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I am converting my Python dictionary of "user-ratings" into a Pandas dataframe.

Here is the dictionary (user_dict) for reference:

{'U1': [3, 4, 2, 5, 0, 4, 1, 3, 0, 0, 4], 'U2': [2, 3, 1, 0, 3, 0, 2, 0, 0, 3, 0], 'U3': [0, 4, 0, 5, 0, 4, 0, 3, 0, 2, 4], 'U4': [0, 0, 2, 1, 4, 3, 2, 0, 0, 2, 0], 'U5': [0, 0, 0, 5, 0, 4, 0, 3, 0, 0, 4], 'U6': [2, 3, 4, 0, 3, 0, 3, 0, 3, 4, 0], 'U7': [0, 4, 3, 5, 0, 5, 0, 0, 0, 0, 4], 'U8': [4, 3, 0, 3, 4, 2, 2, 0, 2, 3, 2], 'U9': [0, 2, 0, 3, 1, 0, 1, 0, 0, 2, 0], 'U10': [0, 3, 0, 4, 3, 3, 0, 3, 0, 4, 4], 'U11': [2, 2, 1, 2, 1, 0, 2, 0, 1, 0, 2], 'U12': [0, 4, 4, 5, 0, 0, 0, 3, 0, 4, 5], 'U13': [3, 3, 0, 2, 2, 3, 2, 0, 2, 0, 3], 'U14': [0, 3, 4, 5, 0, 5, 0, 0, 0, 4, 0], 'U15': [2, 0, 0, 3, 0, 2, 2, 3, 0, 0, 3], 'U16': [4, 4, 0, 4, 3, 4, 0, 3, 0, 3, 0], 'U17': [0, 2, 0, 3, 1, 0, 2, 0, 1, 0, 3], 'U18': [2, 3, 1, 0, 3, 2, 3, 2, 0, 2, 0], 'U19': [0, 5, 0, 4, 0, 3, 0, 4, 0, 0, 5], 'U20': [0, 0, 3, 0, 3, 0, 4, 0, 2, 0, 0], 'U21': [3, 0, 2, 4, 2, 3, 0, 4, 2, 3, 3], 'U22': [4, 4, 0, 5, 3, 5, 0, 4, 0, 3, 0], 'U23': [3, 0, 0, 0, 3, 0, 2, 0, 0, 4, 0], 'U24': [4, 0, 3, 0, 3, 0, 3, 0, 0, 2, 2], 'U25': [0, 5, 0, 3, 3, 4, 0, 3, 3, 4, 4]}

When I run this code:

df=  pd.DataFrame([user_dict], columns=user_dict.keys())
print(df)

The dataframe prints all on one row. How do I get it to print with each user on its own row? With two columns... User and User Ratings?

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2 Answers 2

Reset to default
4

You don't need to give keys when you are converting dictionary to dataframe. The dictionary keys are taken as columns.

In [75]: d = {'U1': [3, 4, 2, 5, 0, 4, 1, 3, 0, 0, 4], 'U2': [2, 3, 1, 0, 3, 0, 2, 0, 0, 3, 0], 'U3': [0, 4, 0, 5, 0,
    ...: 4, 0, 3, 0, 2, 4], 'U4': [0, 0, 2, 1, 4, 3, 2, 0, 0, 2, 0], 'U5': [0, 0, 0, 5, 0, 4, 0, 3, 0, 0, 4], 'U6': [2
    ...: , 3, 4, 0, 3, 0, 3, 0, 3, 4, 0], 'U7': [0, 4, 3, 5, 0, 5, 0, 0, 0, 0, 4], 'U8': [4, 3, 0, 3, 4, 2, 2, 0, 2, 3
    ...: , 2], 'U9': [0, 2, 0, 3, 1, 0, 1, 0, 0, 2, 0], 'U10': [0, 3, 0, 4, 3, 3, 0, 3, 0, 4, 4], 'U11': [2, 2, 1, 2,
    ...: 1, 0, 2, 0, 1, 0, 2], 'U12': [0, 4, 4, 5, 0, 0, 0, 3, 0, 4, 5], 'U13': [3, 3, 0, 2, 2, 3, 2, 0, 2, 0, 3], 'U1
    ...: 4': [0, 3, 4, 5, 0, 5, 0, 0, 0, 4, 0], 'U15': [2, 0, 0, 3, 0, 2, 2, 3, 0, 0, 3], 'U16': [4, 4, 0, 4, 3, 4, 0,
    ...:  3, 0, 3, 0], 'U17': [0, 2, 0, 3, 1, 0, 2, 0, 1, 0, 3], 'U18': [2, 3, 1, 0, 3, 2, 3, 2, 0, 2, 0], 'U19': [0,
    ...: 5, 0, 4, 0, 3, 0, 4, 0, 0, 5], 'U20': [0, 0, 3, 0, 3, 0, 4, 0, 2, 0, 0], 'U21': [3, 0, 2, 4, 2, 3, 0, 4, 2, 3
    ...: , 3], 'U22': [4, 4, 0, 5, 3, 5, 0, 4, 0, 3, 0], 'U23': [3, 0, 0, 0, 3, 0, 2, 0, 0, 4, 0], 'U24': [4, 0, 3, 0,
    ...:  3, 0, 3, 0, 0, 2, 2], 'U25': [0, 5, 0, 3, 3, 4, 0, 3, 3, 4, 4]}
    ...:

