tanu/LSHTM_analysis
1
0
Fork 0
Code Issues Pull requests Releases Wiki Activity

resolving merge conflicts dur to shoddy data

Browse source
This commit is contained in:
Tanushree Tunstall 2020-07-14 14:09:42 +01:00
parent 8dc2fa7326
commit f27c223bdd
2 changed files with 633 additions and 0 deletions
Download patch file Download diff file Expand all files Collapse all files

179
scripts/combining_FIXME.py Executable file
View file

@ -0,0 +1,179 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
Created on Tue Aug 6 12:56:03 2019
@author: tanu
'''
# FIXME: change filename 2(mcsm normalised data)
# to be consistent like (pnca_complex_mcsm_norm.csv) : changed manually, but ensure this is done in the mcsm pipeline
#=======================================================================
# Task: combine 2 dfs on comm_valson cols by detecting them
# includes sainity checks
#=======================================================================
#%% load packages
import sys, os
import pandas as pd
import numpy as np
import re
#from varname import nameof
#%% end of variable assignment for input and output files
#=======================================================================
#%% function/methd to combine dfs
def detect_common_cols (df1, df2):
"""
Detect comm_valson cols
@param df1: df
@type df1: pandas df
@param df2: df
@type df2: pandas df
@return: comm_valson cols
@type: list
"""
common_cols = np.intersect1d(df1.columns, df2.columns).tolist()
print('Length of comm_cols:', len(common_cols)
, '\nMerging column/s:', common_cols
, '\n---------------------------------------------------------------'
, '\nType:', type(common_cols)
, '\n\ndtypes in merging columns:\n', df1[common_cols].dtypes
, '\n---------------------------------------------------------------')
return common_cols
#%% Function to combine 2 dfs by detecting commom cols and performing
# sanity checks on the output df
def combine_dfs_with_checks(df1, df2, my_join = 'outer'):
"""
Combine 2 dfs by finding merging columns automatically
@param df1: data frame
@type df1: pandas df
@param df2: data frame
@type df2: pandas df
@my_join: join type for merging
@type my_join: string
@return: combined_df
@type: pandas df
"""
print('Finding comm_cols and merging cols:'
,'\n=========================================================')
common_cols = np.intersect1d(df1.columns, df2.columns).tolist()
print('Length of comm_cols:', len(common_cols)
, '\nmerging column/s:', common_cols
, '\ntype:', type(common_cols))
#print('\ndtypes in merging columns:\n', df1[common_cols].dtypes)
print('selecting consistent dtypes for merging (object i.e string)')
#merging_cols = df1[comm_valson_cols].select_dtypes(include = [object]).columns.tolist()
#merging_cols = df1[comm_valson_cols].select_dtypes(include = ['int64']).columns.tolist()
merging_cols = common_cols.copy()
nmerging_cols = len(merging_cols)
print(' length of merging cols:', nmerging_cols
, '\nmerging cols:', merging_cols, 'type:', type(merging_cols)
, '\n=========================================================')
#========================
# merge 1 (combined_df)
# concatenating 2dfs:
# df1, df2
#========================
# checking cross-over of mutations in the two dfs to merge
ndiff_1 = df1[merging_cols].squeeze().isin(df2[merging_cols].squeeze()).sum()
ndiff1 = df1.shape[0] - ndiff_1
print('There are', ndiff1, 'unmatched mutations in left df')
#missing_mutinfo = df1[~left_df['mutationinformation'].isin(df2['mutationinformation'])]
#missing_mutinfo.to_csv('infoless_muts.csv')
ndiff_2 = df2[merging_cols].squeeze().isin(df1[merging_cols].squeeze()).sum()
ndiff2 = df2.shape[0] - ndiff_2
print('There are', ndiff2, 'unmatched mutations in right_df')
#comm_vals = np.intersect1d(df1[merging_cols], df2[merging_cols])
#comm_vals_count = len(comm_vals)
#print('length of comm_valson values:', comm_vals_count , '\ntype:', type(comm_vals_count))
#========================
# merging dfs & sanity checks
#========================
fail = False
print('combing with:', my_join)
comb_df = pd.merge(df1, df2, on = merging_cols, how = my_join)
expected_cols = df1.shape[1] + df2.shape[1] - nmerging_cols
if my_join == 'right':
df2_nd = df2.drop_duplicates(merging_cols, keep = 'first')
expected_rows = df2_nd.shape[0]
if my_join == 'left':
expected_rows = df1.shape[0]
#if my_join == 'inner':
# expected_rows = comm_vals_count
#if my_join == 'outer':
