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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()
)

Python Panda's row selection

I've tried doing some searching, but I'm having troubles finding what I specifically need. I currently have this.
location = 'Location'
data = pd.read_csv('testbook.csv')
df = pd.DataFrame(data)
search = 'OR' # This will be replaced with an input
row = (df[df.eq(search).any(1)])
print(row)
Location = row.at[0, location]
print(Location)
This outputs this
row print out
Location City Price Etc
0 FL OR 50 123
Location print out
FL
this is the CSV information that it's pull the data from.
My main question and issue is what I'm trying to find out is at this specific line of code
Location = row.at[0, location]
for Location what I'm trying to do and see if possible is in the brackets [0, location].
I want it to automate in the future since for example if I need to find instead of 'OR' I need to find what data is in 'OR1'. The issue is that the [0] is related to the Row # hence this(this is the entire df).
Location City Price Etc
0 FL OR 50 123
1 FL1 OR1 501 1231
2 FL2 OR2 502 1232
I would have to manually change the code every single time which of course is unfeasible with what I'm trying to accomplish.
My main question is, how do I pull specific row numbers all the way on the left and take that output and make it a variable that I can input anywhere?

I'm having a bit of trouble figuring out what you are looking for but this is my best guess
import pandas as pd
data = {'Location':['FL', 'FL1', 'FL2'],
'City': ['OR', 'OR1', 'OR2'],
'Price':[50, 501, 502],
'Etc': [123,1231,1232]}
data = pd.DataFrame(data)
df = pd.DataFrame(data)
# Given search term -> find location
search = 'OR'
# Outputs 'FL'
df['Location'][df['City'] == search].any()

Reading txt file into Anaconda?

Trying to read a .txt file into my Jupyter notebook.
This is my code:
acm = pd.read_csv('outputacm.txt', header=None, error_bad_lines=False)
print(acm)
Here is a sample of my txt file:
2244018
#*OQL[C++]: Extending C++ with an Object Query Capability.
##José A. Blakeley
#year1995
#confModern Database Systems
#citation14
#index0
#arnetid2
#*Transaction Management in Multidatabase Systems.
##Yuri Breitbart,Hector Garcia-Molina,Abraham Silberschatz
#year1995
#confModern Database Systems
#citation22
#index1
#arnetid3
#*Overview of the ADDS System.
##Yuri Breitbart,Tom C. Reyes
#year1995
#confModern Database Systems
#citation-1
#index2
#arnetid4
And the different symbols are supposed to correspond to:
#* --- paperTitle
## --- Authors
#year ---- Year
#conf --- publication venue
#citation --- citation number (both -1 and 0 means none)
#index ---- index id of this paper
#arnetid ---- pid in arnetminer database
#% ---- the id of references of this paper (there are multiple lines, with each indicating a reference)
#! --- Abstract
Not sure how to set this up so the data gets read correctly. Ideally, I would want a data frame where each category is a different column, and then all the entries in the document are rows. Thanks!

My regex is not as up to speed as it should be, but the below might work so long as the data remains in the same form and the column names aren't duplicated in other lines:
import re
import pandas as pd
path = r"filepath.txt"
f = open(path, 'r')
year = []
confModern = []
#continue for all columns
for ele in f:
if len(re.findall('year', ele)) > 0:
year.append(ele[5:])
if len(re.findall('confModern', ele)) > 0:
year.append(ele[12:])
# continue for all columns with the needed string
df = pd.DataFrame(data={'year' : year ...#continue for each list})

How to calculate a formula in Excel and pull the calculated value with OpenPyXL

I am trying to copy/paste quantities from one excel form into another excel form (that i use as a template) that calculates COGS, and then take the sum of the COGS and assign it to a variable. After some research on OpenPyXL, it looks like you cant calculate the formula in real-time, so i am attempting to save the file with the quantities copied, then re open it, grab my COGS sum, and clear out the quantities to leave it as a blank template.
However, when i do this, i can see that it is pasting the quantity values and clearing them out, but the total COGS value remains 'None.'
This is my first attempt at a Python application after reading through most of Automate the Boring, so I am quite new to this. Any help would be appreciated!
# TODO: Calculate total sell using order form and total cost using ' Costing.xlsx
# Create variables to hold qty of each trim from the total_sheet
costing_wb = openpyxl.load_workbook('Z:\\Costing.xlsx')
costing_sheet = costing_wb['COSTING']
for i in range(3, 46, 1):
costing_sheet.cell(row=i+2, column=4).value = total_sheet.cell(row=i, column=3).value
# Save Costing Sheet
costing_wb.save('Z:\\Costing.xlsx')
# TODO: total_COGS not grabbing value, returning as 'None'
# Reopen costing sheet and set COGS value
costing_wb = openpyxl.load_workbook('Z:\\Costing.xlsx', data_only=True)
costing_sheet = costing_wb['COSTING']
total_COGS = costing_sheet['I6'].value
# Empty Column D contents and save as blank
costing_wb = openpyxl.load_workbook('Z:\\Costing.xlsx')
costing_sheet = costing_wb['COSTING']
for i in range(5, 48, 1):
costing_sheet.cell(row=i, column=4).value = None
costing_wb.save('Z:\\Costing.xlsx')

From 10-K -- extract SIC, CIK, create metadata table

I am working with 10-Ks from Edgar. To assist in file management and data analysis, I would like to create a table containing the path to each file, the CIK number for the company filed (this is a unique ID issued by SEC), and the SIC industry code which it belongs to. Below is an image visually representing what I want to do.
The two things I want to extract are listed at the top of each document. The CIK # will always be a number which is listed after the phrase "CENTRAL INDEX KEY:". The SIC # will always be a number enclosed in brackets after "STANDARD INDUSTRIAL CLASSIFICATION" and then a description of that particular industry.
This is consistent across all filings.
To do's:
Loop through files: extract file path, CIK and SIC numbers -- with attention that I just get one return per document, and each result is in order, so my records between fields align.
Merge these fields together -- I am guessing the best way to do this is to extract each field into their own separate lists and then merge, maybe into a Pandas dataframe?
Ultimately I will be using this table to help me subset the data between SIC industries.
Thank you for taking a look. Please let me know if I can provide additional documentation.

Here are some codes I just wrote for doing something similar. You can output the results to a CSV file. As the first step, you need to walk through the folder and get a list of all the 10-Ks and iterate over it.
year_end = ""
sic = ""
with open(txtfile, 'r', encoding='utf-8', errors='replace') as rawfile:
for cnt, line in enumerate(rawfile):
#print(line)
if "CONFORMED PERIOD OF REPORT" in line:
year_end = line[-9:-1]
#print(year_end)
if "STANDARD INDUSTRIAL CLASSIFICATION" in line:
match = re.search(r"\d{4}", line)
if match:
sic = match.group(0)
#print(sic)
#print(sic)
if (year_end and sic) or cnt > 100:
#print(year_end, sic)
break