Python - Applying A Mask To An Array In A For Loop

I have this code: import numpy as np result = {} result['depth'] = [1,1,1,2,2,2] result['generation'] = [1,1,1,2,2,2] result['dimension'] = [1,2,3,1,2,3] result['data'] = [np.arra

Solution 1:

In order to solve your problem (finding and dropping duplicates) I encourage you to use pandas. It is a Python module that makes your life absurdly simple:

import numpy as np

result = {}
result['depth'] = [1,1,1,2,2,2]
result['generation'] = [1,1,1,2,2,2]
result['dimension'] = [1,2,3,1,2,3]
result['data'] = [np.array([0,0,0]), np.array([0,0,0]), np.array([0,0,0]),\
         np.array([0,0,0]), np.array([0,0,0]), np.array([0,0,0])]

# Here comes pandas!
import pandas as pd

# Converting your dictionary of lists into a beautiful dataframe
df = pd.DataFrame(result)

#>    data         depth dimension generation# 0     [0, 0, 0]   1       1        1# 1     [0, 0, 0]   1       2        1# 2     [0, 0, 0]   1       3        1# 3     [0, 0, 0]   2       1        2# 4     [0, 0, 0]   2       2        2# 5     [0, 0, 0]   2       3        2# Dropping duplicates... in one single command!
df = df.drop_duplicates('depth')

#>    data         depth dimension generation# 0     [0, 0, 0]   1       1        1# 3     [0, 0, 0]   2       1        2

If you want oyur data back in the original format... you need yet again just one line of code!

df.to_dict('list')

#> {'data': [array([0, 0, 0]), array([0, 0, 0])],# 'depth': [1, 2],# 'dimension': [1, 1],# 'generation': [1, 2]}