You can use iloc with numpy.r_:

print (np.r_[0:2, -2:0])
[ 0  1 -2 -1]

df = df.iloc[np.r_[0:2, -2:0]]
print (df)
            A  B  C
2012-11-29  0  0  0
2012-11-30  1  1  1
2012-12-07  8  8  8
2012-12-08  9  9  9

df = df.iloc[np.r_[0:4, -4:0]]
print (df)
            A  B  C
2012-11-29  0  0  0
2012-11-30  1  1  1
2012-12-01  2  2  2
2012-12-02  3  3  3
2012-12-05  6  6  6
2012-12-06  7  7  7
2012-12-07  8  8  8
2012-12-08  9  9  9

You can use df.head(5) and df.tail(5) to get first five and last five. Optionally you can create new data frame and append() head and tail:

new_df = df.tail(5)
new_df = new_df.append(df.head(5))

Not quite the same question but if you just want to show the top / bottom 5 rows (eg with display in jupyter or regular print, there's potentially a simpler way than this if you use the pd.option_context context.

#make 100 3d random numbers
df = pd.DataFrame(np.random.randn(100,3))

# sort them by their axis sum
df = df.loc[df.sum(axis=1).index]

with pd.option_context('display.max_rows',10):
    print(df)

Outputs:

           0         1         2
0  -0.649105 -0.413335  0.374872
1   3.390490  0.552708 -1.723864
2  -0.781308 -0.277342 -0.903127
3   0.433665 -1.125215 -0.290228
4  -2.028750 -0.083870 -0.094274
..       ...       ...       ...
95  0.443618 -1.473138  1.132161
96 -1.370215 -0.196425 -0.528401
97  1.062717 -0.997204 -1.666953
98  1.303512  0.699318 -0.863577
99 -0.109340 -1.330882 -1.455040

[100 rows x 3 columns]