Convert unix time to readable date in pandas dataframe

These appear to be seconds since epoch.

In [20]: df = DataFrame(data['values'])In [21]: df.columns = ["date","price"]In [22]: dfOut[22]: <class 'pandas.core.frame.DataFrame'>Int64Index: 358 entries, 0 to 357Data columns (total 2 columns):date     358  non-null valuesprice    358  non-null valuesdtypes: float64(1), int64(1)In [23]: df.head()Out[23]:          date  price0  1349720105  12.081  1349806505  12.352  1349892905  12.153  1349979305  12.194  1350065705  12.15In [25]: df['date'] = pd.to_datetime(df['date'],unit='s')In [26]: df.head()Out[26]:                  date  price0 2012-10-08 18:15:05  12.081 2012-10-09 18:15:05  12.352 2012-10-10 18:15:05  12.153 2012-10-11 18:15:05  12.194 2012-10-12 18:15:05  12.15In [27]: df.dtypesOut[27]: date     datetime64[ns]price           float64dtype: object

If you try using:

df[DATE_FIELD]=(pd.to_datetime(df[DATE_FIELD],***unit='s'***))

and receive an error :

"pandas.tslib.OutOfBoundsDatetime: cannot convert input with unit 's'"

This means the DATE_FIELD is not specified in seconds.

In my case, it was milli seconds - EPOCH time.

The conversion worked using below:

df[DATE_FIELD]=(pd.to_datetime(df[DATE_FIELD],unit='ms')) 

Assuming we imported pandas as pd and df is our dataframe

pd.to_datetime(df['date'], unit='s')

works for me.