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How to convert strings in a DataFrame in Python to dictionaries

For example, I now have a DataFrame as

id score1 score2 score3 score4 score5 1 0.000000 0.108659 0.000000 0.078597 1 2 0.053238 0.308253 0.286353 0.446433 1 3 0.000000 0.083979 0.808983 0.233052 1

I want to convert it as

 id scoreDict 1 {'1': 0, '2': 0.1086, ...} 2 {...} 3 {...}

Anyway to do this?

Thanks in advance!

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3 answers
 import pandas as pd # your df # ========================= print(df) id score1 score2 score3 score4 score5 0 1 0.0000 0.1087 0.0000 0.0786 1 1 2 0.0532 0.3083 0.2864 0.4464 1 2 3 0.0000 0.0840 0.8090 0.2331 1 # to_dict # ========================= df.to_dict(orient='records') Out[318]: [{'id': 1.0, 'score1': 0.0, 'score2': 0.10865899999999999, 'score3': 0.0, 'score4': 0.078597, 'score5': 1.0}, {'id': 2.0, 'score1': 0.053238000000000001, 'score2': 0.308253, 'score3': 0.28635300000000002, 'score4': 0.44643299999999997, 'score5': 1.0}, {'id': 3.0, 'score1': 0.0, 'score2': 0.083978999999999998, 'score3': 0.80898300000000001, 'score4': 0.23305200000000001, 'score5': 1.0}]
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I think the code below will provide you with a data frame in the format you are looking for. It also allows you to select any column as an index.

 import pandas as pd #IMPORT YOUR DATA #Any other way to import data can also be used. I saved it in .csv file df=pd.read_csv('dftestid.csv') print("INITIAL DATAFRAME") print(df) print() #Convert Data Frame to Dictionary (set_index method allows any column to be used as index) df2dict=df.set_index('id').transpose().to_dict(orient='dict') #Convert Dictionary to List with 'score' replaced dicttolist=[[k,{int(k1.replace('score','')):v1 for k1,v1 in v.items()}] for k,v in df2dict.items()] #"Create the new DataFrame" df2=pd.DataFrame(dicttolist,columns=['id', 'scoreDict']) print("NEW DATAFRAME") print(df2) OUT: INITIAL DATAFRAME id score1 score2 score3 score4 score5 0 1 0.000000 0.108659 0.000000 0.078597 1 1 2 0.053238 0.308253 0.286353 0.446433 1 2 3 0.000000 0.083979 0.808983 0.233052 1 NEW DATAFRAME id scoreDict 0 1 {1: 0.0, 2: 0.108659, 3: 0.0, 4: 0.078597, 5: ... 1 2 {1: 0.053238, 2: 0.308253, 3: 0.286353, 4: 0.4... 2 3 {1: 0.0, 2: 0.083979, 3: 0.808983, 4: 0.233052...
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For others like me, I come up with this question, but I want to do the following: Create a dict row by row to display a column based on the value of the neighboring column.

Here is our chart table:

  Rating y 0 AAA 19 1 AA1 18 2 AA2 17 3 AA3 16 4 A1 15 5 A2 14 6 A3 13 ... 19 D 0

IN:

 import pandas as pd df_map.set_index('y') df_map.transpose() dict_y = df_map['Rating'].to_dict()

OF:

 {19: 'AAA', 18: 'AA1', 17: 'AA2', 16: 'AA3', 15: 'A1', 14: 'A2', 13: 'A3', 12: 'BBB1', 11: 'BBB2', 10: 'BBB3', 9: 'BB1', 8: 'BB2', 7: 'BB3', 6: 'B1', 5: 'B2', 4: 'B3', 3: 'CCC1', 2: 'CCC2', 1: 'D'}
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