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Can I loop the same analysis across multiple csv dataframes then concatenate results from each into one table?

newbie python learner here!
I have 20 participant csv files (P01.csv to P20.csv) with dataframes in them that contain stroop test data. The important columns for each are the condition column which has a random mix of incongruent and congruent conditions, the reaction time column for each condition and the column for if the response was correct, true or false.
Here is an example of the dataframe for P01 I'm not sure if this counts as a code snippet? :
trialnum,colourtext,colourname,condition,response,rt,correct
1,blue,red,incongruent,red,0.767041,True
2,yellow,yellow,congruent,yellow,0.647259,True
3,green,blue,incongruent,blue,0.990185,True
4,green,green,congruent,green,0.720116,True
5,yellow,yellow,congruent,yellow,0.562909,True
6,yellow,yellow,congruent,yellow,0.538918,True
7,green,yellow,incongruent,yellow,0.693017,True
8,yellow,red,incongruent,red,0.679368,True
9,yellow,blue,incongruent,blue,0.951432,True
10,blue,blue,congruent,blue,0.633367,True
11,blue,green,incongruent,green,1.289047,True
12,green,green,congruent,green,0.668142,True
13,blue,red,incongruent,red,0.647722,True
14,red,blue,incongruent,blue,0.858307,True
15,red,red,congruent,red,1.820112,True
16,blue,green,incongruent,green,1.118404,True
17,red,red,congruent,red,0.798532,True
18,red,red,congruent,red,0.470939,True
19,red,blue,incongruent,blue,1.142712,True
20,red,red,congruent,red,0.656328,True
21,red,yellow,incongruent,yellow,0.978830,True
22,green,red,incongruent,red,1.316182,True
23,yellow,yellow,congruent,green,0.964292,False
24,green,green,congruent,green,0.683949,True
25,yellow,green,incongruent,green,0.583939,True
26,green,blue,incongruent,blue,1.474140,True
27,green,blue,incongruent,blue,0.569109,True
28,green,green,congruent,blue,1.196470,False
29,red,red,congruent,red,4.027546,True
30,blue,blue,congruent,blue,0.833177,True
31,red,red,congruent,red,1.019672,True
32,green,blue,incongruent,blue,0.879507,True
33,red,red,congruent,red,0.579254,True
34,red,blue,incongruent,blue,1.070518,True
35,blue,yellow,incongruent,yellow,0.723852,True
36,yellow,green,incongruent,green,0.978838,True
37,blue,blue,congruent,blue,1.038232,True
38,yellow,green,incongruent,yellow,1.366425,False
39,green,red,incongruent,red,1.066038,True
40,blue,red,incongruent,red,0.693698,True
41,red,blue,incongruent,blue,1.751062,True
42,blue,blue,congruent,blue,0.449651,True
43,green,red,incongruent,red,1.082267,True
44,blue,blue,congruent,blue,0.551023,True
45,red,blue,incongruent,blue,1.012258,True
46,yellow,green,incongruent,yellow,0.801443,False
47,blue,blue,congruent,blue,0.664119,True
48,red,green,incongruent,yellow,0.716189,False
49,green,green,congruent,yellow,0.630552,False
50,green,yellow,incongruent,yellow,0.721917,True
51,red,red,congruent,red,1.153943,True
52,blue,red,incongruent,red,0.571019,True
53,yellow,yellow,congruent,yellow,0.651611,True
54,blue,blue,congruent,blue,1.321344,True
55,green,green,congruent,green,1.159240,True
56,blue,blue,congruent,blue,0.861646,True
57,yellow,red,incongruent,red,0.793069,True
58,yellow,yellow,congruent,yellow,0.673190,True
59,yellow,red,incongruent,red,1.049320,True
60,red,yellow,incongruent,yellow,0.773447,True
61,red,yellow,incongruent,yellow,0.693554,True
62,red,red,congruent,red,0.933901,True
63,blue,blue,congruent,blue,0.726794,True
64,green,green,congruent,green,1.046116,True
65,blue,blue,congruent,blue,0.713565,True
66,blue,blue,congruent,blue,0.494177,True
67,green,green,congruent,green,0.626399,True
68,blue,blue,congruent,blue,0.711896,True
69,blue,blue,congruent,blue,0.460420,True
70,green,green,congruent,yellow,1.711978,False
71,blue,blue,congruent,blue,0.634218,True
72,yellow,blue,incongruent,yellow,0.632482,False
73,yellow,yellow,congruent,yellow,0.653813,True
74,green,green,congruent,green,0.808987,True
75,blue,blue,congruent,blue,0.647117,True
76,green,red,incongruent,red,1.791693,True
77,red,yellow,incongruent,yellow,1.482570,True
78,red,red,congruent,red,0.693132,True
79,red,yellow,incongruent,yellow,0.815830,True
80,green,green,congruent,green,0.614441,True
81,yellow,red,incongruent,red,1.080385,True
82,red,green,incongruent,green,1.198548,True
83,blue,green,incongruent,green,0.845769,True
84,yellow,blue,incongruent,blue,1.007089,True
85,green,blue,incongruent,blue,0.488701,True
86,green,green,congruent,yellow,1.858272,False
