scikit learn mean shift clustering in one-dimensional array
It does not make sense to run mean-shift on one-dimensional data.
Do regular kernel density estimation instead. Locate the minima, and split the data set there.
Mean shift is for data that is too complex for proper KDE.
One dimensional data never is.
how can I run a mean shift clustering on a 1D array?
Here there is my dataframe:
    >>>df
        INFO         FREQ
     R2   31    0.2468213
     R5   27  0.003670532
     UR   25   0.00337465
I need to apply the clustering on the "INFO" column.
Whit the kmeans I solved this problem using the reshape(-1,1) command:
kmeans.fit(df["INFO"].values.reshape(-1,1)) , but with the mean shift clustering I get this error:
meanshift.fit(df["INFO"].values.reshape(-1,1)) output: ValueError: Invalid shape in axis 1: 0.