Creation of formatted DataFrame and then adding data line by line - python-2.7

Question

I have a continuous stream of data coming in so I want to define the DataFrame before hand so that I don't have tell pandas to format data or set index
So I want to create a DataFrame like
df = pd.DataFrame(columns=["timestamp","stockname","price","volume"])
but I want to tell Pandas that index of data should be timestamp and that the format would be
"%Y-%m-%d %H:%M:%S:%f"
and one this it set, then I would read through file and pass data to the DataFrame initialized
I get data in variables like these populated every time in loop like
for line in filehandle:
timestamp, stockname, price, volume = fetch(line)
here I want to update the "df"
this update would go on while I would keep using the copy of
df
let us say into a
tempdf
to do re-sampling or any other task at any given point in time because original dataframe
df
is getting updated continuously

Answer 1

import numpy as np
import pandas as pd
import datetime as dt
import time
# create df with timestamp as index
df = pd.DataFrame(columns=["timestamp","stockname","price","volume"], dtype = float)
pd.to_datetime(df['timestamp'], format = "%Y-%m-%d %H:%M:%S:%f")
df.set_index('timestamp', inplace = True)
for i in range(10): # for the purposes of functioning demo code
i += 1 # counter
time.sleep(0.01) # give jupyter notebook a moment
timestamp = dt.datetime.now() # to be used as index
df.loc[timestamp] = ['AAPL', np.random.randint(1000), np.random.randint(10)] # replace with your database read
tempdf = df.copy()
If you are reading a file or database continuously, you can replace the for: loop with what you described in your question. #MattR's questions should also be addressed; if you need to continuously log or update data, I am not sure if pandas is the best solution.