In [1]:

    

import pandas as pd


    

In [2]:

    

df = pd.DataFrame({'A': ['A1', 'A2', 'A3'],
                   'B': ['B1', 'B2', 'B3'],
                   'C': ['C1', 'C2', 'C3']},
                  index=['ONE', 'TWO', 'THREE'])
print(df)


    

    




        A   B   C
ONE    A1  B1  C1
TWO    A2  B2  C2
THREE  A3  B3  C3

In [3]:

    

df.loc['ONE'] = 0
print(df)


    

    




        A   B   C
ONE     0   0   0
TWO    A2  B2  C2
THREE  A3  B3  C3

In [4]:

    

df.loc['FOUR'] = 0
df.loc['FIVE'] = ['A5', 'B5', 'C5']
print(df)


    

    




        A   B   C
ONE     0   0   0
TWO    A2  B2  C2
THREE  A3  B3  C3
FOUR    0   0   0
FIVE   A5  B5  C5

In [5]:

    

# df.loc['SIX'] = ['A6', 'B6']
# ValueError: cannot set a row with mismatched columns


    

In [6]:

    

s = pd.Series(['B6', 'C6', 'D6'], index=['B', 'C', 'D'], name='SIX')
print(s)


    

    




B    B6
C    C6
D    D6
Name: SIX, dtype: object

In [7]:

    

df.loc['XXX'] = df.loc['TWO'] + df.loc['THREE']
df.loc['YYY'] = s
df.loc['ZZZ'] = s.values
print(df)


    

    




          A     B     C
ONE       0     0     0
TWO      A2    B2    C2
THREE    A3    B3    C3
FOUR      0     0     0
FIVE     A5    B5    C5
XXX    A2A3  B2B3  C2C3
YYY     NaN    B6    C6
ZZZ      B6    C6    D6

In [8]:

    

df = pd.DataFrame({'A': ['A1', 'A2', 'A3'],
                   'B': ['B1', 'B2', 'B3'],
                   'C': ['C1', 'C2', 'C3']},
                  index=['ONE', 'TWO', 'THREE'])


    

In [9]:

    

# print(df.append(0))
# TypeError: cannot concatenate object of type '<class 'int'>'; only Series and DataFrame objs are valid


    

In [10]:

    

print(df.append([0, 1, 2]))


    

    




         A    B    C    0
ONE     A1   B1   C1  NaN
TWO     A2   B2   C2  NaN
THREE   A3   B3   C3  NaN
0      NaN  NaN  NaN  0.0
1      NaN  NaN  NaN  1.0
2      NaN  NaN  NaN  2.0

In [11]:

    

print(df.append({'A': 0, 'B': 1, 'C': 2}, ignore_index=True))


    

    




    A   B   C
0  A1  B1  C1
1  A2  B2  C2
2  A3  B3  C3
3   0   1   2

In [12]:

    

s = pd.Series(['A4', 'B4', 'C4'], index=df.columns, name='FOUR')


    

In [13]:

    

print(df.append(s))


    

    




        A   B   C
ONE    A1  B1  C1
TWO    A2  B2  C2
THREE  A3  B3  C3
FOUR   A4  B4  C4

In [14]:

    

s_mismatch = pd.Series(['B5', 'C5', 'D5'], index=['B', 'C', 'D'], name='FIVE')


    

In [15]:

    

print(df.append(s_mismatch))


    

    




         A   B   C    D
ONE     A1  B1  C1  NaN
TWO     A2  B2  C2  NaN
THREE   A3  B3  C3  NaN
FIVE   NaN  B5  C5   D5

In [16]:

    

print(df)


    

    




        A   B   C
ONE    A1  B1  C1
TWO    A2  B2  C2
THREE  A3  B3  C3

In [17]:

    

s_no_name = pd.Series(['B4', 'C4', 'D4'], index=['B', 'C', 'D'])


    

In [18]:

    