87,yellow,yellow,congruent,yellow,0.893149,True
88,yellow,yellow,congruent,yellow,0.569597,True
89,yellow,yellow,congruent,yellow,0.483542,True
90,yellow,red,incongruent,red,1.669842,True
91,blue,green,incongruent,green,1.158416,True
92,blue,red,incongruent,red,1.853055,True
93,green,yellow,incongruent,yellow,1.023785,True
94,yellow,blue,incongruent,blue,0.955395,True
95,yellow,yellow,congruent,yellow,1.303260,True
96,blue,yellow,incongruent,yellow,0.737741,True
97,yellow,green,incongruent,green,0.730972,True
98,green,red,incongruent,red,1.564596,True
99,yellow,yellow,congruent,yellow,0.978911,True
100,blue,yellow,incongruent,yellow,0.508151,True
101,red,green,incongruent,green,1.821969,True
102,red,red,congruent,red,0.818726,True
103,yellow,yellow,congruent,yellow,1.268222,True
104,yellow,yellow,congruent,yellow,0.585495,True
105,green,green,congruent,green,0.673404,True
106,blue,yellow,incongruent,yellow,1.407036,True
107,red,red,congruent,red,0.701050,True
108,red,green,incongruent,red,0.402334,False
109,red,green,incongruent,green,1.537681,True
110,green,yellow,incongruent,yellow,0.675118,True
111,green,green,congruent,green,1.004550,True
112,yellow,blue,incongruent,blue,0.627439,True
113,yellow,yellow,congruent,yellow,1.150248,True
114,blue,yellow,incongruent,yellow,0.774452,True
115,red,red,congruent,red,0.860966,True
116,red,red,congruent,red,0.499595,True
117,green,green,congruent,green,1.059725,True
118,red,red,congruent,red,0.593180,True
119,green,yellow,incongruent,yellow,0.855915,True
120,blue,green,incongruent,green,1.335018,True
But I am only interested in the 'condition', 'rt', and 'correct' columns.
I need to create a table that says the mean reaction time for the congruent conditions, and the incongruent conditions, and the percentage correct for each condition. But I want to create an overall table of these results for each participant. I am aiming to get something like this as an output table:
Participant
Stimulus Type
Mean Reaction Time
Percentage Correct
01
Congruent
0.560966
80
01
Incongruent
0.890556
64
02
Congruent
0.460576
89
02
Incongruent
0.956556
55
Etc. for all 20 participants. This was just an example of my ideal output because later I'd like to plot a graph of the means from each condition across the participants. But if anyone thinks that table does not make sense or is inefficient, I'm open to any advice!
I want to use pandas but don't know where to begin finding the rt means for each condition when there are two different conditions in the same column in each dataframe? And I'm assuming I need to do it in some kind of loop that can run over each participant csv file, and then concatenates the results in a table for all the participants?
Initially, after struggling to figure out the loop I would need and looking on the web, I ran this code, which worked to concatenate all of the dataframes of the participants, I hoped this would help me to do the same analysis on all of them at once but the problem is it doesn't identify the individual participants for each of the rows from each participant csv file (there are 120 rows for each participant like the example I give above) that I had put into one table:
import os
import glob
import pandas as pd
#set working directory
os.chdir('data')
#find all csv files in the folder
#use glob pattern matching -> extension = 'csv'
#save result in list -> all_filenames
extension = 'csv'
all_filenames = [i for i in glob.glob('*.{}'.format(extension))]
#print(all_filenames)
#combine all files in the list
combined_csv = pd.concat([pd.read_csv(f) for f in all_filenames ])
#export to csv
combined_csv.to_csv( "combined_csv.csv", index=False, encoding='utf-8-sig')
Perhaps I could do something to add a participant column to identify each participant's data set in the concatenated table and then perform the mean and percentage correct analysis on the two conditions for each participant in that big concatenated table?
Or would it be better to do the analysis and then loop it over all of the individual participant csv files of dataframes?
I'm sorry if this is a really obvious process, I'm new to python and trying to learn to analyse my data more efficiently, have been scouring the Internet and Panda tutorials but I'm stuck. Any help is welcome! I've also never used Stackoverflow before so sorry if I haven't formatted things correctly here but thanks for the feedback about including examples of the input data, code I've tried, and desired output data, I really appreciate the help.

