I'm having trouble with processing some csv data files for a project. Someone suggested using python/csv reader to help break down the files, which I've had some success with, but not in a way I can use.

This code is a little different from what I was trying before. I am essentially attempting to create an array. In the raw data format, the first 7 rows contain no data, and then each column contains 50 experiments, each with 4000 rows, for 200000 some rows total. What I want to do is take each column, and make it an individual csv file, with each experiment in its own column. So it would be an array of 50 columns and 4000 rows for each data type. The code here does break down the correct values, I think the logic is okay, but it is breaking down the opposite of how I want it. I want the separators without quotes (the commas and spaces) and I want the element values in quotes. Right now it is doing just the opposite for both, element values with no quotes, and the separators in quotes. I've spent several hours trying to figure out how to do this to no avail,

import csv  

ifile  = open('00_follow_maverick.csv')  
epistemicfile = open('00_follower_maverick_EP.csv', 'w')  

reader = csv.reader(ifile)  

colnum = 0  
rownum = 0  
y = 0  
z = 8   
for column in reader:  
    rownum = 4000 * y + z  
    for element in column:  
        writer = csv.writer(epistemicfile)  
        if y <= 50:  
            y = y + 1  
            writer.writerow([element])  
            writer.writerow(',')  
            rownum = x * y + z  
        if y > 50:  
            y = 0  
            z = z + 1  
            writer.writerow(' ')  
            rownum = x * y + z  
        if z >= 4008:  
            break  

What is going on: I am taking each row in the raw data file in iterations of 4000, so that I can separate them with commas for the 50 experiments. When y, the experiment indicator here, reaches 50, it resets back to experiment 0, and adds 1 to z, which tells it which row to look at, by the formula of 4000 * y + z. When i开发者_运维百科t completes the rows for all 50 experiments, it is finished. The problem here is that I don't know how to get python to write the actual values in quotes, and my separators outside of quotes.

Any help will be most appreciated. Apologies if this seems a stupid question, I have no programming experience, this is my first attempt ever. Thank you.

Sorry, I'll try to make this more clear. The original csv file has several columns, each of which are different sets of data.

A miniature example of the raw file looks like:

column1             column2            column3
exp1data1time1      exp1data2time1     exp1data3time1
exp1data1time2      exp1data2time2     exp1data3time2
exp2data1time1      exp2data2time1     exp2data3time1
exp2data1time2      exp2data2time2     exp2data3time2
exp3data1time1      exp3data2time1     exp3data3time1
exp3data1time2      exp3data2time2     exp3data3time2

So, the actual version has 4000 rows instead of 2 for each new experiment. There are 40 columns in the actual version, but basically, the data type in the raw file matches the column number. I want to separate each data type or column into an individual csv file.

This would look like:

csv file1

exp1data1time1   exp2data1time1   exp3data1time1   
exp1data1time2   exp2data1time2   exp3data1time2

csv file2

exp1data2time1   exp2data2time1   exp3data2time1   
exp1data2time2   exp2data2time2   exp3data2time2

csv file3

exp1data3time1   exp2data3time1   exp3data3time1   
exp1data3time2   exp2data3time2   exp3data3time2

So, I'd move the raw data in the file to a new column, and each data type to its own file. Right now I'm only going to do one file, until I can move the separate experiments to separate columns in the new file. So, in the code, the above would make the 4000 into 2. I hope this makes more sense, but if not, I will try again.

If I had a cat for each time I saw a bio or psych or chem database in this state:

"each column contains 50 experiments, each with 4000 rows, for 200000 some rows total. What I want to do is take each column, and make it an individual csv file, with each experiment in its own column. So it would be an array of 50 columns and 4000 rows for each data type"

I'd have way too farking many cats.

I didn't even look at your code because the re-mangling you are proposing is just another problem that will have to be solved. I don't fault you, you claim to be a novice and all your peers make the same sort of error. Beginning programmers who have yet to understand how to use arrays often wind up with variable declarations like:

integer response01, response02, response03, response04, ...

and then very, very redundant code when they try to see if every response is - say - 1. I think this is such a seductive error in bio-informatics because it actually models the paper notations they come from rather well. Unfortunately, the sheet-of-paper model isn't the best way to model data.

