# Pandas: group by and Pivot table difference

I just started learning Pandas and was wondering if there is any difference between `pandas groupby`

and `pandas pivot_table`

functions. Can anyone help me understand the difference between them. Help would be appreciated.

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
```

From: stackoverflow.com/q/34702815