Taking logarithm of column

Question

Im quite new to programming (in python) and I would like to create a new variable that is the logarithm of a column (from an imported excel file). I have tried different solutions from this site, but I keep getting an error. My latest error is AttributeError: 'str' object has no attribute 'log'. I have already dropped all the values that are not "numbers', but I still don't know how to convert the values from strings to integers (if this is the case, because 'int(neighborhood)' doesn't work).

This is the code I have now:

import pandas as pd
import numpy as np

df=pd.read_excel("kwb-2016_del_col_del_row.xls")
df = df[df.m_woz != "."] # drop rows with values "."
neighborhood=df[df.recs=="Neighborhood"]
neighborhood=neighborhood["m_woz"]
print(neighborhood)

np.log(neighborhood)

and this is the error I'm getting:

AttributeError                            Traceback (most recent call last)
<ipython-input-66-46698de51811> in <module>()
     12 print(neighborhood)
     13 
---> 14 np.log(neighborhood)


AttributeError: 'str' object has no attribute 'log'

Could someone help me please?

Answer 1

Perhaps you are not removing the data you think you are?
Try printing the data types to see what they are.
In a DataFrame, your column might be filled with objects instead of numbers.

print(df.dtypes)

Also, you might want to look at these two pages

Select row from a DataFrame based on the type of the object(i.e. str)

Pandas: convert dtype 'object' to int

Here's an example I constructed and ran interactively that correctly gets the logarithms (don't type >>>):

>>> raw_data = {'m_woz': ['abc', 'def', 1.23, 45.6, '.xyz'], 
    'recs': ['Neighborhood', 'Neighborhood', 
    'unknown', 'Neighborhood', 'whatever']}
>>> df = pd.DataFrame(raw_data, columns = ['m_woz', 'recs'])
>>> print(df.dtypes)
m_woz    object
recs     object
dtype: object

Note that the type is object, not float or int or str

Continuing on, here is what df and neighborhood look like:

>>> df
  m_woz          recs
0    42  Neighborhood
1   def  Neighborhood
2  1.23       unknown
3  45.6  Neighborhood
4  .xyz      whatever

>>> neighborhood=df[df.recs=="Neighborhood"]
>>> neighborhood

  m_woz          recs
0    42  Neighborhood
1   def  Neighborhood
3  45.6  Neighborhood

And here are the tricks... This line selects all rows in neighborhood that are int or float (be careful to fix indents if you copy/paste this

>>> df_num_strings = neighborhood[neighborhood['m_woz'].
        apply(lambda x: type(x) in (int, float))]

>>> df_num_strings
  m_woz          recs
0    42  Neighborhood
3  45.6  Neighborhood

Almost there... convert the numbers to floating point from string

>>> df_float = df_num_strings['m_woz'].astype(str).astype(float)
>>> df_float
0    42.0
3    45.6

Finally, compute logarithms:

>>> np.log(df_float)
0    3.737670
3    3.819908
Name: m_woz, dtype: float64