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Split values in CSV that look like JSON

So I have a CSV file with a column called content. However, the contents in column look like it is based on JSON, and, therefore, house more columns. I would like to split these contents into multiple columns or extract the final part of it after "value". See picture below to see an example of the file. Any ideas how to get this? I would prefer using Python. I don't have any experience with JSON.

Using pandas you could do in a simpler way.
EDIT updated to handle the single quotes:
import pandas as pd
import json
data = pd.read_csv('test.csv', delimiter="\n")["content"]
res = [json.loads(row.replace("'", '"')) for row in data]
result = pd.DataFrame(res)
result.head()
# Export result to CSV
result.to_csv("result.csv")
my csv:
result:

This script will create a new csv file with the 'value' added to the csv as an additional column
(make sure that the input_csv and output_csv are different filenames)
import csv
import json
input_csv = "data.csv"
output_csv = "data_updated.csv"
values = []
with open(input_csv) as f_in:
dr = csv.DictReader(f_in)
for row in dr:
value = json.loads(row["content"].replace("'", '"'))["value"]
values.append(value)
with open(input_csv) as f_in:
with open(output_csv, "w+") as f_out:
w = csv.writer(f_out, lineterminator="\n")
r = csv.reader(f_in)
all = []
row = next(r)
row.append("value")
all.append(row)
i = 0
for row in r:
row.append(values[i])
all.append(row)
i += 1
w.writerows(all)

I need to read a csv with unknown number of columns and then write the data to a csv with a set number of columns

So I have a file that looks like this:
name,number,email,job1,job2,job3,job4
I need to convert it to one that looks like this:
name,number,email,job1
name,number,email,job2
name,number,email,job3
name,number,email,job4
How would I do this in Python?

As said in a comment that you can use pandas to read, write and manipulate csv file.
Here is one example of how you can solve your problem with pandas in python
import pandas as pd
# df = pd.read_csv("filename.csv") # read csv file from disk
# comment out below line when open from disk
df = pd.DataFrame([['ss','0152','ss#','student','others']],columns=['name','number','email','job1','job2'])
print(df)
this line output is
name number email job1 job2
0 ss 0152 ss# student others
Now we need to know how many columns are there:
x = len(df.columns)
print(x)
it will store the number of column in x
5
Now let's create a empty Dataframe with columns= [name,number,email,job]
c = pd.DataFrame(columns=['name','number','email','job'])
print(c)
output:
Columns: [name, number, email, job]
Index: []
Now we use loop from range 3 to end of the column and concat datafarme with our empty dataframe:
for i in range(3,x):
df1 = df.iloc[:,0:3].copy() # we took first 3 column
df2 = df.iloc[:,[i]].copy() # we took ith coulmn
df1['job'] = df2; # added ith coulmn to the df1
c = pd.concat([df1,c]); # concat df1 and c
print(c)
output:
name number email job
0 ss 0152 ss# others
0 ss 0152 ss# student
Dataframe c has your desired output. Now you can save it using
c.to_csv('ouput.csv')

Let's assume this is the dataframe:
import pandas as pd
df = pd.DataFrame(columns=['name','number','email','job1','job2','job3','job4'])
df = df.append({'name':'jon', 'number':123, 'email':'smth#smth.smth', 'job1':'a','job2':'b','job3':'c','job4':'d'},ignore_index=True)
We define a new dataframe:
new_df = pd.DataFrame(columns=['name','number','email','job'])
Now, we loop over the old one to split it based on the jobs. I assume you have 4 jobs to split:
for i, row in df.iterrows():
for job in range(1,5):
job_col = "job" + str(job)
new_df = new_df.append({'name':row['name'], 'number':row['number'], 'email':row['email'], 'job':row[job_col]}, ignore_index=True)

You can use the csv module and Python's unpacking syntax to get the data from the input file and write it to the output file.
import csv
with open('input.csv', newline='') as infile, open('output.csv', 'w', newline='') as outfile:
reader = csv.reader(infile)
writer = csv.writer(outfile)
# Skip header row, if necessary
next(reader)
# Use sequence unpacking to get the fixed variables and
# and arbitrary number of "jobs".
for name, number, email, *jobs in reader:
for job in jobs:
writer.writerow([name, number, email, job])

