Right now I have a DF like this
 Word       Word2          Word3
 Hello      NaN            NaN
 My         My Name        NaN
 Yellow     Yellow Bee     Yellow Bee Hive
 Golden     Golden Gates   NaN
 Yellow     NaN            NaN
What I was hoping for was to remove all of the NaN cells from my data frame. So in the end, it would look like this, where 'Yellow Bee Hive' has moved to row 1 (similarly to what happens when you delete cells from a column in excel) :
   Word       Word2             Word3
1  Hello      My Name        Yellow Bee Hive
2  My         Yellow Bee       
3  Yellow     Golden Gates             
4  Golden       
5  Yellow    
Unfortunately, neither of these work because they delete the Entire ROW!
 df = df[pd.notnull(df['Word','Word2','Word3'])]
or
 df = df.dropna() 
Anyone have any suggestions? Should I reindex the table?
I think you can use this:
df = df.apply(lambda x: pd.Series(x.dropna().values))
For example:
import pandas as pd
import numpy as np
df = pd.DataFrame({
    'Word':['Hello', 'My', 'Yellow', 'Golden', 'Yellow'],
    'Word2':[np.nan, 'My Name', 'Yellow Bee', 'Golden Gates', np.nan],
    'Word3':[np.nan, np.nan, 'Yellow Bee Hive', np.nan, np.nan]
})
print(df)
Initial dataframe:
     Word         Word2            Word3
0   Hello           NaN              NaN
1      My       My Name              NaN
2  Yellow    Yellow Bee  Yellow Bee Hive
3  Golden  Golden Gates              NaN
4  Yellow           NaN              NaN
and applying this lambda function:
df = df.apply(lambda x: pd.Series(x.dropna().values))
print(df)
gives:
     Word         Word2            Word3
0   Hello       My Name  Yellow Bee Hive
1      My    Yellow Bee              NaN
2  Yellow  Golden Gates              NaN
3  Golden           NaN              NaN
4  Yellow           NaN              NaN
Then you can fill NaN values with empty strings:
df = df.fillna('')
print(df)
     Word         Word2            Word3
0   Hello       My Name  Yellow Bee Hive
1      My    Yellow Bee                 
2  Yellow  Golden Gates                 
3  Golden                               
4  Yellow    
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