Try this:
from pathlib import Path
# Use the Path class to represent a path. It offers more
# functionalities when perform operations on paths
path = Path("./data").resolve()
# Create a dictionary whose keys are the Participant ID
# (the `01` in `P01.csv`, etc), and whose values are
# the data frames initialized from the CSV
data = {
p.stem[1:]: pd.read_csv(p) for p in path.glob("*.csv")
}
# Create a master data frame by combining the individual
# data frames from each CSV file
df = pd.concat(data, keys=data.keys(), names=["participant", None])
# Calculate the statistics
result = (
df.groupby(["participant", "condition"]).agg(**{
"Mean Reaction Time": ("rt", "mean"),
"correct": ("correct", "sum"),
"size": ("trialnum", "size")
}).assign(**{
"Percentage Correct": lambda x: x["correct"] / x["size"]
}).drop(columns=["correct", "size"])
.reset_index()
)

Alignment of column names and its corresponding rows in Python

I have a CSV file which is very messy in terms of column and row alignment. In the first cell, all column names are stated, but they do not align with the rows beneath. So when I load this CSV in python using pandas
I do not get a clean dataframe
In the below picture, there is an example of how it should look like with the columns separated and matching the rows.
Some details:
Few lines of raw CSV file:
Columns:
VMName;"Cluster";"time";"AvgValue";"MinValue";"MaxValue";"MetricId";"MemoryMB";"CpuMHz";"NumCpu"
Rows:
ITLT4301;1;"1-5-2018";976439;35059255;53842;6545371441;3235864;95200029;"MemActive";"4096";"0";"0"
Code:
df = pd.read_csv(file_location, sep=";")
Output when loading the dataframe in python:
VMName;"Cluster";"time";"AvgValue";"MinValue";"MaxValue";"MetricId";"MemoryMB";"CpuMHz";"NumCpu",,,
ITLT4301;1;"1-5-2018";976439,35059255 53842,6545371441 3235864,"95200029 MemActive"" 4096"" 0"" 0"""
Desired output:
VMName Cluster time AvgValue MinValue MaxValue MetricId MemoryMB CpuMHz
ITLT4301 1 1-5-201 976439 35059255 53842 6545371441 95200029 MemActive
NumCpu
4096
Hopefully this clears up the topic and problem a bit. Desired output is a well-organized data frame where the columns match the rows based on separater sign ";"

Your input data file is not a standard csv file. The correct way would be to fix the previous step in order to get a normal csv file instead of a mess of double quotes preventing any decent csv parser to correctly extract data.
As a workaround, it is possible to remove the initial and terminating double quote, remove any doubled double quote, and split every line on semi-column ignoring any remaining double quote. Optionnaly, you could also try to just remove any double quote and split the lines on ';'. It really depends on what values you expect.
A possible code could be:
def split_line(line):
'''split a line on ; after stripping white spaces, the initial and terminating "
doubles double quotes are also removed'''
return line.strip()[1:-1].replace('""', '').split(';')
with open('file.dat') as fd:
cols = split_line(next(fd)) # extract column names from header line
data = [split_line(line) for line in fd] # process data lines
df = pd.DataFrame(data, columns=cols) # build a dataframe from that
With that input:
"VMName;""Cluster"";""time"";""AvgValue"";""MinValue"";""MaxValue"";""MetricId"";""MemoryMB"";""CpuMHz"";""NumCpu"""
"ITLT4301;1;""1-5-2018"";976439" 35059255;53842 6545371441;3235864 "95200029;""MemActive"";""4096"";""0"";""0"""
"ITLT4301;1;""1-5-2018"";98" 9443749608104;29 3435452286154;673 "067568681366;""CpuUsageMHz"";""0"";""5600"";""2"""
It gives:
VMName Cluster time AvgValue MinValue \
0 ITLT4301 1 1-5-2018 976439" 35059255 53842 6545371441
1 ITLT4301 1 1-5-2018 98" 9443749608104 29 3435452286154
MaxValue MetricId MemoryMB CpuMHz NumCpu
0 3235864 "95200029 MemActive 4096 0 0
1 673 "067568681366 CpuUsageMHz 0 5600 2