# df1_nd = df1.drop_duplicates(merging_cols, keep = 'first')
# df2_nd = df2.drop_duplicates(merging_cols, keep = 'first')
# expected_rows = df1_nd.shape[0] + df2_nd.shape[0] - comm_vals_count
if my_join == ('inner' or 'outer') and len(merging_cols) > 1:
#comm_vals = np.intersect1d(df1['mutationinformation'], df2['mutationinformation'])
print('length of merging_cols > 1, therefore omitting row checks')
combined_df = comb_df.copy()
expected_rows = len(combined_df)
else:
comm_vals = np.intersect1d(df1[merging_cols], df2[merging_cols])
print('length of merging_cols == 1, calculating expected rows in merged_df')
combined_df = comb_df.drop_duplicates(subset = merging_cols, keep ='first')
if my_join == 'inner':
expected_rows = len(comm_vals)
if my_join == 'outer':
df1_nd = df1.drop_duplicates(merging_cols, keep = 'first')
df2_nd = df2.drop_duplicates(merging_cols, keep = 'first')
expected_rows = df1_nd.shape[0] + df2_nd.shape[0] - len(comm_vals)
if len(combined_df) == expected_rows and len(combined_df.columns) == expected_cols:
print('PASS: successfully combined dfs with:', my_join, 'join')
else:
print('FAIL: combined_df\'s expected rows and cols not matched')
fail = True
print('\nExpected no. of rows:', expected_rows
, '\nGot:', len(combined_df)
, '\nExpected no. of cols:', expected_cols
, '\nGot:', len(combined_df.columns))
if fail:
sys.exit()
#if clean:
#foo = combined_df2.filter(regex = r'.*_x|_y', axis = 1)
#print(foo.columns)
#print('Detected duplicate cols with suffix: _x _y'
# , '\Dropping duplicate cols and cleaning')
# drop position col containing suffix '_y' and then rename col without suffix
combined_df_clean = combined_df.drop(combined_df.filter(regex = r'.*_y').columns, axis = 1)
combined_df_clean.rename(columns=lambda x: re.sub('_x$','', x), inplace = True)
return combined_df_clean
#%% end of function
#=======================================================================

454
scripts/combining_dfs.py Executable file
View file

@ -0,0 +1,454 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
Created on Tue Aug 6 12:56:03 2019
@author: tanu
'''
#=======================================================================
# Task: combining all dfs to a single one
# Input: 8 dfs
#1) <gene>.lower()'_complex_mcsm_norm.csv'
#2) <gene>.lower()_foldx.csv'
#3) <gene>.lower()_dssp.csv'
#4) <gene>.lower()_kd.csv'
#5) <gene>.lower()_rd.csv'
#6) 'ns' + <gene>.lower()_snp_info.csv'
#7) <gene>.lower()_af_or.csv'
#8) <gene>.lower() _af_or_kinship.csv
# combining order
#Merge1 = 1 + 2
#Merge2 = 3 + 4
#Merge3 = Merge2 + 5
#Merge4 = Merge1 + Merge3
#Merge5 = 6 + 7
#Merge6 = Merge5 + 8
#Merge7 = Merge4 + Merge6
# Output: single csv of all 8 dfs combined
# useful link
# https://stackoverflow.com/questions/23668427/pandas-three-way-joining-multiple-dataframes-on-columns
#=======================================================================
#%% load packages
import sys, os
import pandas as pd
from pandas import DataFrame
import numpy as np
#from varname import nameof
import argparse
#=======================================================================
#%% specify input and curr dir
homedir = os.path.expanduser('~')
# set working dir
os.getcwd()
os.chdir(homedir + '/git/LSHTM_analysis/scripts')
os.getcwd()
# FIXME: local imports
#from combining import combine_dfs_with_checks
from combining_FIXME import detect_common_cols
#=======================================================================
#%% command line args
#arg_parser = argparse.ArgumentParser()
#arg_parser.add_argument('-d', '--drug', help='drug name', default = 'pyrazinamide')
#arg_parser.add_argument('-g', '--gene', help='gene name', default = 'pncA') # case sensitive
#arg_parser.add_argument('--datadir', help = 'Data Directory. By default, it assmumes homedir + git/Data')
#arg_parser.add_argument('-i', '--input_dir', help = 'Input dir containing pdb files. By default, it assmumes homedir + <drug> + input')
#arg_parser.add_argument('-o', '--output_dir', help = 'Output dir for results. By default, it assmes homedir + <drug> + output')
#arg_parser.add_argument('--debug', action ='store_true', help = 'Debug Mode')
#args = arg_parser.parse_args()
#=======================================================================
#%% variable assignment: input and output
drug = 'pyrazinamide'
gene = 'pncA'
gene_match = gene + '_p.'
