Create multiple dictionaries from a single iterator in nested for loops

Question

I have a nested list comprehension which has created a list of six lists of ~29,000 items. I'm trying to parse this list of final data, and create six separate dictionaries from it. Right now the code is very unpythonic, I need the right statement to properly accomplish the following:

1.) Create six dictionaries from a single statement.

2.) Scale to any length list, ie, not hardcoding a counter shown as is.

I've run into multiple issues, and have tried the following:

1.) Using while loops

2.) Using break statements, will break out of the inner most loop, but then does not properly create other dictionaries. Also break statements set by a binary switch.

3.) if, else conditions for n number of indices, indices iterate from 1-29,000, then repeat.

Note the ellipses designate code omitted for brevity.

# Parse csv files for samples, creating a dictionary of key, value pairs and multiple lists.
with open('genes_1') as f:
    cread_1 = list(csv.reader(f, delimiter = '\t'))
    sample_1_values = [j for i, j in (sorted([x for x in {i: float(j) 
                        for i, j in cread_1}.items()], key = lambda v: v[1]))]
    sample_1_genes = [i for i, j in (sorted([x for x in {i: float(j) 
                            for i, j in cread_1}.items()], key = lambda v: v[1]))]

... 

# Compute row means.
mean_values = []
for i, (a, b, c, d, e, f) in enumerate(zip(sample_1_values, sample_2_values, sample_3_values, sample_4_values, sample_5_values, sample_6_values)):
    mean_values.append((a + b + c + d + e + f)/6)

# Provide proper gene names for mean values and replace original data values by corresponding means.
sample_genes_list = [i for i in sample_1_genes, sample_2_genes, sample_3_genes, sample_4_genes, sample_5_genes, sample_6_genes]

sample_final_list = [sorted(zip(sg, mean_values)) for sg in sample_genes_list]

# Create multiple dictionaries from normalized values for each dataset.
class BreakIt(Exception): pass
try: 
    count = 1         
    for index, items in enumerate(sample_final_list):
        sample_1_dict_normalized = {}             
        for index, (genes, values) in enumerate(items):
            sample_1_dict_normalized[genes] = values
            count = count + 1
            if count == 29595:
                raise BreakIt
except BreakIt:
    pass

...

try: 
    count = 1         
    for index, items in enumerate(sample_final_list):
        sample_6_dict_normalized = {}             
        for index, (genes, values) in enumerate(items):
            if count > 147975:
                sample_6_dict_normalized[genes] = values
            count = count + 1
            if count == 177570:
                raise BreakIt
except BreakIt:
    pass

# Pull expression values to qualify overexpressed proteins.
print 'ERG values:'
print 'Sample 1:', round(sample_1_dict_normalized.get('ERG'), 3) 
print 'Sample 6:', round(sample_6_dict_normalized.get('ERG'), 3)  

Answer 1

Your code is too long for me to give exact answer. I will answer very generally.

First, you are using enumerate for no reason. if you don't need both index and value, you probably don't need enumerate.

This part:

with open('genes.csv') as f:
    cread_1 = list(csv.reader(f, delimiter = '\t'))
    sample_1_dict = {i: float(j) for i, j in cread_1}
    sample_1_list = [x for x in sample_1_dict.items()]
    sample_1_values_sorted = sorted(sample_1_list, key=lambda expvalues: expvalues[1])
    sample_1_genes = [i for i, j in sample_1_values_sorted]
    sample_1_values = [j for i, j in sample_1_values_sorted]
    sample_1_graph_raw = [float(j) for i, j in cread_1] 

should be (a) using a list named samples and (b) much shorter, since you don't really need to extract all this information from sample_1_dict and move it around right now. It can be something like:

samples = [None] * 6
for k in range(6):
    with open('genes.csv') as f: #but something specific to k
        cread = list(csv.reader(f, delimiter = '\t'))
        samples[k] = {i: float(j) for i, j in cread}

after that, calculating the sum and mean will be way more natural.

In this part:

class BreakIt(Exception): pass
try: 
    count = 1         
    for index, items in enumerate(sample_final_list):
        sample_1_dict_normalized = {}             
        for index, (genes, values) in enumerate(items):
            sample_1_dict_normalized[genes] = values
            count = count + 1
            if count == 29595:
                raise BreakIt
except BreakIt:
    pass

you should be (a) iterating of the samples list mentioned earlier, and (b) not using count at all, since you can iterate naturally over samples or sample[i].list or something like that.

Answer 2

Your code has several problems. You should put your code in functions that preferably do one thing each. Than you can call a function for each sample without repeating the same code six times (I assume that is what the ellipsis is hiding.). Give each function a self-describing name and a doc string that explains what it does. There is quite a bit unnecessary code. Some of this might become obvious once you have it in functions. Since functions take arguments you can hand in your 29595, for example.