I have hundreds of large CSV files that I would like to merge into one. However, not all CSV files contain all columns. Therefore, I need to merge files based on column name, not column position.
Just to be clear: in the merged CSV, values should be empty for a cell coming from a line which did not have the column of that cell.
I cannot use the pandas module, because it makes me run out of memory.
Is there a module that can do that, or some easy code?
The csv.DictReader and csv.DictWriter classes should work well (see Python docs). Something like this:
import csv
inputs = ["in1.csv", "in2.csv"] # etc
# First determine the field names from the top line of each input file
# Comment 1 below
fieldnames = []
for filename in inputs:
with open(filename, "r", newline="") as f_in:
reader = csv.reader(f_in)
headers = next(reader)
for h in headers:
if h not in fieldnames:
fieldnames.append(h)
# Then copy the data
with open("out.csv", "w", newline="") as f_out: # Comment 2 below
writer = csv.DictWriter(f_out, fieldnames=fieldnames)
for filename in inputs:
with open(filename, "r", newline="") as f_in:
reader = csv.DictReader(f_in) # Uses the field names in this file
for line in reader:
# Comment 3 below
writer.writerow(line)
Comments from above:
You need to specify all the possible field names in advance to DictWriter, so you need to loop through all your CSV files twice: once to find all the headers, and once to read the data. There is no better solution, because all the headers need to be known before DictWriter can write the first line. This part would be more efficient using sets instead of lists (the in operator on a list is comparatively slow), but it won't make much difference for a few hundred headers. Sets would also lose the deterministic ordering of a list - your columns would come out in a different order each time you ran the code.
The above code is for Python 3, where weird things happen in the CSV module without newline="". Remove this for Python 2.
At this point, line is a dict with the field names as keys, and the column data as values. You can specify what to do with blank or unknown values in the DictReader and DictWriter constructors.
This method should not run out of memory, because it never has the whole file loaded at once.
For those of us using 2.7, this adds an extra linefeed between records in "out.csv". To resolve this, just change the file mode from "w" to "wb".
The solution by #Aaron Lockey, which is the accepted answer has worked well for me except, there were no headers for the file. The out put had no headers and only the row data. Each column was without headings (keys). So I inserted following:
writer.writeheader()
and it worked perfectly fine for me! So now the entire code appears like this:
import csv
inputs = ["in1.csv", "in2.csv"] # etc
# First determine the field names from the top line of each input file
fieldnames = []
for filename in inputs:
with open(filename, "r", newline="") as f_in:
reader = csv.reader(f_in)
headers = next(reader)
for h in headers:
if h not in fieldnames:
fieldnames.append(h)
# Then copy the data
with open("out.csv", "w", newline="") as f_out:
writer = csv.DictWriter(f_out, fieldnames=fieldnames)
writer.writeheader() #this is the addition.
for filename in inputs:
with open(filename, "r", newline="") as f_in:
reader = csv.DictReader(f_in) # Uses the field names in this file
for line in reader:
writer.writerow(line)
You can use the pandas module to do this pretty easily. This snippet assumes all your csv files are in the current folder.
import pandas as pd
import os
all_csv = [file_name for file_name in os.listdir(os.getcwd()) if '.csv' in file_name]
li = []
for filename in all_csv:
df = pd.read_csv(filename, index_col=None, header=0, parse_dates=True, infer_datetime_format=True)
li.append(df)
frame = pd.concat(li, axis=0, ignore_index=True)
frame.to_csv('melted_csv.csv', index=False)
I've faced a situation where not only the number of columns are different, but also some column names are missing. For this kind of situation and obviously for your case, this code snippet can help you :)
The tricky part is naming the columns which have no names and adding them to dictionary. The read_csv_file function is playing the main role here.