#drug = args.drug
#gene = args.gene
#datadir = args.datadir
#indir = args.input_dir
#outdir = args.output_dir
#%%=======================================================================
#==============
# directories
#==============
if not datadir:
datadir = homedir + '/' + 'git/Data'
if not indir:
indir = datadir + '/' + drug + '/input'
if not outdir:
outdir = datadir + '/' + drug + '/output'
#=======
# input
#=======
in_filename_mcsm = gene.lower() + '_complex_mcsm_norm.csv'
in_filename_foldx = gene.lower() + '_foldx.csv'
in_filename_dssp = gene.lower() + '_dssp.csv'
in_filename_kd = gene.lower() + '_kd.csv'
in_filename_rd = gene.lower() + '_rd.csv'
in_filename_snpinfo = 'ns' + gene.lower() + '_snp_info.csv'
in_filename_afor = gene.lower() + '_af_or.csv'
in_filename_afor_kin = gene.lower() + '_af_or_kinship.csv'
infile_mcsm = outdir + '/' + in_filename_mcsm
infile_foldx = outdir + '/' + in_filename_foldx
infile_dssp = outdir + '/' + in_filename_dssp
infile_kd = outdir + '/' + in_filename_kd
infile_rd = outdir + '/' + in_filename_rd
infile_snpinfo = indir + '/' + in_filename_snpinfo
infile_afor = outdir + '/' + in_filename_afor
infile_afor_kin = outdir + '/' + in_filename_afor_kin
print('\nInput path:', indir
, '\nOutput path:', outdir
, '\nInput filename mcsm:', infile_mcsm
, '\nInput filename foldx:', infile_foldx
, '\nInput filename dssp:', infile_dssp
, '\nInput filename kd:', infile_kd
, '\nInput filename rd', infile_rd
, '\nInput filename snp info:', infile_snpinfo
, '\nInput filename af or:', infile_afor
, '\nInput filename afor kinship:', infile_afor_kin
, '\n============================================================')
#=======
# output
#=======
out_filename_comb = gene.lower() + '_all_params.csv'
outfile_comb = outdir + '/' + out_filename_comb
print('Output filename:', outfile_comb
, '\n===================================================================')
o_join = 'outer'
l_join = 'left'
r_join = 'right'
i_join = 'inner'
# end of variable assignment for input and output files
#%%============================================================================
print('==================================='
, '\nFirst merge: mcsm + foldx'
, '\n===================================')
mcsm_df = pd.read_csv(infile_mcsm, sep = ',')
#mcsm_df.columns = mcsm_df.columns.str.lower()
foldx_df = pd.read_csv(infile_foldx , sep = ',')
#mcsm_foldx_dfs = combine_dfs_with_checks(mcsm_df, foldx_df, my_join = o_join)
merging_cols_m1 = detect_common_cols(mcsm_df, foldx_df)
mcsm_foldx_dfs = pd.merge(mcsm_df, foldx_df, on = merging_cols_m1, how = o_join)
ncols_m1 = len(mcsm_foldx_dfs.columns)
print('\n\nResult of first merge:', mcsm_foldx_dfs.shape
, '\n===================================================================')
#%%============================================================================
print('==================================='
, '\nSecond merge: dssp + kd'
, '\n===================================')
dssp_df = pd.read_csv(infile_dssp, sep = ',')
kd_df = pd.read_csv(infile_kd, sep = ',')
rd_df = pd.read_csv(infile_rd, sep = ',')
#dssp_kd_dfs = combine_dfs_with_checks(dssp_df, kd_df, my_join = o_join)
merging_cols_m2 = detect_common_cols(dssp_df, kd_df)
dssp_kd_dfs = pd.merge(dssp_df, kd_df, on = merging_cols_m2, how = o_join)
print('\n\nResult of second merge:', dssp_kd_dfs.shape
, '\n===================================================================')
