Polars Dataframe from nested dictionaries as columns - Stack Overflow

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I have a dictionary of nested columns with the index as key in each one. When i try to convert it to a polars dataframe, it fetches the column names and the values right, but each column has just one element that's the dictionary of the column elements, without "expanding" it into a series.

An example, let's say i have:

d = {'col1': {'0':'A','1':'B','2':'C'}, 'col2': {'0':1,'1':2,'2':3}}

Then, when i do a pl.DataFrame(d) or pl.from_dict(d), i'm getting:

col1           col2
---            ---
struct[3]      struct[3]
{"A","B","C"}  {1,2,3}

Instead of the regular dataframe.

Any idea how to fix this?

Thanks in advance!

I have a dictionary of nested columns with the index as key in each one. When i try to convert it to a polars dataframe, it fetches the column names and the values right, but each column has just one element that's the dictionary of the column elements, without "expanding" it into a series.

An example, let's say i have:

d = {'col1': {'0':'A','1':'B','2':'C'}, 'col2': {'0':1,'1':2,'2':3}}

Then, when i do a pl.DataFrame(d) or pl.from_dict(d), i'm getting:

col1           col2
---            ---
struct[3]      struct[3]
{"A","B","C"}  {1,2,3}

Instead of the regular dataframe.

Any idea how to fix this?

Thanks in advance!

• python-polars
• polars

Share Improve this question Follow edited Mar 24 at 18:23 Dean MacGregor 18.9k1010 gold badges5252 silver badges102102 bronze badges asked Mar 24 at 17:51 GhostGhost 1,54855 gold badges2020 silver badges4545 bronze badges

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1 Answer 1

Sorted by: Reset to default 2

There's not a particularly straight forward way to do that. You essentially have to take each column one at a time and unpivot it and then join each column back together.

Setup

d = {'col1': {'0':'A','1':'B','2':'C'}, 'col2': {'0':1,'1':2,'2':3}}
df = pl.DataFrame(d)

To (what I think is the) desired output

df_final=None
for col in df.columns:
    df_new = df[col].to_frame().unnest(col)
    df_new = df_new.unpivot(variable_name="index", value_name=col)
    if df_final is None:
        df_final=df_new
    else:
        df_final=df_final.join(df_new, on="index", how="full", coalesce=True)
df_final
shape: (3, 3)
┌───────┬──────┬──────┐
│ index ┆ col1 ┆ col2 │
│ ---   ┆ ---  ┆ ---  │
│ str   ┆ str  ┆ i64  │
╞═══════╪══════╪══════╡
│ 0     ┆ A    ┆ 1    │
│ 1     ┆ B    ┆ 2    │
│ 2     ┆ C    ┆ 3    │
└───────┴──────┴──────┘

Simplified if index keys are guaranteed to be balanced

If you can be assured that the keys of your nested cols will always be uniform and sorted you can do it as a map_batches instead of a for loop with joins.

df.select(pl.all().map_batches(lambda s: (
    s.to_frame().unnest(s.name).unpivot()['value']
)))
shape: (3, 2)
┌──────┬──────┐
│ col1 ┆ col2 │
│ ---  ┆ ---  │
│ str  ┆ i64  │
╞══════╪══════╡
│ A    ┆ 1    │
│ B    ┆ 2    │
│ C    ┆ 3    │
└──────┴──────┘

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