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['A'] = 0
print(df)


    

    




       A   B   C
ONE    0  B1  C1
TWO    0  B2  C2
THREE  0  B3  C3

In [4]:

    

df['D'] = 0
print(df)


    

    




       A   B   C  D
ONE    0  B1  C1  0
TWO    0  B2  C2  0
THREE  0  B3  C3  0

In [5]:

    

df['E'] = [0, 1, 2]
print(df)


    

    




       A   B   C  D  E
ONE    0  B1  C1  0  0
TWO    0  B2  C2  0  1
THREE  0  B3  C3  0  2

In [6]:

    

# df['F'] = [0, 1, 2, 3]
# ValueError: Length of values does not match length of index


    

In [7]:

    

df['F'] = df['B'] + df['C']
df['G'] = df['B'].str.lower()
print(df)


    

    




       A   B   C  D  E     F   G
ONE    0  B1  C1  0  0  B1C1  b1
TWO    0  B2  C2  0  1  B2C2  b2
THREE  0  B3  C3  0  2  B3C3  b3

In [8]:

    

s = pd.Series(['X2', 'X3', 'X4'], index=['TWO', 'THREE', 'FOUR'], name='X')
print(s)


    

    




TWO      X2
THREE    X3
FOUR     X4
Name: X, dtype: object

In [9]:

    

df['H'] = s
print(df)


    

    




       A   B   C  D  E     F   G    H
ONE    0  B1  C1  0  0  B1C1  b1  NaN
TWO    0  B2  C2  0  1  B2C2  b2   X2
THREE  0  B3  C3  0  2  B3C3  b3   X3

In [10]:

    

print(s.values)


    

    




['X2' 'X3' 'X4']

In [11]:

    

df['I'] = s.values
print(df)


    

    




       A   B   C  D  E     F   G    H   I
ONE    0  B1  C1  0  0  B1C1  b1  NaN  X2
TWO    0  B2  C2  0  1  B2C2  b2   X2  X3
THREE  0  B3  C3  0  2  B3C3  b3   X3  X4

In [12]:

    

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


    

In [13]:

    

print(df.assign(A=0))


    

    




       A   B   C
ONE    0  B1  C1
TWO    0  B2  C2
THREE  0  B3  C3

In [14]:

    

print(df.assign(D=0))


    

    




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

In [15]:

    

print(df)


    

    




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

In [16]:

    

s = pd.Series(['X2', 'X3', 'X4'], index=['TWO', 'THREE', 'FOUR'], name='X')
print(s)


    

    




TWO      X2
THREE    X3
FOUR     X4
Name: X, dtype: object

In [17]:

    

df_new = df.assign(C='XXX',
                   D=0, E=[0, 1, 2],
                   F=s, G=s.values,
                   H=df['A'] + df['B'])
print(df_new)


    

    




        A   B    C  D  E    F   G     H
ONE    A1  B1  XXX  0  0  NaN  X2  A1B1
TWO    A2  B2  XXX  0  1   X2  X3  A2B2
THREE  A3  B3  XXX  0  2   X3  X4  A3B3

In [18]:

    

df = pd.DataFrame({'A': ['A1', 'A2', 'A3'],
                   'B': ['B1', 'B2', 'B3'],
                   'C': ['C1', 'C2', 'C3']},
                  index=['ONE', 'TWO', 'THREE'])
s = pd.Series(['X2', 'X3', 'X4'], index=['TWO', 'THREE', 'FOUR'], name='X')


    

In [19]:

    

df.insert(0, 'D', 0)
print(df)


    

    




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

In [20]:

    

df.insert(len(df.columns), 'E', s)
print(df)


    

    




       D   A   B   C    E
ONE    0  A1  B1  C1  NaN
TWO    0  A2  B2  C2   X2
THREE  0  A3  B3  C3   X3

In [21]:

    

# df.insert(10, 'F', 10)
# ValueError: cannot insert F, already exists


    

In [22]:

    

# df.insert(-1, 'F', 10)
# ValueError: unbounded slice


    

In [23]:

    

# df.insert(0, 'D', 10)
# ValueError: cannot insert D, already exists


    

In [24]:

    

df.insert(0, 'D', 10, allow_duplicates=True)
print(df)


    

    




        D  D   A   B   C    E
ONE    10  0  A1  B1  C1  NaN
TWO    10  0  A2  B2  C2   X2
THREE  10  0  A3  B3  C3   X3

In [25]:

    

df = pd.DataFrame({'A': ['A1', 'A2', 'A3'],
                   'B': ['B1', 'B2', 'B3'],
                   'C': ['C1', 'C2', 'C3']},
                  index=['ONE', 'TWO', 'THREE'])
s = pd.Series(['X2', 'X3', 'X4'], index=['TWO', 'THREE', 'FOUR'], name='X')


    

In [26]:

    

print(pd.concat([df, s], axis=1))


    

    




         A    B    C    X
ONE     A1   B1   C1  NaN
TWO     A2   B2   C2   X2
THREE   A3   B3   C3   X3
FOUR   NaN  NaN  NaN   X4

In [27]:

    

print(pd.concat([df, s], axis=1, join='inner'))


    

    




        A   B   C   X
TWO    A2  B2  C2  X2
THREE  A3  B3  C3  X3

In [28]:

    

s1 = pd.Series(['X1', 'X2', 'X3'], index=df.index, name='X')
s2 = pd.Series(['Y1', 'Y2', 'Y3'], index=df.index, name='Y')


    

In [29]:

    

print(pd.concat([df, s1, s2], axis=1))


    

    




        A   B   C   X   Y
ONE    A1  B1  C1  X1  Y1
TWO    A2  B2  C2  X2  Y2
THREE  A3  B3  C3  X3  Y3

In [30]:

    

df2 = pd.DataFrame({'df_col1': 0, 'df_col2': range(3)}, index=df.index)
print(df2)


    

    




       df_col1  df_col2
ONE          0        0
TWO          0        1
THREE        0        2

In [31]:

    

print(pd.concat([df, df2], axis=1))


    

    




        A   B   C  df_col1  df_col2
ONE    A1  B1  C1        0        0
TWO    A2  B2  C2        0        1
THREE  A3  B3  C3        0        2