Below:
with open('input.csv') as f_in:
lines = [l.strip() for l in f_in.readlines()]
with open('output.csv','w') as f_out:
for idx,line in enumerate(lines):
if idx > 0:
fields = line.split(',')
for idx in range(3,len(fields)):
f_out.write(','.join(fields[:3]) + ',' + fields[idx] + '\n')
input.csv
header row
name,number,email,job1,job2,job3,job4
name1,number1,email1,job11,job21,job31,job41
output.csv
name,number,email,job1
name,number,email,job2
name,number,email,job3
name,number,email,job4
name1,number1,email1,job11
name1,number1,email1,job21
name1,number1,email1,job31
name1,number1,email1,job41

How do i write csv basis on comparing two csv file[column based]

I have two csv files:
csv1
csv2
(*note headers can be differ)
csv1 has 1 single column an csv2 has 5 columns
now column 1 of csv1 has some matching values in column2 of csv2
my concern is how can i write a csv where column1 of csv1 does not have a MATCHING VALUES to column2 of csv2
I have attached three files csv1, csv2 and expected output..
Expected Output:
ProfileID,id,name,class ,rollnumber
1,lkha,prince,sfasd,DAS
2,hgfhfk,kabir,AD,AD
5,jlh,antriskh,ASDA,AD
CSV 1:
id,name
10927,prince
109582,kabir
f546416,rahul
g44674,saini
r7341,antriskh
CSV 2:
ProfileID,id,name,class ,rollnumber
1,lkha,prince,sfasd,DAS
2,hgfhfk,kabir,AD,AD
3,f546416,rahul,AD,FF
44,g44674,saini,DD,FF
5,jlh,antriskh,ASDA,AD
I tried using converting them into dictionary and match them csv1 keys to csv2 values but it is not working as expected
def read_csv1(filename):
prj_structure = {}
f = open(filename, "r")
data = f.read()
f.close()
lst = data.split("\n")
prj = ""
for i in range(0, len(lst)):
val = lst[i].split(",")
if len(val)>0:
prj = val[0]
if prj!="":
if prj not in prj_structure.keys():
prj_structure[prj] = []
prj_structure[prj].append([val[1], val[2], val[3], val[4])
return prj_structure
def read_csv2(filename):
prj_structure = {}
f = open(filename, "r")
data = f.read()
f.close()
lst = data.split("\n")
prj = ""
for i in range(0, len(lst)):
val = lst[i].split(",")
if len(val)>0:
prj = val[0]
if prj!="":
if prj not in prj_structure.keys():
prj_structure[prj] = []
prj_structure[prj].append([val[0])
return prj_structure
csv1_data = read_csv1("csv1.csv")
csv2_data = read_csv2("csv2.csv")
for k, v in csv1_data.items():
for ks, vs in csv2_data.items():
if k==vs[0][0]:
#here it is not working
sublist = []
sublist.append(k)

Use the DictReader from the csv package.
import csv
f1 = open('csv1.csv')
csv_1 = csv.DictReader(f1)
f2 = open('csv2.csv')
csv_2 = csv.DictReader(f2)
first_dict = {}
for row in csv_1:
first_dict[row['name']]=row
f1.close()
f_out = open('output.csv','w')
csv_out = csv.DictWriter(f_out,csv_2.fieldnames)
csv_out.writeheader()
for second_row in csv_2:
if second_row['name'] in first_dict:
first_row = first_dict[second_row['name']]
if first_row['id']!=second_row['id']:
csv_out.writerow(second_row)
f2.close()
f_out.close()

If you have the option, I have always found pandas as a great tool to import and manipulate CSV files.
import pandas as pd
# Read in both the CSV files
df_1 = pd.DataFrame(pd.read_csv('csv1.csv'))
df_2 = pd.DataFrame(pd.read_csv('csv2.csv'))
# Iterate over both DataFrames and if any id's from in df_2 match
# df_1, remove them from df_2
for num1, row1 in df_1.iterrows():
for num2, row2 in df_2.iterrows():
if row1['id'] == row2['id']:
df_2.drop(num2, inplace=True)
df_2.head()

