Advanced Aggregation [2/2]
Great! What is you need to apply certain functions to specific columns separately, like find out median of one column, and mean of another one. Surely, you can perform grouping twice. But this case is also covered by pandas and .agg() method. If you want to apply specific functions to certain columns, pass them as a dictionary {'column': 'function'}. For instance, we can calculate mean total income ('totinch') and median number of people in a dwelling ('hhsize') for each number of bedrooms.
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('broomh').agg({'totinch': 'mean', 'hhsize': 'median'}))
Note that if you pass dictionary as the
.agg()method parameter, then there is no need to select columns after grouping.
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Completion rate improved to 2.56Advanced Aggregation [2/2]
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Great! What is you need to apply certain functions to specific columns separately, like find out median of one column, and mean of another one. Surely, you can perform grouping twice. But this case is also covered by pandas and .agg() method. If you want to apply specific functions to certain columns, pass them as a dictionary {'column': 'function'}. For instance, we can calculate mean total income ('totinch') and median number of people in a dwelling ('hhsize') for each number of bedrooms.
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('broomh').agg({'totinch': 'mean', 'hhsize': 'median'}))
Note that if you pass dictionary as the
.agg()method parameter, then there is no need to select columns after grouping.
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