I havefound this answer Convert a Pandas DataFrame to a dictionary which almost works for the following scenario, but I will explain why it does not.
example
nestedList = [['A', 'B'], ['A', 'C']]
df = pd.DataFrame(nestedList,columns=["1","2"])
Returns the following dataframe
1 2
0 A B
1 A C
Which I would like to then use .to_dict() to convert to a dictionary with keys column 1 and list column 2
sampledict = {"A":["B","C"]}
using the linked example I set the column I would like to use as the key to column 1 and orient to list
sampledict = df.set_index('1').T.to_dict('list')
which returns
{'A': ['C']}
Because Dataframe columns are not unique once you transpose which is pointed out with the userwarning:
UserWarning: DataFrame columns are not unique, some columns will be omitted.
Which is kind of confusing to me so I dug a bit deeper and found Export pandas to dictionary by combining multiple row values which I was able to modify to get the answer I wanted with the following code:
d = {}
for i in df["1"].unique():
d[i] = [df["2"][j] for j in df[df["1"]==i].index]
Which returns
{'A': ['B', 'C']}
Which is great, however I still do not understand what it is that they did, as there was little explanation. Could a user please explain or write this in a more verbose manner? as well as potentially suggest a more simple solution?