In [76]: df = pd.DataFrame(d)

In [77]: df
Out[77]:
    U1  U2  U3  U4  U5  U6  U7  U8  U9  U10  U11  U12  ...  U14  U15  U16  U17  U18  U19  U20  U21  U22  U23  U24  U25
0    3   2   0   0   0   2   0   4   0    0    2    0  ...    0    2    4    0    2    0    0    3    4    3    4    0
1    4   3   4   0   0   3   4   3   2    3    2    4  ...    3    0    4    2    3    5    0    0    4    0    0    5
2    2   1   0   2   0   4   3   0   0    0    1    4  ...    4    0    0    0    1    0    3    2    0    0    3    0
3    5   0   5   1   5   0   5   3   3    4    2    5  ...    5    3    4    3    0    4    0    4    5    0    0    3
4    0   3   0   4   0   3   0   4   1    3    1    0  ...    0    0    3    1    3    0    3    2    3    3    3    3
5    4   0   4   3   4   0   5   2   0    3    0    0  ...    5    2    4    0    2    3    0    3    5    0    0    4
6    1   2   0   2   0   3   0   2   1    0    2    0  ...    0    2    0    2    3    0    4    0    0    2    3    0
7    3   0   3   0   3   0   0   0   0    3    0    3  ...    0    3    3    0    2    4    0    4    4    0    0    3
8    0   0   0   0   0   3   0   2   0    0    1    0  ...    0    0    0    1    0    0    2    2    0    0    0    3
9    0   3   2   2   0   4   0   3   2    4    0    4  ...    4    0    3    0    2    0    0    3    3    4    2    4
10   4   0   4   0   4   0   4   2   0    4    2    5  ...    0    3    0    3    0    5    0    3    0    0    2    4

[11 rows x 25 columns]

In [78]: df.T
Out[78]:
     0  1  2  3  4  5  6  7  8  9  10
U1   3  4  2  5  0  4  1  3  0  0   4
U2   2  3  1  0  3  0  2  0  0  3   0
U3   0  4  0  5  0  4  0  3  0  2   4
U4   0  0  2  1  4  3  2  0  0  2   0
U5   0  0  0  5  0  4  0  3  0  0   4
U6   2  3  4  0  3  0  3  0  3  4   0
U7   0  4  3  5  0  5  0  0  0  0   4
U8   4  3  0  3  4  2  2  0  2  3   2
U9   0  2  0  3  1  0  1  0  0  2   0
U10  0  3  0  4  3  3  0  3  0  4   4
U11  2  2  1  2  1  0  2  0  1  0   2
U12  0  4  4  5  0  0  0  3  0  4   5
U13  3  3  0  2  2  3  2  0  2  0   3
U14  0  3  4  5  0  5  0  0  0  4   0
U15  2  0  0  3  0  2  2  3  0  0   3
U16  4  4  0  4  3  4  0  3  0  3   0
U17  0  2  0  3  1  0  2  0  1  0   3
U18  2  3  1  0  3  2  3  2  0  2   0
U19  0  5  0  4  0  3  0  4  0  0   5
U20  0  0  3  0  3  0  4  0  2  0   0
U21  3  0  2  4  2  3  0  4  2  3   3
U22  4  4  0  5  3  5  0  4  0  3   0
U23  3  0  0  0  3  0  2  0  0  4   0
U24  4  0  3  0  3  0  3  0  0  2   2

df.T - transpose of dataframe

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You could also melt down the DataFrame after creating the DataFrame from the dictionary to have the two final columns

# Create DataFrame from user_dict 
df = pd.DataFrame(user_dict)

# melt down DataFrame and rename columns accordingly
final_df = pd.melt(df,var_name='User',value_name='User Ratings')
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