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I have a CSV file with a huge list of house pricing information. I need to calculate the mean price overall and the most common zip codes. My code does these things fine so far but i need to split the housing now into its various types (of which there are about 5) and i need to calculate the mean price for these individual types.

Sly_Lamp
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  • groupby housetype and calculate the mean of the groups? – Patrick Artner Apr 30 '20 at 12:29
  • Probably a dupe of [pandas-group-by-in-group-by-and-average](https://stackoverflow.com/questions/30328646/python-pandas-group-by-in-group-by-and-average) – Patrick Artner Apr 30 '20 at 12:30
  • I've been trying to implement some of the code suggested in that post but I am getting the wrong output. The groupby function doesn't seem to be grouping the correct column as the output seems to still contain info from other columns. – Sly_Lamp Apr 30 '20 at 12:48

2 Answers2

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If you have a column containing the types of house, you can just group for that column ad then compute the mean:

avgs = df.groupby('Housetype').mean()
Mazziotti Raffaele
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Separate them to different dataframes, e.g.

df1 = df[df['HouseType'] == 1]

will create a new dataframe with only type1 houses

gtomer
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