If you pass a Series to apply, the (lambda) function receive a scalar (a float here). If you pass a DataFrame to apply, the (lambda) function receive a Series.

So you don't need apply here:

zscore_fun_improved(df.Close)

Demo:

# DataFrame -> apply get Series
>>> df.apply(type)
open     <class 'pandas.core.series.Series'>
close    <class 'pandas.core.series.Series'>
dtype: object

# Series -> apply get scalar values
>>> df['close'].apply(type)
0    <class 'float'>
1    <class 'float'>
2    <class 'float'>
3    <class 'float'>
4    <class 'float'>
Name: close, dtype: object

As pointed out by warren-weckesser this can also happen if you use dtype object (and in fact this is likelier the issue you are facing):

>>> s = pd.Series([1.0], dtype='object')
>>> s
0    1
dtype: object
>>> np.log(s)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'float' object has no attribute 'log'

You can address this by setting the dtype to float explicitly:

>>> np.log(s.astype('float64'))
0    0.0
dtype: float64

In your case:

np.log(df['price'].astype('float'))

Note: You can have more control using to_numeric.

First/alternative answer:

You have a float variable np in scope.

The problem is that:

import numpy as np
np = 1
np.log

is perfectly valid python.

>>> import numpy as np
>>> np = 1.
>>> np.log
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'float' object has no attribute 'log'

The solution is not to use np are a variable name, or other popular import abbreviations pd or dt etc. You can pick this kind of error up using a linter.

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

Try this instead,

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

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

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']))