For the single row case:

In [35]:

df.loc[(df[0]==101) & (df[1]==3)] = [[200,10]]
df
Out[35]:
     0   1
0  100   0
1  100   1
2  101   2
3  200  10
4  102   4
5  102   5

For the multiple row-case the following would work:

In [60]:

a = np.array(([100, 100, 101, 101, 102, 102],
                 [0,1,3,3,3,4]))
df = pd.DataFrame(a.T)
df
Out[60]:
     0  1
0  100  0
1  100  1
2  101  3
3  101  3
4  102  3
5  102  4
In [61]:

df.loc[(df[0]==101) & (df[1]==3)] = 200,10
df
Out[61]:
     0   1
0  100   0
1  100   1
2  200  10
3  200  10
4  102   3
5  102   4

For multi-row update like you propose the following would work where the replacement site is a single row, first construct a dict of the old vals to search for and use the new values as the replacement value:

In [78]:

old_keys = [(x[0],x[1]) for x in old_vals]
new_valss = [(x[0],x[1]) for x in new_vals]
replace_vals = dict(zip(old_keys, new_vals))
replace_vals
Out[78]:
{(100, 0): array([300,  20]),
 (101, 3): array([200,  10]),
 (102, 5): array([400,  30])}

We can then iterate over the dict and then set the rows using the same method as my first answer:

In [93]:

for k,v in replace_vals.items():
    df.loc[(df[0] == k[0]) & (df[1] == k[1])] = [[v[0],v[1]]]
df
     0  1
0  100  0
     0  1
5  102  5
     0  1
3  101  3
Out[93]:
     0   1
0  300  20
1  100   1
2  101   2
3  200  10
4  102   4
5  400  30

You can select by loc with condition rows of column Cabin and set to scalar:

train.loc[train['Pclass'] == 1, 'Cabin'] = 1

And your code replace all values to 1 because is is same as:

train['Cabin'] = 1

Sample:

train = pd.DataFrame({'Pclass':[1,2,3,1,2],
                      'Cabin':[10,20,30,40,50]})
print (train)
   Cabin  Pclass
0     10       1
1     20       2
2     30       3
3     40       1
4     50       2

train.loc[train['Pclass'] == 1, 'Cabin'] = 1
print (train)
   Cabin  Pclass
0      1       1
1     20       2
2     30       3
3      1       1
4     50       2