There is actually 2 warnings :

FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values. If you want the future behaviour and silence this warning, you can specify "categories='auto'". In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.

and the second :

The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.
"use the ColumnTransformer instead.", DeprecationWarning)

In the future, you should not define the columns in the OneHotEncoder directly, unless you want to use "categories='auto'". The first message also tells you to use OneHotEncoder directly, without the LabelEncoder first. Finally, the second message tells you to use ColumnTransformer, which is like a Pipe for columns transformations.

Here is the equivalent code for your case :

from sklearn.compose import ColumnTransformer 
ct = ColumnTransformer([("Name_Of_Your_Step", OneHotEncoder(),[0])], remainder="passthrough")) # The last arg ([0]) is the list of columns you want to transform in this step
ct.fit_transform(X)    

See also : ColumnTransformer documentation

For the above example;

Encoding Categorical data (Basically Changing Text to Numerical data i.e, Country Name)

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer
#Encode Country Column
labelencoder_X = LabelEncoder()
X[:,0] = labelencoder_X.fit_transform(X[:,0])
ct = ColumnTransformer([("Country", OneHotEncoder(), [0])], remainder = 'passthrough')
X = ct.fit_transform(X)

If you read the docs for OneHotEncoder you'll see the input for fit is "Input array of type int". So you need to do two steps for your one hot encoded data

from sklearn import preprocessing
cat_features = ['color', 'director_name', 'actor_2_name']
enc = preprocessing.LabelEncoder()
enc.fit(cat_features)
new_cat_features = enc.transform(cat_features)
print new_cat_features # [1 2 0]
new_cat_features = new_cat_features.reshape(-1, 1) # Needs to be the correct shape
ohe = preprocessing.OneHotEncoder(sparse=False) #Easier to read
print ohe.fit_transform(new_cat_features)

Output:

[[ 0.  1.  0.]
 [ 0.  0.  1.]
 [ 1.  0.  0.]]

EDIT

As of 0.20 this became a bit easier, not only because OneHotEncoder now handles strings nicely, but also because we can transform multiple columns easily using ColumnTransformer, see below for an example

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import numpy as np

X = np.array([['apple', 'red', 1, 'round', 0],
              ['orange', 'orange', 2, 'round', 0.1],
              ['bannana', 'yellow', 2, 'long', 0],
              ['apple', 'green', 1, 'round', 0.2]])
ct = ColumnTransformer(
    [('oh_enc', OneHotEncoder(sparse=False), [0, 1, 3]),],  # the column numbers I want to apply this to
    remainder='passthrough'  # This leaves the rest of my columns in place
)
print(ct2.fit_transform(X)) # Notice the output is a string

Output:

[['1.0' '0.0' '0.0' '0.0' '0.0' '1.0' '0.0' '0.0' '1.0' '1' '0']
 ['0.0' '0.0' '1.0' '0.0' '1.0' '0.0' '0.0' '0.0' '1.0' '2' '0.1']
 ['0.0' '1.0' '0.0' '0.0' '0.0' '0.0' '1.0' '1.0' '0.0' '2' '0']
 ['1.0' '0.0' '0.0' '1.0' '0.0' '0.0' '0.0' '0.0' '1.0' '1' '0.2']]

Why do you call OneHotEncoder constructor twise? enc has been created by default constructor, so for enc you have categorical_features='all' (all feature are categorical). As I understand you need somthing like this:

enc = OneHotEncoder(categorical_features=[0,2],
    handle_unknown='error', n_values='auto', sparse=True)
enc.fit([[0, 40, 3], [1, 50, 0], [0, 45, 1], [1, 30, 2]])
print(enc.n_values_)
print(enc.feature_indices_)
enc.transform([[0, 45, 3]]).toarray()

and you will have

[2 4]
[0 2 6]
Out[23]: array([[  1.,   0.,   0.,   0.,   0.,   1.,  45.]])

You can do this to get rid of the deprecation messages

from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer

ct = ColumnTransformer(
    [('one_hot_encoder', OneHotEncoder(), [0])],    # The column numbers to be transformed (here is [0] but can be [0, 1, 3])
    remainder='passthrough'                         # Leave the rest of the columns untouched
)

x = np.array(ct.fit_transform(x), dtype=np.float)

I think for this case you should stick to pd.get_dummies:

>>> data
   age seniority  gender  salary
0    1    junior    male       5
1    2    senior  female       6
2    3    junior  female       7

# One hot encode with get_dummies
data = pd.concat((data,pd.get_dummies(data.seniority)),1)

>>> data
   age seniority  gender  salary  junior  senior
0    1    junior    male       5       1       0
1    2    senior  female       6       0       1
2    3    junior  female       7       1       0

The problem is that sklearn's OneHotEncoder needs to have an array of ints as input. But in the array data.values, you still have the string representation of gender. You could, if you wanted, just one hot encode the seniority values, but if you want to know the meaning of those features, it's not very nice, you have to pass it the column names manually (which is unfeasible in a lot of cases):

from sklearn.preprocessing import LabelEncoder
label_encoder = LabelEncoder()
data['seniority'] = label_encoder.fit_transform(data['seniority'])

from sklearn.preprocessing import OneHotEncoder
one_hot_encoder = OneHotEncoder(sparse=False)
data[['junior','senior']] = one_hot_encoder.fit_transform(data['seniority'].values.reshape(-1,1))

>>> data
   age  seniority  gender  salary  junior  senior
0    1          0    male       5     1.0     0.0
1    2          1  female       6     0.0     1.0
2    3          0  female       7     1.0     0.0

Or, if the feature names don't matter:

from sklearn.preprocessing import LabelEncoder
label_encoder = LabelEncoder()
data['seniority'] = label_encoder.fit_transform(data['seniority'])

from sklearn.preprocessing import OneHotEncoder
one_hot_encoder = OneHotEncoder(sparse=False)
data = pd.concat((data,pd.DataFrame(one_hot_encoder.fit_transform(data['seniority'].values.reshape(-1,1)))),1)

   age  seniority  gender  salary    0    1
0    1          0    male       5  1.0  0.0
1    2          1  female       6  0.0  1.0
2    3          0  female       7  1.0  0.0

But in the end, pd.get_dummies does the job in a much nicer way (IMO)