#%%============================================================================
print('==================================='
, '\nThird merge: second merge + rd_df'
, '\ndssp_kd_dfs + rd_df'
, '\n===================================')
#dssp_kd_rd_dfs = combine_dfs_with_checks(dssp_kd_dfs, rd_df, my_join = o_join)
merging_cols_m3 = detect_common_cols(dssp_df, kd_df)
dssp_kd_rd_dfs = pd.merge(dssp_kd_dfs, rd_df, on = merging_cols_m3, how = o_join)
ncols_m3 = len(dssp_kd_rd_dfs.columns)
print('\n\nResult of Third merge:', dssp_kd_rd_dfs.shape
, '\n===================================================================')
#%%============================================================================
print('======================================='
, '\nFourth merge: First merge + Third merge'
, '\nmcsm_foldx_dfs + dssp_kd_rd_dfs'
, '\n=======================================')
#combined_df = combine_dfs_with_checks(mcsm_foldx_dfs, dssp_kd_rd_dfs, my_join = i_join)
merging_cols_m4 = detect_common_cols(mcsm_foldx_dfs, dssp_kd_rd_dfs)
combined_df = pd.merge(mcsm_foldx_dfs, dssp_kd_rd_dfs, on = merging_cols_m4, how = i_join)
combined_df_expected_cols = ncols_m1 + ncols_m3 - len(merging_cols_m4)
if len(combined_df) == len(mcsm_df) and len(combined_df.columns) == combined_df_expected_cols:
print('PASS: successfully combined 5 dfs'
, '\nNo. of rows combined_df:', len(combined_df)
, '\nNo. of cols combined_df:', len(combined_df.columns))
else:
sys.exit('FAIL: check individual df merges')
print('\nResult of Fourth merge:', combined_df.shape
, '\n===================================================================')
#%%============================================================================
# OR merges: TEDIOUSSSS!!!!
#%%============================================================================
print('==================================='
, '\nFifth merge: afor_df + snpinfo_df'
, '\n===================================')
# OR combining
afor_df = pd.read_csv(infile_afor, sep = ',')
#afor_df.columns = afor_df.columns.str.lower()
snpinfo_df_all = pd.read_csv(infile_snpinfo, sep = ',')
#snpinfo_df_all.columns = snpinfo_df_all.columns.str.lower()
#afor_snpinfo_dfs = combine_dfs_with_checks(afor_df, snpinfo_df_all, my_join = i_join)
merging_cols_m5 = detect_common_cols(afor_df, snpinfo_df_all)
afor_snpinfo_dfs = pd.merge(afor_df, snpinfo_df_all, on = merging_cols_m5, how = l_join)
# finding mutations lacking meta data
foo = afor_df[~afor_df['mutation'].isin(snpinfo_df_all['mutation'])]
foo1 = afor_df[afor_df['mutation'].isin(snpinfo_df_all['mutation'])]
bar = snpinfo_df_all[~snpinfo_df_all['mutation'].isin(afor_df['mutation'])]
bar1 = snpinfo_df_all[snpinfo_df_all['mutation'].isin(afor_df['mutation'])]
# checks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# afor_df
if afor_df['mutation'].shape[0] == afor_df['mutation'].nunique():
print('No duplicate mutations detected in afor_df')
else:
print('Dropping duplicate mutations detected in afor_df')
afor_df = afor_df.drop_duplicates(subset = 'mutation', keep = 'first')
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# finding mutations lacking meta data
# FIXME: should get fixmed with JP's resolved dataset!?
print('There are', len(afor_df[~afor_df['mutation'].isin(snpinfo_df_all['mutation'])])
, 'mutations with various or calculated that have no additional info...STRANGE'
, 'Reported to Jody on 14 july 2020 on skype!')
