I am using Pandas
0.19.1 on Python 3. I am getting a warning on these lines of code. I"m trying to get a list that contains all the row numbers where string
Peter is present at column
df = pd.read_excel(xls_path) myRows = df[df["Unnamed: 5"] == "Peter"].index.tolist()
It produces a Warning:
"Python36libsite-packagespandascoreops.py:792: FutureWarning: elementwise comparison failed; returning scalar, but in the future will perform elementwise comparison result = getattr(x, name)(y)"
What is this FutureWarning and should I ignore it since it seems to work.
This FutureWarning isn"t from Pandas, it is from numpy and the bug also affects matplotlib and others, 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
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
== 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.
Numpy and Python are in a standoff, for now the operation returns a scalar, but in the future it may change.
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
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.
pandas are Jedi;
numpy are the hutts; and
python is the galactic empire.