Pandas DataFrames
dict = {"country": ["Brazil", "Russia", "India", "China", "South Africa"], "capital": ["Brasilia", "Moscow", "New Dehli", "Beijing", "Pretoria"], "area": [8.516, 17.10, 3.286, 9.597, 1.221], "population": [200.4, 143.5, 1252, 1357, 52.98] } import pandas as pd brics = pd.DataFrame(dict) print(brics)
Adding index to DataFrame
# Set the index for brics brics.index = ["BR", "RU", "IN", "CH", "SA"] # Print out brics with new index values print(brics)
Reading CSV by Pandas DataFrame
# 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)
Reading CSV file by Pandas DataFrame with 1st column as index
# Import pandas and cars.csv import pandas as pd cars = pd.read_csv('cars.csv', index_col = 0) # Print out country column as Pandas Series print(cars['cars_per_cap']) # Print out country column as Pandas DataFrame print(cars[['cars_per_cap']]) # Print out DataFrame with country and drives_right columns print(cars[['cars_per_cap', 'country']])
Print partial rows (observations) from a DataFrame
# Import cars data import pandas as pd cars = pd.read_csv('cars.csv', index_col = 0) # Print out first 4 observations print(cars[0:4]) # Print out fifth, sixth, and seventh observation print(cars[4:6])
Data access by loc and iloc in Pandas DaraFrame
loc is label-based, and iloc is integer index based
# Import cars data import pandas as pd cars = pd.read_csv('cars.csv', index_col = 0) # Print out observation for Japan print(cars.iloc[2]) # Print out observations for Australia and Egypt print(cars.loc[['AUS', 'EG']])