# print(df.append(s_no_name))
# TypeError: Can only append a Series if ignore_index=True or if the Series has a name


    

In [19]:

    

print(df.append(s_no_name, ignore_index=True))


    

    




     A   B   C    D
0   A1  B1  C1  NaN
1   A2  B2  C2  NaN
2   A3  B3  C3  NaN
3  NaN  B4  C4   D4

In [20]:

    

print(df.append([s, s_mismatch]))


    

    




         A   B   C    D
ONE     A1  B1  C1  NaN
TWO     A2  B2  C2  NaN
THREE   A3  B3  C3  NaN
FOUR    A4  B4  C4  NaN
FIVE   NaN  B5  C5   D5

In [21]:

    

df2 = pd.DataFrame([['B6', 'C6', 'D6'], ['B7', 'C7', 'D7']], 
                   index=['SIX', 'SEVEN'], columns=['B', 'C', 'D'])
print(df2)


    

    




        B   C   D
SIX    B6  C6  D6
SEVEN  B7  C7  D7

In [22]:

    

print(df.append(df2))


    

    




         A   B   C    D
ONE     A1  B1  C1  NaN
TWO     A2  B2  C2  NaN
THREE   A3  B3  C3  NaN
SIX    NaN  B6  C6   D6
SEVEN  NaN  B7  C7   D7

In [23]:

    

# print(df.append([s, df2]))
# ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 5 and the array at index 1 has size 3


    

In [24]:

    

print(df.append(s).append(df2))


    

    




         A   B   C    D
ONE     A1  B1  C1  NaN
TWO     A2  B2  C2  NaN
THREE   A3  B3  C3  NaN
FOUR    A4  B4  C4  NaN
SIX    NaN  B6  C6   D6
SEVEN  NaN  B7  C7   D7

In [25]:

    

print(pd.concat([df, df2]))


    

    




         A   B   C    D
ONE     A1  B1  C1  NaN
TWO     A2  B2  C2  NaN
THREE   A3  B3  C3  NaN
SIX    NaN  B6  C6   D6
SEVEN  NaN  B7  C7   D7

In [26]:

    

print(pd.concat([df, df2], join='inner'))


    

    




        B   C
ONE    B1  C1
TWO    B2  C2
THREE  B3  C3
SIX    B6  C6
SEVEN  B7  C7

In [27]:

    

print(df.append(s))


    

    




        A   B   C
ONE    A1  B1  C1
TWO    A2  B2  C2
THREE  A3  B3  C3
FOUR   A4  B4  C4

In [28]:

    

print(pd.concat([df, s]))


    

    




         A    B    C    0
ONE     A1   B1   C1  NaN
TWO     A2   B2   C2  NaN
THREE   A3   B3   C3  NaN
A      NaN  NaN  NaN   A4
B      NaN  NaN  NaN   B4
C      NaN  NaN  NaN   C4

In [29]:

    

print(pd.DataFrame(s).T)


    

    




       A   B   C
FOUR  A4  B4  C4

In [30]:

    

print(pd.concat([df, pd.DataFrame(s).T]))


    

    




        A   B   C
ONE    A1  B1  C1
TWO    A2  B2  C2
THREE  A3  B3  C3
FOUR   A4  B4  C4

In [31]:

    

print(df.T.assign(FOUR=0, FIVE=['A5', 'B5', 'C5']).T)


    

    




        A   B   C
ONE    A1  B1  C1
TWO    A2  B2  C2
THREE  A3  B3  C3
FOUR    0   0   0
FIVE   A5  B5  C5

In [32]:

    

# df_insert = df.T.insert(0, 'FOUR', 0).T
# AttributeError: 'NoneType' object has no attribute 'T'


    

In [33]:

    

df_T = df.T
df_T.insert(0, 'FOUR', 0)
print(df_T.T)


    

    




        A   B   C
FOUR    0   0   0
ONE    A1  B1  C1
TWO    A2  B2  C2
THREE  A3  B3  C3