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I have a csv file looking like this:

COL0;COl1;COL2;COL3;...;COL9999
SomeText0;[-3.45,0.23];[-1.40,0.21];[-1.35,0.13];...;[-1.87,0.12]
SomeText1;[-3.05,0.20];[-0.40,0.01];[-0.05,0.03];...;[-1.65,0.33]
SomeText2;[-0.40,0.03];[-1.00,0.20];[-0.35,0.03];...;[-1.43,0.12]
...

All cells are strings (e.g. "[-3.45,0.23]" ), but I want them to be np.float64-1d arrays (except COL0 of course)

How do I do this efficiently?

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1 Answer 1

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1

Just read the CSV normally and then use the built-in function ast.literal_eval to parse the strings into arrays of floats:

import ast
df = pd.read_csv('YOUR FILE.csv', sep=';')
df.loc[:, 'COl1':] = df.loc[:, 'COl1':].apply(lambda col: col.apply(ast.literal_eval).apply(np.asarray))

Output:

>>> df
        COL0           COl1          COL2           COL3        COL9999
0  SomeText0  [-3.45, 0.23]  [-1.4, 0.21]  [-1.35, 0.13]  [-1.87, 0.12]
1  SomeText1   [-3.05, 0.2]  [-0.4, 0.01]  [-0.05, 0.03]  [-1.65, 0.33]
2  SomeText2   [-0.4, 0.03]   [-1.0, 0.2]  [-0.35, 0.03]  [-1.43, 0.12]
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