1. Read in the three baseball datasets and set the index as playerID:

>>> baseball_14 = pd.read_csv('data/baseball14.csv',
                              index_col='playerID')
>>> baseball_15 = pd.read_csv('data/baseball15.csv',
                              index_col='playerID')
>>> baseball_16 = pd.read_csv('data/baseball16.csv',
                              index_col='playerID')
>>> baseball_14.head()

2. Use the index method difference to discover which index labels are in baseball_14 and not in baseball_15, and vice versa:

>>> baseball_14.index.difference(baseball_15.index)
Index(['corpoca01', 'dominma01', 'fowlede01', 'grossro01',
       'guzmaje01', 'hoeslj01', 'krausma01', 'preslal01',
       'singljo02'], dtype='object', name='playerID')

>>> baseball_14.index.difference(baseball_16.index)
Index(['congeha01', 'correca01', 'gattiev01', 'gomezca01',
       'lowrije01', 'rasmuco01', 'tuckepr01', 'valbulu01'],
       dtype='object', name='playerID')

3. There are quite a few players unique to each index. Let's find out how many hits each player has in total over the three-year period. The H column contains the number of hits:

>>> hits_14 = baseball_14['H']
>>> hits_15 = baseball_15['H']
>>> hits_16 = baseball_16['H']
>>> hits_14.head()
playerID
altuvjo01    225
cartech02    115
castrja01    103
corpoca01     40
dominma01    121
Name: H, dtype: int64

4. Let's first add together two Series using the plus operator:

>>> (hits_14 + hits_15).head()
playerID
altuvjo01    425.0
cartech02    193.0
castrja01    174.0
congeha01      NaN
corpoca01      NaN
Name: H, dtype: float64

5. Even though players congeha01 and corpoca01 have recorded hits for 2015, their result is missing. Let's use the add method and its parameter, fill_value, to avoid missing values:

>>> hits_14.add(hits_15, fill_value=0).head()
playerID
altuvjo01    425.0
cartech02    193.0
castrja01    174.0
congeha01     46.0
corpoca01     40.0
Name: H, dtype: float64

6. We add hits from 2016 by chaining the add method once more:

>>> hits_total = hits_14.add(hits_15, fill_value=0) 
                        .add(hits_16, fill_value=0)
>>> hits_total.head()
playerID
altuvjo01    641.0
bregmal01     53.0
cartech02    193.0
castrja01    243.0
congeha01     46.0
Name: H, dtype: float64

7. Check for missing values in the result:

>>> hits_total.hasnans
False