## Python | Pandas Series.describe()

Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.

Pandas

function generate a descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution for the given series object. All the calculations are performed by excluding NaN values.** Series.describe()**

Syntax:Series.describe(percentiles=None, include=None, exclude=None)

Parameter :

percentiles :The percentiles to include in the output.

include :A white list of data types to include in the result. Ignored for Series.

exclude :A black list of data types to omit from the result. Ignored for Series

Returns :Summary statistics of the Series

**Example #1:** Use `Series.describe()`

function to find the summary statistics of the given series object.

`# importing pandas as pd` `import` `pandas as pd` ` ` `# Creating the Series` `sr ` `=` `pd.Series([` `80` `, ` `25` `, ` `3` `, ` `25` `, ` `24` `, ` `6` `])` ` ` `# Create the Index` `index_ ` `=` `[` `'Coca Cola'` `, ` `'Sprite'` `, ` `'Coke'` `, ` `'Fanta'` `, ` `'Dew'` `, ` `'ThumbsUp'` `]` ` ` `# set the index` `sr.index ` `=` `index_` ` ` `# Print the series` `print` `(sr)` |

**Output :**

Now we will use `Series.describe()`

function to find the summary statistics of the underlying data in the given series object.

`# find summary statistics of the underlying ` `# data in the given series object.` `result ` `=` `sr.describe()` ` ` `# Print the result` `print` `(result)` |

**Output :**

As we can see in the output, the `Series.describe()`

function has successfully returned the summary statistics of the given series object.

**Example #2 :** Use `Series.describe()`

function to find the summary statistics of the underlying data in the given series object. The given series object contains some missing values.

`# importing pandas as pd` `import` `pandas as pd` ` ` `# Creating the Series` `sr ` `=` `pd.Series([` `100` `, ` `None` `, ` `None` `, ` `18` `, ` `65` `, ` `None` `, ` `32` `, ` `10` `, ` `5` `, ` `24` `, ` `None` `])` ` ` `# Create the Index` `index_ ` `=` `pd.date_range(` `'2010-10-09'` `, periods ` `=` `11` `, freq ` `=` `'M'` `)` ` ` `# set the index` `sr.index ` `=` `index_` ` ` `# Print the series` `print` `(sr)` |

**Output :**

Now we will use `Series.describe()`

function to find the summary statistics of the underlying data in the given series object.

`# find summary statistics of the underlying ` `# data in the given series object.` `result ` `=` `sr.describe()` ` ` `# Print the result` `print` `(result)` |

**Output :**

As we can see in the output, the `Series.describe()`

function has successfully returned the summary statistics of the given series object. `NaN`

values has been ignored while calculating these statistical values.

Last Updated on October 24, 2021 by admin