For any kind of csv processing, using the builtin csv module makes most of the error prone processing trivial. Given your example values, the following code should produce the desired results. I use comprehensions to do the filtering.
import csv
import io
# example data, as StringIO that will behave like file objects
raw_csv_1 = io.StringIO('''\
id,name
10927,prince
109582,kabir
f546416,rahul
g44674,saini
r7341,antriskh''')
raw_csv_2 = io.StringIO('''\
ProfileID,id,name,class,rollnumber
1,lkha,prince,sfasd,DAS
2,hgfhfk,kabir,AD,AD
3,f546416,rahul,AD,FF
44,g44674,saini,DD,FF
5,jlh,antriskh,ASDA,AD''')
# in your actual data, you would use actual file objects instead, like
# with open('location/of/your/csv_1') as file_1:
# raw_csv_1 = file_1.read()
# with open('location/of/your/csv_2') as file_2:
# raw_csv_2 = file_2.read()
Then we need to transform then into csv.reader objects:
csv_1 = csv.reader(raw_csv_1)
next(csv_1) # consume once to skip the header
csv_2 = csv.reader(raw_csv_2)
header = next(csv_2) # consume once to skip the header, but store it
Last but not least, collect the names of the first csv in a set to use them as lookup table, filter the second csv with it, and write it back as 'result.csv' into your file system.
skip_keys = {id_ for id_, name in vals_1}
result = [row for row in vals_2 if row[1] not in skip_keys]
# at this point, result contains
# [['1', 'lkha', 'prince', 'sfasd', 'DAS'],
# ['2', 'hgfhfk', 'kabir', 'AD', 'AD'],
# ['5', 'jlh', 'antriskh', 'ASDA', 'AD']]
with open('result.csv', 'w') as result_file:
csv.writer(result_file).writerows(header+result)

Mapping CSV Header using a Dictionary

I have a reference file that looks like this:
Experiment,Array,Drug
8983,Genechip,Famotidine
8878,Microarray,Dicyclomine
8988,Genechip,Etidronate
8981,Microarray,Flunarizine
I successfully created a dictionary mapping the Experiment numbers to the Drug name using the following:
reader = csv.reader(open('C:\Users\Troy\Documents\ExPSRef.txt'))
#Configure dictionary
result = {}
for row in reader:
key = row[0]
result[key] = row[2]
di = result
I want to map this dictionary to the header of another file which consists of the experiment number. It currently looks like this:
Gene,8988,8981,8878,8983
Vcp,0.011,-0.018,-0.032,-0.034
Ube2d2,0.034,0.225,-0.402,0.418
Becn1,0.145,-0.108,-0.421,-0.048
Lypla2,-0.146,-0.026,-0.101,-0.011
But it should look like this:
Gene,Etidronate,Flunarizine,Dicyclomine,Famotidine
Vcp,0.011,-0.018,-0.032,-0.034
Ube2d2,0.034,0.225,-0.402,0.418
Becn1,0.145,-0.108,-0.421,-0.048
Lypla2,-0.146,-0.026,-0.101,-0.011
I tried using:
import csv
import pandas as pd
reader = csv.reader(open('C:\Users\Troy\Documents\ExPSRef.txt'))
result = {}
for row in reader:
key = row[0]
result[key] = row[2]
di = result
df = pd.read_csv('C:\Users\Troy\Documents\ExPS2.txt')
df['row[0]'].replace(di, inplace=True)
but it returned a KeyError: 'row[0]'.
I tried the following as well, even transposing in order to merge:
import pandas as pd
df1 = pd.read_csv('C:\Users\Troy\Documents\ExPS2.txt',).transpose()
df2 = pd.read_csv('C:\Users\Troy\Documents\ExPSRef.txt', delimiter=',', engine='python')
df3 = df1.merge(df2)
df4 = df3.set_index('Drug').drop(['Experiment', 'Array'], axis=1)
df4.index.name = 'Drug'
print df4
and this time received MergeError('No common columns to perform merge on').
Is there a simpler way to map my dictionary to the header that would work?

One of the things to keep in mind would be to making sure that both the keys corresponding to the mapper dictionary as well as the header which it is mapped to are of the same data type.
Here, one is a string and the other of integer type. So while reading itself, we'll let it not interpret dtype by setting it to str for the reference DF.
df1 = pd.read_csv('C:\Users\Troy\Documents\ExPS2.txt') # Original
df2 = pd.read_csv('C:\Users\Troy\Documents\ExPSRef.txt', dtype=str) # Reference
Convert the columns of the original DF to it's series representation and then replace the old value which were Experiment Nos. with the new Drug name retrieved from the reference DF.
df1.columns = df1.columns.to_series().replace(df2.set_index('Experiment').Drug)
df1

I used csv for the whole script. This fixes the header you wanted and saves into a new file. The new filename can be replaced with the same one if that's what you prefer. This program is written with python3.
import csv
with open('sample.txt', 'r') as ref:
reader = csv.reader(ref)
# skip header line
next(reader)
# make dictionary
di = dict([(row[0], row[2]) for row in reader])
data = []
with open('sample1.txt', 'r') as df:
reader = csv.reader(df)
header = next(reader)
new_header = [header[0]] + [di[i] for i in header if i in di]
data = list(reader)
# used to make new file, can also replace with the same file name
with open('new_sample1.txt', 'w') as df_new:
writer = csv.writer(df_new)
writer.writerow(new_header)
writer.writerows(data)

