## Python Pandas Series.mean()

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 return the mean of the underlying data in the given Series object.** Series.mean()**

Syntax:Series.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Parameter :

axis :Axis for the function to be applied on.

skipna :Exclude NA/null values when computing the result.

level :If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.

numeric_only :Include only float, int, boolean columns.

**kwargs :Additional keyword arguments to be passed to the function.

Returns :mean : scalar or Series (if level specified)

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

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

`# importing pandas as pd` `import` `pandas as pd` ` ` `# Creating the Series` `sr ` `=` `pd.Series([` `10` `, ` `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.mean()`

function to find the mean of the given series object.

`# return the mean` `result ` `=` `sr.mean()` ` ` `# Print the result` `print` `(result)` |

**Output :**

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

function has successfully returned the mean of the given series object.

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

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

`# importing pandas as pd` `import` `pandas as pd` ` ` `# Creating the Series` `sr ` `=` `pd.Series([` `19.5` `, ` `16.8` `, ` `None` `, ` `22.78` `, ` `16.8` `, ` `20.124` `, ` `None` `, ` `18.1002` `, ` `19.5` `])` ` ` `# Print the series` `print` `(sr)` |

**Output :**

Now we will use `Series.mean()`

function to find the mean of the given series object. we are going to skip all the missing values while calculating the mean.

`# return the mean` `# skip all the missing values` `result ` `=` `sr.mean(skipna ` `=` `True` `)` ` ` `# Print the result` `print` `(result)` |

**Output :**

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

function has successfully returned the mean of the given series object.

Last Updated on October 18, 2021 by admin