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.