The default return dtype is float64 or int64 depending on the data supplied Use the downcast parameter to obtain other dtypes. Pandas.to_numeric ¶ pandas.to_numeric(arg, errors='raise', downcast=none) [source] ¶ convert argument to a numeric type. ['sai', 'bo staff']}) >>> df.to_csv('out.csv', index=false) In a future version of pandas, parsing datetimes with mixed time zones will raise an error unless `utc=true` Please specify `utc=true` to opt in to the new behaviour and silence this warning.
If the input is already of a numeric dtype, the dtype will be preserved Check whether all characters in each string are numeric This is equivalent to running the python string method str.isnumeric() for each element of the series/index. As this behaviour is separate from the core conversion to numeric values, any errors raised during the downcasting will be surfaced regardless of the value of the ‘errors’ input. Pass a list with length equal to the number of columns when calling get_dummies on a dataframe Alternatively, prefix can be a dictionary mapping column names to prefixes.
