My issue is from a much larger program but I shrunk and dramatically simplified the specific problem for the purpose of this question.
I've used the dictreader method to create a dictionary from a csv file. I want to loop through the dictionary printing out its contents, which I can, but I want to do this multiple times.
The content of test.csv is simply one column with the numbers 1-3 and a header row called Number.
GetData is a class with a method called create_dict() that I wrote that creates and returns a dictionary from test.csv using csv.dictreader
My code is as follows:
dictionary = GetData('test.csv').create_dict()
for i in range(5):
print("outer loop")
for row in dictionary:
print(row['Number'])
class GetData:
def __init__(self, file):
self._name = file
def create_dict(self):
data = csv.DictReader(open(self._name, 'r'), delimiter=",")
return data
The output is as follows:
outer loop
1
2
3
outer loop
outer loop
outer loop
outer loop
My desired output is:
outer loop
1
2
3
outer loop
1
2
3
outer loop
1
2
3
outer loop
1
2
3
outer loop
1
2
3
Does anyone know why this happens in Python?
Since you're using a file object, it's reading from the cursor position. This isn't a problem the first time through because the position is at the beginning of the file. After that, it's reading from the end of the file to the, well, end of the file.
I'm not sure how GetData works, but see if it has a seek command in which case:
for i in range(5):
print('outer loop')
dictionary.seek(0)
for row in dictionary:
print(row['Number'])
As g.d.d.c points out in a comment, it may also be a generator instead of a file object, in which case this approach is flawed. The generator will only run once, so you may have to dict() it. It all depends on how GetData.create_dict works!
As per your comment that GetData.create_dict gives you a csv.DictReader, your options are somewhat limited. Remember that the DictReader is essentially just a list of dicts, so you may be able to get away with:
list_of_dicts = [row for row in dictionary]
then you can iterate through the list_of_dicts
for i in range(5):
print('outer loop')
for row in list_of_dicts:
print(row['Number'])
csv.DictReader is an iterator for the associated open file. After one loop over the file, you're at the end (EOF).
To loop over it again, simply seek to the beginning of the file: your_filehandle.seek(0)
Related
def read_prices(tikrList):
#read each file and get the price list dictionary
def getPriceDict():
priceDict = {}
TLL = len(tikrList)
for x in range(0,TLL):
with open(tikrList[x] + '.csv','r') as csvFile:
csvReader = csv.reader(csvFile)
for column in csvReader:
priceDict[column[0]] = float(column[1])
return priceDict
#populate the final dictionary with the price dictionary from the previous function
def popDict():
combDict = {}
TLL = len(tikrList)
for x in range(0,TLL):
for y in tikrList:
combDict[y] = getPriceDict()
return combDict
return(popDict())
print(read_prices(['GOOG','XOM','FB']))
What is wrong with the code is that when I return the final dictionary the key for GOOG,XOM,FB is represnting the values for the FB dictionary only.
As you can see with this output:
{'GOOG': {'2015-12-31': 104.660004, '2015-12-30': 106.220001},
'XOM': {'2015-12-31': 104.660004, '2015-12-30': 106.220001},
'FB': {'2015-12-31': 104.660004, '2015-12-30': 106.220001}
I have 3 different CSV files but all of them are just reading the CSV file for FB.
I want to apologize ahead of time if my code is not easy to read or doesn't make sense. I think there is an issue with storing the values and returning the priceDict in the getPriceDict function but I cant seem to figure it out.
Any help is appreciated, thank you!
Since this is classwork I won't provide a solution but I'll point a few things out.
You have defined three functions - two are defined inside the third. While structuring functions like that can make sense for some problems/solutions I don't see any benefit in your solution. It seems to make it more complicated.
The two inner functions don't have any parameters, you might want to refactor them so that when they are called you pass them the information they need. One advantage of a function is to encapsulate an idea/process into a self-contained code block that doesn't rely on resources external to itself. This makes it easy to test so you know that the function works and you can concentrate on other parts of the code.
This piece of your code doesn't make much sense - it never uses x from the outer loop:
...
for x in range(0,TLL):
for y in tikrList:
combDict[y] = getPriceDict()
When you iterate over a list the iteration will stop after the last item and it will iterate over the items themselves - no need to iterate over numbers to access the items: don't do for i in range(thelist): print(thelist[i])
>>> tikrList = ['GOOG','XOM','FB']
>>> for name in tikrList:
... print(name)
GOOG
XOM
FB
>>>
When you read through a tutorial or the documentation, don't just look at the examples - read and understand the text .