Python: creating dataframe with filename and file last modify time

i want to read file name in folder which i already did using file=glob.glob... function.
and add in 'file_last_mod_t' column last modify file time.
my part of code:
df=pd.DataFrame(columns=['filename','file_last_mod_t','else'])
df.set_index('filename')
for file in glob.glob('folder_path'): #inside this folder is file.txt
file_name=os.path.basename('folder_path')
df.loc[file_name]= os.path.getmtime(file)
which gives me:
df:
filename,file_last_mod_t,else
file.txt,123456,123456 #123456 its time result example
i want to add this last modify time only to file_last_mod_t column, not for all.
i want to receive :
df:
filename,file_last_mod_t,else
file.txt,123456,
thanks in advice
after code modification:
df=pd.read_csv('C:/df.csv')
filename_list= pd.Series(result_from_other_definition)# it looks same as in #filename column
df['filename']=filename_list # so now i have dataframe with 3 columns and firs column have files list
df.set_index('filename')
for file in glob.glob('folder_path'):#inside this folder is file.txt
df['file_last_mod_t']=df['filename'].apply(lambda x: (os.path.getmtime(x)) #the way how getmtime is present is now no matter, could be #float numbers
df.to_csv('C:/df.csv')
#printing samples:
first run:
df['filename']=filename_list
print (df)
,'filename','file_last_mod_t','else'
0,file1.txt,NaN,NaN
1,file2.txt,NaN,NaN
code above works fine after first run when df is empty , only with headers.
after next run when i run the code and df.csv have some content i am changing manually value of timestamp in file, i am receiving an error : TypeError: stat: path should be string, bytes, os.PathLike or integer,not float this code should replace manually modified cell with good timestamp. i think it's connected with apply
also i dont know why index appear in df
**solved **

Please see comment on code as following:
import os
import pandas as pd
import datetime as dt
import glob
# this is the function to get file time as string
def getmtime(x):
x= dt.datetime.fromtimestamp(os.path.getmtime(x)).strftime("%Y-%m-%d %H:%M:%d")
return x
df=pd.DataFrame(columns=['filename','file_last_mod_t','else'])
df.set_index('filename')
# I set filename list to df['filename']
df['filename'] = pd.Series([file for file in glob.glob('*')])
# I applied a time modified file to df['file_last_mod_t'] by getmtime function
df['file_last_mod_t'] = df['filename'].apply(lambda x: getmtime(x))
print (df)
The result is
filename file_last_mod_t else
0 dataframe 2019-05-04 18:43:04 NaN
1 fer2013.csv 2018-05-26 12:18:26 NaN
2 file.txt 2019-05-04 18:49:04 NaN
3 file2.txt 2019-05-04 18:51:04 NaN
4 Untitled.ipynb 2019-05-04 17:41:04 NaN
5 Untitled1.ipynb 2019-05-04 20:51:04 NaN
For the updated question, I started with df.csv what have data as following:
filename,file_last_mod_t,else
file1.txt,,
And, I think you want to add new files. So, I made the code as following:
import os
import pandas as pd
df=pd.read_csv('df.csv')
df_adding=pd.DataFrame(columns=['filename','file_last_mod_t','else'])
df_adding['filename'] = pd.Series(['file2.txt'])
df = df.append(df_adding)
df = df.drop_duplicates('filename')
df['file_last_mod_t']=df['filename'].apply(lambda x: (os.path.getmtime(x))) #the way how getmtime is present is now no matter, could be #float numbers
df.to_csv('df.csv', index=False)
I created df_adding dataframe for new files and I appended it to df which read df.csv.
Finally, we can apply getmtime and save if to df.csv.

