Pandas an pathlib pt2

In [1]:

# Pathlib edith multiplevalues

In [22]:

import pandas as pd
from  pathlib import Path
import shutil

In [2]:

def replace_in_file(file_path, str1, str2):
    #read input file
    f_in = open(file_path, "rt")
    #read file contents to string
    data = f_in.read()
    #replace all occurrences of the required string
    data = data.replace(str1, str2)
    #close the input file
    f_in.close()
    #open the same file in write mode
    f_out = open(file_path, "wt")
    #overrite the file with the resulting data
    f_out.write(data)
    #close the file
    f_out.close()

In [45]:

files_dmc = Path('./data_simulation/').glob('**/**/*values*.txt')
files_PDCCA = Path('./data_simulation/').glob('**/**/*tables*.txt')
for file in files_PDCCA:
    replace_in_file(file, ',', '')

In [50]:

files_PDCCA = Path('./data_out/').glob('**/**/*PDCCA*.txt')
path_01 = list(files_PDCCA)[0]

In [51]:

import pandas as pd

In [53]:

df = pd.read_csv(path_01,  sep=' ')

In [54]:

df

Out[54]:

	n	rho12	rho13	rho14	rho23	rho24	rho34
0	4	0.760061	0.684270	0.508418	0.513245	0.508449	0.711413
1	5	0.764320	0.668200	0.486119	0.478104	0.471227	0.688854
2	7	0.781551	0.651747	0.466251	0.443449	0.431775	0.666639
3	9	0.800796	0.645638	0.466188	0.434620	0.420630	0.661609
4	11	0.816372	0.642035	0.473159	0.433055	0.419693	0.662708
5	13	0.828812	0.639615	0.483356	0.434795	0.423914	0.666626
6	16	0.843128	0.637587	0.500960	0.440563	0.435018	0.674478
7	20	0.856425	0.637008	0.523840	0.449767	0.451689	0.684833
8	23	0.863436	0.637316	0.538322	0.455968	0.462372	0.690960
9	28	0.871284	0.638074	0.556323	0.463410	0.474545	0.697337
10	33	0.875581	0.638884	0.568598	0.466995	0.480815	0.700410
11	38	0.877237	0.639596	0.577043	0.467009	0.482564	0.701830
12	45	0.876467	0.640072	0.583833	0.462281	0.478809	0.701987
13	52	0.873414	0.639615	0.586269	0.453720	0.469767	0.700487
14	60	0.868307	0.637922	0.585748	0.441348	0.455874	0.697579
15	69	0.861494	0.635339	0.583239	0.426509	0.438734	0.694187
16	79	0.853424	0.632594	0.579813	0.411055	0.420112	0.691023
17	91	0.843822	0.630126	0.576024	0.395589	0.400228	0.688057
18	104	0.834231	0.628455	0.573007	0.382984	0.382723	0.685663
19	119	0.824804	0.627456	0.571114	0.373461	0.368305	0.683506
20	135	0.817120	0.627146	0.570672	0.368735	0.359524	0.681523
21	154	0.811207	0.627304	0.571261	0.369216	0.356249	0.679024
22	174	0.808063	0.627567	0.571980	0.374684	0.358535	0.675950
23	198	0.806848	0.627602	0.572156	0.384599	0.365654	0.671797
24	223	0.806645	0.627402	0.571489	0.395762	0.374961	0.667542
25	252	0.805431	0.627078	0.570054	0.406522	0.384524	0.663173
26	285	0.801432	0.625991	0.567398	0.413280	0.390904	0.658420
27	321	0.795125	0.622777	0.562381	0.414375	0.392598	0.652446
28	362	0.788254	0.616244	0.553995	0.410533	0.391414	0.644081
29	407	0.782478	0.606658	0.542966	0.402544	0.389501	0.633788
30	457	0.778289	0.596822	0.532642	0.392815	0.389975	0.623522
31	513	0.775795	0.589098	0.524813	0.383588	0.393465	0.614296
32	575	0.775403	0.583926	0.518941	0.376264	0.398467	0.607003
33	645	0.776380	0.579773	0.512178	0.371013	0.402677	0.602293
34	723	0.778667	0.575429	0.503394	0.367814	0.405080	0.600395
35	809	0.782200	0.570337	0.493671	0.366048	0.406000	0.600701
36	905	0.786615	0.565534	0.485843	0.367141	0.408390	0.604788
37	1011	0.791235	0.563392	0.483736	0.373832	0.416242	0.613622
38	1130	0.795885	0.565740	0.489388	0.386496	0.431217	0.626992
39	1261	0.798482	0.573270	0.503071	0.403482	0.452505	0.643278
40	1407	0.799359	0.583587	0.520701	0.421681	0.476888	0.659848
41	1570	0.798991	0.591055	0.535089	0.436085	0.499760	0.674025
42	1750	0.798090	0.590516	0.542369	0.441946	0.517148	0.684802
43	1950	0.797723	0.580736	0.542156	0.438549	0.526096	0.691883

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