I'm working on cs50's pset6, DNA, and I want to read a csv file that looks like this:
name,AGATC,AATG,TATC
Alice,2,8,3
Bob,4,1,5
Charlie,3,2,5
But the problem is that dictionaries only have a key, and a value, so I don't know how I could structure this. What I currently have is this piece of code:
import sys
with open(argv[1]) as data_file:
data_reader = csv.DictReader(data_file)
And also, my csv file has multiple columns and rows, with a header and the first column indicating the name of the person. I don't know how to do this, and I will later need to access the individual amount of say, Alice's value of AATG.
Also, I'm using the module sys, to import DictReader and also reader
You can always try to create the function on your own.
You can use my code here:
def csv_to_dict(csv_file):
key_list = [key for key in csv_file[:csv_file.index('\n')].split(',')] # save the keys
data = {} # every dictionary
info = [] # list of dicitionaries
# for each line
for line in csv_file[csv_file.index('\n') + 1:].split('\n'):
count = 0 # this variable saves the key index in my key_list.
# for each string before comma
for value in line.split(','):
data[key_list[count]] = value # for each key in key_list (which I've created before), I put the value. This is the way to set a dictionary values.
count += 1
info.append(data) # after updating my data (dictionary), I append it to my list.
data = {} # I set the data dictionary to empty dictionary.
print(info) # I print it.
### Be aware that this function prints a list of dictionaries.
Related
I am trying to add content to my existing csv file. meaning I would not like to remove any existing content but just like to add new lines based on list of lists.
However, in the final output I don't see any new row being added in the csv. Also the variable is returning empty list values (please see the comment on the code line).
Here i am asking the user first the number of entries. then value for each entry (each row value for each column). Then I am simple appending it to a final list i.e total_number_of_rows which should look like:
[[x,x,x][xy,xy,xy].
In the final code I am trying to write the values from the two lists to the CSV file for example the final output should look like:
id, name, color
oldvalue1, oldvalue2, oldvalues3 #assuming this is an already existing row in the csv file
x,x,x
xy,xy,xy
number_of_entries = int(input("how many entries will you need to enter:"))
each_new_row = []
total_number_of_rows = []
for i in range(number_of_entries):
new_id = input("add new new_id")
new_name = input ("add new date")
new_color = input ("add new color")
each_new_row.extend((new_id,new_name,new_color))
print("each_new_row",each_new_row)
total_number_of_rows.append(each_new_row)
print("total number of rows", total_number_of_rows) # this is showing [[1,1,red],[2,2,blue]]
each_new_row.clear()
print("total_number_of_rows", total_number_of_rows) ## this is showing [[],[]]
with open('file.csv', 'a',newline ="") as f:
writer = csv.writer(f)
writer.writerows(total_number_of_rows)
#not seeing any new row added
each_new_row = []
# ...
for i in range(number_of_entries):
# ...
total_number_of_rows.append(each_new_row)
# ...
each_new_row.clear()
This appends the same list multiple times to total_number_of_rows. At the end you clear this list, and that's why total_number_of_rows contains only empty lists (it's the same empty list multiple times).
Instead, you want to create a new empty list in every iteration instead of reusing the same list over and over.
for i in range(number_of_entries):
each_new_row = [] # do this
# ...
total_number_of_rows.append(each_new_row)
# ...
#each_new_row.clear() # don't do this
I have a .json file of all of my AWS target groups. This was created using aws elbv2 describe-target-groups. I want to extract every TargetGroupArn from this file and store it into a Python list.
