Groupby¶
The groupby method allows you to group together rows based on a column and perform an aggregate function on them.
Create dataframe
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import pandas as pd
data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],
'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah'],
'Sales':[200,120,340,124,243,350]}
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df = pd.DataFrame(data)
df
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Now you can use the .groupby() method to group rows together based off of a column name. For instance let's group based off of Company. This will create a DataFrameGroupBy object:
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df.groupby('Company')
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You can save this object as a new variable:
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by_comp = df.groupby("Company")
And then call aggregate methods off the object:
Get the average sales by each company
- Pandas automatically ignore the non-numeric column "Person"
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by_comp.mean()
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df.groupby('Company').mean()
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Using loc() with groupby()¶
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df.groupby('Company').mean().loc['FB']
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More examples of aggregate methods:¶
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by_comp.std()
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Note that "Person" is returned as well. Python is able to sort in descending order
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by_comp.min()
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Note that "Person" is returned as well. Python is able to sort in ascending order
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by_comp.max()
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Note that Pandas will count "Person" as well
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by_comp.count()
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Using describe() with groupby()¶
The function describe() returns the count, mean, std, min, max and quartile values
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by_comp.describe()
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Transpose describe()¶
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by_comp.describe().transpose()
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by_comp.describe().transpose()['GOOG']
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