Pandas Series: describe() function

Last update on September 15 2022 12:56:36 (UTC/GMT +8 hours)

Generate descriptive statistics in Pandas

The describe() function is used to 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.

Syntax:

Series.describe(self, percentiles=None, include=None, exclude=None)

Parameters:

Name	Description	Type/Default Value	Required / Optional
percentiles	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.	list-like of numbers	Optional
include	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 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.	‘all’, list-like of dtypes or None (default),	Optional
exclude	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.	list-like of dtypes or None (default)	Options

Returns: scalar or Series
Summary statistics of the Series or Dataframe provided.

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 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.

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.

Example - Describing a numeric Series:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([2, 3, 4])
s.describe()

Output:

count    3.0
mean     3.0
std      1.0
min      2.0
25%      2.5
50%      3.0
75%      3.5
max      4.0
dtype: float64

Example - Describing a categorical Series:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series(['p', 'p', 'q', 'r'])
s.describe()

Output:

count     4
unique    3
top       p
freq      2
dtype: object

Example - Describing a timestamp Series:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([
  np.datetime64("2012-02-02"),
  np.datetime64("2019-02-02"),
  np.datetime64("2019-02-02")
])
s.describe()

Output:

count                       3
unique                      2
top       2019-02-02 00:00:00
freq                        2
first     2012-02-02 00:00:00
last      2019-02-02 00:00:00
dtype: object

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