With my current code, I get no output. I can confirm that the dictionary has data in it, but nothing is being appended to the list that I'm trying to create.
import json
from pprint import pprint
with open('target_groups.json') as f:
data = json.load(f)
items = data['TargetGroups']
arn_list = []
for key, val in data.items():
if key == 'TargetGroupArn':
arn_list.append(val)
print(arn_list)
Expected results would be for arn_list to print out looking like this:
[arn:aws:elb:xxxxxxx:targetgroup1, arn:aws:elb:xxxxxxx:targetgroup2, arn:aws:elb:xxxxxxx:targetgroup3]
Change your code to this:
import json
from pprint import pprint
with open('target_groups.json') as f:
data = json.load(f)
arn_list = []
if 'TargetGroups' in data:
items = data['TargetGroups']
for item in items:
if 'TargetGroupArn' in item:
arn_list.append(item['TargetGroupArn'])
print(arn_list)
else:
print('No data')
There are many ways to make this python code more concise. However, I prefer a more wordy style that easier to read.
Also note that this code checks that keys exist so that the code will not stackdump for missing data.
it would be better if you could post the file you are trying to get data from, but this part:
for key, val in data.items():
if key == 'TargetGroupArn':
arn_list.append(val)
need to be changed to:
for key, val in items.items():
if key == 'TargetGroupArn':
arn_list.append(val)
you get data from 'data' and add it to items, but you never actually used it.
give it a shot.
I have a python script that looks at a json file and lists variables to a CSV. The problem I am having is latitude and logitude are listed twice. Therefore, when I write the row, it looks at those variables and creates an output with duplicate values.
import csv, json, sys
def find_deep_value(d, key):
# Modified from https://stackoverflow.com/questions/48568649/convert-json-to-csv-using-python/48569129#48569129
if key in d:
return d[key]
for k in d.keys():
if isinstance(d[k], dict):
for j in find_deep_value(d[k], key):
return j
inputFile = open("pywu.cache.json", 'r') # open json file
outputFile = open("CurrentObs.csv", 'w') # load csv file
data = json.load(inputFile) # load json content
inputFile.close() # close the input file
output = csv.writer(outputFile) # create a csv.write
# Gives you latitude coordinates from within the json
lat = find_deep_value(data, "latitude")
# Gives you longitude coordinates from within the json
lon = find_deep_value(data, "longitude")
# Gives you a list of weather from within the json
weather = find_deep_value(data, "weather")
# Gives you a list of temperature_strings from within the json
temp = find_deep_value(data, "temperature_string")
output.writerow([lat, lon, weather, temp])
outputFile.close()
Is there a way to only list them once?
You need to use return rather than yield. Yield is for generators. Once you fix that, you'll also need to change
list(find_deep_value(data, "latitude"))
to
find_deep_value(data, "latitude")
for each of those lines. And finally, change
output.writerow(lat + lon + weather + temp)
to
output.writerow([lat, lon, weather, temp])
What's happening (you might want to read up on generators first) is when a key is not in the top-level dictionary, you start looping through them, and when the first 'latitude' is reached, the yield keyword returns a generator object. You have that generator wrapped in list() which immediately unpacks the entire generator into a list. So if you have more than one sub-dictionary with the given key in it, you're going to end up looking through and finding every single one.
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
I am reading a CSV file and combining rows into dictionaries, with the first row containing the keys and the subsequent rows containing the values.
I want my dictionary keys to be in the same order as the original csv file, but the dict(zip)) function seems to order them randomly. I tried OrderedDict and that didn't work.
If there is a better way to produce my dictionaries I'm open to suggestions, but I would really like to know how i can do this while keeping my existing code, just because I am very new to Python (and programming in general) and I would like to be able to understand my own code at this point.
import csv # imports the csv module
with open("csvfile.csv", "r") as file_var:
reader = csv.reader(file_var)
my_list = []
for row in reader:
if (len(row)!=0):
my_list = my_list + [row]
for i in range(1, len(my_list)):
user = dict(zip(my_list[0], my_list[i]))
print "----------------------"
print user['first_name'], user['last_name']
for key in user:
print key, user[key]
Dictionaries have an arbitrary order. You should use an OrderedDict instead.
from collections import OrderedDict
user = OrderedDict(zip(my_list[0], my_list[i]))
etc.
I note you say it didn't work, but I see no reason why it wouldn't. In what way did it fail?