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]: df
Out[22]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 358 entries, 0 to 357
Data columns (total 2 columns):
date 358 non-null values
price 358 non-null values
dtypes: float64(1), int64(1)
In [23]: df.head()
Out[23]:
date price
0 1349720105 12.08
1 1349806505 12.35
2 1349892905 12.15
3 1349979305 12.19
4 1350065705 12.15
In [25]: df['date'] = pd.to_datetime(df['date'],unit='s')
In [26]: df.head()
Out[26]:
date price
0 2012-10-08 18:15:05 12.08
1 2012-10-09 18:15:05 12.35
2 2012-10-10 18:15:05 12.15
3 2012-10-11 18:15:05 12.19
4 2012-10-12 18:15:05 12.15
In [27]: df.dtypes
Out[27]:
date datetime64[ns]
price float64
dtype: 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.