python pandas - Help in processing large set of data

Question

Need help in optimizing code below.

code is working for small set of data but not large set. it is throwing 'unable to allocate array with shape()

code works fine with small file (less than 50 mb) but it failed with large file (more than 200 mb).
the chunck code comes from stackoverflow only. Split pandas dataframe in two if it has more than 10 rows

Here is the original txt file

id, no, julianDate
123,6683969930867371,24118
123,9.22465322574855E+015,21232
123,9.88635756801637E+015,19219
123,3422437279060279,18110
123,3340444112843298,20124
123,4059401965256193,20131
123,7419046088033512,22331

here is the code which is doing:
update id length to 3 if some other length,
convert julian date format YYDDD to YYMM
and jumble the whole df.

import pandas as pd
from os import getcwd, listdir


def single_file(folder):
    # list_of_files = listdir(getcwd())
    for f in listdir(getcwd()):
        if f.endswith('.txt'):
            return f


def convert_file_to_df(file):
    colnames = ['Id', 'no', 'julianDate']
    df = pd.read_csv(file, names=colnames, header=None, converters={'Id': '{:0>3}'.format})
    return df

def convert_no_to_int(df):
    df['no'] = df['no'].astype('int')
    return df


def convert_julian_to_yymm(df, ex_year):
    df['julianDate'] = df['julianDate'].astype('str')
    df['year'] = df['julianDate'].str[:2]
    df['year'] = df['year'].astype(int)
    df = df.drop(df[df['year'] < ex_year].index)
    df['year'] = df['year'].astype(str)
    df['day'] = df['julianDate'].str[2:]
    df['month'] = df['day'].astype(int)
    df['month'] = df['month'] // 30
    df['month'] = df['month'].astype(str).str.zfill(2)
    df['yymm'] = df['year'] + df['month']
    df['yymm'] = df['yymm'].astype(int)
    return df

def split_dataframe_to_chunks(df, n):
    df_len = len(df)
    count = 0
    dfs = []

    while True:
        if count > df_len-1:
            break

        start = count
        count += n
        # print('{0} : {1}'.format(start, count))
        dfs.append(df.iloc[start : count])
    return dfs


for f in listdir(getcwd()):
    # print('for loop {}'.format(i))
    if f.endswith('.txt'):
        # print('file if loop {}'.format(i))
        df = convert_file_to_df(f)
        convert_no_to_int(df)
        dfs = split_dataframe_to_chunks(df, 100)
        # loop = 1
        for df in dfs:
            df_data = convert_julian_to_yymm(df, 20)
            small_chunck_df = drop_columns(df_data)
            final_df = pd.concat([final_df, small_chunck_df])

here is the data after processing

id, no, yymm
123,7623696460219421,2007
123,5638234312228287,2304
123,7285561860395750,2010
123,9232591144713690,2109
123,3214483466634390,2311
123,7443958909235846,2308
123,2479507924021739,2306
123,1477320189903881,2108