Converting a PD DF to a dictionary-CodePudding

I have a pd dataframe as seen in image: image of data

imported via the pd.read_csv method.

I would like to convert it to a dictionary, where the key is 'Countries', and the value is a list of the numbers 1 to 300. How is the best way to do this? I have tried other methods listed on stack but since my df doesn't have column headings it is not working

CodePudding user response：

Something like this should do what your question asks:

d = {row[1]:list(row[2:]) for row in df.itertuples()}

Here is sample code showing the above in the context of your question:

records = [
    ['afghanistan', -0.9,-0.7,-0.5,-0.3,-0.1,0.1,0.3,0.5,0.7,0.9],
    ['albania', -0.9,-0.7,-0.5,-0.3,-0.1,0.1,0.3,0.5,0.7,0.9],
    ['algeria', -0.9,-0.7,-0.5,-0.3,-0.1,0.1,0.3,0.5,0.7,0.9],
    ['andorra', -0.9,-0.7,-0.5,-0.3,-0.1,0.1,0.3,0.5,0.7,0.9]
]

import pandas as pd
df = pd.DataFrame(records)
print(df)

d = {row[1]:list(row[2:]) for row in df.itertuples()}
print()
[print(f"{k} : {v}") for k, v in d.items()]

Output:

            0    1    2    3    4    5    6    7    8    9    10
0  afghanistan -0.9 -0.7 -0.5 -0.3 -0.1  0.1  0.3  0.5  0.7  0.9
1      albania -0.9 -0.7 -0.5 -0.3 -0.1  0.1  0.3  0.5  0.7  0.9
2      algeria -0.9 -0.7 -0.5 -0.3 -0.1  0.1  0.3  0.5  0.7  0.9
3      andorra -0.9 -0.7 -0.5 -0.3 -0.1  0.1  0.3  0.5  0.7  0.9

afghanistan : [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9]
albania : [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9]
algeria : [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9]
andorra : [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9]