02_Dictionary_n_Pandas

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

# Get index of 'germany': ind_ger
ind_ger = countries.index('germany')

# Use ind_ger to print out capital of Germany
print(capitals[ind_ger])

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

# From string in countries and capitals, create dictionary europe
europe = {"spain":"madrid", "france":"paris", "germany": "berlin", 
"norway": "oslo"}

# Print europe
print(europe)

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

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

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

===========Dictionary Manipulation (1)
# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin', 'norway':'oslo' }

# Add italy to europe
europe['italy'] = 'rome'

# Print out italy in europe
print('italy' in europe)

# Add poland to europe
europe['poland'] = 'warsaw'

# Print europe
print(europe)

=============Dictionary Manipulation (2)
# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'bonn',
          'norway':'oslo', 'italy':'rome', 'poland':'warsaw',
          'australia':'vienna' }

# Update capital of germany 
#[The capital of Germany is not 'bonn'; it's 'berlin'. Update its value.]
europe['germany'] = 'berlin'

# Remove australia
#Australia is not in Europe, Austria is! Remove the key 'australia' from europe.
del(europe['australia'])

# Print europe
print(europe)

============Dictionariception
# Dictionary of dictionaries
europe = { 'spain': { 'capital':'madrid', 'population':46.77 },
           'france': { 'capital':'paris', 'population':66.03 },
           'germany': { 'capital':'berlin', 'population':80.62 },
           'norway': { 'capital':'oslo', 'population':5.084 } }


# Print out the capital of France
print(europe['france']['population'])

# Create sub-dictionary data
data = {'capital': 'rome',
        'population': 59.83}

# Add data to europe under key 'italy'
europe['italy'] = data

# Print europe
print(europe)

==============Dictionary to DataFrame (1)
# Pre-defined lists
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]

# Import pandas as pd
import pandas as pd

# Create dictionary my_dict with three key:value pairs: my_dict
my_dict = {"country":names, "drives_right":dr, "cars_per_cap":cpc}

# Build a DataFrame cars from my_dict: cars
cars = pd.DataFrame(my_dict)

# Print cars
print(cars)

==============Dictionary to DataFrame (2)
import pandas as pd

# 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]
cars_dict = { 'country':names, 'drives_right':dr, 'cars_per_cap':cpc }
cars = pd.DataFrame(cars_dict)
print(cars)

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

# Specify row labels of cars
cars.index = row_labels

# Print cars again
print(cars)
===============CSV to DataFrame (1)
# Import pandas as pd
import pandas as pd

# Import the cars.csv data: cars
cars = pd.read_csv('cars.csv')

# Print out cars
print(cars)
==============CSV to DataFrame (2)
Remember index_col, an argument of read_csv(), that you can use to 
specify which column in the CSV file should be used as a row label? 
Well, that's exactly what you need here!

# Import pandas as pd
import pandas as pd

# Fix import by including index_col
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out cars
print(cars)

==============Square Brackets (1)
# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out country column as Pandas Series
print(cars['country'])

# Print out country column as Pandas DataFrame
print(cars[['country']])

# Print out DataFrame with country and drives_right columns
print(cars[['country', 'drives_right']])

===============Square Brackets (2)
You can also use them to get rows, or observations, from a DataFrame.
#You can only select rows using square brackets if you specify a slice, 
like 0:4. 

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out first 3 observations
print(cars[0:3])

# Print out fourth, fifth and sixth observation
print(cars[3:6])

==============loc and iloc (1)
loc is label-based, which means that you have to specify rows and 
columns based on their row and column labels. iloc is integer index 
based, so you have to specify rows and columns by their integer index 
like you did in the previous exercise.

cars.loc['RU']
cars.iloc[4]

cars.loc[['RU']]
cars.iloc[[4]]

cars.loc[['RU', 'AUS']]
cars.iloc[[4, 1]]


# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out observation for Japan
print(cars.loc['JPN'])
print(cars.iloc[2])

# Print out observations for Australia and Egypt
print(cars.loc[['AUS', 'EG']])
print(cars.iloc[[1,-1]])

===============loc and iloc (2)

loc and iloc also allow you to select both rows and columns from 
a DataFrame.

Example:
cars.loc['IN', 'cars_per_cap']
cars.iloc[3, 0]

cars.loc[['IN', 'RU'], 'cars_per_cap']
cars.iloc[[3, 4], 0]

cars.loc[['IN', 'RU'], ['cars_per_cap', 'country']]
cars.iloc[[3, 4], [0, 1]]

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out drives_right value of Morocco
print(cars.loc[['MOR'], ['drives_right']])

# Print sub-DataFrame
print(cars.loc[['RU', 'MOR'], ['country', 'drives_right']])

==============loc and iloc (3)
It's also possible to select only columns with loc and iloc. In both 
cases, you simply put a slice going from beginning to end in front of 
the comma:

cars.loc[:, 'country']
cars.iloc[:, 1]

cars.loc[:, ['country','drives_right']]
cars.iloc[:, [1, 2]]

---# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out drives_right column as Series
print(cars.loc[:, "drives_right"])

# Print out drives_right column as DataFrame
print(cars.loc[:, [ 'drives_right']])

# Print out cars_per_cap and drives_right as DataFrame
print(cars.loc[:, ['cars_per_cap', 'drives_right']])