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I have the following data in pandas dataframe:
state 1st 2nd 3rd 0 California $11,593,820 $109,264,246 $8,496,273 1 New York $10,861,680 $45,336,041 $6,317,300 2 Florida $7,942,848 $69,369,589 $4,697,244 3 Texas $7,536,817 $61,830,712 $5,736,941 I want to perform some simple analysis (e.g., sum, groupby) with three columns (1st, 2nd, 3rd), but the data type of those three columns is object (or string).
So I used the following code for data conversion:
data = data.convert_objects(convert_numeric=True) But, conversion does not work, perhaps, due to the dollar sign. Any suggestion?
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to_numeric() The best way to convert one or more columns of a DataFrame to numeric values is to use pandas. to_numeric(). This function will try to change non-numeric objects (such as strings) into integers or floating-point numbers as appropriate.
To encode non-numeric data to numeric you can use scikit-learn's LabelEncoder. It will encode each category such as COL1's a , b , c to integers. enc. fit() creates the corresponding integer values.
@EdChum's answer is clever and works well. But since there's more than one way to bake a cake.... why not use regex? For example:
df[df.columns[1:]] = df[df.columns[1:]].replace('[\$,]', '', regex=True).astype(float) To me, that is a little bit more readable.
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