I have a list of strings, and two separate pandas dataframes. One of the dataframes contains NaNs. I am trying to find a fast way of checking if any item in the list is contained in either of the dataframes, and if so, to remove it from the list.
Currently, I do this with list comprehension. I first concatenate the two dataframes. I then loop through the list, and using an if statement check if it is contained in the concatenated dataframe values.
patches = [patch for patch in patches if not patch in bad_patches.values]
The first 5 elements of my list of strings:
patches[1:5]
['S2A_MSIL2A_20170613T101031_11_52',
'S2A_MSIL2A_20170717T113321_35_89',
'S2A_MSIL2A_20170613T101031_12_39',
'S2A_MSIL2A_20170613T101031_11_77']
An example of one of my dataframes, with the second being the same but containing less rows. Note first row contains patches[2].
cloud_patches.head()
0 S2A_MSIL2A_20170717T113321_35_89
1 S2A_MSIL2A_20170717T113321_39_84
2 S2B_MSIL2A_20171112T114339_0_13
3 S2B_MSIL2A_20171112T114339_0_52
4 S2B_MSIL2A_20171112T114339_0_53
The concatenated dataframe:
bad_patches = pd.concat([cloud_patches, snow_patches], axis=1)
bad_patches.head()
0 S2A_MSIL2A_20170717T113321_35_89 S2B_MSIL2A_20170831T095029_27_76
1 S2A_MSIL2A_20170717T113321_39_84 S2B_MSIL2A_20170831T095029_27_85
2 S2B_MSIL2A_20171112T114339_0_13 S2B_MSIL2A_20170831T095029_29_75
3 S2B_MSIL2A_20171112T114339_0_52 S2B_MSIL2A_20170831T095029_30_75
4 S2B_MSIL2A_20171112T114339_0_53 S2B_MSIL2A_20170831T095029_30_78
and the tail, showing the NaNs of one column:
bad_patches.tail()
61702 NaN S2A_MSIL2A_20180228T101021_43_6
61703 NaN S2A_MSIL2A_20180228T101021_43_8
61704 NaN S2A_MSIL2A_20180228T101021_43_11
61705 NaN S2A_MSIL2A_20180228T101021_43_13
61706 NaN S2A_MSIL2A_20180228T101021_43_16
Column headers are all (poorly) named 0.
The second element of patches should be removed as it's contained in the first row of bad_patches. My method does work but takes absolutely ages. Bad_patches is 60,000 rows and the length of patches is variable. Right now for a length of 1000 patches it takes a 2.04 seconds but I need to scale up to 500k patches so hoping there is a faster way. Thanks!
I would create a set with the values from cloud_patches and snow_patches. Then also create a set of patches:
patch_set = set(cloud_patches[0]).union(set(snow_patches[0])
patches = set(patches)
Now you just subtract all values in patch_set from the values in patches, and you will be left with only values in patches that do not show up in cloud_patches nor snow_patches:
cleaned_list = list(patches - patch_set)