I load a row of data in a class, row by row with a loop. I'd like to append each row to a list.
class Biz:
def __init__(self, dba_name, legal_name):
self.dba_name = dba_name
self.legal_name = legal_name
MasterList = []
File_From_Excel = pd.read_excel('C:\file.xlsx')
for index, row in File_From_Excel.iterrows():
record = Biz(row['Field 1'], row['Field 2'])
MasterList.append(record)
print(MasterList)
When I run code like this, I do not get an error, but I get info like this printed:
"[<main.Biz object at 0x0C11BFB0>, <main.Biz object at 0x00BDED50>]"
I'm a newbie and I haven't figured out how to overcome this one. Thank you!
You are printing a list of class instances, so the output is their memory addresses. What you probably want instead is the attributes of these instances. It can be achieved as follows (for illustrative purposes):
# this goes at the end of your code, outside of the loop!
print([[record.dba_name, record.legal_name] for record in MasterList])
This approach is by no means optimal and and will give you memory issues if there are a lot of elements in MasterList. In that case you would want to use either a generator or a class iterator.
Edit: Come to think of it, there is no need for a generator here since a simple for loop can iterate over the list:
for record in MasterList:
print([record.dba_name, record.legal_name], end=' ')
I was a little curious because when I add a single line in my code, that counts the number of rows in the csv file, the for loop is stop working and just skipping everything inside.
My code shown below, is working now, but if I uncomment the row_count it's not working, so my question is why?
with open(r"C:\Users\heltbork\Desktop\test\ICM.csv", newline='') as csvfile:
sensor = csv.reader(csvfile, delimiter=',', quotechar='|')
#row_count = sum(1 for row in sensor)
#print(row_count)
for row in sensor:
#alot of stuff here
The reader is an iterable (see the iterator protocol):
... One notable exception is code which attempts multiple iteration passes. A container object (such as a list) produces a fresh new iterator each time you pass it to the iter() function or use it in a for loop. Attempting this with an iterator will just return the same exhausted iterator object used in the previous iteration pass, making it appear like an empty container.
The iterable is consumed when you iterate it. It is not a concrete data structure:
sensor = csv.reader(...) # creates an iterator
row_count = sum(1 for row in sensor) # *consumes* the iterator
for row in sensor: # nothing in the iterator, consumed by `sum`
# a lot of stuff here
You should count while you iterate (inside for row in sensor:), because once you iterate and consume it - you can't iterate again.
Alternatives are using list for concreting the data, or if you need the iterable interface - itertools.tee (if you don't have a lot if data). You can also use enumerate and keep the last index.
Example:
sensor = csv.reader(...) # creates an iterator
count = 0
for idx, row in enumerate(sensor):
# a lot of stuff here
# ...
count = idx
print(count)
Or:
count = 0
for row in sensor:
# a lot of stuff here
# ...
count += 1
print(count)
this type of question has been asked many times. So apologies; I have searched hard to get an answer - but have not found anything that is close enough to my needs (and I am not sufficiently advanced (I am a total newbie) to customize an existing answer). So thanks in advance for any help.
Here's my query:
I have 30 or so csv files and each contains between 500 and 15,000 rows.
Within each of them (in the 1st column) - are rows of alphabetical IDs (some contain underscores and some also have numbers).
I don't care about the unique IDs - but I would like to identify the duplicate IDs and the number of times they appear in all the different csv files.
Ideally I'd like the output for each duped ID to appear in a new csv file and be listed in 2 columns ("ID", "times_seen")
It may be that I need to compile just 1 csv with all the IDs for your code to run properly - so please let me know if I need to do that
I am using python 2.7 (a crawling script that I run needs this version, apparently).
