df

	A	B	C
0	0	0	a
1	0	1	b
2	1	2	c
3	1	3	d
4	2	4	e
5	2	5	f

Replace a value in a column.

df['A'] = df['A'].replace(1, 100)

df

	A	B	C
0	0	0	a
1	0	1	b
2	100	2	c
3	100	3	d
4	2	4	e
5	2	5	f

Replace multiple values in a column.

df['A'] = df['A'].replace([0,2], 999)
df

	A	B	C
0	999	0	a
1	999	1	b
2	1	2	c
3	1	3	d
4	999	4	e
5	999	5	f

Replace in a specific column with regex.

df['A'] = df['A'].astype('str') # regex only works for string types.
df.replace({'A': r'^(1)$'}, {'A': r'\1_new'}, regex=True)

	A	B	C
0	0	0	a
1	0	1	b
2	1_new	2	c
3	1_new	3	d
4	2	4	e
5	2	5	f

Replace a value in a column with a function value.

nums = {'0': 'zero', '1': 'one',
        '2': 'two',  '3': 'three',
        '4': 'four', '5': 'five'}

def repl(m):
    return nums[m.group()]

# regex only works for string types
df['B'] = df['B'].astype('string')

df['B'] = df['B'].str.replace(r"^([0-9])$", repl, regex=True)
df

	A	B	C
0	0	zero	a
1	0	one	b
2	1	two	c
3	1	three	d
4	2	four	e
5	2	five	f