Healthy Algorithms

One fun thing about using the IPython Notebook as my lab book for all my research is that I can do “me”-search in my copious spare time, for example to see the top 25 `numpy` calls I’ve used this year:

In [1]:
import glob

In [2]:
lines = ''
for fname in glob.glob('*.py'):
    with file(fname) as f:
        lines += f.read()
        lines += '\n'

In [3]:
import re

# Find top np.* calls

In [9]:
np_calls = re.findall('np\.[\w\.]+', lines)
np_calls[:5]
Out[9]:
['np.linspace',
 'np.random.random',
 'np.random.normal',
 'np.sqrt',
 'np.random.normal']

In [10]:
import pandas as pd

In [12]:
pd.Series(np_calls).value_counts().head(20)
Out[12]:
np.array            219
np.random.normal    170
np.mean             130
np.random.seed      126
np.round            124
np.log              119
np.exp              114
np.linspace          96
np.random.choice     84
np.where             84
np.zeros             78
np.ones              65
np.dot               62
np.empty             62
np.sum               52
np.absolute          49
np.nan               47
np.arange            45
np.inf               38
np.sqrt              37

Number one thing: `np.array`! I wonder why I use that.