I am generating a dictionary out of multiple .csv files and it looks like this (example):

dtDict = {'AV-IM-1-13991730': {'6/1/2014 0:10': '0.96','6/1/2014 0:15': '0.92','6/1/2014 0:20': '0.97'},'AV-IM-1-13991731': {'6/1/2014 0:10': '1.96','6/1/2014 0:15': '1.92','6/1/2014 0:20': '1.97'},'AV-IM-1-13991732': {'6/1/2014 0:10': '2.96','6/1/2014 0:15': '2.92','6/1/2014 0:20': '2.97'},'AV-IM-1-13991733': {'6/1/2014 0:10': '3.96','6/1/2014 0:15': '3.96','6/1/2014 0:20': '3.97'}}

I want to save it to a .csv file in the following format:

timestamp,AV-IM-1-13991730,AV-IM-1-13991731,AV-IM-1-13991732,AV-IM-1-13991733
6/1/2014 0:10,0.96,1.96,2.96,3.96
6/1/2014 0:15,0.92,1.92,2.92,3.96
6/1/2014 0:20,0.97,1.97,2.97,3.97

The piece of code I have as of now (related to this objective):

header = '''# file...... Recorder file
# date...... Thu Mar 12 14:35:32 2015
# user...... Sri
# host...... (null)
# group..... None
# property.. AVA Measurements
# limit..... 
# interval..''' testpower        = open("custpower.csv",'w')
testpower.writelines([header,'\n','# timestamp\n'])
...
for key, value in dtDict.iteritems():#Still trying to figure out how to write to custpower.csv

I tried doing something similar to this:

for key, value in dtDict.iteritems():testpower.writelines([key,',',','.join(value),'\n'])

but it didnot quite do what I was trying to do.

This is beyond simple if you can use pandas.

import pandas as pddata = {'AV-IM-1-13991730': {'6/1/2014 0:10': '0.96','6/1/2014 0:15': '0.92','6/1/2014 0:20': '0.97'},'AV-IM-1-13991731': {'6/1/2014 0:10': '1.96','6/1/2014 0:15': '1.92','6/1/2014 0:20': '1.97'},'AV-IM-1-13991732': {'6/1/2014 0:10': '2.96','6/1/2014 0:15': '2.92','6/1/2014 0:20': '2.97'},'AV-IM-1-13991733': {'6/1/2014 0:10': '3.96','6/1/2014 0:15': '3.96','6/1/2014 0:20': '3.97'}}df = pd.DataFrame(data)
df.to_csv(PATH_TO_OUTPUT_FILE)

df becomes a DataFrame that looks like

              AV-IM-1-13991730 AV-IM-1-13991731 AV-IM-1-13991732 AV-IM-1-13991733
6/1/2014 0:10             0.96             1.96             2.96             3.96
6/1/2014 0:15             0.92             1.92             2.92             3.96
6/1/2014 0:20             0.97             1.97             2.97             3.97

And your resulting csv looks like

,AV-IM-1-13991730,AV-IM-1-13991731,AV-IM-1-13991732,AV-IM-1-13991733
6/1/2014 0:10,0.96,1.96,2.96,3.96
6/1/2014 0:15,0.92,1.92,2.92,3.96
6/1/2014 0:20,0.97,1.97,2.97,3.97

Pandas is also nice because you can then do:

df.convert_objects(convert_numeric=True).plot()
# the converts change "0.97" -> 0.97 so it's plottable

To get: