Directories and Pandas

# 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)

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

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

# Remove australia
del(europe['australia'])

# Print europe
print(europe)

Use chained square brackets to select and print out the capital of France.
Create a dictionary, named data, with the keys 'capital' and 'population'. Set them to 'rome' and 59.83, respectively.
Add a new key-value pair to europe; the key is 'italy' and the value is data, the dictionary you just built.

# 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']['capital'])

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

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

# Print europe
print(europe)

# 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 = names, dr, cpc

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

# Print cars
print(cars)


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

# 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, …
# Print cars again
print(cars)
IPython Shell
Slides
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)

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

# 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)
  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 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)

Sqaure Brackets

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']])