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 Unnamed: 5.

df = pd.read_excel(xls_path)
myRows = df[df['Unnamed: 5'] == 'Peter'].index.tolist()

It produces a Warning:

"\Python36\lib\site-packages\pandas\core\ops.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

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 left operand is python’s turf, a primitive string, and the middle operation is python’s turf, but the right 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. https://youtu.be/OZczsiCfQQk?t=3

I get the same error when I try to set the index_col reading a file into a Panda‘s data-frame:

df = pd.read_csv('my_file.tsv', sep='\t', header=0, index_col=['0'])  ## or same with the following
df = pd.read_csv('my_file.tsv', sep='\t', header=0, index_col=[0])

I have never encountered such an error previously. I still am trying to figure out the reason behind this (using @Eric Leschinski explanation and others).

Anyhow, the following approach solves the problem for now until I figure the reason out:

df = pd.read_csv('my_file.tsv', sep='\t', header=0)  ## not setting the index_col
df.set_index(['0'], inplace=True)

I will update this as soon as I figure out the reason for such behavior.

My experience to the same warning message was caused by TypeError.

TypeError: invalid type comparison

So, you may want to check the data type of the Unnamed: 5

for x in df['Unnamed: 5']:
  print(type(x))  # are they 'str' ?

Here is how I can replicate the warning message:

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(3, 2), columns=['num1', 'num2'])
df['num3'] = 3
df.loc[df['num3'] == '3', 'num3'] = 4  # TypeError and the Warning
df.loc[df['num3'] == 3, 'num3'] = 4  # No Error

Hope it helps.

Can’t beat Eric Leschinski’s awesomely detailed answer, but here’s a quick workaround to the original question that I don’t think has been mentioned yet – put the string in a list and use .isin instead of ==

For example:

import pandas as pd
import numpy as np

df = pd.DataFrame({"Name": ["Peter", "Joe"], "Number": [1, 2]})

# Raises warning using == to compare different types:
df.loc[df["Number"] == "2", "Number"]

# No warning using .isin:
df.loc[df["Number"].isin(["2"]), "Number"]

A quick workaround for this is to use numpy.core.defchararray. I also faced the same warning message and was able to resolve it using above module.

import numpy.core.defchararray as npd
resultdataset = npd.equal(dataset1, dataset2)

Eric’s answer helpfully explains that the trouble comes from comparing a Pandas Series (containing a NumPy array) to a Python string. Unfortunately, his two workarounds both just suppress the warning.

To write code that doesn’t cause the warning in the first place, explicitly compare your string to each element of the Series and get a separate bool for each. For example, you could use map and an anonymous function.

myRows = df[df['Unnamed: 5'].map( lambda x: x == 'Peter' )].index.tolist()

If your arrays aren’t too big or you don’t have too many of them, you might be able to get away with forcing the left hand side of == to be a string:

myRows = df[str(df['Unnamed: 5']) == 'Peter'].index.tolist()

But this is ~1.5 times slower if df['Unnamed: 5'] is a string, 25-30 times slower if df['Unnamed: 5'] is a small numpy array (length = 10), and 150-160 times slower if it’s a numpy array with length 100 (times averaged over 500 trials).

a = linspace(0, 5, 10)
b = linspace(0, 50, 100)
n = 500
string1 = 'Peter'
string2 = 'blargh'
times_a = zeros(n)
times_str_a = zeros(n)
times_s = zeros(n)
times_str_s = zeros(n)
times_b = zeros(n)
times_str_b = zeros(n)
for i in range(n):
    t0 = time.time()
    tmp1 = a == string1
    t1 = time.time()
    tmp2 = str(a) == string1
    t2 = time.time()
    tmp3 = string2 == string1
    t3 = time.time()
    tmp4 = str(string2) == string1
    t4 = time.time()
    tmp5 = b == string1
    t5 = time.time()
    tmp6 = str(b) == string1
    t6 = time.time()
    times_a[i] = t1 - t0
    times_str_a[i] = t2 - t1
    times_s[i] = t3 - t2
    times_str_s[i] = t4 - t3
    times_b[i] = t5 - t4
    times_str_b[i] = t6 - t5
print('Small array:')
print('Time to compare without str conversion: {} s. With str conversion: {} s'.format(mean(times_a), mean(times_str_a)))
print('Ratio of time with/without string conversion: {}'.format(mean(times_str_a)/mean(times_a)))

print('\nBig array')
print('Time to compare without str conversion: {} s. With str conversion: {} s'.format(mean(times_b), mean(times_str_b)))
print(mean(times_str_b)/mean(times_b))

print('\nString')
print('Time to compare without str conversion: {} s. With str conversion: {} s'.format(mean(times_s), mean(times_str_s)))
print('Ratio of time with/without string conversion: {}'.format(mean(times_str_s)/mean(times_s)))

Result:

