How to remove blanks/NA’s from dataframe and shift the values up

You can use apply with dropna:

np.random.seed(100)
df = pd.DataFrame(np.random.randn(5,4))
df.iloc[1,2] = np.NaN
df.iloc[0,1] = np.NaN
df.iloc[2,1] = np.NaN
df.iloc[2,0] = np.NaN
print (df)
          0         1         2         3
0 -1.749765       NaN  1.153036 -0.252436
1  0.981321  0.514219       NaN -1.070043
2       NaN       NaN -0.458027  0.435163
3 -0.583595  0.816847  0.672721 -0.104411
4 -0.531280  1.029733 -0.438136 -1.118318

df1 = df.apply(lambda x: pd.Series(x.dropna().values))
print (df1)
          0         1         2         3
0 -1.749765  0.514219  1.153036 -0.252436
1  0.981321  0.816847 -0.458027 -1.070043
2 -0.583595  1.029733  0.672721  0.435163
3 -0.531280       NaN -0.438136 -0.104411
4       NaN       NaN       NaN -1.118318

And then if need replace to empty space, what create mixed values – strings with numeric – some functions can be broken:

df1 = df.apply(lambda x: pd.Series(x.dropna().values)).fillna('')
print (df1)
          0         1         2         3
0  -1.74977  0.514219   1.15304 -0.252436
1  0.981321  0.816847 -0.458027 -1.070043
2 -0.583595   1.02973  0.672721  0.435163
3  -0.53128           -0.438136 -0.104411
4                               -1.118318