I've got a data frame and want to filter or bin by a range of values and then get the counts of values in each bin.
Currently, I'm doing this:
x = 5 y = 17 z = 33 filter_values = [x, y, z] filtered_a = df[df.filtercol <= x] a_count = filtered_a.filtercol.count() filtered_b = df[df.filtercol > x] filtered_b = filtered_b[filtered_b <= y] b_count = filtered_b.filtercol.count() filtered_c = df[df.filtercol > y] c_count = filtered_c.filtercol.count()
But is there a more concise way to accomplish the same thing?
Perhaps you are looking for pandas.cut:
import pandas as pd import numpy as np df = pd.DataFrame(np.arange(50), columns=['filtercol']) filter_values = [0, 5, 17, 33] out = pd.cut(df.filtercol, bins=filter_values) counts = pd.value_counts(out) # counts is a Series print(counts)
(17, 33] 16 (5, 17] 12 (0, 5] 5
To reorder the result so the bin ranges appear in order, you could use
(0, 5] 5 (5, 17] 12 (17, 33] 16
See also Discretization and quantiling.