No, str objects do not have an isnumeric method. isnumeric is only available for unicode objects. In other words:

>>> d = unicode('some string', 'utf-8')
>>> d.isnumeric()
False
>>> d = unicode('42', 'utf-8')
>>> d.isnumeric()
True

numpy.log10 is a "ufunc", and the method Series.apply(func) has a special test for numpy ufuncs which makes test.apply(log10) equivalent to np.log10(test). This means test, a Pandas Series instance, is passed to log10. The data type of test is object, which means that the elements in test can be arbitrary Python objects. np.log10 doesn't know how to handle such a collection of objects (it doesn't "know" that those objects are, in fact, all np.float64 instances), so it attempts to dispatch the calculation to the individual elements in the Series. To do that, it expects the elements themselves to have a log10 method. That's when the error occurs: the elements in the Series (in this case, np.float64 instances) do not have a log10 method.

A couple alternative expression that should do what you want are np.log10(test.astype(np.float64)) or test.astype(np.float64).apply(np.log10). The essential part is that test.astype(np.float64) converts the data type of the Series object from object to np.float64.

It looks like you are calling standard library statistics functions on a NumPy array. statistics is not meant to accept NumPy arrays, so it is raising the error. To fix the problem, use + instead of np.concatenate to concatenate the lists, or use the corresponding NumPy functions; e.g., np.mean instead of statistics.mean and np.std (with ddof=1) instead of statistics.stdev.

import numpy as np
import statistics
a = [1, 2, 3, 4, 5]
b = [6, 7, 8, 9]
# statistics.stdev(np.concatenate([a, b]))  # AttributeError
statistics.stdev(a + b)  # 2.7386127875258306
np.std(np.concatenate([a, b]), ddof=1)  # np.float64(2.7386127875258306)
np.std(a + b, ddof=1)  # np.float64(2.7386127875258306)

When you use df.loc[0, 'Count'] you are only returning the item that is actually that location which is a float. apply is a method of a Pandas or numpy series so you need to have a Series class to use that method. So most likely you want to do something like this but without a test data set it's hard to know.

df['Colour'] = df['Count'].apply(lambda x: 'Red' if x <=500 else 'Green')

Creating a test dataset shows you how this works all together:

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randint(0,1000,size=(10, 4)), columns=list('ABCD'))
df['Colour'] = df['A'].apply(lambda x: 'Red' if x <=500 else 'Green')
df.head()

Which returns:

    A   B   C   D   Colour
0   223 360 133 285 Red
1   300 125 642 238 Red
2   118 293 442 382 Red
3   448 357 55  124 Red
4   995 395 658 559 Green

If you want to use .loc to access the whole column you can use df.loc[:, 'Count'] You can read more about the .loc method here

It helps to post a full stack trace, but based on what you’ve said it sounds like you’re trying to call the quantile function on a single integer, rather than the whole column