Thanks again
It seems the most easy way to achieve want you want would make use of dictionaries.
import csv
import os
# Assuming all your csv are in a single directory we will iterate on the
# files in this directory, selecting only those ending with .csv
# to list files in the directory we will use the walk function in the
# os module. os.walk(path_to_dir) returns a generator (a lazy iterator)
# this generator generates tuples of the form root_directory,
# list_of_directories, list_of_files.
# So: declare the generator
file_generator = os.walk("/path/to/csv/dir")
# get the first values, as we won't recurse in subdirectories, we
# only ned this one
root_dir, list_of_dir, list_of_files = file_generator.next()
# Now, we only keep the files ending with .csv. Let me break that down
csv_list = []
for f in list_of_files:
if f.endswith(".csv"):
csv_list.append(f)
# That's what was contained in the line
# csv_list = [f for _, _, f in os.walk("/path/to/csv/dir").next() if f.endswith(".csv")]
# The dictionary (key value map) that will contain the id count.
ref_count = {}
# We loop on all the csv filenames...
for csv_file in csv_list:
# open the files in read mode
with open(csv_file, "r") as _:
# build a csv reader around the file
csv_reader = csv.reader(_)
# loop on all the lines of the file, transformed to lists by the
# csv reader
for row in csv_reader:
# If we haven't encountered this id yet, create
# the corresponding entry in the dictionary.
if not row[0] in ref_count:
ref_count[row[0]] = 0
# increment the number of occurrences associated with
# this id
ref_count[row[0]]+=1
# now write to csv output
with open("youroutput.csv", "w") as _:
writer = csv.writer(_)
for k, v in ref_count.iteritems():
# as requested we only take duplicates
if v > 1:
# use the writer to write the list to the file
# the delimiters will be added by it.
writer.writerow([k, v])
You may need to tweek a little csv reader and writer options to fit your needs but this should do the trick. You'll find the documentation here https://docs.python.org/2/library/csv.html. I haven't tested it though. Correcting the little mistakes that may have occurred is left as a practicing exercise :).
That's rather easy to achieve. It would look something like:
import os
# Set to what kind of separator you have. '\t' for TAB
delimiter = ','
# Dictionary to keep count of ids
ids = {}
# Iterate over files in a dir
for in_file in os.listdir(os.curdir):
# Check whether it is csv file (dummy way but it shall work for you)
if in_file.endswith('.csv'):
with open(in_file, 'r') as ifile:
for line in ifile:
my_id = line.strip().split(delimiter)[0]
# If id does not exist in a dict = set count to 0
if my_id not in ids:
ids[my_id] = 0
# Increment the count
ids[my_id] += 1
# saves ids and counts to a file
with open('ids_counts.csv', 'w') as ofile:
for key, val in ids.iteritems():
# write down counts to a file using same column delimiter
ofile.write('{}{}{}\n'.format(key, delimiter, value))
Check out the pandas package. You can read an write csv files quite easily with it.
http://pandas.pydata.org/pandas-docs/stable/10min.html#csv
Then, when having the csv-content as a dataframe you convert it with the as_matrix function.
Use the answers to this question to get the duplicates as a list.
Find and list duplicates in a list?
I hope this helps
As you are a newbie, Ill try to give some directions instead of posting an answer. Mainly because this is not a "code this for me" platform.
Python has a library called csv, that allows to read data from CSV files (Boom!, surprised?). This library allows you to read the file. Start by reading the file (preferably an example file that you create with just 10 or so rows and then increase the amount of rows or use a for loop to iterate over different files). The examples in the bottom of the page that I linked will help you printing this info.
As you will see, the output you get from this library is a list with all the elements of each row. Your next step should be extracting just the ID that you are interested in.
Next logical step is counting the amount of appearances. There is also a class from the standard library called counter. They have a method called update that you can use as follows:
from collections import Counter
c = Counter()
c.update(['safddsfasdf'])
c # Counter({'safddsfasdf': 1})
c['safddsfasdf'] # 1
c.update(['safddsfasdf'])
c # Counter({'safddsfasdf': 2})
c['safddsfasdf'] # 2
c.update(['fdf'])
c # Counter({'safddsfasdf': 2, 'fdf': 1})
c['fdf'] # 1
So basically you will have to pass it a list with the elements you want to count (you could have more than 1 id in the list, for exampling reading 10 IDs before inserting them, for improved efficiency, but remember not constructing a thousands of elements list if you are seeking good memory behaviour).
If you try this and get into some trouble come back and we will help further.
Edit
Spoiler alert: I decided to give a full answer to the problem, please avoid it if you want to find your own solution and learn Python in the progress.
