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efueyo
Asked: 2020-01-16 04:02:29 +0800 CST 2020-01-16 04:02:29 +0800 CST

Pandas convert non-null objects

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After importing using urlibInternet data, I manage to create a DataFrame (df)with two columns (Fecha y Valor)of the types:

Fecha    1563 non-null object, 
Valor    1563 non-null object.

With the statement df["Fecha"] = pd.to_datetime(df["Fecha"])I get to pass the values ​​of the column Fecha, to the formatFecha 1563 non-null datetime64[ns]

Next I try to change the format of the column valorwith the statement df["Valor"] = pd.to_numeric(df["Valor"])and I get the error:

ValueError: Unable to parse string "185,130000" at position 0.

I try again with the statement df['Valor'] = df['Valor'].apply(np.float)and it gives me the error:

ValueError: Unable to parse string "185,130000" at position 0

What else could I do?

python

1 Answers

  1. Best Answer

    The problem apparently is the format of the string: the value 185,130000cannot be converted to any numeric value because it cannot interpret the ,as a decimal separator. A possible solution is to replace this character with the .:

    # Reemplazamos la , por .
df["Valor"] = df["Valor"].str.replace(",", ".")
# Y ahora sí deberíamos poder convertirlo a numérico
df["Valor"] = pd.to_numeric(df["Valor"])

    On the other hand, if you are reading this data from a file csvusing read_csv, you may want to set the comma ( ,) as the decimal separator at that time, using the parameter decimal=','.

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