Row-wise maximum value is the largest value in each row of a matrix or a data frame. It is commonly used in data analysis and machine learning tasks to extract important information from a dataset or to perform certain operations.
To find the column that contains the highest number of row-wise maximum values in a Pandas DataFrame, you can use the following methods:
1. Using "idxmax()" function:
You can use the idxmax() function in Pandas to find the column with the maximum value in each row of the DataFrame, and then count the occurrences of each column to find the column with the highest number of row-wise maximum values.
Example:
2. Using "apply()" function:
You can use the apply() method along the axis=1 to apply a function to each row of a pandas DataFrame. You can define a lambda function that returns the maximum value of each row using the max() method.
Example:
3. Using NumPy's "argmax()" function:
You can use the argmax() function in NumPy to find the index of the maximum value in each row of the DataFrame, and then count the occurrences of each column to find the column with the highest number of row-wise maximum values.