79
205
75
222
73
195
76
205
74
220
74
220
73
170
72
185
74
195
73
220
74
230
72
180
73
220
69
180
72
180
73
170
75
210
75
215
73
200
72
213
72
180
76
192
74
235
72
185
77
235
74
210
77
222
75
210
76
230
80
220
74
180
74
190
75
200
78
210
73
194
73
180
74
190
75
240
76
200
71
198
73
200
74
195
76
210
76
220
74
190
73
210
74
225
70
180
72
185
73
170
73
185
73
185
73
180
71
178
74
175
74
200
72
204
74
211
71
190
74
210
73
190
75
190
75
185
79
290
73
175
75
185
76
200
74
220
76
170
78
220
74
190
76
220
72
205
74
200
76
250
74
225
75
215
78
210
75
215
72
195
74
200
72
194
74
220
70
180
71
180
70
170
75
195
71
180
71
170
73
206
72
205
71
200
73
225
72
201
75
225
74
233
74
180
75
225
73
180
77
220
73
180
76
237
75
215
74
190
76
235
75
190
73
180
71
165
76
195
75
200
72
190
71
190
77
185
73
185
74
205
71
190
72
205
74
206
75
220
73
208
72
170
75
195
75
210
74
190
72
211
74
230
71
170
70
185
74
185
77
241
77
225
75
210
75
175
78
230
75
200
76
215
73
198
75
226
75
278
79
215
77
230
76
240
71
184
75
219
74
170
69
218
71
190
76
225
72
220
72
176
70
190
72
197
73
204
71
167
72
180
71
195
73
220
72
215
73
185
74
190
74
205
72
205
75
200
74
210
74
215
77
200
75
205
73
211
72
190
71
208
74
200
77
210
75
232
75
230
75
210
78
220
78
210
74
202
76
212
78
225
76
170
70
190
72
200
80
237
74
220
74
170
71
193
70
190
72
150
71
220
74
200
71
190
72
185
71
185
74
200
69
172
76
220
75
225
75
190
76
195
73
219
76
190
73
197
77
200
73
195
72
210
72
177
77
220
77
235
71
180
74
195
74
195
73
190
78
230
75
190
73
200
70
190
74
190
72
200
73
200
73
184
75
200
75
180
74
219
76
187
73
200
74
220
75
205
75
190
72
170
73
160
73
215
72
175
74
205
78
200
76
214
73
200
74
190
75
180
70
205
75
220
71
190
72
215
78
235
75
191
73
200
73
181
71
200
75
210
77
240
72
185
69
165
73
190
74
185
72
175
70
155
75
210
70
170
72
175
72
220
74
210
73
205
74
200
76
205
75
195
80
240
72
150
75
200
73
215
74
202
74
200
73
190
75
205
75
190
71
160
73
215
75
185
74
200
74
190
72
210
74
185
74
220
74
190
73
202
76
205
75
220
72
175
73
160
73
190
73
200
72
229
72
206
72
220
72
180
71
195
75
175
75
188
74
230
73
190
75
200
79
190
74
219
76
235
73
180
74
180
74
180
72
200
74
234
74
185
75
220
78
223
74
200
74
210
74
200
77
210
70
190
73
177
74
227
73
180
71
195
75
199
71
175
72
185
77
240
74
210
70
180
77
194
73
225
72
180
76
205
71
193
76
230
78
230
75
220
73
200
78
249
74
190
79
208
75
245
76
250
72
160
75
192
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"""
EXERCICE 1
---
- Write a for loop that iterates over the rows of cars and on each iteration perform two print() calls: one to print out the row label and one to print out all of the rows contents.
"""
# Import cars data
import pandas as pd
cars = pd.read_csv('/Users/samiadrappeau/Documents/TBS/PYTHON-101/code/02-intermediate/cars.csv', index_col = 0) # ADAPT THE PATH TO YOURS


# Iterate over rows of cars

for label, row in cars.iterrows():
    print(label)
    print(row)

US
cars_per_cap              809
country         United States
drives_right             True
Name: US, dtype: object
AUS
cars_per_cap          731
country         Australia
drives_right        False
Name: AUS, dtype: object
JPN
cars_per_cap      588
country         Japan
drives_right    False
Name: JPN, dtype: object
IN
cars_per_cap       18
country         India
drives_right    False
Name: IN, dtype: object
RU
cars_per_cap       200
country         Russia
drives_right      True
Name: RU, dtype: object
MOR
cars_per_cap         70
country         Morocco
drives_right       True
Name: MOR, dtype: object
EG
cars_per_cap       45
country         Egypt
drives_right     True
Name: EG, dtype: object


