pandas timeseries dataframe from source

pandas_timeseries.py

import pandas as pd
import datetime as dt

testdata={
  "host1":[
    (dt.datetime(2015, 3, 5, 12), 100),
    (dt.datetime(2015, 3, 5, 12,1), 100),
    (dt.datetime(2015, 3, 5, 12,2), 100),
    (dt.datetime(2015, 3, 5, 12,3), 100),
    (dt.datetime(2015, 3, 5, 12,4), 100),
  ],
  "host2":[
    (dt.datetime(2015, 3, 5, 12), 300),
    (dt.datetime(2015, 3, 5, 12,2), 300),
    (dt.datetime(2015, 3, 5, 12,3), 300),
    (dt.datetime(2015, 3, 5, 12,4), 300)
  ]
}


host1=pd.Series([100, 101, 102, 103, 104], index=[dt.datetime(2015, 3, 5, 12),
    dt.datetime(2015, 3, 5, 12,1),
    dt.datetime(2015, 3, 5, 12,2),
    dt.datetime(2015, 3, 5, 12,3),
    dt.datetime(2015, 3, 5, 12,4)])

host2=pd.Series([300, 302, 303, 304], index=[dt.datetime(2015, 3, 5, 12),
    dt.datetime(2015, 3, 5, 12,2),
    dt.datetime(2015, 3, 5, 12,3),
    dt.datetime(2015, 3, 5, 12,4)])


d={
  "host1":host1,
  "host2": host2
}


idx=[dt.datetime(2015, 3, 5, 12),
    dt.datetime(2015, 3, 5, 12,1),
    dt.datetime(2015, 3, 5, 12,2),
    dt.datetime(2015, 3, 5, 12,3),
    dt.datetime(2015, 3, 5, 12,4),
    dt.datetime(2015, 3, 5, 12,5)
    ]

df=pd.DataFrame(d, index=idx)
"""
>>> df
2015-03-05 12:00:00    100    100
2015-03-05 12:01:00    101    NaN
2015-03-05 12:02:00    102    102
2015-03-05 12:03:00    103    103
2015-03-05 12:04:00    104    104
2015-03-05 12:05:00    NaN    NaN


>>> df.index
<class 'pandas.tseries.index.DatetimeIndex'>
[2015-03-05 12:00:00, ..., 2015-03-05 12:05:00]
Length: 6, Freq: None, Timezone: None
>>> df.columns
Index([u'host1', u'host2'], dtype='object')


# select a column
>>> df['host1']
2015-03-05 12:00:00    100
2015-03-05 12:01:00    101
2015-03-05 12:02:00    102
2015-03-05 12:03:00    103
2015-03-05 12:04:00    104
2015-03-05 12:05:00    NaN
Name: host1, dtype: float64
"""

df=df.interpolate()   # fills a gap in the data replacing NaN with interpolated numbers

"""
# drop a column
df=.drop('_span', 1)

# set a column as the index
df= df.set_index("_time")

"""

used to parse and clean up dumped zabbix data