# this extracts the pairs of indices that are considered duplicates:ĭupes_mi = drop_ĭupes_left = matches_mi.get_level_values(0)ĭupes_right = matches_mi. Pandas dropduplicates () method helps in removing duplicates from the Pandas Dataframe In Python. # that mask is comparisons that are considered duplicates. Mi = om_tuples(combinations(df.index, 2))ĭf_cross_diff = df.loc.set_index(mi) - df.loc.set_index(mi)
# using combinations ensures a lower-triangular matrix of comparison indices This is a general solution that for any data frame. To deduplicate a data frame within a threshold, you need to calculate the difference between each value within each column and see if those values are within the threshold difference. For example, if two coordinates are rounded to (100, 300, 756.2) and (200, 400, 756.1), they should be considered duplicates and should be removed. What I want is to remove duplicates if the columns 'xr' and 'yr' are duplicates +-100 and 'zr' duplicates +-0.1.
#Pd drop duplicates code
My code looks something like this: import pandas as pdĭf_test_1 = pd.DataFrame(np.array(,, , ]), columns = )ĭf_test_2 = pd.DataFrame(np.array(,, , ]), columns = )ĭf_test_3 = pd.concat()ĭf_test_3 = df_test_3.drop_duplicates(subset=, keep=False)
For example, if one point lies at x = 149 and another at x = 151, rounding them to the nearest hundred gives different values. My idea was to round the values to the nearest significant number, but this also does not always work, since if some values are rounded to different numbers, they won't match and won't be removed. One of these contains points that should be masked in the other one, but the values are slightly offset from each other, meaning a direct match with drop_duplicates is not possible. What I have is two Pandas dataframes of coordinates in xyz-format.
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