def read_csv_file(csv_file_path):
headers = []
data = []
with open(csv_file_path, 'r') as f:
csv_reader = csv.reader(f)
rows = []
for i, row in enumerate(csv_reader):
if i == 0:
for j in range(len(row)):
if row[j].strip() == "":
col_name = f"col-{j+1}"
else:
col_name = row[j]
if col_name not in headers:
headers.append(col_name)
else:
rows.append(row)
if len(row) > len(headers):
for j in range(len(row)):
if j+1 > len(headers):
col_name = f"col-{j+1}"
if col_name not in headers:
headers.append(col_name)
for i, row in enumerate(rows):
row_data = {}
for j in range(len(headers)):
if len(row) > j:
row_data[headers[j]] = row[j]
else:
row_data[headers[j]] = ''
data.append(row_data)
return headers, data
def write_csv_file(file_path, rows):
if len(rows) > 0:
headers = list(rows[0].keys())
with open(file_path, 'w', newline='', encoding='UTF8') as f:
writer = csv.DictWriter(f, fieldnames=headers)
writer.writeheader()
writer.writerows(rows)
# The list of the csv file paths which will be merged
files_to_be_merged = [
'file-1.csv',
'file-2.csv',
'file-3.csv'
]
# Read and store all the file data in new_file_data
final_headers = []
new_file_data = []
for f1 in files_to_be_merged:
single_file_data = read_csv_file(f1)
for h in single_file_data[0]:
if h not in final_headers:
final_headers.append(h)
new_file_data += single_file_data[1]
# Add the missing keys to the dictionaries
for d in new_file_data:
for h in final_headers:
if d.get(h) is None:
d[h] = ""
# Write a new file
target_file_name = 'merged_file.csv'
write_csv_file(target_file_name, new_file_data)
Related
Hey intelligent community,
I need a little bit of help because i think i don't see the the wood in the trees.
i have to CSV files that look like this:
Name,Number
AAC;2.2.3
AAF;2.4.4
ZCX;3.5.2
Name,Number
AAC;2.2.3
AAF;2.4.4
ZCX;3.5.5
I would like to compare both files and than write any changes like this:
Name,Number,Changes
AAC;2.2.3
AAF;2.4.4
ZCX;5.5.5;change: 3.5.2
So on every line when there is a difference in the number, i want to add this as a new column at the end of the line.
The Files are formated the same but sometimes have a new row so thats why i think i have to map the keys.
I come this far but now iam lost in my thoughts:
Python 3.10.9
import csv
Reading the first csv and set mapping
with open('test1.csv', 'r') as csvfile:
reader= csv.reader(csvfile)
rows = list(reader)
file1_dict = {row[1]: row[0] for row in rows}
Reading the second csv and set mapping
with open('test2.csv', 'r') as csvfile:
reader= csv.reader(csvfile)
rows = list(reader)
file2_dict = {row[1]: row[0] for row in rows}
comparing the keys and find the diff
for k in test1_dict:
if test1_dict[k] != test2:dict[k]
test1_dict[k] = test2_dict[k]
for row in rows:
if row[1] == k:
row.append(test2_dict[k])
#write the csv (not sure how to add the word "change:")
with open('test1.csv', 'w', newline ='') as csvfile:
writer = csv.writer(csvfile)
writer.writerows(rows)
If i try this, i don't get a new column, it just "updates" the csv file with the same columns.
For example this code gives me the diff row but i'am not able to just add it to existing file and row.
with open('test1.csv') as fin1:
with open('test2.csv') as fin2:
read1 = csv.reader(fin1)
read2 = csv.reader(fin2)
diff_rows = (row1 for row1, row2 in zip(read1, read2) if row1 != row2)
with open('test3.csv', 'w') as fout:
writer = csv.writer(fout)
writer.writerows(diff_rows)
Does someone have any tips or help for my problem? I read many answers on here but can't figure it out.
Thanks alot.
#bigkeefer
Thanks for your answer, i tried to change it for the delimiter ; but it gives an "list index out of range error".
with open('test3.csv', 'r') as file1:
reader = csv.reader(file1, delimiter=';')
rows = list(reader)[1:]
file1_dict = {row[0]: row[1] for row in rows}
with open('test4.csv', 'r') as file2:
reader = csv.reader(file2, delimiter=';')
rows = list(reader)[1:]
file2_dict = {row[0]: row[1] for row in rows}
new_file = ["Name;Number;Changes\n"]
with open('output.csv', 'w') as nf:
for key, value in file1_dict.items():
if value != file2_dict[key]:
new_file.append(f"{key};{file2_dict[key]};change: {value}\n")
else:
new_file.append(f"{key};{value}\n")
nf.writelines(new_file)
You will need to adapt this to overwrite your first file etcetera, as you mentioned above, but I've left it like this for your testing purposes. Hopefully this will help you in some way.
