Reformat a dataframe based on final empty columns in python - python-2.7

Question

I am working on scraping a table that has major and minor column names. When I do this, the table comes in having read both the column names and column groups, so the column names are misaligned in the dataframe like so (simplified):
unnamed1 unnamed2 unnamed3 Year Passing Rushing Receiving
2015 NA 200 60 NA NA NA
2014 NA 180 70 NA NA NA
My challenge is in shifting the column names so that 'Year' aligns over '2015' and so forth. The problem is then that the number of columns to shift does not remain constant from table to table (this is only one of many). My code at the moment looks like the following:
table1=read_html('http://www.pro-football-reference.com/players/T/TyexWi00.htm')
df=table1[0]
to_shift=len(df.dropna(how='all', axis=1).columns) #Number of empty columns to shift by
df2=df.dropna(how='all',axis=1) #Drop the empty columns
df2.columns=df.columns[-to_shift:] #Shift all columns left by the number i've found
The problem is that for a player that has none of one stat (passing in this simple example), there are completely blank columns in the middle of the dataframe as well as at the right end, so that the code shifts too far. Is there a clean way of counting the columns from right to left until one is not completely empty?
Much thanks, and I hope my question is clear!

Answer 1

Is there a clean way of counting the columns from right to left until one is not completely empty?
from itertools import takewhile
len(df.columns) - len(list(takewhile(lambda col: df[col].isnull().all(), reversed(df.columns)))) - 1
Explanation:
takewhile returns all elements of a list (beginning at the front) until the given condition is False. When we call it on reversed(df.columns), we get all elements from the end. With df[col].isnull().all() we can check whether all entries of a column are null (a.k.a. nan). Consequently the above takewhile expression returns the suffix of columns which are completely 'empty'. By calculating total_length - bad_suffix_length - 1, we get the first index for which the condition is not satisfied.

Answer 2

Adding to the correct response from Michael Hoff (Thank you very much!), the code has been edited to
to_shift=len(df.columns) - len(list(takewhile(lambda col: df[col].isnull().all(), reversed(df.columns)))) #Index of origianl dataframe to keep
df2=df.drop(list(takewhile(lambda col: df[col].isnull().all(), reversed(df.columns))),axis=1) #Drop the empty right side columns
colnames=df.columns[-to_shift:]
df2.columns=colnames