import pandas

def get_hourly_entries(df):
    '''
    The data in the MTA Subway Turnstile data reports on the cumulative
    number of entries and exits per row.  Assume that you have a dataframe
    called df that contains only the rows for a particular turnstile machine
    (i.e., unique SCP, C/A, and UNIT).  This function should change
    these cumulative entry numbers to a count of entries since the last reading
    (i.e., entries since the last row in the dataframe).
    
    More specifically, you want to do two things:
       1) Create a new column called ENTRIESn_hourly
       2) Assign to the column the difference between ENTRIESn of the current row 
          and the previous row. If there is any NaN, fill/replace it with 1.
    
    You may find the pandas functions shift() and fillna() to be helpful in this exercise.
    
    Examples of what your dataframe should look like:
    
           C/A  UNIT       SCP     DATEn     TIMEn    DESCn  ENTRIESn    EXITSn  ENTRIESn_hourly
    0     A002  R051  02-00-00  05-01-11  00:00:00  REGULAR   3144312   1088151                0
    1     A002  R051  02-00-00  05-01-11  04:00:00  REGULAR   3144335   1088159               23
    2     A002  R051  02-00-00  05-01-11  08:00:00  REGULAR   3144353   1088177               18
    3     A002  R051  02-00-00  05-01-11  12:00:00  REGULAR   3144424   1088231               71
    4     A002  R051  02-00-00  05-01-11  16:00:00  REGULAR   3144594   1088275              170
    5     A002  R051  02-00-00  05-01-11  20:00:00  REGULAR   3144808   1088317              214
    6     A002  R051  02-00-00  05-02-11  00:00:00  REGULAR   3144895   1088328               87
    7     A002  R051  02-00-00  05-02-11  04:00:00  REGULAR   3144905   1088331               10
    8     A002  R051  02-00-00  05-02-11  08:00:00  REGULAR   3144941   1088420               36
    9     A002  R051  02-00-00  05-02-11  12:00:00  REGULAR   3145094   1088753              153
    10    A002  R051  02-00-00  05-02-11  16:00:00  REGULAR   3145337   1088823              243
    ...
    ...

    '''
    df['ENTRIESn_hourly'] = df['ENTRIESn'] - df['ENTRIESn'].shift(1)
    return df.fillna(1)
