Python script taking too long to run?

Question

I am writing a python script that essentially does the following

1. Reads a CSV file as a dataframe object.
2. Selects some columns based on names and stores them in a new DF object.
3. Does some math and string manipulation on the values in cells. I use the for loop and the iterrows() method here.
4. Writes the modified DF to a CSV
5. Writes the CSV to json using a for loop.

This code takes forever to run. I am trying to understand why this is taking so long, and if I should do my tasks differently to speed up the execution.

import pandas
import json
import pendulum
import csv
import os
import time

start_time = time.time()
print("--- %s seconds ---" % (time.time() - start_time))

os.chdir('/home/csv_files_from_REC')
df11 = pandas.read_csv('RTP_Gap_2018-01-21.csv') ### Reads the CSV FILE

print df11.shape ### Prints the shape of the DF

### Filter the initial DF by selecting some columns based on NAME
df1 = df11[['ENODEB','DAY','HR','SITE','RTP_Gap_Length_Total_sec','RTP_Session_Duration_Total_sec','RTP_Gap_Duration_Ratio_Avg%']]

print df1.shape ## Prints Shape

#### Math and String manupulation stuff ###
for index, row in df1.iterrows():
    if row['DAY'] == 'Total':
        df1.drop(index, inplace=True)
    else:
        stamp = row['DAY'] + ' ' + str(row['HR']) + ':00:00'
        sitename = str(row['ENODEB'])+'_'+row['SITE']
        if row['RTP_Session_Duration_Total_sec'] == 0:
            rtp_gap = 0
        else:
            rtp_gap = row['RTP_Gap_Length_Total_sec']/row['RTP_Session_Duration_Total_sec']
        time1 = pendulum.parse(stamp,tz='America/Chicago').isoformat()
        df1.loc[index,'DAY'] = time1
        df1.loc[index,'SITE'] = sitename
        df1.loc[index,'HR'] = rtp_gap

### Write DF to CSV ###
df1.to_csv('RTP_json.csv',index=None)
json_file_ind = 'RTP_json.json'
file = open(json_file_ind, 'w')
file.write("")
file.close()

#### Write CSV to JSON ###
with open('RTP_json.csv', 'r') as csvfile:
    reader_ind = csv.DictReader(csvfile)
    row=[]
    for row in reader_ind:         
        row["RTP_Gap_Length_Total_sec"] = float(row["RTP_Gap_Length_Total_sec"])
        row["RTP_Session_Duration_Total_sec"] = float(row["RTP_Session_Duration_Total_sec"])
                row["RTP_Gap_Duration_Ratio_Avg%"]=float(row["RTP_Gap_Duration_Ratio_Avg%"])
        row["HR"] = float(row["HR"])
        with open('RTP_json.json', 'a') as json_file_ind:
            json.dump(row, json_file_ind)
            json_file_ind.write('\n')

 end_time = time.time()
 print("--- %s seconds ---" % (time.time() - end_time))

Output

    --- 2018-01-23T12:25:07.411691-06:00 seconds ---### START TIME
    (2055, 36) ### SIZE of initial DF
    (2055, 7) ### Size of Filtered DF
    --- 2018-01-23T12:31:54.480568-06:00 seconds --- --- ### END TIME

Answer 1

This piece should speed up your dataframe calcuations significantly

import numpy as np

df1 = df11[['ENODEB','DAY','HR','SITE','RTP_Gap_Length_Total_sec','RTP_Session_Duration_Total_sec','RTP_Gap_Duration_Ratio_Avg%']]

print df1.shape ## Prints Shape

df1 = df1[df1.DAY != 'Total'].reset_index()
df1['DAY'] = pendulum.parse(df1['DAY'] + ' ' + str(df1['HR']) + ':00:00',tz='America/Chicago').isoformat()
df1['SITE'] = str(df1['ENODEB'])+'_'+df1['SITE']
df1['HR'] = np.where(df1['RTP_Session_Duration_Total_sec']==0,0,df1['RTP_Gap_Length_Total_sec']/df1['RTP_Session_Duration_Total_sec'])

Also, why bother writing to a csv and reading from it again.

to get the df to a json format

format_json =  df1.to_json(orient='records') # converts df to json list
json_file_ind = 'RTP_json.json'
file = open(json_file_ind, 'w')
for i in format_json:
    file.write(i)
    file.write('\n')

This should speed up your code significantly