python - Convert list of dictionaries to a pandas DataFrame


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I have a list of dictionaries like this:

[{'points': 50, 'time': '5:00', 'year': 2010}, 
{'points': 25, 'time': '6:00', 'month': "february"}, 
{'points':90, 'time': '9:00', 'month': 'january'}, 
{'points_h1':20, 'month': 'june'}]

And I want to turn this into a pandas DataFrame like this:

      month  points  points_h1  time  year
0       NaN      50        NaN  5:00  2010
1  february      25        NaN  6:00   NaN
2   january      90        NaN  9:00   NaN
3      june     NaN         20   NaN   NaN

Note: Order of the columns does not matter.

How can I turn the list of dictionaries into a pandas DataFrame as shown above?


All Answers
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    Supposing d is your list of dicts, simply:

    pd.DataFrame(d)
    

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    In pandas 16.2, I had to do pd.DataFrame.from_records(d) to get this to work.


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    How do I convert a list of dictionaries to a pandas DataFrame?

    The other answers are correct, but not much has been explained in terms of advantages and limitations of these methods. The aim of this post will be to show examples of these methods under different situations, discuss when to use (and when not to use), and suggest alternatives.


    DataFrame(), DataFrame.from_records(), and .from_dict()

    Depending on the structure and format of your data, there are situations where either all three methods work, or some work better than others, or some don't work at all.

    Consider a very contrived example.

    np.random.seed(0)
    data = pd.DataFrame(
        np.random.choice(10, (3, 4)), columns=list('ABCD')).to_dict('r')
    
    print(data)
    [{'A': 5, 'B': 0, 'C': 3, 'D': 3},
     {'A': 7, 'B': 9, 'C': 3, 'D': 5},
     {'A': 2, 'B': 4, 'C': 7, 'D': 6}]
    

    This list consists of "records" with every keys present. This is the simplest case you could encounter.

    # The following methods all produce the same output.
    pd.DataFrame(data)
    pd.DataFrame.from_dict(data)
    pd.DataFrame.from_records(data)
    
       A  B  C  D
    0  5  0  3  3
    1  7  9  3  5
    2  2  4  7  6
    

    Word on Dictionary Orientations: orient='index'/'columns'

    Before continuing, it is important to make the distinction between the different types of dictionary orientations, and support with pandas. There are two primary types: "columns", and "index".

    orient='columns'
    Dictionaries with the "columns" orientation will have their keys correspond to columns in the equivalent DataFrame.

    For example, data above is in the "columns" orient.

    data_c = [
     {'A': 5, 'B': 0, 'C': 3, 'D': 3},
     {'A': 7, 'B': 9, 'C': 3, 'D': 5},
     {'A': 2, 'B': 4, 'C': 7, 'D': 6}]
    

    pd.DataFrame.from_dict(data_c, orient='columns')
    
       A  B  C  D
    0  5  0  3  3
    1  7  9  3  5
    2  2  4  7  6
    

    Note: If you are using pd.DataFrame.from_records, the orientation is assumed to be "columns" (you cannot specify otherwise), and the dictionaries will be loaded accordingly.

    orient='index'
    With this orient, keys are assumed to correspond to index values. This kind of data is best suited for pd.DataFrame.from_dict.

    data_i ={
     0: {'A': 5, 'B': 0, 'C': 3, 'D': 3},
     1: {'A': 7, 'B': 9, 'C': 3, 'D': 5},
     2: {'A': 2, 'B': 4, 'C': 7, 'D': 6}}
    

    pd.DataFrame.from_dict(data_i, orient='index')
    
       A  B  C  D
    0  5  0  3  3
    1  7  9  3  5
    2  2  4  7  6
    

    This case is not considered in the OP, but is still useful to know.

    Setting Custom Index

    If you need a custom index on the resultant DataFrame, you can set it using the index=... argument.

    pd.DataFrame(data, index=['a', 'b', 'c'])
    # pd.DataFrame.from_records(data, index=['a', 'b', 'c'])
    
       A  B  C  D
    a  5  0  3  3
    b  7  9  3  5
    c  2  4  7  6
    

    This is not supported by pd.DataFrame.from_dict.

    Dealing with Missing Keys/Columns

    All methods work out-of-the-box when handling dictionaries with missing keys/column values. For example,

    data2 = [
         {'A': 5, 'C': 3, 'D': 3},
         {'A': 7, 'B': 9, 'F': 5},
         {'B': 4, 'C': 7, 'E': 6}]
    

    # The methods below all produce the same output.
    pd.DataFrame(data2)
    pd.DataFrame.from_dict(data2)
    pd.DataFrame.from_records(data2)
    
         A    B    C    D    E    F
    0  5.0  NaN  3.0  3.0  NaN  NaN
    1  7.0  9.0  NaN  NaN  NaN  5.0
    2  NaN  4.0  7.0  NaN  6.0  NaN
    

    Reading Subset of Columns

    "What if I don't want to read in every single column"? You can easily specify this using the columns=... parameter.

