pandas.core.groupby.DataFrameGroupBy.describe

DataFrameGroupBy.describe(**kwargs) [source]

Parameters:

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. 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 dtypes : Limits the results to the provided data types. To limit the result to numeric types submit numpy.number. To limit it instead to categorical objects submit the numpy.object data type. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])) None (default) : The result will include all numeric columns. 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 dtypes : Excludes the provided data types from the result. To select numeric types submit numpy.number. To select categorical objects submit the data type numpy.object. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])) None (default) : The result will exclude nothing.

Returns:

summary: Series/DataFrame of summary statistics

Notes

For numeric data, the result’s index will include count, mean, std, min, max as well as lower, 50 and upper percentiles. By default the lower percentile is 25 and the upper percentile is 75. The 50 percentile is the same as the median.

For object data (e.g. strings or timestamps), the result’s index will include count, unique, top, and freq. The top is the most common value. The freq is the most common value’s frequency. Timestamps also include the first and last items.

If multiple object values have the highest count, then the count and top results will be arbitrarily chosen from among those with the highest count.

For mixed data types provided via a DataFrame, the default is to return only an analysis of numeric columns. If include='all' is provided as an option, the result will include a union of attributes of each type.

The include and exclude parameters can be used to limit which columns in a DataFrame are analyzed for the output. The parameters are ignored when analyzing a Series.

Examples

Describing a numeric Series.

>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0

Describing a categorical Series.

>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count     4
unique    3
top       a
freq      2
dtype: object

Describing a timestamp Series.

>>> s = pd.Series([
...   np.datetime64("2000-01-01"),
...   np.datetime64("2010-01-01"),
...   np.datetime64("2010-01-01")
... ])
>>> s.describe()
count                       3
unique                      2
top       2010-01-01 00:00:00
freq                        2
first     2000-01-01 00:00:00
last      2010-01-01 00:00:00
dtype: object

Describing a DataFrame. By default only numeric fields are returned.

>>> df = pd.DataFrame([[1, 'a'], [2, 'b'], [3, 'c']],
...                   columns=['numeric', 'object'])
>>> df.describe()
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Describing all columns of a DataFrame regardless of data type.

>>> df.describe(include='all')
        numeric object
count       3.0      3
unique      NaN      3
top         NaN      b
freq        NaN      1
mean        2.0    NaN
std         1.0    NaN
min         1.0    NaN
25%         1.5    NaN
50%         2.0    NaN
75%         2.5    NaN
max         3.0    NaN

Describing a column from a DataFrame by accessing it as an attribute.

>>> df.numeric.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
Name: numeric, dtype: float64

Including only numeric columns in a DataFrame description.

>>> df.describe(include=[np.number])
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Including only string columns in a DataFrame description.

>>> df.describe(include=[np.object])
       object
count       3
unique      3
top         b
freq        1

Excluding numeric columns from a DataFrame description.

>>> df.describe(exclude=[np.number])
       object
count       3
unique      3
top         b
freq        1

Excluding object columns from a DataFrame description.

>>> df.describe(exclude=[np.object])
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0