Use pd. DataFrame. from_dict() to transform a list of dictionaries to pandas DatFrame. This function is used to construct DataFrame from dict of array-like or dicts.
Create Dataframe from list of dicts with custom indexes. As all the dictionaries have similar keys, so the keys became the column names. Then for each key all the values associated with that key in all the dictionaries became the column values.
When we create dataframe from a list of dictionaries, matching keys will be the columns and corresponding values will be the rows of the dataframe. If there is no matching values and columns in the dictionary, then NaN value will be inserted in the resulted dataframe.
To convert Pandas DataFrame to List in Python, use the DataFrame. values(). tolist() function.
Use df.to_dict('records')
-- gives the output without having to transpose externally.
In [2]: df.to_dict('records')
Out[2]:
[{'customer': 1L, 'item1': 'apple', 'item2': 'milk', 'item3': 'tomato'},
{'customer': 2L, 'item1': 'water', 'item2': 'orange', 'item3': 'potato'},
{'customer': 3L, 'item1': 'juice', 'item2': 'mango', 'item3': 'chips'}]
Use df.T.to_dict().values()
, like below:
In [1]: df
Out[1]:
customer item1 item2 item3
0 1 apple milk tomato
1 2 water orange potato
2 3 juice mango chips
In [2]: df.T.to_dict().values()
Out[2]:
[{'customer': 1.0, 'item1': 'apple', 'item2': 'milk', 'item3': 'tomato'},
{'customer': 2.0, 'item1': 'water', 'item2': 'orange', 'item3': 'potato'},
{'customer': 3.0, 'item1': 'juice', 'item2': 'mango', 'item3': 'chips'}]
As an extension to John Galt's answer -
For the following DataFrame,
customer item1 item2 item3
0 1 apple milk tomato
1 2 water orange potato
2 3 juice mango chips
If you want to get a list of dictionaries including the index values, you can do something like,
df.to_dict('index')
Which outputs a dictionary of dictionaries where keys of the parent dictionary are index values. In this particular case,
{0: {'customer': 1, 'item1': 'apple', 'item2': 'milk', 'item3': 'tomato'},
1: {'customer': 2, 'item1': 'water', 'item2': 'orange', 'item3': 'potato'},
2: {'customer': 3, 'item1': 'juice', 'item2': 'mango', 'item3': 'chips'}}
If you are interested in only selecting one column this will work.
df[["item1"]].to_dict("records")
The below will NOT work and produces a TypeError: unsupported type: . I believe this is because it is trying to convert a series to a dict and not a Data Frame to a dict.
df["item1"].to_dict("records")
I had a requirement to only select one column and convert it to a list of dicts with the column name as the key and was stuck on this for a bit so figured I'd share.
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