#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Mar 24 15:01:59 2022 @author: tanu """ import sys, os import pandas as pd import numpy as np from statistics import mean, median, mode from statistics import multimode from collections import Counter from tidy_split import tidy_split #import math # https://stackoverflow.com/questions/43321455/pandas-count-null-values-in-a-groupby-function # https://stackoverflow.com/questions/33457191/python-pandas-gene_LF2frame-fill-nans-with-a-conditional-mean # round up # int(math.ceil(mean(foo))) # https://stackoverflow.com/questions/33457191/python-pandas-gene_LF2frame-fill-nans-with-a-conditional-mean # https://stackoverflow.com/questions/37189878/pandas-add-column-to-groupby-gene_LF2frame # https://stackoverflow.com/questions/43847520/how-to-get-the-distinct-count-of-values-in-a-python-pandas-gene_LF2frame #%% Read gene_LF2 and formatting drug = "pyrazinamide" gene_LF2 = pd.read_csv("/home/tanu/git/ML_AI_training/test_gene_LF2/sample_gene_LF2.csv") gene_LF2.columns gene_LF2.head() #%% Quick checks: Lineage and sample count for each mutation gene_LF2['id'].nunique() gene_LF2['mutationinformation'].nunique() total_id_ucount = gene_LF2['id'].nunique() total_id_ucount gene_LF2.groupby('mutationinformation')['lineage'].size() gene_LF2.groupby('mutationinformation')['lineage_corrupt'].size() gene_LF2.groupby('mutationinformation')['id'].size() gene_LF2.groupby('mutationinformation')['lineage'].value_counts() gene_LF2.groupby('mutationinformation')['lineage'].nunique() #%% id count: add all id ids and count of unique ids per mutation gene_LF2['id_list'] = gene_LF2['mutationinformation'].map(gene_LF2.groupby('mutationinformation')['id'].apply(list)) gene_LF2['id_ucount'] = gene_LF2['mutationinformation'].map(gene_LF2.groupby('mutationinformation')['id'].nunique()) gene_LF2[['mutationinformation', 'id', 'id_list', 'id_ucount']] #%% Lineages: add all lineages and count of unique lineages per mutation # Lineages good: lineage column has only a single lineage for each mutationinformation gene_LF2['lineage'] gene_LF2['lineage_list'] = gene_LF2['mutationinformation'].map(gene_LF2.groupby('mutationinformation')['lineage'].apply(list)) gene_LF2['lineage_ucount'] = gene_LF2['mutationinformation'].map(gene_LF2.groupby('mutationinformation')['lineage'].nunique()) gene_LF2[['mutationinformation', 'lineage', 'lineage_list', 'lineage_ucount']] # Lineage corrupt: lineage column has only multiple lineages for each mutationinformation separated by ';' gene_LF2['lineage_corrupt'] # split using tidy_split() gene_LF2_split = tidy_split(gene_LF2, 'lineage_corrupt', sep = ';') # remove leading white space else these are counted as distinct mutations as well #gene_LF2_split['lineage_corrupt'] = gene_LF2_split['lineage_corrupt'].str.lstrip() gene_LF2_split['lineage_corrupt'] = gene_LF2_split['lineage_corrupt'].str.strip() gene_LF2_split.head() gene_LF2_split['lineage_corrupt_list'] = gene_LF2_split['mutationinformation'].map(gene_LF2_split.groupby('mutationinformation')['lineage_corrupt'].apply(list)) gene_LF2_split['lineage_corrupt_ucount'] = gene_LF2_split['mutationinformation'].map(gene_LF2_split.groupby('mutationinformation')['lineage_corrupt'].nunique()) gene_LF2_split[['mutationinformation', 'lineage_corrupt_list', 'lineage_corrupt_ucount']] gene_LF2_split[['mutationinformation', 'lineage_ucount', 'lineage_corrupt_ucount']] #%% AF: calculate AF for each mutation #1) calculate no. of unique ids gene_LF2['id_ucount']/total_id_ucount #%% DM OM labels # COPY mutation_info_labels column gene_LF2['mutation_info_labels_orig'] = gene_LF2['mutation_info_labels'] # Convert DM/OM labels to numeric dm_om_map = {'DM': 1, 'OM': 0} # pnca, OM is minority, other genes: DM is minority gene_LF2['dm_om_numeric'] = gene_LF2['mutation_info_labels'].map(dm_om_map) # sanity check gene_LF2['dm_om_numeric'].value_counts() gene_LF2['mutation_info_labels'].value_counts() # Convert drtype column to numeric drtype_map = {'XDR': 5 , 'Pre-XDR': 4 , 'MDR': 3 , 'Pre-MDR': 2 , 'Other': 1 , 'Sensitive': 0} gene_LF2['drtype_numeric'] = gene_LF2['drtype'].map(drtype_map) # COPY dst column gene_LF2['dst'] = gene_LF2[drug] # to allow cross checking gene_LF2['dst_multimode'] = gene_LF2[drug] # sanity check gene_LF2[drug].value_counts() gene_LF2['dst_multimode'].value_counts() gene_LF2[drug].isnull().sum() gene_LF2['dst_multimode'].isnull().sum() gene_LF2['mutationinformation'].value_counts() #gene_LF2.C.isnull().groupby([df['A'],df['B']]).sum().astype(int).reset_index(name='count') gene_LF2[drug].isnull().groupby(gene_LF2['mutationinformation']).sum() # GOAL