Binning is a common technique used to group a set of continuous or numeric values into discrete and finite intervals or bins. This technique is useful for simplifying the data visualization and analysis processes by reducing the number of distinct values in the dataset. I found a code that makes it possible to implement this technique in Python but I am wondering if there are other alternatives to doing this, if there are please let me know. The code I found is attached below and it involves using the `cut()`

method of Pandas and bins a `series`

into 10 bins:

Hi @mubashir_rizvi you can use` np.histogram()`

method, which divides the data into equally spaced bins and counts the number of values that fall within each bin. It takes two main arguments: the data to be binned and the number of bins to use and it returns two arrays: an array of bin boundaries (`bin_edges`

) and an array of counts for each bin (`hist`

).

Hello @mubashir_rizvi , you can use `qcut()`

function in Pandas to bin a numeric series into equally sized bins based on quantiles. It takes two main arguments: the series to be binned and the number of bins to create. This method creates bins of equal frequency based on the quantiles of the data. In other words, it ensures that each bin contains the same number of data points, but the bin boundaries may not be equidistant.

Hey @mubashir_rizvi , the `linspace()`

function in NumPy can be used which creates a one-dimensional array of evenly spaced numbers over a specified interval. In this method, `np.linspace()`

is used to create an array of bin boundaries. The `np.digitize()`

function then maps the values of the numeric series to the corresponding bins based on these boundaries. A simple dataframe is again created to show the results in a way that is easy to interpret.