## Python | Pandas Series.var

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. ** Pandas **is one of those packages and makes importing and analyzing data much easier.

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 unbiased variance over requested axis. The variance is normalized by N-1 by default. This can be changed using the ddof argument.** Series.var()**

Syntax:Series.var(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Parameter :

axis :{index (0)}

skipna :Exclude NA/null values. If an entire row/column is NA, the result will be NA

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

ddof :Delta Degrees of Freedom. The divisor used in calculations is N – ddof, where N represents the number of elements.

numeric_only :Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

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

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

function to find the variance of the given Series object.

`# importing pandas as pd` `import` `pandas as pd` ` ` `# Creating the Series` `sr ` `=` `pd.Series([` `19.5` `, ` `16.8` `, ` `22.78` `, ` `20.124` `, ` `18.1002` `])` ` ` `# Print the series` `print` `(sr)` |

**Output :**

Now we will use `Series.var()`

function to find the variance of the given series object.

`# find the variance` `sr.var()` |

**Output :**

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

function has returned the variance of the given Series object.

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

function to find the variance of the given Series object. The given Series object contains some missing values.

**Note : **We can skip the missing values by setting the skipna parameter to `True`

.

`# importing pandas as pd` `import` `pandas as pd` ` ` `# Creating the Series` `sr ` `=` `pd.Series([` `100` `, ` `214` `, ` `325` `, ` `88` `, ` `None` `, ` `325` `, ` `None` `, ` `68` `])` ` ` `# Print the series` `print` `(sr)` |

**Output :**

Now we will use `Series.var()`

function to find the variance of the given series object.

`# find the variance` `sr.var(skipna ` `=` `True` `)` |

**Output :**

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

function has returned the variance of the given Series object.

Last Updated on October 23, 2021 by admin