Advanced Aggregation [1/2]
Sometimes one aggregate function is not enought to make complete conclusions. For instance, we may need to get not only minimal, but also maximal value per group. Can pandas handle it? Surely, it can!
If you want to apply more than one aggregate function to each group, use the .agg() method. Pass a list of function names (as strings!) you want to apply to each group as the parameter. For instance, we can get the minimal and maximal price for each value of the 'roomh' column (number of rooms).
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# Importing the library
import pandas as pd
# Reading the file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/f2947b09-5f0d-4ad9-992f-ec0b87cd4b3f/data4.csv')
# Minimal and maximal prices for each dwelling type
print(df.groupby('roomh')['valueh'].agg(['min', 'max']))
12345678# Importing the library import pandas as pd # Reading the file df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/f2947b09-5f0d-4ad9-992f-ec0b87cd4b3f/data4.csv') # Minimal and maximal prices for each dwelling type print(df.groupby('roomh')['valueh'].agg(['min', 'max']))
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Abschnitt 4. Kapitel 1
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