is to populate na in the dst column from the count of the dm_om_numeric column gene_LF2['dst_multimode'].isnull().groupby(gene_LF2['mutationinformation']).sum() gene_LF2['mutationinformation'] #%% Recalculating dst: my gene_LF2 #------------------------------ # Revised dst: max(multimode) #------------------------------ # For each mutation, generate the revised dst which is the mode of dm_om_numeric # PROBLEM: Returns the smallest of the two when bimodal, and fails when all equally likely # SOLUTION: Using max of the 'dst_noNA' column #gene_LF22.groupby('mutationinformation')['dm_om_numeric'].agg(multimode) # Get multimode for dm_om_numeric column dm_om_multimode = gene_LF2.groupby('mutationinformation')['dm_om_numeric'].agg(multimode) #dm_om_multimode # Fill using multimode ONLY where NA in dst_multimode column #gene_LF22['dst_multimode'] = gene_LF22['dst_multimode'].fillna(dm_om_multimode) gene_LF2['dst_multimode'] = gene_LF2['dst_multimode'].fillna(dm_om_multimode) # gene_LF22['dst_multimode'] # Now get the max from multimode gene_LF22['dst_noNA'] = gene_LF2['dst_multimode'].apply(lambda x: np.nanmax(x)) print(gene_LF2) # Finally created a revised dst with the max from the multimode gene_LF22['dst_mode'] = gene_LF2.groupby('mutationinformation')['dst_noNA'].max() #============================================================================== #%% Recalculating drtype: my gene_LF2 #-------------------------------- # drtype: ALL values: # numeric and names in an array #-------------------------------- gene_LF2['drtype_all_vals'] = gene_LF2['drtype_numeric'] gene_LF2['drtype_all_names'] = gene_LF2['drtype'] # example: https://stackoverflow.com/questions/55125680/pandas-get-all-groupby-values-in-an-array # print(df.groupby('key').gene_LF2.apply(list).reset_index()) # my use case, don't need the reset_index() gene_LF2['drtype_all_vals'] = gene_LF2.groupby('mutationinformation').drtype_all_vals.apply(list) gene_LF2['drtype_all_names'] = gene_LF2.groupby('mutationinformation').drtype_all_names.apply(list) #--------------------------------- # Revised drtype: max(Multimode) #-------------------------------- gene_LF2['drtype_multimode'] = gene_LF2.groupby(['mutationinformation'])['drtype_numeric'].agg(multimode) gene_LF2['drtype_multimode'] # Now get the max from multimode gene_LF2['drtype_mode'] = gene_LF2['drtype_multimode'].apply(lambda x: np.nanmax(x)) gene_LF2.head() #---------------------- # Revised drtype: Max #---------------------- gene_LF2.head() gene_LF2['drtype_max'] = gene_LF2.groupby(['mutationinformation'])['drtype_numeric'].max() #gene_LF2 = gene_LF22.reset_index() gene_LF2.head() #%% Finally reset index gene_LF2 = gene_LF2.reset_index() #============================================================================== #--------------------------------------- # Create revised mutation_info_column #--------------------------------------- gene_LF2['dst_mode'].value_counts() gene_LF2[drug].value_counts() # note this is overriding, since downstream depends on it # make a copy you if you need to keep that gene_LF2['mutation_info_labels_orig'] = gene_LF2['mutation_info_labels'] gene_LF2['mutation_info_labels'] = gene_LF2['dst_mode'].map({1: 'DM' , 0: 'OM'}) gene_LF2['mutation_info_labels_orig'].value_counts() gene_LF2['mutation_info_labels'].value_counts() #============================================================================== # sanity check if (all(gene_LF2['mutation'] == gene_LF2['mutationinformation'])): print('\nPass: Mutationinformation check successful') else: sys.exit('\nERROR: mutationin cross checks failed. Please check your group_by() aggregate functions') # Drop mutation column gene_LF2.drop(['mutation'], axis=1, inplace=True) #%% subset: equivalent of merged_df3? # https://stackoverflow.com/questions/39900061/sort-lists-in-a-pandas-gene_LF2frame-column # result = gene_LF2['dst_multimode'].sort_values().apply(lambda x: sorted(x)) # newdf = pd.gene_LF2Frame({'dst_multimode': Series(list(set(result['a'].apply(tuple))))}) # newdf.sort_values(by='a') # gene_LF2['dst_multimode'].value_counts() # gene_LF2.sort_values(['dst_multimode'], ascending=False) #gene_LF2_df3 = gene_LF2.drop_duplicates(['mutationinformation']) #gene_LF2_df3_v2 = gene_LF2.drop_duplicates(['mutationinformation']) #all(gene_LF2_df3 == gene_LF2_df3_v2) #%%