How to efficiently merge / merge / merge a large data frame in pandas?

Question

The goal is to create a large frame of data on which I can perform operations such as the average of each row in columns, etc.

The problem is that as the data frame grows, the speed for each iteration increases, so I cannot finish my calculations.

Notes: my dfonly has two columns where it is col1not needed, so I join it. col1is a string, and col2is a float. The number of rows is 3k. The following is an example:

folder_paths    float
folder/Path     1.12630137
folder/Path2    1.067517426
folder/Path3    1.06443264
folder/Path4    1.049119625
folder/Path5    1.039635769

Question Any ideas on how this code can be made more efficient and where are the bottlenecks? Also, I'm not sure if that mergeis the way to go.

. , , , : col1 - , col2 - float.

df = pd.DataFrame() # create an empty data frame

for i in range(1000):
    if i is 0:
        df = generate_new_df(arg1, arg2)
    else:
        df = pd.merge(df, generate_new_df(arg1, arg2), on='col1', how='outer')

pd.concat, :

df = pd.concat([df, get_os_is_from_folder(pnlList, sampleSize, randomState)], axis=1)

pd.concat

run 1
time 0.34s
run 2    
time 0.34s
run 3    
time 0.32s
run 4    
time 0.33s
run 5    
time 0.42s
run 6    
time 0.41s
run 7    
time 0.45s
run 8    
time 0.46s
run 9    
time 0.54s
run 10   
time 0.58s
run 11   
time 0.73s
run 12   
time 0.72s
run 13   
time 0.79s
run 14   
time 0.87s
run 15   
time 0.95s
run 16   
time 1.06s
run 17   
time 1.19s
run 18   
time 1.24s
run 19   
time 1.37s
run 20   
time 1.57s
run 21   
time 1.68s
run 22   
time 1.93s
run 23   
time 1.86s
run 24   
time 1.96s
run 25   
time 2.11s
run 26   
time 2.32s
run 27   
time 2.42s
run 28   
time 2.57s

dfList pd.concat . .

dfList=[]
for i in range(1000):
    dfList.append(generate_new_df(arg1, arg2))

df = pd.concat(dfList, axis=1)

:

run 1 took 0.35 sec.
run 2 took 0.26 sec.
run 3 took 0.3 sec.
run 4 took 0.33 sec.
run 5 took 0.45 sec.
run 6 took 0.49 sec.
run 7 took 0.54 sec.
run 8 took 0.51 sec.
run 9 took 0.51 sec.
run 10 took 1.06 sec.
run 11 took 1.74 sec.
run 12 took 1.47 sec.
run 13 took 1.25 sec.
run 14 took 1.04 sec.
run 15 took 1.26 sec.
run 16 took 1.35 sec.
run 17 took 1.7 sec.
run 18 took 1.73 sec.
run 19 took 6.03 sec.
run 20 took 1.63 sec.
run 21 took 1.93 sec.
run 22 took 1.84 sec.
run 23 took 2.25 sec.
run 24 took 2.65 sec.
run 25 took 6.84 sec.
run 26 took 2.88 sec.
run 27 took 2.58 sec.
run 28 took 2.81 sec.
run 29 took 2.84 sec.
run 30 took 2.99 sec.
run 31 took 3.12 sec.
run 32 took 3.48 sec.
run 33 took 3.35 sec.
run 34 took 3.6 sec.
run 35 took 4.0 sec.
run 36 took 4.41 sec.
run 37 took 4.88 sec.
run 38 took 4.92 sec.
run 39 took 4.78 sec.
run 40 took 5.02 sec.
run 41 took 5.32 sec.
run 42 took 5.31 sec.
run 43 took 5.78 sec.
run 44 took 5.77 sec.
run 45 took 6.15 sec.
run 46 took 6.4 sec.
run 47 took 6.84 sec.
run 48 took 7.08 sec.
run 49 took 7.48 sec.
run 50 took 7.91 sec.

Answer 1

, , , , dataframes , / . , , , , generate_new_df .

merge_with_concat , dataframes , .

, 1000 dataframes, 100 , 10 dataframes, 10 . , , .

( , generate_new_df ) - :

def chunk_dfs(file_names, chunk_size):
    """" yields n dataframes at a time where n == chunksize """
    dfs = []
    for f in file_names:
        dfs.append(generate_new_df(f))
        if len(dfs) == chunk_size:
            yield dfs
            dfs  = []
    if dfs:
        yield dfs


def merge_with_concat(dfs, col):                                             
    dfs = (df.set_index(col, drop=True) for df in dfs)
    merged = pd.concat(dfs, axis=1, join='outer', copy=False)
    return merged.reset_index(drop=False)

col_name = "name_of_column_to_merge_on"
file_names = ['list/of', 'file/names', ...]
chunk_size = 100

merged = merge_with_concat((merge_with_concat(dfs, col_name) for dfs in chunk_dfs(file_names, chunk_size)), col_name)