CODEWITHSUNDEEP
pythonpandasdictionary
I am Nik
I am Nik

Reputation: 129

to_dict() creates brackets around values

I am trying to create default dictionary from my pandas DataFrame, but to_dict() method creates unwanted square brackets around values of the columns i want to write. The example code is as follows:

# Create DF
my_df = pd.DataFrame({'numbers': (1, 2, 3, 4, 5), 'letters': ('a', 'b', 'c', 'd', 'e')})

# Create dictionary from the DF
my_dict = my_df.set_index('numbers').T.to_dict('list')

# Create collections dictionary
my_collections_dict = collections.defaultdict(int, my_dict)

results in:

defaultdict(int, {1: ['a'], 2: ['b'], 3: ['c'], 4: ['d'], 5: ['e']})

what i want is:

defaultdict(int, {1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e'})

how to obtain 'pure' column values?

Upvotes: 1

Views: 2082

Answers (2)

asongtoruin
asongtoruin

Reputation: 10359

You don't need to transpose your frame, you can instead select your column and do:

my_dict = my_df.set_index('numbers')['letters'].to_dict()

if you have multiple columns you want to use in your dictionary, it'll take an extra line but you can use:

my_dict = my_df.set_index('numbers').to_dict(orient='index')
my_dict = {k: list(v.values()) for k, v in my_dict.items()}

Upvotes: 5

Piotrek
Piotrek

Reputation: 1530

It's because you specify to_dict('list') -> this way entries will be returned as lists (which is why they are shown in [].

Try using records instead:

# Create DF
my_df = pd.DataFrame({'numbers': (1, 2, 3, 4, 5), 'letters': ('a', 'b', 'c', 'd', 'e')})

# Create dictionary from the DF
my_dict = my_df.set_index('numbers').T.to_dict('records')

# Create collections dictionary
my_collections_dict = collections.defaultdict(int, my_dict)

Output from the second line:

[{1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e'}]

Upvotes: 1

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