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
foo = afor_df[~afor_df['mutation'].isin(snpinfo_df_all['mutation'])]
foo1 = afor_df[afor_df['mutation'].isin(snpinfo_df_all['mutation'])]
# snpinfo_df_all
ndups = 0
if not snpinfo_df_all['mutation'].shape[0] == snpinfo_df_all['mutation'].nunique():
ndups = snpinfo_df_all['mutation'].duplicated().sum()
print(ndups, 'duplicated muts detected in snpinfo_df_all.'
, '\nHowever these may have different nucleotide changes. Checking further...')
#expected_nrows = afor_df.shape[0] + ndups
cols_to_check = ['mutation', 'mutationinformation', 'ref_allele', 'alt_allele']
if snpinfo_df_all.duplicated(subset = cols_to_check).sum() == 0:
print('No *REAL* duplicate muts detected in snpinfo_df_all'
, '\nDim of df:', snpinfo_df_all.shape)
snpinfo_df_all = snpinfo_df_all.copy()
else:
print(snpinfo_df_all.duplicated(subset = cols_to_check).sum()
, ' Actual duplicate mutations detected in snpinfo_df_all')
dup_muts = snpinfo_df_all[['mutation', 'mutationinformation']][snpinfo_df_all.duplicated(subset = cols_to_check)]
print(len(dup_muts), 'duplicated mutation detected'
, '\nDropping duplicated mutations before merging')
snpinfo_df_all = snpinfo_df_all.drop_duplicates(subset = cols_to_check, keep = 'first')
print('Dim of df after removing duplicates:', snpinfo_df_all.shape)
if len(afor_snpinfo_dfs) == afor_df.shape[0] + ndups:
print('PASS: succesfully combined with left join')
else:
print('FAIL: unsuccessful merge'
, '\nDim of df1:', afor_df.shape
, '\nDim of df2:', snpinfo_df_all.shape)
sys.exit()
print('\nResult of Fifth merge:', afor_snpinfo_dfs.shape
, '\n===================================================================')
#%%============================================================================
print('==================================='
, '\nSixth merge: fifth merge + afor_kin_df'
, '\nafor_snpinfo_dfs + afor_kin_df'
, '\n===================================')
afor_kin_df = pd.read_csv(infile_afor_kin, sep = ',')
afor_kin_df.columns = afor_kin_df.columns.str.lower()
#ors_df = combine_dfs_with_checks(afor_snpinfo_dfs, afor_kin_df, my_join = o_join)
merging_cols_m6 = detect_common_cols(afor_snpinfo_dfs, afor_kin_df)
print('Dim of df1:', afor_snpinfo_dfs.shape
, '\nDim of df2:', afor_kin_df.shape
, '\nNo. of merging_cols:', len(merging_cols_m6))
ors_df = pd.merge(afor_snpinfo_dfs, afor_kin_df, on = merging_cols_m6, how = o_join)
# Dropping unncessary columns
cols_to_drop = ['reference_allele', 'alternate_allele', 'symbol' ]
print('Dropping', len(cols_to_drop), 'columns:\n'
, cols_to_drop)
ors_df.drop(cols_to_drop, axis = 1, inplace = True)
print('Reordering', ors_df.shape[1], 'columns'
, '\n===============================================')
cols = ors_df.columns
column_order = ['mutation'
, 'mutationinformation'
, 'wild_type'
, 'position'
, 'mutant_type'
, 'chr_num_allele'
, 'ref_allele'
, 'alt_allele'
, 'mut_info'
, 'mut_type'
, 'gene_id'
, 'gene_number'
, 'mut_region'
#, 'reference_allele'
#, 'alternate_allele'
, 'chromosome_number'
, 'af'
, 'af_kin'
, 'est_chisq'
, 'or_mychisq'
, 'or_fisher'
, 'or_logistic'
, 'or_kin'
, 'pval_chisq'
, 'pval_fisher'
, 'pval_logistic'
, 'pwald_kin'
, 'ci_low_fisher'
, 'ci_hi_fisher'
, 'ci_low_logistic'
, 'ci_hi_logistic'
, 'beta_logistic'
, 'beta_kin'
, 'se_logistic'
, 'se_kin'
, 'zval_logistic'
, 'logl_h1_kin'
, 'l_remle_kin'
, 'wt_3let'
, 'mt_3let'
#, 'symbol'
, 'n_miss']
if len(column_order) == ors_df.shape[1] == len(DataFrame(column_order).isin(ors_df.columns)):
print('PASS: Column order generated for all columns in df', len(column_order), 'columns'
, '\nApplying column order to df...' )
ors_df_ordered = ors_df[column_order]
else:
print('FAIL: Mismatch in no. of cols to reorder'
, '\nNo. of cols in df to reorder:', ors_df.shape[1]