Python: extracting data values from one file with IDs from a second file

I’m new to coding, and trying to extract a subset of data from a large file.
File_1 contains the data in two columns: ID and Values.
File_2 contains a large list of IDs, some of which may be present in File_1 while others will not be present.
If an ID from File_2 is present in File_1, I would like to extract those values and write the ID and value to a new file, but I’m not sure how to do this. Here is an example of the files:
File_1: data.csv
ID Values
HOT224_1_0025m_c100047_1 16
HOT224_1_0025m_c10004_1 3
HOT224_1_0025m_c100061_1 1
HOT224_1_0025m_c10010_2 1
HOT224_1_0025m_c10020_1 1
File_2: ID.xlsx
IDs
HOT224_1_0025m_c100047_1
HOT224_1_0025m_c100061_1
HOT225_1_0025m_c100547_1
HOT225_1_0025m_c100561_1
I tried the following:
import pandas as pd
data_file = pd.read_csv('data.csv', index_col = 0)
ID_file = pd.read_excel('ID.xlsx')
values_from_ID = data_file.loc[['ID_file']]
The following error occurs:
KeyError: "None of [['ID_file']] are in the [index]"
Not sure if I am reading in the excel file correctly.
I also do not know how to write the extracted data to a new file once I get the code to do it.
Thanks for your help.

With pandas:
import pandas as pd
data_file = pd.read_csv('data.csv', index_col=0, delim_whitespace=True)
ID_file = pd.read_excel('ID.xlsx', index_col=0)
res = data_file.loc[ID_file.index].dropna()
res.to_csv('result.csv')
Content of result.csv:
IDs,Values
HOT224_1_0025m_c100047_1,16.0
HOT224_1_0025m_c100061_1,1.0
In steps:
You need to read your csv with whitespace delimited:
data_file = pd.read_csv('data.csv', index_col=0, delim_whitespace=True)
it looks like this:
>>> data_file
Values
ID
HOT224_1_0025m_c100047_1 16
HOT224_1_0025m_c10004_1 3
HOT224_1_0025m_c100061_1 1
HOT224_1_0025m_c10010_2 1
HOT224_1_0025m_c10020_1 1
Now, read your Excel file, using the ids as index:
ID_file = pd.read_excel('ID.xlsx', index_col=0)
and you use its index with locto get the matching entries from your first dataframe. Drop the missing values with dropna():
res = data_file.loc[ID_file.index].dropna()
Finally, write to the result csv:
res.to_csv('result.csv')

You can do it using a simple dictionary in Python. You can make a dictionary from file 1 and read the IDs from File 2. The IDS from file 2 can be checked in the dictionary and only the matching ones can be written to your output file. Something like this could work :
with open('data.csv','r') as f:
lines = f.readlines()
#Skip the CSV Header
lines = lines[1:]
table = {l.split()[0]:l.split()[1] for l in lines if len(l.strip()) != 0}
with open('id.csv','r') as f:
lines = f.readlines()
#Skip the CSV Header
lines = lines[1:]
matchedIDs = [(l.strip(),table[l.strip()]) for l in line if l.strip() in table]
Now you will have your matched IDs and their values in a list of tuples called matchedIDs. You can write them in any format you like in a file.

I'm also new to python programming. So the code that I used below might not be the most efficient. The situation I assumed is that find ids in data.csv also in id.csv, there might be some ids in data.csv not in id.csv and vise versa.
import pandas as pd
data = pd.read_csv('data.csv')
id2 = pd.read_csv('id.csv')
data.ID = data['ID']
id2.ID = idd['IDs']
d=[]
for row in data.ID:
d.append(row)
f=[]
for row in id2.ID:
f.append(row)
g=[]
for i in d:
if i in f:
g.append(i)
data = pd.read_csv('data.csv',index_col='ID')
new_data = data.loc[g,:]
new_data.to_csv('new_data.csv')

This is the code I ended up using. It worked perfectly. Thanks to everyone for their responses.
import pandas as pd
data_file = pd.read_csv('data.csv', index_col=0)
ID_file = pd.read_excel('ID.xlsx', index_col=0)
res = data_file.loc[ID_file.index].dropna()
res.to_csv('result.csv')