**This FutureWarning isn’t from Pandas, it is from numpy, here’s how to reproduce the warning nearer to the source of the trouble:**

```
import numpy as np
print(np.__version__) # Numpy version '1.12.0'
'x' in np.arange(5) #Future warning thrown here
FutureWarning: elementwise comparison failed; returning scalar instead, but in the
future will perform elementwise comparison
False
```

Another way to reproduce this bug using the double equals operator:

```
import numpy as np
np.arange(5) == np.arange(5).astype(str) #FutureWarning thrown here
```

An example of Matplotlib affected by this FutureWarning under their quiver plot implementation: https://matplotlib.org/examples/pylab_examples/quiver_demo.html

## What’s going on here?

There is a disagreement between Numpy and native python on what should happen when you compare a strings to numpy’s numeric types. Notice the right operand is python’s turf, a primitive string, and the middle operation is python’s turf, but the left operand is numpy’s turf. Should you return a Python style Scalar or a Numpy style ndarray of Boolean? Numpy says ndarray of bool, Pythonic developers disagree. Classic standoff.

Should it be elementwise comparison or Scalar if item exists in the array?

If your code or library is using the `in`

or `==`

operators to compare python string to numpy ndarrays, they aren’t compatible, so when if you try it, it returns a scalar, but only for now. The Warning indicates that in the future this behavior might change so your code pukes all over the carpet if python/numpy decide to do adopt Numpy style.

## Submitted Bug reports:

Numpy and Python are in a standoff, for now the operation returns a scalar, but in the future it may change.

https://github.com/numpy/numpy/issues/6784

https://github.com/pandas-dev/pandas/issues/7830

## Two workaround solutions:

Either lockdown your version of python and numpy, ignore the warnings and expect the behavior to not change, or convert both left and right operands of `==`

and `in`

to be from a numpy type or primitive python numeric type.

Suppress the warning globally:

```
import warnings
import numpy as np
warnings.simplefilter(action='ignore', category=FutureWarning)
print('x' in np.arange(5)) #returns False, without Warning
```

Suppress the warning on a line by line basis.

```
import warnings
import numpy as np
with warnings.catch_warnings():
warnings.simplefilter(action='ignore', category=FutureWarning)
print('x' in np.arange(2)) #returns False, warning is suppressed
print('x' in np.arange(10)) #returns False, Throws FutureWarning
```

Just suppress the warning by name, then put a loud comment next to it mentioning the current version of python and numpy, saying this code is brittle and requires these versions and put a link to here. Kick the can down the road.

**TLDR:** `pandas`

are Jedi; `numpy`

are the hutts; and `python`

is the galactic empire.