# pandas.Series.describe¶

Generate descriptive statistics that summarize the central tendency,
dispersion and shape of a dataset’s distribution, excluding
`NaN`

values.

Analyzes both numeric and object series, as well
as `DataFrame`

column sets of mixed data types. The output
will vary depending on what is provided. Refer to the notes
below for more detail.

- param percentiles
- list-like of numbers, optional
The percentiles to include in the output. All should fall between 0 and 1. The default is

`[.25, .5, .75]`

, which returns the 25th, 50th, and 75th percentiles.

- param include
- ‘all’, list-like of dtypes or None (default), optional
A white list of data types to include in the result. Ignored for

`Series`

. Here are the options:‘all’ : All columns of the input will be included in the output.

- A list-like of dtypesLimits the results to the
provided data types. To limit the result to numeric types submit

`numpy.number`

. To limit it instead to object columns submit the`numpy.object`

data type. Strings can also be used in the style of`select_dtypes`

(e.g.`df.describe(include=['O'])`

). To select pandas categorical columns, use`'category'`

None (default) : The result will include all numeric columns.

- param exclude
- list-like of dtypes or None (default), optional,
A black list of data types to omit from the result. Ignored for

`Series`

. Here are the options:- A list-like of dtypesExcludes the provided data types
from the result. To exclude numeric types submit

`numpy.number`

. To exclude object columns submit the data type`numpy.object`

. Strings can also be used in the style of`select_dtypes`

(e.g.`df.describe(include=['O'])`

). To exclude pandas categorical columns, use`'category'`

None (default) : The result will exclude nothing.

- return
Series or DataFrame Summary statistics of the Series or Dataframe provided.

## Limitations¶

Parameters

`include`

and`exclude`

are currently unsupported by Intel Scalable Dataframe Compiler.For string Series resulting values are returned as strings.

## Examples¶

```
import numpy as np
import pandas as pd
from numba import njit
@njit
def series_describe():
s = pd.Series([5., 0, 3.3, 4.4, 9.2])
return s.describe()
print(series_describe())
```

```
$ python ./series/series_describe.py
count 5.000000
mean 4.380000
std 3.315419
min 0.000000
25% 3.300000
50% 4.400000
75% 5.000000
max 9.200000
dtype: float64
```

See also

- DataFrame.count
Count number of non-NA/null observations.

- DataFrame.max
Maximum of the values in the object.

- DataFrame.min
Minimum of the values in the object.

- DataFrame.mean
Mean of the values.

- DataFrame.std
Standard deviation of the observations.

- DataFrame.select_dtypes
Subset of a DataFrame including/excluding columns based on their dtype.