stil fiddling iwth combining dfs
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2 changed files with 118 additions and 86 deletions
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@ -8,69 +8,74 @@ Created on Tue Aug 6 12:56:03 2019
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# FIXME: change filename 2(mcsm normalised data)
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# to be consistent like (pnca_complex_mcsm_norm.csv) : changed manually, but ensure this is done in the mcsm pipeline
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#=======================================================================
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# Task: combine 2 dfs with aa position as linking column
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# Task: combine 2 dfs on comm_valson cols by detecting them
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# includes sainity checks
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# Input: 2 dfs
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# <gene.lower()>_complex_mcsm_norm.csv
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# <gene.lower()>_foldx.csv
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# Output: .csv of all 2 dfs combined
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# useful link
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# https://stackoverflow.com/questions/23668427/pandas-three-way-joining-multiple-dataframes-on-columns
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#=======================================================================
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#%% load packages
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import sys, os
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import pandas as pd
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import numpy as np
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import re
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#from varname import nameof
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#%% end of variable assignment for input and output files
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#=======================================================================
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#%% function/methd to combine 4 dfs
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#%% function/methd to combine dfs
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#def combine_stability_dfs(mcsm_df, foldx_df, out_combined_df):
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def combine_stability_dfs(mcsm_df, foldx_df, my_join = 'outer'):
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def detect_common_cols (df1, df2):
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"""
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Combine 2 dfs
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Detect comm_valson cols
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@param mcsm_df: csv file (output from mcsm pipeline)
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@type mcsm_df: string
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@param df1: df
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@type df1: pandas df
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@param foldx_df: csv file (output from runFoldx.py)
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@type foldx_df: string
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@param df2: df
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@type df2: pandas df
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@param out_combined_df: csv file output
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@type out_combined_df: string
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@return: none, writes combined df as csv
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@return: comm_valson cols
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@type: list
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"""
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#========================
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# read input csv files to combine
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#========================
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print('Reading input files:')
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common_cols = np.intersect1d(df1.columns, df2.columns).tolist()
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#print('Length of comm_cols:', len(comm_cols)
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# , '\nmerging column/s:', comm_cols
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# , '\ntype:', type(comm_cols)
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# , '\ndtypes in merging columns:\n', df1[comm_cols].dtypes)
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left_df = pd.read_csv(mcsm_df, sep = ',')
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left_df.columns = left_df.columns.str.lower()
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return common_cols
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right_df = pd.read_csv(foldx_df, sep = ',')
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right_df.columns = right_df.columns.str.lower()
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print('Dimension left df:', left_df.shape
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, '\nDimesnion right_df:', right_df.shape
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# , '\njoin type:', join_type
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, '\n=========================================================')
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def combine_stability_dfs(df1, df2, my_join = 'outer'):
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"""
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Combine 2 dfs by finding merging columns automatically
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print('Finding common cols and merging cols:'
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@param df1: data frame
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@type df1: pandas df
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@param df2: data frame
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@type df2: pandas df
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@my_join: join type for merging
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@type my_join: string
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@return: combined_df
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@type: pandas df
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"""
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print('Finding comm_valson cols and merging cols:'
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,'\n=========================================================')
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common_cols = np.intersect1d(left_df.columns, right_df.columns).tolist()
