dask_expr._collection.Series.describe
dask_expr._collection.Series.describe¶
- Series.describe(split_every=False, percentiles=None, percentiles_method='default', include=None, exclude=None)[source]¶
Generate descriptive statistics.
This docstring was copied from pandas.core.series.Series.describe.
Some inconsistencies with the Dask version may exist.
Descriptive statistics include those 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.- Parameters
- percentileslist-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 object columns submit thenumpy.object
data type. Strings can also be used in the style ofselect_dtypes
(e.g.df.describe(include=['O'])
). To select pandas categorical columns, use'category'
None (default) : The result will include all numeric columns.
- excludelist-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 exclude numeric types submit
numpy.number
. To exclude object columns submit the data typenumpy.object
. Strings can also be used in the style ofselect_dtypes
(e.g.df.describe(exclude=['O'])
). To exclude pandas categorical columns, use'category'
None (default) : The result will exclude nothing.
- Returns
- Series or DataFrame
Summary statistics of the Series or Dataframe provided.
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.
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 is25
and the upper percentile is75
. The50
percentile is the same as the median.For object data (e.g. strings or timestamps), the result’s index will include
count
,unique
,top
, andfreq
. Thetop
is the most common value. Thefreq
is the most common value’s frequency. Timestamps also include thefirst
andlast
items.If multiple object values have the highest count, then the
count
andtop
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 the dataframe consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. Ifinclude='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 aSeries
.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 dtype: float64
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 mean 2006-09-01 08:00:00 min 2000-01-01 00:00:00 25% 2004-12-31 12:00:00 50% 2010-01-01 00:00:00 75% 2010-01-01 00:00:00 max 2010-01-01 00:00:00 dtype: object
Describing a
DataFrame
. By default only numeric fields are returned.>>> df = pd.DataFrame({'categorical': pd.Categorical(['d', 'e', 'f']), ... 'numeric': [1, 2, 3], ... 'object': ['a', 'b', 'c'] ... }) >>> 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') categorical numeric object count 3 3.0 3 unique 3 NaN 3 top f NaN a freq 1 NaN 1 mean NaN 2.0 NaN std NaN 1.0 NaN min NaN 1.0 NaN 25% NaN 1.5 NaN 50% NaN 2.0 NaN 75% NaN 2.5 NaN max NaN 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=[object]) object count 3 unique 3 top a freq 1
Including only categorical columns from a
DataFrame
description.>>> df.describe(include=['category']) categorical count 3 unique 3 top d freq 1
Excluding numeric columns from a
DataFrame
description.>>> df.describe(exclude=[np.number]) categorical object count 3 3 unique 3 3 top f a freq 1 1
Excluding object columns from a
DataFrame
description.>>> df.describe(exclude=[object]) categorical numeric count 3 3.0 unique 3 NaN top f NaN freq 1 NaN mean NaN 2.0 std NaN 1.0 min NaN 1.0 25% NaN 1.5 50% NaN 2.0 75% NaN 2.5 max NaN 3.0