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.
Healthy Algorithms 