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print('Length of common cols:', len(common_cols)
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, '\ncommon column/s:', common_cols, 'type:', type(common_cols))
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common_cols = np.intersect1d(df1.columns, df2.columns).tolist()
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print('Length of comm_valson cols:', len(common_cols)
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, '\nmerging column/s:', common_cols
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, '\ntype:', type(common_cols)
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, '\ndtypes in merging columns:\n', df1[common_cols].dtypes)
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print('selecting consistent dtypes for merging (object i.e string)')
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merging_cols = left_df[common_cols].select_dtypes(include = [object]).columns.tolist()
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#merging_cols = df1[comm_valson_cols].select_dtypes(include = [object]).columns.tolist()
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#merging_cols = df1[comm_valson_cols].select_dtypes(include = ['int64']).columns.tolist()
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merging_cols = common_cols.copy()
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nmerging_cols = len(merging_cols)
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print(' length of merging cols:', nmerging_cols
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, '\nmerging cols:', merging_cols, 'type:', type(merging_cols)
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@ -79,67 +84,94 @@ def combine_stability_dfs(mcsm_df, foldx_df, my_join = 'outer'):
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#========================
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# merge 1 (combined_df)
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# concatenating 2dfs:
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# mcsm_df, foldx_df
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# df1, df2
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#========================
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# checking cross-over of mutations in the two dfs to merge
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#ndiff1 = left_df.shape[0] - left_df['mutationinformation'].isin(right_df['mutationinformation']).sum()
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ndiff_1 = left_df[merging_cols].squeeze().isin(right_df[merging_cols].squeeze()).sum()
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print('ndiff_1:', ndiff_1)
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ndiff_1 = df1[merging_cols].squeeze().isin(df2[merging_cols].squeeze()).sum()
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ndiff1 = df1.shape[0] - ndiff_1
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print('There are', ndiff1, 'unmatched mutations in left df')
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ndiff1 = left_df.shape[0] - ndiff_1
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#print('There are', ndiff1, 'unmatched mutations in left df')
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#missing_mutinfo = left_df[~left_df['mutationinformation'].isin(right_df['mutationinformation'])]
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#missing_mutinfo = df1[~left_df['mutationinformation'].isin(df2['mutationinformation'])]
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#missing_mutinfo.to_csv('infoless_muts.csv')
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#ndiff2 = right_df.shape[0] - right_df['mutationinformation'].isin(left_df['mutationinformation']).sum()
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ndiff_2 = right_df[merging_cols].squeeze().isin(left_df[merging_cols].squeeze()).sum()
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print('ndiff_2:', ndiff_2)
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ndiff_2 = df2[merging_cols].squeeze().isin(df1[merging_cols].squeeze()).sum()
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ndiff2 = df2.shape[0] - ndiff_2
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print('There are', ndiff2, 'unmatched mutations in right_df')
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ndiff2 = right_df.shape[0] - ndiff_2
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#print('There are', ndiff2, 'unmatched mutations in right_df')
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comm = np.intersect1d(left_df[merging_cols], right_df[merging_cols])
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comm_count = len(comm)
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print('inner:', comm, '\nlength:', comm_count , '\ntype:', type(comm_count))
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#comm_vals = np.intersect1d(df1[merging_cols], df2[merging_cols])
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#comm_vals_count = len(comm_vals)
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#print('length of comm_valson values:', comm_vals_count , '\ntype:', type(comm_vals_count))
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#========================
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# sanity checks for join type
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# merging dfs & sanity checks
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#========================
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fail = False
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print('combing with:', my_join)
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combined_df = pd.merge(left_df, right_df, on = merging_cols, how = my_join)
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combined_df1 = combined_df.drop_duplicates(subset = merging_cols, keep ='first')
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comb_df = pd.merge(df1, df2, on = merging_cols, how = my_join)
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combined_df = comb_df.drop_duplicates(subset = merging_cols, keep ='first')
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if my_join == 'inner':
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#expected_rows = left_df.shape[0] - ndiff1
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expected_rows = comm_count
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expected_cols = df1.shape[1] + df2.shape[1] - nmerging_cols
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if my_join == 'outer':
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#expected_rows = right_df.shape[0] + ndiff1
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expected_rows = max(left_df.shape[0], right_df.shape[0])
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if my_join == 'right':
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expected_rows = right_df.shape[0]
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df2_nd = df2.drop_duplicates(merging_cols, keep = 'first')
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expected_rows = df2_nd.shape[0]
