How to keep column names when converting from pandas to numpy

Question

According to this post, I should be able to access the names of columns in an ndarray as a.dtype.names

Howevever, if I convert a pandas DataFrame to an ndarray with df.a

Answer 1

Yet more methods of converting a pandas.DataFrame to numpy.array while preserving label/column names

This is mainly for demonstrating how to set dtype/column_dtypes, because sometimes a data source iterator's output'll need some pre-normalization.

---

Method one inserts by column into a zeroed array of predefined height and is loosely based on a Creating Structured Arrays guide that just a bit of web-crawling turned up

import numpy


def to_tensor(dataframe, columns = [], dtypes = {}):
    # Use all columns from data frame if none where listed when called
    if len(columns) <= 0:
        columns = dataframe.columns
    # Build list of dtypes to use, updating from any `dtypes` passed when called
    dtype_list = []
    for column in columns:
        if column not in dtypes.keys():
            dtype_list.append(dataframe[column].dtype)
        else:
            dtype_list.append(dtypes[column])
    # Build dictionary with lists of column names and formatting in the same order
    dtype_dict = {
        'names': columns,
        'formats': dtype_list
    }
    # Initialize _mostly_ empty nupy array with column names and formatting
    numpy_buffer = numpy.zeros(
        shape = len(dataframe),
        dtype = dtype_dict)
    # Insert values from dataframe columns into numpy labels
    for column in columns:
        numpy_buffer[column] = dataframe[column].to_numpy()
    # Return results of conversion
    return numpy_buffer

Method two is based on user7138814's answer and will likely be more efficient as it is basically a wrapper for the built in to_records method available to pandas.DataFrames

def to_tensor(dataframe, columns = [], dtypes = {}, index = False):
    to_records_kwargs = {'index': index}
    if not columns:  # Default to all `dataframe.columns`
        columns = dataframe.columns
    if dtypes:       # Pull in modifications only for dtypes listed in `columns`
        to_records_kwargs['column_dtypes'] = {}
        for column in dtypes.keys():
            if column in columns:
                to_records_kwargs['column_dtypes'].update({column: dtypes.get(column)})
    return dataframe[columns].to_records(**to_records_kwargs)

---

With either of the above one could do...

X = pandas.DataFrame(dict(age = [40., 50., 60.], sys_blood_pressure = [140., 150., 160.]))

# Example of overwriting dtype for a column
X_tensor = to_tensor(X, dtypes = {'age': 'int32'})

print("Ages -> {0}".format(X_tensor['age']))
print("SBPs -> {0}".format(X_tensor['sys_blood_pressure']))

... which should output...

Ages -> array([40, 50, 60])
SBPs -> array([140., 150., 160.])

... and a full dump of X_tensor should look like the following.

array([(40, 140.), (50, 150.), (60, 160.)],
      dtype=[('age', '

Some thoughts

While method two will likely be more efficient than the first, method one (with some modifications) may be more useful for merging two or more pandas.DataFrames into one numpy.array.

Additionally (after swinging back through to review), method one will likely face-plant as it's written with errors about to_records_kwargs not being a mapping if dtypes is not defined, next time I'm feeling Pythonic I may resolve that with an else condition.