Pandas: group by and Pivot table difference

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

It's more appropriate to use .pivot_table() instead of .groupby() when you need to show aggregates with both rows and column labels.

.pivot_table() makes it easy to create row and column labels at the same time and is preferable, even though you can get similar results using .groupby() with few extra steps.

Pandas: group by and Pivot table difference

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