# mars.dataframe.DataFrame.quantile#

DataFrame.quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear')#

Return values at the given quantile over requested axis.

Parameters
• q (float or array-like, default 0.5 (50% quantile)) – Value between 0 <= q <= 1, the quantile(s) to compute.

• axis ({0, 1, 'index', 'columns'} (default 0)) – Equals 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.

• numeric_only (bool, default True) – If False, the quantile of datetime and timedelta data will be computed as well.

• interpolation ({'linear', 'lower', 'higher', 'midpoint', 'nearest'}) –

This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points i and j: * linear: i + (j - i) * fraction, where fraction is the

fractional part of the index surrounded by i and j.

• lower: i.

• higher: j.

• nearest: i or j whichever is nearest.

• midpoint: (i + j) / 2.

Returns

If `q` is an array or a tensor, a DataFrame will be returned where the

index is `q`, the columns are the columns of self, and the values are the quantiles.

If `q` is a float, a Series will be returned where the

index is the columns of self and the values are the quantiles.

Return type

`core.window.Rolling.quantile`

Rolling quantile.

`numpy.percentile`

Numpy function to compute the percentile.

Examples

```>>> import mars.dataframe as md
>>> df = md.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
...                   columns=['a', 'b'])
>>> df.quantile(.1).execute()
a    1.3
b    3.7
Name: 0.1, dtype: float64
```
```>>> df.quantile([.1, .5]).execute()
a     b
0.1  1.3   3.7
0.5  2.5  55.0
```

Specifying numeric_only=False will also compute the quantile of datetime and timedelta data.

```>>> df = md.DataFrame({'A': [1, 2],
...                    'B': [md.Timestamp('2010'),
...                          md.Timestamp('2011')],
...                    'C': [md.Timedelta('1 days'),
...                          md.Timedelta('2 days')]})
>>> df.quantile(0.5, numeric_only=False).execute()
A                    1.5
B    2010-07-02 12:00:00
C        1 days 12:00:00
Name: 0.5, dtype: object
```