Python data wrangling issues

I'm currently stumped by some basic issues with a small data set. Here are the first three lines to illustrate the format of the data:
"Sport","Entry","Contest_Date_EST","Place","Points","Winnings_Non_Ticket","Winnings_Ticket","Contest_Entries","Entry_Fee","Prize_Pool","Places_Paid"
"NBA","NBA 3K Crossover #3 [3,000 Guaranteed] (Early Only) (1/15)","2015-03-01 13:00:00",35,283.25,"13.33","0.00",171,"20.00","3,000.00",35
"NBA","NBA 1,500 Layup #4 [1,500 Guaranteed] (Early Only) (1/25)","2015-03-01 13:00:00",148,283.25,"3.00","0.00",862,"2.00","1,500.00",200
The issues I am having after using read_csv to create a DataFrame:
The presence of commas in certain categorical values (such as Prize_Pool) results in python considering these entries as strings. I need to convert these to floats in order to make certain calculations. I've used python's replace() function to get rid of the commas, but that's as far as I've gotten.
The category Contest_Date_EST contains timestamps, but some are repeated. I'd like to subset the entire dataset into one that has only unique timestamps. It would be nice to have a choice in which repeated entry or entries are removed, but at the moment I'd just like to be able to filter the data with unique timestamps.

Use thousands=',' argument for numbers that contain a comma
In [1]: from pandas import read_csv
In [2]: d = read_csv('data.csv', thousands=',')
You can check Prize_Pool is numerical
In [3]: type(d.ix[0, 'Prize_Pool'])
Out[3]: numpy.float64
To drop rows - take first observed, you can also take last
In [7]: d.drop_duplicates('Contest_Date_EST', take_last=False)
Out[7]:
Sport Entry \
0 NBA NBA 3K Crossover #3 [3,000 Guaranteed] (Early ...
Contest_Date_EST Place Points Winnings_Non_Ticket Winnings_Ticket \
0 2015-03-01 13:00:00 35 283.25 13.33 0
Contest_Entries Entry_Fee Prize_Pool Places_Paid
0 171 20 3000 35

Edit: Just realized you're using pandas - should have looked at that.
I'll leave this here for now in case it's applicable but if it gets
downvoted I'll take it down by virtue of peer pressure :)
I'll try and update it to use pandas later tonight
Seems like itertools.groupby() is the tool for this job;
Something like this?
import csv
import itertools
class CsvImport():
def Run(self, filename):
# Get the formatted rows from CSV file
rows = self.readCsv(filename)
for key in rows.keys():
print "\nKey: " + key
i = 1
for value in rows[key]:
print "\nValue {index} : {value}".format(index = i, value = value)
i += 1
def readCsv(self, fileName):
with open(fileName, 'rU') as csvfile:
reader = csv.DictReader(csvfile)
# Keys may or may not be pulled in with extra space by DictReader()
# The next line simply creates a small dict of stripped keys to original padded keys
keys = { key.strip(): key for (key) in reader.fieldnames }
# Format each row into the final string
groupedRows = {}
for k, g in itertools.groupby(reader, lambda x : x["Contest_Date_EST"]):
groupedRows[k] = [self.normalizeRow(v.values()) for v in g]
return groupedRows;
def normalizeRow(self, row):
row[1] = float(row[1].replace(',','')) # "Prize_Pool"
# and so on
return row
if __name__ == "__main__":
CsvImport().Run("./Test1.csv")
Output:
More info:
https://docs.python.org/2/library/itertools.html
Hope this helps :)

How to retrieve rows from a csv file using a tag string

I have a CSV file contains data like this:
I have write down a code which is able to retrieve the rows which contains "Active" at second column "outcome":
Data:
No,Outcome,target,result
1,Active,PGS2,positive
2,inactive,IM2,negative
3,inactive,IGI,positive
4,Active,IIL,positive
5,Active,P53,negative
Code:
new_file = open(my_file)
lines = new_file.readlines()
for line in lines:
if "Active" in line:
print line,
Outcome:
No,Outcome,target,result
1,Active,PGS2,positive
4,Active,IIL,positive
5,Active,P53,negative
How can i write down this code using pandas library so that i can make this code shorter if i am using pandas functionality after retrieving the rows.
Also this code is not suitable when you have "Active" key word same where else in yor row because that can retrieve a false row. I found after previewing some posts that "pandas" is very suitable library for CSV Handling.

Why not just filter this aftewards, it will be faster than parsing line by line. Just do this:
In [172]:
df[df['Outcome']=='Active']
Out[172]:
No Outcome target result
0 1 Active PGS2 positive
3 4 Active IIL positive
4 5 Active P53 negative