CODEWITHSUNDEEP
pythonpandascsvdictionary
Eran Moshe
Eran Moshe

Reputation: 3208

Python writing dictionary to csv and read dictionary from csv

I'm using pandas to handle data frames. There's one data frame I create which its rows are as follows: [id, vector] Where id is of type string and vector is of type dictionary.

Now when I write it to a csv file The row looks like this (in the csv file):

25377bc2-d3b6-4699-a466-6b9f544e8ba3    {u'sport>sports event>world championship': 0.5058, u'sport>sports event': 0.7032, u'sport>soccer': 0.6377, u'lifestyle and leisure>game': 0.4673, u'sport>sports event>world cup': 0.6614, u'sport>sports event>international tournament': 0.454, u'sport>sports event>national tournament': 0.541, u'sport': 0.9069, u'sport>sports organisations>international federation': 0.5046, u'sport>sports organisations': 0.6982}

I've tried to read it back from csv into pandas data frame, but when I look at the type of the vector that once was a dict it is now of <type 'str'>

I know I can solve it with pickle and save that pandas data frame into a pickle file. But is there a way to read the csv correctly (where the vector in it is of type dictionary)

Upvotes: 1

Views: 373

Answers (1)

jezrael
jezrael

Reputation: 863531

I think you can use json what is better structure as csv for save dicts.

For write use to_json and for read read_json with parameter orient='records', thanks piRSquared for comment:

df = pd.DataFrame({'vector':[{'a':1, 'b':3}, {'a':4, 'b':6}], 'ID':[2,3]})
print (df)
   ID            vector
0   2  {'b': 3, 'a': 1}
1   3  {'b': 6, 'a': 4}

df.to_json('file.json', orient='records')
   ID            vector
0   2  {'b': 3, 'a': 1}
1   3  {'b': 6, 'a': 4}

df = pd.read_json('file.json', orient='records')
print (df)

print (df.applymap(type))
              ID          vector
0  <class 'int'>  <class 'dict'>
1  <class 'int'>  <class 'dict'>

EDIT1:

If is necessary same order of columns, index values use:

df.to_json('file.json', orient='split')

df = pd.read_json('file.json', orient='split')

Upvotes: 2

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