Extract first and last row of a dataframe in pandas

I think you can try add parameter axis=1 to concat, because output of df.iloc[0,:] and df.iloc[-1,:] are Series and transpose by T:

print df.iloc[0,:]
a    1
b    a
Name: 0, dtype: object

print df.iloc[-1,:]
a    4
b    d
Name: 3, dtype: object

print pd.concat([df.iloc[0,:], df.iloc[-1,:]], axis=1)
   0  3
a  1  4
b  a  d

print pd.concat([df.iloc[0,:], df.iloc[-1,:]], axis=1).T
   a  b
0  1  a
3  4  d

You can also use head and tail:

In [29]: pd.concat([df.head(1), df.tail(1)])
Out[29]:
   a  b
0  1  a
3  4  d

The accepted answer duplicates the first row if the frame only contains a single row. If that's a concern

df[0::len(df)-1 if len(df) > 1 else 1]

works even for single row-dataframes.

Example: For the following dataframe this will not create a duplicate:

df = pd.DataFrame({'a': [1], 'b':['a']})
df2 = df[0::len(df)-1 if len(df) > 1  else 1]

print df2

   a  b
0  1  a

whereas this does:

df3 = df.iloc[[0, -1]]

print df3 

   a  b
0  1  a
0  1  a

because the single row is the first AND last row at the same time.

I think the most simple way is .iloc[[0, -1]].

df = pd.DataFrame({'a':range(1,5), 'b':['a','b','c','d']})
df2 = df.iloc[[0, -1]]
    
print(df2)

   a  b
0  1  a
3  4  d