How to Convert Unix Time to Date in Pandas   

To convert Unix timestamp to readable date in Pandas we can use method: pd.to_datetime

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

So this will convert:

[1655822072.437469, 1655815574.333629, 1655797456.516109]

to datetime in Pandas:

DatetimeIndex(['2022-06-21 14:34:32.437469006',
               '2022-06-21 12:46:14.333628893',
               '2022-06-21 07:44:16.516108990'],
              dtype='datetime64[ns]', freq=None)

Let's cover all the steps in to practical example - converting Unix timestamp to any date format (including dd/mm/yyyy).

Setup

Suppose we have DataFrame with Unix timestamp column as follows:

dict = {'ts': {0: 1655822072.437469,
  1: 1655815574.333629,
  2: 1655797456.516109,
  3: 1655743965.358579,
  4: 1655712623.707739},
 'reply_count': {0: 2.0, 1: 3.0, 2: 3.0, 3: 2.0, 4: None}}

pd.DataFrame(dict)

So data will look like:

Step 1: Convert Unix time column to datetime

The first step is to convert the Unix timestamp to Pandas datetime by:

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

The important part of the conversion is unit='s' which stands for seconds. There other options like:

• ns - nanoseconds
• ms - milliseconds

Default value is None and all available options can be found here: pandas.Timestamp

.astype(int)

df['ts'] = df['ts'].astype(int)

Step 2: Convert Unix time to readable date

The second step is to convert Pandas datetime to a readable date. This is possible by using dt attribute:

df['date'].dt.date

The output will be the date component of the original Unit time:

0      2022-06-21
1      2022-06-21
2      2022-06-21
3      2022-06-20
4      2022-06-20

Step 3: Convert Unix time to readable time

To convert the Unix time to a well formatted time string we can use again the dt attribute:

df['date'].dt.time

will give us:

0      14:34:32.437468
1      12:46:14.333628
2      07:44:16.516109
3      16:52:45.358578
4      08:10:23.707739

Step 4: Convert Unix time to custom date or time format

Suppose we would like to get different time pattern like:

• dd/mm/yy
• HH:MM
  etc

This is possible by using method .dt.strftime():

df['date'].dt.strftime('%m/%Y')

which will result into:

0      06/2022
1      06/2022
2      06/2022
3      06/2022
4      06/2022

To find more examples you can consult with: How to Extract Month and Year from DateTime column in Pandas

Step 5: Use datetime.datetime.utcfromtimestamp

Alternative solution is to use datetime.datetime.utcfromtimestamp to convert Unix timestamp to date in Pandas.

To use method like datetime.utcfromtimestamp we will need to apply it to the Unix column:

from datetime import datetime
df["ts"].apply(lambda x: datetime.utcfromtimestamp(x).strftime('%Y-%m-%dT%H:%M:%SZ'))

In this way we can specify the format like:

• %Y-%m-%dT%H:%M:%SZ
• %d-%m-%Y %H:%M:%S

Conclusion

In this article, we saw multiple ways to convert timestamp columns to datetime.

We also covered multiple date and time formats, plus possible problems.