Fast pandas filtering

Question

I want to filter a pandas dataframe, if the name column entry has an item in a given list.

Here we have a DataFrame

x = DataFrame(
    [[\'sam\', 328], [         


        

Answer 1

Try using isin (thanks to DSM for suggesting loc over ix here):

In [78]: x = pd.DataFrame([['sam',328],['ruby',3213],['jon',121]], columns = ['name', 'score'])

In [79]: names = ['sam', 'ruby']

In [80]: x['name'].isin(names)
Out[80]: 
0     True
1     True
2    False
Name: name, dtype: bool

In [81]: x.loc[x['name'].isin(names), 'score'].sum()
Out[81]: 3541

---

CT Zhu suggests a faster alternative using np.in1d:

In [105]: y = pd.concat([x]*1000)
In [109]: %timeit y.loc[y['name'].isin(names), 'score'].sum()
1000 loops, best of 3: 413 µs per loop

In [110]: %timeit y.loc[np.in1d(y['name'], names), 'score'].sum()
1000 loops, best of 3: 335 µs per loop