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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) |
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Adding index to DataFrame
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dict = {"country":# Set the index for brics
brics.index = ["BrazilBR", "RussiaRU", "IndiaIN", "ChinaCH", "South AfricaSA"],
# Print out brics with new "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] }
index values
print(brics) |
Reading CSV by Pandas DataFrame
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# 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 a CSV file by Pandas DataFrame with 1st column as index
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# Import pandas and cars.csv
import pandas as pd
bricscars = pd.DataFrame(dict)
brics.toread_csv('examplecars.csv') |
Adding index to DataFrame
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# Set the index for brics
brics.index = ["BR", "RU", "IN", "CH", "SA"], index_col = 0)
# Print out country column as Pandas Series
print(cars['cars_per_cap'])
# Print out brics with new index values
print(brics) |
Reading CSV by Pandas DataFrame
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# 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
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# 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
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# 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
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']]) |
Save a Pandas DaraFrame by CSV format
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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)
brics.to_csv('example.csv') |
Save a Pandas DaraFrame by CSV format with header and no index
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from pandas import DataFrame
Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
'Price': [22000,25000,27000,35000]
}
df = DataFrame(Cars, columns= ['Brand', 'Price'])
export_csv = df.to_csv (r'C:\Users\Ron\Desktop\export_dataframe.csv', index = None, header=True) #Don't forget to add '.csv' at the end of the path
print (df) |
Print partial rows (observations) from a Pandas DataFrame
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# 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
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# 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']]) |
Sort a Pandas DataFrame in an ascending order
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df.sort_values(by=['Brand'], inplace=True) |
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# sort - ascending order
from pandas import DataFrame
Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
'Price': [22000,25000,27000,35000],
'Year': [2015,2013,2018,2018]
}
df = DataFrame(Cars, columns= ['Brand', 'Price','Year'])
# sort Brand - ascending order
df.sort_values(by=['Brand'], inplace=True)
print (df) |
Sort a Pandas DataFrame in a descending order
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df.sort_values(by=['Brand'], inplace=True, ascending=False) |
Code Block |
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# sort - descending order
from pandas import DataFrame
Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
'Price': [22000,25000,27000,35000],
'Year': [2015,2013,2018,2018]
}
df = DataFrame(Cars, columns= ['Brand', 'Price','Year'])
# sort Brand - descending order
df.sort_values(by=['Brand'], inplace=True, ascending=False)
print (df) |
Sort a Pandas DataFrame by multiple columns
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df.sort_values(by=['First Column','Second Column',...], inplace=True) |
Code Block |
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# sort by multiple columns
from pandas import DataFrame
Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
'Price': [22000,25000,27000,35000],
'Year': [2015,2013,2018,2018]
}
df = DataFrame(Cars, columns= ['Brand', 'Price','Year'])
# sort by multiple columns: Year and Price
df.sort_values(by=['Year','Price'], inplace=True)
print (df |
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# 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']]) |
Random number generation
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