将 pandas.DataFrame 转换为字节

Question

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.

Answer 1

You can use df.to_records() to convert your dataframe to a numpy recarray, then call .tostring() to convert this to a string of bytes:

rec = df.to_records(index=False)

print(repr(rec))
# rec.array([(10, 18446744073709551615, 13240000000.0), (15, 230498234019, 3.14159),
#  (20, 32094812309, 234.1341)], 
#           dtype=[('a', '|u1'), ('b', '<u8'), ('c', '<f8')])

s = rec.tostring()
rec2 = np.fromstring(s, rec.dtype)

print(np.all(rec2 == rec))
# True

Answer 2

import pandas as pd

df = pd.DataFrame([ 10, 15, 20], dtype='u1', columns=['a'])
df_byte = df.to_json().encode()
print(df_byte)