# The csv module will help us read and write to the files
from csv import reader, writer
# The collections module has a useful type called Counter that fulfills our needs
from collections import Counter
# Getting the names/paths of the files is not this question goal,
# so I'll just have them in a list
files = [
"file_1.csv",
"file_2.csv",
]
# The output file name/path will also be stored in a variable
output = "output.csv"
# We create the item that is gonna count for us
appearances = Counter()
# Now we will loop each file
for file in files:
# We open the file in reading mode and get a handle
with open(file, "r") as file_h:
# We create a csv parser from the handle
file_reader = reader(file_h)
# Here you may need to do something if your first row is a header
# We loop over all the rows
for row in file_reader:
# We insert the id into the counter
appearances.update(row[:1])
# row[:1] will get explained afterwards, it is the first column of the row in list form
# Now we will open/create the output file and get a handle
with open(output, "w") as file_h:
# We create a csv parser for the handle, this time to write
file_writer = writer(file_h)
# If you want to insert a header to the output file this is the place
# We loop through our Counter object to write them:
# here we have different options, if you want them sorted
# by number of appearances Counter.most_common() is your friend,
# if you dont care about the order you can use the Counter object
# as if it was a normal dict
# Option 1: ordered
for id_and_times in apearances.most_common():
# id_and_times is a tuple with the id and the times it appears,
# so we check the second element (they start at 0)
if id_and_times[1] == 1:
# As they are ordered, we can stop the loop when we reach
# the first 1 to finish the earliest possible.
break
# As we have ended the loop if it appears once,
# only duplicate IDs will reach to this point
file_writer.writerow(id_and_times)
# Option 2: unordered
for id_and_times in apearances.iteritems():
# This time we can not stop the loop as they are unordered,
# so we must check them all
if id_and_times[1] > 1:
file_writer.writerow(id_and_times)
I offered 2 options, printing them ordered (based on Counter.most_common() doc) and unoredered (based on normal dict method dict.iteritems()). Choose one. From a speed point of view I'm not sure which one would be faster, as one first needs to order the Counter but also stops looping when finding the first element non-duplicated while the second doesn't need to order the elements but needs to loop every ID. The speed will probably be dependant on your data.
About the row[:1] thingy:
row is a list
You can get a subset of a list telling the initial and final positions
In this case the initial position is omited, so it defaults to the start
The final position is 1, so just the first element gets selected
So the output is another list with just the first element
row[:1] == [row[0]] They have the same output, getting a sublist of only the same element is the same that constructing a new list with only the first element
below is my CSV dataset.
a,d,g
b,e,h
c,f,i
I would like to separate these 3 column as row[0], row[1], and row[2].
And also to make them as 3 different lists.
Here is my code:
import csv
file1 = open('....my_path.csv')
Row_0 = [row[0].upper() for row in csv.reader(file1)]
Row_1 = [row[1].upper() for row in csv.reader(file1)]
Row_2 = [row[2].upper() for row in csv.reader(file1)]
print Row_0
print Row_1
print Row_2
However, I only can see Row_0 result from the console. But the Row_1 and Row_2 are always showing [ ]. Which means I only can see the first row, but not the second and following rows.
['A', 'B', 'C']
[]
[]
Does anyone can help me to deal with this "simple" issue?
open returns an iterator, which becomes exhausted (no longer usable) after you iterate through it once. Moreover, each time you do csv.reader(file1), csv.reader will try to iterate over the iterator referenced by file1. This means that, after the first call, this iterator will become exhausted and all subsequent calls to csv.reader will be using an empty iterator.
To do what you want, you would need something like:
import csv
file1 = open('....my_path.csv')
Row_0, Row_1, Row_2 = ([row[0].upper(), row[1].upper(), [row[2].upper()]
for row in csv.reader(file1))
print Row_0
print Row_1
print Row_2
Now, we get all of the data in one read and only iterate over the iterator once.
Also, in case you are wondering, the code I used is known as a generator expression. You could also use a list comprehension:
Row_0, Row_1, Row_2 = [[row[0].upper(), row[1].upper(), [row[2].upper()]
for row in csv.reader(file1)]
but I don't see a point in building a list just to throw it away.
You've read through the whole file! check out the file position after your first comprehension,
You could seek(0) between comprehensions or just iterate once, or reopen the file as #kdopen stated