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:
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.newline=""
. Remove this for Python 2.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.
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)
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