Merging 1300 data frames into a single frame becomes really slow

Question

I have 1300 csv files in a directory.

Each file has a date in the first column, followed by daily data for the last 20-30 years which spans another 8 columns.

So like this, Data1.csv

Date source1 source2 source3 source4 source5 source6 source 7 source 8

I have 1300 uniquely named files.

I am trying to merge all of these into one dataframe using pandas like this

import pandas as pd 
frame = pd.DataFrame()

length = len(os.listdir(filepath))
for filename in os.listdir(filepath):
    file_path = os.path.join(filepath, filename)
    print(length,end=" ")
    df = pd.read_csv(file_path,index_col=0)
    df = pd.concat([df[[col]].assign(Source=f'{filename[:-4]}-{col}').rename(columns={col: 'Data'}) for col in df])
    frame = frame.append(df)
    length-=1

But around the 300th file I have around 12 million rows and my code really slows down...

Is there a way to speed this up before my computer runs out of memory.

My goal is actually to have a massive dataframe which is 1+ (1300x8) by number of dates for 30 years.

Answer 1

The reason your loop slows down is because of at each .append(), the dataframe has to create a copy in order to allocate more memory, as described here.

If your memory can fit it all, you could first fill a list of fixed size(1300) with all data frames, and then use df = pd.concat(list_of_dataframes), which would probably avoid the issue you are having right now. Your code could be adjusted as such:

import pandas as pd 
lst = [None for _ in range(1300)] # Creates empty list

for i, filename in enumerate(os.listdir(filepath)):
    file_path = os.path.join(filepath, filename)
    df = pd.read_csv(file_path,index_col=0)
    df = pd.concat([df[[col]].assign(Source=f'{filename[:-4]}-{col}').rename(columns={col: 'Data'}) for col in df])
    lst[i] = df
    

frame = pd.concat(lst)