Grouping in pandas [2/2]
As you saw in the previous chapter, you received one number per group. But what will be the result after applying, for example, the .mean()
method? Let's find out!
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/data5.csv') # Grouping and aggregating data print(df.groupby('morgh').mean())
As you can see, you received means for all numerical columns. If you want to get aggregated statistics for only certain columns, make a selection right after applying the .groupby()
function. For example, for each value of the 'morth'
column we want to know average values of 'valueh', 'grosrth', 'omphtotinch'
columns.
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/data5.csv') # Grouping and aggregating data print(df.groupby('morgh')[['valueh', 'grosrth', 'omphtotinch']].mean())
This time output looks much better. You got means for only selected columns. Zeros as means appeared as the result of house being mortgaged or not.
¡Gracias por tus comentarios!
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Grouping in pandas [2/2]
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As you saw in the previous chapter, you received one number per group. But what will be the result after applying, for example, the .mean()
method? Let's find out!
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/data5.csv') # Grouping and aggregating data print(df.groupby('morgh').mean())
As you can see, you received means for all numerical columns. If you want to get aggregated statistics for only certain columns, make a selection right after applying the .groupby()
function. For example, for each value of the 'morth'
column we want to know average values of 'valueh', 'grosrth', 'omphtotinch'
columns.
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/data5.csv') # Grouping and aggregating data print(df.groupby('morgh')[['valueh', 'grosrth', 'omphtotinch']].mean())
This time output looks much better. You got means for only selected columns. Zeros as means appeared as the result of house being mortgaged or not.
¡Gracias por tus comentarios!