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if my_join == 'left':
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expected_rows = left_df.shape[0]
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expected_rows = df1.shape[0]
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expected_cols = left_df.shape[1] + right_df.shape[1] - nmerging_cols
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if len(combined_df1) == expected_rows and len(combined_df1.columns) == expected_cols:
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#if my_join == 'inner':
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# expected_rows = comm_vals_count
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#if my_join == 'outer':
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# df1_nd = df1.drop_duplicates(merging_cols, keep = 'first')
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# df2_nd = df2.drop_duplicates(merging_cols, keep = 'first')
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# expected_rows = df1_nd.shape[0] + df2_nd.shape[0] - comm_vals_count
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if my_join == 'inner' or 'outer' and len(merging_cols)>1:
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comm_vals = np.intersect1d(df1['mutationinformation'], df2['mutationinformation'])
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print('length of comm_values for merge:', len(comm_vals))
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if my_join == 'inner':
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expected_rows = len(comm_vals)
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if my_join == 'outer':
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df1_nd = df1.drop_duplicates(merging_cols, keep = 'first')
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df2_nd = df2.drop_duplicates(merging_cols, keep = 'first')
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expected_rows = df1_nd.shape[0] + df2_nd.shape[0] - len(comm_vals)
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else:
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comm_vals = np.intersect1d(df1[merging_cols], df2[merging_cols])
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print('length of comm_values for merge:', len(comm_vals))
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if my_join == 'inner':
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expected_rows = len(comm_vals)
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if my_join == 'outer':
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df1_nd = df1.drop_duplicates(merging_cols, keep = 'first')
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df2_nd = df2.drop_duplicates(merging_cols, keep = 'first')
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expected_rows = df1_nd.shape[0] + df2_nd.shape[0] - len(comm_vals)
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if len(combined_df) == expected_rows and len(combined_df.columns) == expected_cols:
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print('PASS: successfully combined dfs with:', my_join, 'join')
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else:
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print('FAIL: combined_df\'s expected rows and cols not matched')
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fail = True
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print('\nExpected no. of rows:', expected_rows
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, '\nGot:', len(combined_df1)
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, '\nGot:', len(combined_df)
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, '\nExpected no. of cols:', expected_cols
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, '\nGot:', len(combined_df1.columns))
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, '\nGot:', len(combined_df.columns))
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if fail:
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sys.exit()
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return combined_df1
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#if clean:
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#foo = combined_df2.filter(regex = r'.*_x|_y', axis = 1)
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#print(foo.columns)
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#print('Detected duplicate cols with suffix: _x _y'
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# , '\Dropping duplicate cols and cleaning')
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# drop position col containing suffix '_y' and then rename col without suffix
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combined_df_clean = combined_df.drop(combined_df.filter(regex = r'.*_y').columns, axis = 1)
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combined_df_clean.rename(columns=lambda x: re.sub('_x$','', x), inplace = True)
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return combined_df_clean
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#%% end of function
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#=======================================================================
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@ -52,7 +52,7 @@ gene = args.gene
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# dirs
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#======
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datadir = homedir + '/' + 'git/Data'
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indir = datadir + '/' + drug + '/' + 'output'
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indir = datadir + '/' + drug + '/' + 'input'
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outdir = datadir + '/' + drug + '/' + 'output'
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#=======
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@ -61,10 +61,10 @@ outdir = datadir + '/' + drug + '/' + 'output'
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in_filename_mcsm = gene.lower() + '_complex_mcsm_norm.csv'
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in_filename_foldx = gene.lower() + '_foldx.csv'
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infile_mcsm = indir + '/' + in_filename_mcsm
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infile_foldx = indir + '/' + in_filename_foldx
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infile_mcsm = outdir + '/' + in_filename_mcsm
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infile_foldx = outdir + '/' + in_filename_foldx
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print('\nInput path:', indir
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print('\nInput path:', outdir
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, '\nInput filename1:', in_filename_mcsm
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, '\nInput filename2:', in_filename_foldx
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, '\n============================================================')
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