, '\nNo. of cols order generated for:', len(column_order))
sys.exit()
print('\nResult of Sixth merge:', ors_df_ordered.shape
, '\n===================================================================')
#%%============================================================================
print('==================================='
, '\nSeventh merge: Fourth + Sixth merge'
, '\ncombined_df + ors_df_ordered'
, '\n===================================')
#combined_df_all = combine_dfs_with_checks(combined_df, ors_df_ordered, my_join = i_join)
merging_cols_m7 = detect_common_cols(combined_df, ors_df_ordered)
print('Dim of df1:', combined_df.shape
, '\nDim of df2:', ors_df_ordered.shape
, '\nNo. of merging_cols:', len(merging_cols_m7))
print('Checking mutations in the two dfs:'
, '\nmuts in df1 but NOT in df2:'
, combined_df['mutationinformation'].isin(ors_df_ordered['mutationinformation']).sum()
, '\nmuts in df2 but NOT in df1:'
, ors_df_ordered['mutationinformation'].isin(combined_df['mutationinformation']).sum())
#print('\nNo. of common muts:', np.intersect1d(combined_df['mutationinformation'], ors_df_ordered['mutationinformation']) )
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
combined_df_all = pd.merge(combined_df, ors_df, on = merging_cols_m7, how = o_join)
#combined_df_all.shape
# FIXME: DIM
# only with left join!
outdf_expected_rows = len(combined_df)
outdf_expected_cols = len(combined_df.columns) + len(ors_df_ordered.columns) - len(merging_cols_m7)
#if combined_df_all.shape[1] == outdf_expected_cols and combined_df_all.shape[0] == outdf_expected_rows:
if combined_df_all.shape[1] == outdf_expected_cols and combined_df_all['mutationinformation'].nunique() == outdf_expected_rows:
print('PASS: Df dimension match'
, '\nDim of combined_df_all with join type:', o_join
, '\n', combined_df_all.shape
, '\n===============================================================')
else:
print('FAIL: Df dimension mismatch'
, 'Cannot generate expected dim. See details of merge performed'
, '\ndf1 dim:', combined_df.shape
, '\ndf2 dim:', ors_df.shape
, '\nGot:', combined_df_all.shape
, '\nmuts in df1 but NOT in df2:'
, combined_df['mutationinformation'].isin(ors_df['mutationinformation']).sum()
, '\nmuts in df2 but NOT in df1:'
, ors_df['mutationinformation'].isin(combined_df['mutationinformation']).sum())
sys.exit()
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# nan in mutation col
# FIXME: should get fixmed with JP's resolved dataset!?
combined_df_all['mutation'].isna().sum()
baz = combined_df_all[combined_df_all['mutation'].isna()]
#%%============================================================================
output_cols = combined_df_all.columns
print('Output cols:', output_cols)
#%%============================================================================
# write csv
print('Writing file: combined output of all params needed for plotting and ML')
combined_df_all.to_csv(outfile_comb, index = False)
print('\nFinished writing file:'
, '\nNo. of rows:', combined_df_all.shape[0]
, '\nNo. of cols:', combined_df_all.shape[1])
#=======================================================================
#%% incase you FIX the the function: combine_dfs_with_checks
#def main():
# print('Reading input files:')
#mcsm_df = pd.read_csv(infile_mcsm, sep = ',')
#mcsm_df.columns = mcsm_df.columns.str.lower()
#foldx_df = pd.read_csv(infile_foldx , sep = ',')
#dssp_df = pd.read_csv(infile_dssp, sep = ',')
#dssp_df.columns = dssp_df.columns.str.lower()
#kd_df = pd.read_csv(infile_kd, sep = ',')
#kd_df.columns = kd_df.columns.str.lower()
#rd_df = pd.read_csv(infile_kd, sep = ',')
#if __name__ == '__main__':
# main()
#=======================================================================
#%% end of script