Asked By – Gyan Veda
I have an n-by-m Pandas DataFrame
df defined as follows. (I know this is not the best way to do it. It makes sense for what I’m trying to do in my actual code, but that would be TMI for this post so just take my word that this approach works in my particular scenario.)
>>> df = DataFrame(columns=['col1']) >>> df.append(Series([None]), ignore_index=True) >>> df Empty DataFrame Columns: [col1] Index: 
I stored lists in the cells of this DataFrame as follows.
>>> df['column1'] = [1.23, 2.34] >>> df col1 0 [1, 2]
For some reason, the DataFrame stored this list as a string instead of a list.
>>> df['column1'] '[1.23, 2.34]'
I have 2 questions for you.
- Why does the DataFrame store a list as a string and is there a way around this behavior?
- If not, then is there a Pythonic way to convert this string into a list?
The DataFrame I was using had been saved and loaded from a CSV format. This format, rather than the DataFrame itself, converted the list from a string to a literal.
Now we will see solution for issue: Pandas DataFrame stored list as string: How to convert back to list
As you pointed out, this can commonly happen when saving and loading pandas DataFrames as
.csv files, which is a text format.
In your case this happened because list objects have a string representation, allowing them to be stored as
.csv files. Loading the
.csv will then yield that string representation.
If you want to store the actual objects, you should use
DataFrame.to_pickle() (note: objects must be picklable!).
To answer your second question, you can convert it back with
>>> from ast import literal_eval >>> literal_eval('[1.23, 2.34]') [1.23, 2.34]
This question is answered By – anon582847382
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