DataFrame.
describe
Generate descriptive statistics.
Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
NaN
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.
DataFrame
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.
[.25, .5, .75]
A white list of data types to include in the result. Ignored for Series. Here are the options:
Series
‘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 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'
numpy.number
numpy.object
select_dtypes
df.describe(include=['O'])
'category'
None (default) : The result will include all numeric columns.
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 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.
Whether to treat datetime dtypes as numeric. This affects statistics calculated for the column. For DataFrame input, this also controls whether datetime columns are included by default.
New in version 1.1.0.
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 is 25 and the upper percentile is 75. The 50 percentile is the same as the median.
count
mean
std
min
max
50
25
75
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.
unique
top
freq
first
last
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 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. If include='all' is provided as an option, the result will include a union of attributes of each type.
include='all'
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 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(datetime_is_numeric=True) 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 f 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