You should read and understand why database normalization was developed, codified and has come to dominate how people think about structured data. One Wikipedia article may not be sufficient. Using the example I excerpted let me try to explain how I think of it. Your data consists of observations; put the other way the primary datum is a singular observation. That observation has a context though: it is one of a set of 4000 observations, where each set belongs to one of 50 experiments. If you had to attach a context to each observation you'd wind up with an addressing scheme that looks like:

<experiment_number, observation_number, value>

In database jargon, that's a tuple, and it is capable of representing, with no ambiguity and perfect symmetry the entirety of your data. I'm not certain that I've understood the exact structure of your data, so perhaps it is something more like:

<experiment_number, protocol_number, observation_number, value>

where the protocol may be some form of variable treatment type - let's say pH. But note that I didn't call the protocol a pH and I don't record it as such in the database. What I would then need is an ancillary table showing the relevant parameters of the protocol, e.g.:

<protocol_number, acidity, temperature, pressure>

Now we've just built a "relation" that those database people like to talk about; we've also begun normalizing the data. If you need to know the pH for a given protocol, there is one and only one place to find it, in the proper row of the protocol table. Note that I've divorced the data that fit so nicely together on a data-sheet and from the observation table I can't see the pH for a particular dataum. But that's okay, because I can just look it up in my protocol table if needed. This is a "relational join" and if I needed to, I could coalesce all the various parameters from all the various tables and reconstitute the original datasheet in its original, unstructured glory.

I hope this answer is of some use to you. I'm certain that I don't even know what field of study your data is from, but these principles apply across domains from drug trials to purchase requisition processing. Please understand that I'm trying to inform, per your request, and there is zero condescension intended. I welcome further questions on the matter.

Normalization of the dataset

Thanks for giving the example. You have the context I described already, perhaps I can make it more clear.

column1             column2            column3
exp1data1time1      exp1data2time1     exp1data3time1
exp1data1time2      exp1data2time2     exp1data3time2

The columns are an artifice made by the last guy; that is, they carry no relevant information. When parsed into a normal form, your data looks just like my first proposed tuple:

<experiment_number, time, response_number, response>

where I suspect time may actually mean "subject_id" or "trial_number". It may very well look incongruous to you to conjoin all the different response values into the same dataset; indeed based on your desired output, I suspect that it does. At first blush, the objection "but the subject's response to a question about epistemic properties of chairs has no connection to their meta-epistemic beliefs regarding color", but this would be mistaken. The data are related because they have a common experimental subject, and self-correlation is an important concept in sociological analytics.

For example, you may find that respondent A gives the same responses as respondent B, except all of A's responses are biased one higher because of how the subject understood the criteria. This would make a very real difference in the absolute values of the data, but I hope you can see that the question "do A and B actually have different epistemic models?" is salient and valid. One method of data modeling allows this question to be answered easily, your desired method does not.

Working parsing code to follow shortly.

The normalizing code

#!/usr/bin/python

"""parses a csv file containing a particular data layout and normalizes

    The raw data set is a csv file of the form::

        column1                column2               column3
        exp01data01time01      exp01data02time01     exp01data03time01
        exp01data01time02      exp01data02time02     exp01data03time02

    where there are 40 such columns and the literal column title
    is added as context to the output row

    it is assumed that the columns are comma separated but
    the lexical form of the subcolumns is unspecified.

    Output will consist of a single CSV output stream
    on stdout of the form::

        exp01, time01, data01, column1

    for varying actual values of each field.
"""

import csv
import sys

def split_subfields(s):
    """returns a list of subfields of s
       this function is expected to be re-written to match the actual,
       unspecified lexical structure of s."""
    return [s[0:5], s[5:11], s[11:17]]


def normalise_data(reader, writer):
    """returns a list of the column headings from the reader"""

    # obtain the headings for use in normalization
    names = reader.next()

    # get the data rows, split them out by column, add the column name
    for row in reader:
        for column, datum in enumerate(row):
            fields = split_subfields(datum)
            fields.append(names[column])
            writer.writerow(fields)

def main():
    if len(sys.argv) != 2:
        print  >> sys.stderr,  ('usage: %s input.csv' % sys.argv[0])
        sys.exit(1)

    in_file = sys.argv[1]

    reader = csv.reader(open(in_file))
    writer = csv.writer(sys.stdout)
    normalise_data(reader, writer)

if __name__ == '__main__': main()

Such that the command python epistem.py raw_data.csv > cooked_data.csv yields excerpted output looking like:

exp01,data01,time01,column1
...
exp01,data40,time01,column40
exp01,data01,time02,column1
exp01,data01,time03,column1
...
exp02,data40,time15,column40