I need convert the data stored in a pandas.DataFrame into a byte string where each column can have a separate data type (integer or floating point). Here is a simple set of data:
df = pd.DataFrame([ 10, 15, 20], dtype='u1', columns=['a']) df['b'] = np.array([np.iinfo('u8').max, 230498234019, 32094812309], dtype='u8') df['c'] = np.array([1.324e10, 3.14159, 234.1341], dtype='f8') and df looks something like this:
a b c 0 10 18446744073709551615 1.324000e+10 1 15 230498234019 3.141590e+00 2 20 32094812309 2.341341e+02 The DataFrame knows about the types of each column df.dtypes so I'd like to do something like this:
data_to_pack = [tuple(record) for _, record in df.iterrows()] data_array = np.array(data_to_pack, dtype=zip(df.columns, df.dtypes)) data_bytes = data_array.tostring() This typically works fine but in this case (due to the maximum value stored in df['b'][0]. The second line above converting the array of tuples to an np.array with a given set of types causes the following error:
OverflowError: Python int too large to convert to C long The error results (I believe) in the first line which extracts the record as a Series with a single data type (defaults to float64) and the representation chosen in float64 for the maximum uint64 value is not directly convertible back to uint64.
1) Since the DataFrame already knows the types of each column is there a way to get around creating a row of tuples for input into the typed numpy.array constructor? Or is there a better way than outlined above to preserve the type information in such a conversion?
2) Is there a way to go directly from DataFrame to a byte string representing the data using the type information for each column.
