Try this instead,

  print(
"{:.3f}% {} ({} sentences)".format(pcent, gender, nsents)
)

Refer the latest docs for more examples and check the Py version!

Would you please check your function code. It's not like openerp function code.

Can you check this question link: How add a function in openERP 7?

Probably there's something wrong with the input values for X and/or T. The function from the question works ok:

import numpy as np
from math import e

def sigmoid(X, T):
  return 1.0 / (1.0 + np.exp(-1.0 * np.dot(X, T)))

X = np.array([[1, 2, 3], [5, 0, 0]])
T = np.array([[1, 2], [1, 1], [4, 4]])

print(X.dot(T))
# Just to see if values are ok
print([1. / (1. + e ** el) for el in [-5, -10, -15, -16]])
print()
print(sigmoid(X, T))

Result:

[[15 16]
 [ 5 10]]

[0.9933071490757153, 0.9999546021312976, 0.999999694097773, 0.9999998874648379]

[[ 0.99999969  0.99999989]
 [ 0.99330715  0.9999546 ]]

Probably it's the dtype of your input arrays. Changing X to:

X = np.array([[1, 2, 3], [5, 0, 0]], dtype=object)

Gives:

Traceback (most recent call last):
  File "/[...]/stackoverflow_sigmoid.py", line 24, in <module>
    print sigmoid(X, T)
  File "/[...]/stackoverflow_sigmoid.py", line 14, in sigmoid
    return 1.0 / (1.0 + np.exp(-1.0 * np.dot(X, T)))
AttributeError: exp

The error points to this line:

df['content'] = df['content'].apply(lambda x: " ".join(x.lower() for x in x.split() \
                                    if x not in stop_words))

split is being used here as a method of Python's built-in str class. Your error indicates one or more values in df['content'] is of type float. This could be because there is a null value, i.e. NaN, or a non-null float value.

One workaround, which will stringify floats, is to just apply str on x before using split:

df['content'] = df['content'].apply(lambda x: " ".join(x.lower() for x in str(x).split() \
                                    if x not in stop_words))

Alternatively, and possibly a better solution, be explicit and use a named function with a try / except clause:

def converter(x):
    try:
        return ' '.join([x.lower() for x in str(x).split() if x not in stop_words])
    except AttributeError:
        return None  # or some other value

df['content'] = df['content'].apply(converter)

Since pd.Series.apply is just a loop with overhead, you may find a list comprehension or map more efficient:

df['content'] = [converter(x) for x in df['content']]
df['content'] = list(map(converter, df['content']))

The answer is already provided in the comments by @mattdmo and @tdelaney:

• NumPy 1.20 (release notes) deprecated numpy.float, numpy.int, and similar aliases, causing them to issue a deprecation warning

• NumPy 1.24 (release notes) removed these aliases altogether, causing an error when they are used

In many cases you can simply replace the deprecated NumPy types by the equivalent Python built-in type, e.g., numpy.float becomes a "plain" Python float.

For detailed guidelines on how to deal with various deprecated types, have a closer look at the table and guideline in the release notes for 1.20:

...

To give a clear guideline for the vast majority of cases, for the types bool, object, str (and unicode) using the plain version is shorter and clear, and generally a good replacement. For float and complex you can use float64 and complex128 if you wish to be more explicit about the precision.

For np.int a direct replacement with np.int_ or int is also good and will not change behavior, but the precision will continue to depend on the computer and operating system. If you want to be more explicit and review the current use, you have the following alternatives:

• np.int64 or np.int32 to specify the precision exactly. This ensures that results cannot depend on the computer or operating system.
• np.int_ or int (the default), but be aware that it depends on the computer and operating system.
• The C types: np.cint (int), np.int_ (long), np.longlong.
• np.intp which is 32bit on 32bit machines 64bit on 64bit machines. This can be the best type to use for indexing.

...

If you have dependencies that use the deprecated types, a quick workaround would be to roll back your NumPy version to 1.24 or less (as suggested in some of the other answers), while waiting for the dependency to catch up. Alternatively, you could create a patch yourself and open a pull request, or monkey patch the dependency in your own code.