Why are simple operations on pandas.DataFrames so slow compared to the same operations on numpy.ndarrays?

Why are operations on pandas.DataFrames so slow?! Look at the following examples.

Measurement:

• Create a numpy.ndarray populated with random floating point numbers
• Create a pandas.DataFrame populated with the same numpy array

The I measure the time of the following operations

1. For the numpy.ndarray

   • Take the sum along the 0-axis
   • Take the sum along the 1-axis

2. For the pandas.DataFrame

   • Take the sum along the 0-axis
   • Take the sum along the 1-axis

3. For the pandas.DataFrame.values -> np.ndarray

   • Take the sum along the 0-axis
   • Take the sum along the 1-axis

Observations

• Summing over numpy.ndarrays' is much faster then operating onpandas.DataFrames`.
• This is even true, if the pd.DataFrame does not contain only floating point numbers and has nothing special attached (MultiIndex or whatever).
• Operations on numpy.ndarray are about 7 to 10 times faster.

Questions

1. Why does this happen?
2. How can this be optimized?
3. Is pandas not able to call or pass through numpys' operations?

import numpy as np
import pandas as pd

n = 50000
m = 5000
array = np.random.uniform(0, 1, (n, m))
dataframe = pd.DataFrame(array)

Numpy

%%timeit
array.sum(axis=0)

206 ms ± 3.78 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
array.sum(axis=1)

233 ms ± 33.9 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Pandas

%%timeit
dataframe.sum(axis=0)

1.65 s ± 14.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
dataframe.sum(axis=1)

1.74 s ± 15.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Pandas without Pandas

Let's operate on the values alone ...

%%timeit
dataframe.values.sum(axis=0)

206 ms ± 7.13 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
dataframe.values.sum(axis=1)

181 ms ± 1.66 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)