In Pandas, how to create a unique ID based on the combination of many columns?
<p>I have a very large dataset, that looks like</p>

<pre><code>df = pd.DataFrame({'B': ['john smith', 'john doe', 'adam smith', 'john doe', np.nan], 'C': ['indiana jones', 'duck mc duck', 'batman','duck mc duck',np.nan]})

df
Out[173]: 
            B              C
0  john smith  indiana jones
1    john doe   duck mc duck
2  adam smith         batman
3    john doe   duck mc duck
4         NaN            NaN
</code></pre>

<p>I need to create a ID variable, that is unique for every B-C combination. That is, the output should be </p>

<pre><code>            B              C   ID
0  john smith  indiana jones   1
1    john doe   duck mc duck   2
2  adam smith         batman   3
3    john doe   duck mc duck   2 
4         NaN            NaN   0
</code></pre>

<p>I actually dont care about whether the index starts at zero or not, and whether the value for the missing columns is 0 or any other number. I just want something fast, that does not take a lot of memory and can be sorted quickly.
I use:</p>

<pre><code>df['combined_id']=(df.B+df.C).rank(method='dense')
</code></pre>

<p>but the output is <code>float64</code> and takes a lot of memory. Can we do better?
Thanks!</p>