I have the following snippet of Python code:
import pandas as pd
# print normal index
print data.index
# convert from df to JSON and back
data_json = data.to_json()
df = pd.read_json(data_json)
df.index = pd.to_datetime(df.index)
print df.index
for some reason running this returns in:
<class 'pandas.tseries.index.DatetimeIndex'>
[1950-01-03 00:00:00, ..., 2014-08-21 00:00:00]
Length: 16264, Freq: None, Timezone: None
<class 'pandas.tseries.index.DatetimeIndex'>
[1966-10-31 00:00:00, ..., 2001-09-07 00:00:00]
Length: 16264, Freq: None, Timezone: None
Can someone explain to me what is going on and how I can have the index persist through the transformations?
The error here is that to_json saves dates with ms resolution by defaul, while to_datetime converts with nanosecond resolution by default. To fix, either of these (but not both!) would work.
pd.to_datetime(df.index, unit='ms')
#OR
data_json = data.to_json(date_unit='ns')
As noted in comments, you can also just save the json with the dates in iso format.
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