Fastest way to filter csv using pandas and create a matrix-CodePudding

input dict

{'basename_AM1.csv': ['AM1286', 'AM1287', 'AM1288']}

I have large csv files in the below format basename_AM1.csv I have large csv files in the below format

basename_AM1.csv

           ID1           ID2   Score
0        AM1287       AM1286  97.55
1        AM1288       AM1286  78.91
2        AM1289       AM1286  95.38
3        AM1290       AM1286  94.83
4        AM1291       AM1286  82.91

Now I need to create a similarity dict like below for the given input_dict by searching/filter the csv files

{'AM1286': {'AM1286': 0, 'AM287': 97.55, 'AM288': 78.91},
 'AM1287': {'AM1286': 97.55, 'AM1287': 100.0, 'AM1288': 78.91},
 'AM1288': {'AM1286': 78.91, 'AM1287': 78.91, 'AM1288': 100.0}}

I have come up with the below logic but for an input_dict of 100 samples this takes too long, Can someone please suggest the optimized and fastest way to achieve this

for key,value in input_dict.items():
            base_name_df = pd.read_csv('csv_file_path')
            base_name_df.columns = "ID1","ID2","Score"
            if os.path.exists('csv_file_path'):
                for id1 in range(len(value)):
                    for id2 in range(len(value)):
                        scan_df = base_name_df[(base_name_df['ID1'] == value[id1]) & (base_name_df['ID2'] == value[id2])]
                        if not scan_df.empty:
                            scan_df = scan_df.groupby(['LIMSID1','LIMSID2'], as_index=False)['Score'].max()
                            final_dict[value[id1]][value[id2]] = scan_df.iloc[0]['Score']

CodePudding user response：

Pandas has a built in read_csv method.

Docs available at: https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html

CodePudding user response：

IIUC, you can use:

input_dict = {'basename_AM1.csv': ['AM1286', 'AM1287', 'AM1288']}

import pandas as pd
for fname, lst in input_dict.items():
    df = pd.read_csv(fname, sep='\s ', names=['ID1', 'ID2', 'score'])
    df2 = df.pivot('ID1', 'ID2', 'score').reindex(index=lst, columns=lst)
    df2 = df2.combine_first(df2.T).fillna(0)
    # print for example
    print(df2.to_dict())

If you want 100 on the diagonal:

import numpy as np
a = df2.to_numpy()
np.fill_diagonal(a, 100)
df2 = pd.DataFrame(a, index=lst, columns=lst)

output:

{'AM1286': {'AM1286': 0.0, 'AM1287': 97.55, 'AM1288': 78.91},
 'AM1287': {'AM1286': 97.55, 'AM1287': 0.0, 'AM1288': 0.0},
 'AM1288': {'AM1286': 78.91, 'AM1287': 0.0, 'AM1288': 0.0}}