How to Solve Python AttributeError: ‘Series’ object has no attribute ‘to_datetime’

by | Programming, Python, Tips

n Python, a Pandas Series is a one-dimensional labelled array capable of holding data of any type. Pandas Series is the same as a column in an Excel spreadsheet. If you have values in a Series that you want to convert to datetime, you cannot use to_datetime() directly on the Series. If you try to call to_datetime() directly on a Series object, you will raise the AttributeError: ‘Series’ object has no attribute ‘to_datetime’. to_datetime() is a built-in method to Pandas, which can accept a Series object as an argument, for example, pandas.to_datetime(series).

This tutorial will go through the error in detail and how to solve it with code examples.


AttributeError: ‘Series’ object has no attribute ‘to_datetime’

AttributeError occurs in a Python program when we try to access an attribute (method or property) that does not exist for a particular object. The part “‘Series’ object has no attribute ‘to_datetime’” tells us that the Series object we are handling does not have the to_datetime attribute. The to_datetime() method is a built-in Pandas method that we can use to convert a Series argument to a datetime type. We cannot call to_datetime on a Series like series.to_datetime(). Instead, we have to pass the Series to the to_datetime() method.

Example

Let’s look at an example with a Series containing epoch timestamp values. We want to use to_datetime() method to convert the values to human-readable dates. Let’s look at the original Series object:

import pandas as pd

s = pd.Series([1490195805, 1598495821, 1237495321, 1444899912])

print(s)
0    1490195805
1    1598495821
2    1237495321
3    1444899912
dtype: int64

The Series contains integer values representing epoch timestamp values. Let’s look at the code to convert the values to datetime:

s_datetime = s.to_datetime(unit='s')

print(s_datetime)

Let’s run the code to see the result:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-0b06c7ebfe5b> in <module>
      3 s = pd.Series([1490195805, 1598495821, 1237495321, 1444899912])
      4 
----> 5 s_datetime = s.to_datetime(unit='s')
      6 
      7 print(s_datetime)

~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py in __getattr__(self, name)
   5581         ):
   5582             return self[name]
-> 5583         return object.__getattribute__(self, name)
   5584 
   5585     def __setattr__(self, name: str, value) -> None:

AttributeError: 'Series' object has no attribute 'to_datetime'

The Python interpreter raises the AttributeError because to_datetime is an attribute of Pandas, not Series.

Solution

We need to pass the Series to the to_datetime() method as an argument to solve this error. Let’s look at the revised code:

import pandas as pd

s = pd.Series([1490195805, 1598495821, 1237495321, 1444899912])

# Unit of the epoch timestamps is in seconds, set unit to 's'

s_datetime = pd.to_datetime(s, unit='s')

print(s_datetime)

Let’s run the code to see the result:

0   2017-03-22 15:16:45
1   2020-08-27 02:37:01
2   2009-03-19 20:42:01
3   2015-10-15 09:05:12
dtype: datetime64[ns]

We converted the epoch timestamp values to human-readable dates using to_datetime(). The dtype of the Series object is datetime64.

Summary

Congratulations on reading to the end of this tutorial! The AttributeError: ‘Series’ object has no attribute ‘to_datetime’ occurs when you try to call the to_datetime() method on a Series object. to_datetime() is a built-in method, which means you can call it directly once you have imported Pandas. For example:

import pandas as pd

pd.to_datetime(...)

You can find out more about the uses of pandas.to_datetime() by reading the Pandas documentation.

For further reading on Series, go to the articles:

For further reading on the datetime module, go to the article:

How to Solve Python AttributeError: ‘datetime.datetime’ has no attribute ‘datetime’

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Suf is a senior advisor in data science with deep expertise in Natural Language Processing, Complex Networks, and Anomaly Detection. Formerly a postdoctoral research fellow, he applied advanced physics techniques to tackle real-world, data-heavy industry challenges. Before that, he was a particle physicist at the ATLAS Experiment of the Large Hadron Collider. Now, he’s focused on bringing more fun and curiosity to the world of science and research online.

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