Re-evaluate the minimum number of elements to use numexpr for elementwise ops
Currently we have a MIN_ELEMENTS set at 10,000:

https://github.com/pandas-dev/pandas/blob/00a622401e06bd7afaaa508707a46f3dcc494fe4/pandas/core/computation/expressions.py#L42-L43

However, I have been noticing while running lots of performance comparisons recently, that numexpr still seems to show some overhead at that array size compared to numpy.

I did a few specific timings for a few ops comparing numpy and numexpr for a set of different array sizes:

![image](https://user-images.githubusercontent.com/1020496/111657798-10562c00-880c-11eb-8701-dd59fd8d0bfe.png)

<details>
<summary>Code used to create the plot</summary>

```python
import operator

import numpy as np
import pandas as pd
import numexpr as ne

import seaborn as sns

results = []

for s in [10**3, 10**4, 10**5, 10**6, 10**7, 10**8]:
    arr1 = np.random.randn(s)
    arr2 = np.random.randn(s)
    
    for op_str, op in [("+", operator.add), ("*", operator.mul), ("==", operator.eq), ("<=", operator.le)]:
        
        res_ne = %timeit -o ne.evaluate(f"a {op_str} b", local_dict={"a": arr1, "b": arr2}, casting="safe")
        res_np = %timeit -o op(arr1, arr2)
        
        results.append({"size": s, "op": op_str, "engine": "numepxr", "timing": res_ne.average, "timing_stdev": res_np.stdev})
        results.append({"size": s, "op": op_str, "engine": "numpy", "timing": res_np.average, "timing_stdev": res_np.stdev})
        

df = pd.DataFrame(results)

fig = sns.relplot(data=df, x="size", y="timing", hue="engine", col="op", kind="line", col_wrap=2)
fig.set(xscale='log', yscale='log')
```

</details>

So in general, numexpr is not *that* much faster for the large arrays. But specifically, it still has a significant overhead compared to numpy up to 1e5 - 1e6, while the current minimum number of elements is 1e4.

Further, this might depend on your specific hardware and versions etc (this was run on my linux laptop with 8 cores, using latest versions of numpy and numexpr). So it is always hard to give a default suitable for all.

But based on the analysis above, I would propose raising the minimum from 1e4 to 1e5 (or maybe even 1e6).