The frequency of unique values in the entire dataframe in Pandas is used to understand the distribution of values in the dataset across all columns. This can be particularly useful for exploratory data analysis and data cleaning tasks. In Pandas, we can get the frequency of unique values in the entire dataframe using various methods. Here are some common methods:

#### 1. Using "value_counts()" function:

The `value_counts()`

function in Pandas can be used to get the frequency of unique values in the entire dataframe.

##### Example:

In this example, we use the `stack()`

function to stack the dataframe into a single column. The `value_counts()`

function is then used to get the frequency of unique values in the stacked column.

#### 2. Using "numpy.ravel()" and "numpy.unique()" functions:

Another way to get the frequency of unique values in the entire dataframe is to use the `numpy.ravel()`

and `numpy.unique()`

functions together.

##### Example:

In this example, we use the `numpy.ravel()`

function to flatten the dataframe into a one-dimensional array. The `numpy.unique()`

function is then used to get the unique values and their counts in the flattened array. Finally, the `zip()`

and `dict()`

functions are used to convert the arrays into a dictionary.