    For example, from the example dictionary of data2 above, if you wanted to read only columns "A', 'D', and 'F', you can do so by passing a list:

    pd.DataFrame(data2, columns=['A', 'D', 'F'])
    # pd.DataFrame.from_records(data2, columns=['A', 'D', 'F'])
    
         A    D    F
    0  5.0  3.0  NaN
    1  7.0  NaN  5.0
    2  NaN  NaN  NaN
    

    This is not supported by pd.DataFrame.from_dict with the default orient "columns".

    pd.DataFrame.from_dict(data2, orient='columns', columns=['A', 'B'])
    

    ValueError: cannot use columns parameter with orient='columns'
    

    Reading Subset of Rows

    Not supported by any of these methods directly. You will have to iterate over your data and perform a reverse delete in-place as you iterate. For example, to extract only the 0th and 2nd rows from data2 above, you can use:

    rows_to_select = {0, 2}
    for i in reversed(range(len(data2))):
        if i not in rows_to_select:
            del data2[i]
    
    pd.DataFrame(data2)
    # pd.DataFrame.from_dict(data2)
    # pd.DataFrame.from_records(data2)
    
         A    B  C    D    E
    0  5.0  NaN  3  3.0  NaN
    1  NaN  4.0  7  NaN  6.0
    

    The Panacea: json_normalize for Nested Data

    A strong, robust alternative to the methods outlined above is the json_normalize function which works with lists of dictionaries (records), and in addition can also handle nested dictionaries.

    pd.io.json.json_normalize(data)
    
       A  B  C  D
    0  5  0  3  3
    1  7  9  3  5
    2  2  4  7  6
    

    pd.io.json.json_normalize(data2)
    
         A    B  C    D    E
    0  5.0  NaN  3  3.0  NaN
    1  NaN  4.0  7  NaN  6.0
    

    Again, keep in mind that the data passed to json_normalize needs to be in the list-of-dictionaries (records) format.

    As mentioned, json_normalize can also handle nested dictionaries. Here's an example taken from the documentation.

    data_nested = [
      {'counties': [{'name': 'Dade', 'population': 12345},
                    {'name': 'Broward', 'population': 40000},
                    {'name': 'Palm Beach', 'population': 60000}],
       'info': {'governor': 'Rick Scott'},
       'shortname': 'FL',
       'state': 'Florida'},
      {'counties': [{'name': 'Summit', 'population': 1234},
                    {'name': 'Cuyahoga', 'population': 1337}],
       'info': {'governor': 'John Kasich'},
       'shortname': 'OH',
       'state': 'Ohio'}
    ]
    

    pd.io.json.json_normalize(data_nested, 
                              record_path='counties', 
                              meta=['state', 'shortname', ['info', 'governor']])
    
             name  population    state shortname info.governor
    0        Dade       12345  Florida        FL    Rick Scott
    1     Broward       40000  Florida        FL    Rick Scott
    2  Palm Beach       60000  Florida        FL    Rick Scott
    3      Summit        1234     Ohio        OH   John Kasich
    4    Cuyahoga        1337     Ohio        OH   John Kasich
    

    For more information on the meta and record_path arguments, check out the documentation.


    Summarising

    Here's a table of all the methods discussed above, along with supported features/functionality.

    enter image description here

    * Use orient='columns' and then transpose to get the same effect as orient='index'.


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    You can also use pd.DataFrame.from_dict(d) as :

    In [8]: d = [{'points': 50, 'time': '5:00', 'year': 2010}, 
       ...: {'points': 25, 'time': '6:00', 'month': "february"}, 
       ...: {'points':90, 'time': '9:00', 'month': 'january'}, 
       ...: {'points_h1':20, 'month': 'june'}]
    
    In [12]: pd.DataFrame.from_dict(d)
    Out[12]: 
          month  points  points_h1  time    year
    0       NaN    50.0        NaN  5:00  2010.0
    1  february    25.0        NaN  6:00     NaN
    2   january    90.0        NaN  9:00     NaN
    3      june     NaN       20.0   NaN     NaN
    

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    I know a few people will come across this and find nothing here helps. The easiest way I have found to do it is like this:

    dict_count = len(dict_list)
    df = pd.DataFrame(dict_list[0], index=[0])
    for i in range(1,dict_count-1):
        df = df.append(dict_list[i], ignore_index=True)
    

    Hope this helps someone!