I've assumed you've actually got the headers above in each file. If not, remove the slicing on the list creations, and change the new_file variable assignment to an empty list ([]).
with open('f1.csv', 'r') as file1:
reader = csv.reader(file1, delimiter=";")
rows = list(reader)[1:]
file1_dict = {row[0]: row[1] for row in rows if row}
with open('f2.csv', 'r') as file2:
reader = csv.reader(file2, delimiter=";")
rows = list(reader)[1:]
file2_dict = {row[0]: row[1] for row in rows if row}
new_file = ["Name,Number,Changes\n"]
for key, value in file1_dict.items():
if value != file2_dict[key]:
new_file.append(f"{key};{file2_dict[key]};change: {value}\n")
else:
new_file.append(f"{key};{value}\n")
with open('new.csv', 'w') as nf:
nf.writelines(new_file)
Trying to read in a CSV, adding a row at the bottom, and deleting a row at the top. I have not been able to find a way to delete a row in the dictwriter object without converting to a list, deleting the row in the list, then writing it out using csv.writer.
Should be a better way than reading/writing twice.
python3.8, ubuntu
Thx.
stime = get_time_str()
new_dict = {'Time': stime, 'Queries': Querycounter.value}
Querycounter.value = 0
# list of column names
field_names = ['Time', 'Queries']
# Open CSV file in append mode
#append the new queries count at the end of the file
with open(AlpacaQueriesCSVfile, 'a') as f_object:
dictwriter_object = DictWriter(f_object, fieldnames=field_names)
dictwriter_object.writerow(new_dict)
f_object.close()
#open using csv.reader, delete the rows(s).
with open(AlpacaQueriesCSVfile, "r") as f:
reader = csv.reader(f, delimiter=",")
data = list(reader) #should be a better way of doing this by deleting rows in the dictwriter_object above....later
row_count = len(data)
if row_count > 2880:
logger.debug('Deleting row from Queries.csv ')
to_skip = row_count-2880
del data[1:to_skip] # leave first row
with open(QueriesCSVfile, 'w') as f:
write = csv.writer(f)
write.writerows(data)
What I want to do is actually as it is written in the title.
with open(path, "r+", newline='') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
list_of_column_names = []
num_cols = len(next(csv_reader))
for i in range(num_cols):
list_of_column_names.append(i)
fields = list_of_column_names
with open(example.csv, "r+", newline='') as writeFile:
csvwriter = csv.DictWriter(writeFile, delimiter=',', lineterminator='\n', fieldnames=fields)
writeFile.seek(0, 0)
csvwriter.writeheader()
I want to enumerate the columns which initially doesn't have any column names. But when I run the code, it replaces the data in the first row. For example:
example.csv:
a,b
c,d
e,f
what I want:
0,1
a,b
c,d
e,f
what happens after running the code:
0,1
c,d
e,f
Is there a way to prevent this from happening?
There's no magical way to insert a line into an existing text file.
The following is how I think of doing this, and your code is already getting steps 2-4. Also, I wouldn't mess with the DictWriter since you're not trying to convert a Python dict to CSV (I can see you using it for writing the header, but that's easy enough to do with the regular reader/writer):
open a new file for writing
read the first row of your CSV
interpret the column indexes as the header
write the header
write the first row
read/write the rest of the rows
move the new file back to the old file, overwrite (not shown)
Here's what that looks like in code:
import csv
with open('output.csv', 'w', newline='') as out_f:
writer = csv.writer(out_f)
with open('input.csv', newline='') as in_f:
reader = csv.reader(in_f)
# Read the first row
first_row = next(reader)
# Count the columns in first row; equivalent to your `for i in range(len(first_row)): ...`
header = [i for i, _ in enumerate(first_row)]
# Write header and first row
writer.writerow(header)
writer.writerow(first_row)
# Write rest of rows
for row in reader:
writer.writerow(row)
source.csv as follows.
AB;CD
a;1;
b;2;
c;3;
target.csv as follows.
DE;FG;HI
1;e;1;
2;a;2;
3;f;3;
I need to do a vlookup using file handling mechanisms in python.
So need to update column 'FG' of 'target.csv' by looking up the column 'AB' of 'source.csv' and update with 'CD' column value of 'source.csv'.
So my desired output is like below.
DE;FG;HI
1;e;1;
2;1;2; #a is replaced with 1
3;f;3;
Without using pandas or any other method how I can approach this.
Below is how I approached this.
with open('D:/target.csv', "w+", encoding="utf-8") as Tgt_csvFile:
with open('D:/source.csv', "r", encoding="utf-8") as Src_csvFile:
for line in Src_csvFile:
fields = line.split(";")
x = fields[0]
for line_1 in Tgt_csvFile:
fields_1 = line_1.split(";")
y = fields[1]
if y == x:
update # not sure how to do this
else:
keep as it is
Appreciate on the support
This will solve your particular problem, but if the number of input/output columns changes you will need to adjust the logic accordingly.
It's also worth noting the trailing ; on each non-header row of your csv file will cause most packages to assume there is an extra column. I don't think you want that.
