In your documentation you often mention awkward.topandas(), but how about the other way, a awkward.frompandas()?

• Say I created a Pandas DataFrame from a .csv, do some filtering and stuff, and now I want to continue with an awkward array (for example I want to add a named numpy array). Is this possible?
• Or nested inside a Jagged Array is a Table (loaded from HDF5). I want to give this Table as a DataFrame to a colleague who is only familiar with Pandas. After he/she is done, I want to store it back in the HDF5 as a Jagged Array. Can this be done?

I looked in the Python file where .topandas() was defined:
https://github.com/scikit-hep/awkward-array/blob/d942fb8d4fae5e1dec35c70938e24c05207b3f31/awkward/util.py#L213
, but nothing about loading DataFrames there.

I also tried with some code, but this failed:

import pandas as pd

df = pd.DataFrame({"foo": [2, 8], "bar": [0.3, -0.9]})
print(type(df))
# <class 'pandas.core.frame.DataFrame'>
print(df.head())
#    foo  bar
# 0    2  0.3
# 1    8 -0.9

af = awkward.fromiter(df)
print(af)
# ['foo' 'bar']

df_awk = awkward.topandas(af, flatten=True)
print(type(df_awk))
# <class 'pandas.core.series.Series'>
print(df_awk.head())
# 0    foo
# 1    bar
# dtype: object

Applying .fromiter() only gets the column names.

TL;DR How to convert a Pandas DataFrame to an awkward-array and vice-versa?