"""
EXERCICE 2
---
- Using the iterators lab and row, adapt the code in the for loop such that the first iteration prints out "US: 809", the second iteration "AUS: 731", and so on.
- The output should be in the form "country: cars_per_cap". Make sure to print out this exact string (with the correct spacing).
    - You can use str() to convert your integer data to a string so that you can print it in conjunction with the country label.
"""
# Adapt for loop
for lab, row in cars.iterrows() :
    print(lab)
    print(row)
    
    print(f"{lab}: {row['cars_per_cap']}")
    print("-"*15)

US
cars_per_cap              809
country         United States
drives_right             True
Name: US, dtype: object
US: 809
---------------
AUS
cars_per_cap          731
country         Australia
drives_right        False
Name: AUS, dtype: object
AUS: 731
---------------
JPN
cars_per_cap      588
country         Japan
drives_right    False
Name: JPN, dtype: object
JPN: 588
---------------
IN
cars_per_cap       18
country         India
drives_right    False
Name: IN, dtype: object
IN: 18
---------------
RU
cars_per_cap       200
country         Russia
drives_right      True
Name: RU, dtype: object
RU: 200
---------------
MOR
cars_per_cap         70
country         Morocco
drives_right       True
Name: MOR, dtype: object
MOR: 70
---------------
EG
cars_per_cap       45
country         Egypt
drives_right     True
Name: EG, dtype: object
EG: 45
---------------


"""
EXERCICE 3
---
- Use a for loop to add a new column, named COUNTRY, that contains a uppercase version of the country names in the "country" column. You can use the string method upper() for this.
- To see if your code worked, print out cars. Don't indent this code, so that it's not part of the for loop.
"""
# Code for loop that adds COUNTRY column
for x, row in cars.iterrows():
    cars.loc[x, "COUNTRY"] = row['country'].upper()


# Print cars
print(cars)

     cars_per_cap        country  drives_right        COUNTRY
US            809  United States          True  UNITED STATES
AUS           731      Australia         False      AUSTRALIA
JPN           588          Japan         False          JAPAN
IN             18          India         False          INDIA
RU            200         Russia          True         RUSSIA
MOR            70        Morocco          True        MOROCCO
EG             45          Egypt          True          EGYPT


"""
EXERCICE 4
---
- Replace the for loop with a one-liner that uses .apply(str.upper). The call should give the same result: a column COUNTRY should be added to cars, containing an uppercase version of the country names.
- As usual, print out cars to see the fruits of your hard labor
"""
# Use .apply(str.upper)
cars["COUNTRY_2"] = cars["country"].apply(str.upper)

# Print cars
cars

	country	capital	area	population
BR	Brazil	Brasilia	8.516	200.40
RU	Russia	Moscow	17.100	143.50
IN	India	New Delhi	3.286	1252.00
CH	China	Beijing	9.597	1357.00
SA	South Africa	Pretoria	1.221	52.98

	country	capital	area	population
BR	Brazil	Brasilia	8.516	200.40
RU	Russia	Moscow	17.100	143.50
IN	India	New Delhi	3.286	1252.00
CH	China	Beijing	9.597	1357.00
SA	South Africa	Pretoria	1.221	52.98

	country	capital	area	population
BR	Brazil	Brasilia	8.516	200.40
RU	Russia	Moscow	17.100	143.50
IN	India	New Delhi	3.286	1252.00
CH	China	Beijing	9.597	1357.00
SA	South Africa	Pretoria	1.221	52.98

	cars_per_cap	country	drives_right	COUNTRY	COUNTRY_2
US	809	United States	True	UNITED STATES	UNITED STATES
AUS	731	Australia	False	AUSTRALIA	AUSTRALIA
JPN	588	Japan	False	JAPAN	JAPAN
IN	18	India	False	INDIA	INDIA
RU	200	Russia	True	RUSSIA	RUSSIA
MOR	70	Morocco	True	MOROCCO	MOROCCO
EG	45	Egypt	True	EGYPT	EGYPT