I just started learning Pandas and was wondering if there is any difference between groupby and pivot_table functions. Can anyone help me understand the difference between them?
Both pivot_table and groupby are used to aggregate your dataframe. The difference is only with regard to the shape of the result.
Using pd.pivot_table(df, index=["a"], columns=["b"], values=["c"], aggfunc=np.sum) a table is created where a is on the row axis, b is on the column axis, and the values are the sum of c.
Example:
df = pd.DataFrame({"a": [1,2,3,1,2,3], "b":[1,1,1,2,2,2], "c":np.random.rand(6)})
pd.pivot_table(df, index=["a"], columns=["b"], values=["c"], aggfunc=np.sum)
b 1 2
a
1 0.528470 0.484766
2 0.187277 0.144326
3 0.866832 0.650100
Using groupby, the dimensions given are placed into columns, and rows are created for each combination of those dimensions.
In this example, we create a series of the sum of values c, grouped by all unique combinations of a and b.
df.groupby(['a','b'])['c'].sum()
a b
1 1 0.528470
2 0.484766
2 1 0.187277
2 0.144326
3 1 0.866832
2 0.650100
Name: c, dtype: float64
A similar usage of groupby is if we omit the ['c']. In this case, it creates a dataframe (not a series) of the sums of all remaining columns grouped by unique values of a and b.
print df.groupby(["a","b"]).sum()
c
a b
1 1 0.528470
2 0.484766
2 1 0.187277
2 0.144326
3 1 0.866832
2 0.650100