Small array:
Time to compare without str conversion: 6.58464431763e-06 s. With str conversion: 0.000173756599426 s
Ratio of time with/without string conversion: 26.3881526541

Big array
Time to compare without str conversion: 5.44309616089e-06 s. With str conversion: 0.000870866775513 s
159.99474375821288

String
Time to compare without str conversion: 5.89370727539e-07 s. With str conversion: 8.30173492432e-07 s
Ratio of time with/without string conversion: 1.40857605178

In my case, the warning occurred because of just the regular type of boolean indexing — because the series had only np.nan. Demonstration (pandas 1.0.3):

>>> import pandas as pd
>>> import numpy as np
>>> pd.Series([np.nan, 'Hi']) == 'Hi'
0    False
1     True
>>> pd.Series([np.nan, np.nan]) == 'Hi'
~/anaconda3/envs/ms3/lib/python3.7/site-packages/pandas/core/ops/array_ops.py:255: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  res_values = method(rvalues)
0    False
1    False

I think with pandas 1.0 they really want you to use the new 'string' datatype which allows for pd.NA values:

>>> pd.Series([pd.NA, pd.NA]) == 'Hi'
0    False
1    False
>>> pd.Series([np.nan, np.nan], dtype='string') == 'Hi'
0    <NA>
1    <NA>
>>> (pd.Series([np.nan, np.nan], dtype='string') == 'Hi').fillna(False)
0    False
1    False

Don’t love at which point they tinkered with every-day functionality such as boolean indexing.

I got this warning because I thought my column contained null strings, but on checking, it contained np.nan!

if df['column'] == '':

Changing my column to empty strings helped :)

I’ve compared a few of the methods possible for doing this, including pandas, several numpy methods, and a list comprehension method.

First, let’s start with a baseline:

>>> import numpy as np
>>> import operator
>>> import pandas as pd

>>> x = [1, 2, 1, 2]
>>> %time count = np.sum(np.equal(1, x))
>>> print("Count {} using numpy equal with ints".format(count))
CPU times: user 52 µs, sys: 0 ns, total: 52 µs
Wall time: 56 µs
Count 2 using numpy equal with ints

So, our baseline is that the count should be correct 2, and we should take about 50 us.

Now, we try the naive method:

>>> x = ['s', 'b', 's', 'b']
>>> %time count = np.sum(np.equal('s', x))
>>> print("Count {} using numpy equal".format(count))
CPU times: user 145 µs, sys: 24 µs, total: 169 µs
Wall time: 158 µs
Count NotImplemented using numpy equal
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  """Entry point for launching an IPython kernel.

And here, we get the wrong answer (NotImplemented != 2), it takes us a long time, and it throws the warning.

So we’ll try another naive method:

>>> %time count = np.sum(x == 's')
>>> print("Count {} using ==".format(count))
CPU times: user 46 µs, sys: 1 µs, total: 47 µs
Wall time: 50.1 µs
Count 0 using ==

Again, the wrong answer (0 != 2). This is even more insidious because there’s no subsequent warnings (0 can be passed around just like 2).

Now, let’s try a list comprehension:

>>> %time count = np.sum([operator.eq(_x, 's') for _x in x])
>>> print("Count {} using list comprehension".format(count))
CPU times: user 55 µs, sys: 1 µs, total: 56 µs
Wall time: 60.3 µs
Count 2 using list comprehension

We get the right answer here, and it’s pretty fast!

Another possibility, pandas:

>>> y = pd.Series(x)
>>> %time count = np.sum(y == 's')
>>> print("Count {} using pandas ==".format(count))
CPU times: user 453 µs, sys: 31 µs, total: 484 µs
Wall time: 463 µs
Count 2 using pandas ==

Slow, but correct!

And finally, the option I’m going to use: casting the numpy array to the object type:

>>> x = np.array(['s', 'b', 's', 'b']).astype(object)
>>> %time count = np.sum(np.equal('s', x))
>>> print("Count {} using numpy equal".format(count))
CPU times: user 50 µs, sys: 1 µs, total: 51 µs
Wall time: 55.1 µs
Count 2 using numpy equal

Fast and correct!

I had this code which was causing the error:

for t in dfObj['time']:
  if type(t) == str:
    the_date = dateutil.parser.parse(t)
    loc_dt_int = int(the_date.timestamp())
    dfObj.loc[t == dfObj.time, 'time'] = loc_dt_int

I changed it to this:

for t in dfObj['time']:
  try:
    the_date = dateutil.parser.parse(t)
    loc_dt_int = int(the_date.timestamp())
    dfObj.loc[t == dfObj.time, 'time'] = loc_dt_int
  except Exception as e:
    print(e)
    continue

to avoid the comparison, which is throwing the warning – as stated above. I only had to avoid the exception because of dfObj.loc in the for loop, maybe there is a way to tell it not to check the rows it has already changed.