# Read in input, creating a dict where key is column 1 and value is column 2
with open('source.csv', mode='r') as infile:
reader = csv.reader(infile, delimiter=';')
s = {x[0]:x[1] for x in reader}
print(s)
# If column 2 is a key in dict s update with value from dict
output = []
with open('target.csv', mode='r') as infile:
reader = csv.reader(infile, delimiter=';')
for row in reader:
if row[1] in s.keys():
row[1] = s[row[1]]
output.append(row)
# Output to csv
with open('output.csv', mode='w', newline='') as outfile:
writer = csv.writer(outfile, delimiter=';')
writer.writerows(output)
Here is my suggestion:
with open('D:/source.csv', "r", encoding="utf-8") as Src_csvFile:
l=Src_csvFile.readlines()
d={}
for i in l[1:]:
x=i.split(';')
d[x[0]]=x[1]
with open('D:/target.csv', "r", encoding="utf-8") as Tgt_csvFile:
m=Tgt_csvFile.readlines()
for i in range(1,len(m)):
x=m[i].split(';')
if x[1] in d:
x[1]=d.get(x[1])
m[i]=';'.join(x)
with open('D:/target.csv', "w", encoding="utf-8") as Tgt_csvFile:
Tgt_csvFile.writelines(m)
Output:
DE;FG;HI
1;e;1;
2;1;2;
3;f;3;
I've just started coding, I'm trying to remove certain columns from a CSV for a project, we aren't supposed to use pandas. For instance, one of the fields I have to delete is called DwTm, but there's about 15 columns I have to get rid of; I only want the first few, Here's what I've gotten:
import csv
FTemp = "D:/tempfile.csv"
FOut = "D:/NewFile.csv"
with open(FTemp, 'r') as csv_file:
csv_reader = csv.reader(csv_file)
with open(FOut, 'w') as new_file:
fieldnames = ['Stn_Name', 'Lat', 'Long', 'Prov', 'Tm']
csv_writer = csv.DictWriter(new_file, fieldnames=fieldnames)
for line in csv_reader:
del line['DwTm']
csv_writer.writerow(line)
When I run this, I get the error
del line['DwTm']
TypeError: list indices must be integers or slices, not str
This is the only method I've found to almost work without using pandas. Any ideas?
The easiest way around this is to use a DictReader to read the file. Like DictWriter, which you are using to write the file, DictReader uses dictionaries for rows, so your approach of deleting keys from the old row then writing to the new file will work as you expect.
import csv
FTemp = "D:/tempfile.csv"
FOut = "D:/NewFile.csv"
with open(FTemp, 'r') as csv_file:
# Adjust the list to be have the correct order
old_fieldnames = ['Stn_Name', 'Lat', 'Long', 'Prov', 'Tm', 'DwTm']
csv_reader = csv.DictReader(csv_file, fieldnames=old_fieldnames)
with open(FOut, 'w') as new_file:
fieldnames = ['Stn_Name', 'Lat', 'Long', 'Prov', 'Tm']
csv_writer = csv.DictWriter(new_file, fieldnames=fieldnames)
for line in csv_reader:
del line['DwTm']
csv_writer.writerow(line)
Below
import csv
# We only want to read the 'department' field
# We are not interested in 'name' and 'birthday month'
# Make sure the list items are in ascending order
NON_INTERESTING_FIELDS_IDX = [2,0]
rows = []
with open('example.csv') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
for row in csv_reader:
for idx in NON_INTERESTING_FIELDS_IDX:
del row[idx]
rows.append(','.join(row))
with open('example_out.csv','w') as out:
for row in rows:
out.write(row + '\n')
example.csv
name,department,birthday month
John Smith,Accounting,November
Erica Meyers,IT,March
example_out.csv
department
Accounting
IT
It's possible to simultaneously open the file to read from and the file to write to. Let's say you know the indices of the columns you want to keep, say, 0,2, and 4:
good_cols = (0,2,4)
with open(Ftemp, 'r') as fin, open(Fout, 'w') as fout:
for line in fin:
line = line.rstrip() #clean up newlines
temp = line.split(',') #make a list from the line
data = [temp[x] for x in range(len(temp)) if x in good_cols]
fout.write(','.join(data) + '\n')
The list comprehension (data) pulls only the columns you want to keep out of each row and immediately writes line-by-line to your new file, using the join method (plus tacking on an endline for each new row).
If you only know the names of the fields you want to keep/remove it's a bit more involved, you have to extract the indices from the first line of the csv file, but it's not much more difficult.