print 'hello world'
plot(np.random.randn(1000).cumsum())



cd stackexchange/

import pandas as pd
files = ['Python200901-07.csv',
         'Python200907-201007.csv',
         'Python201007-201107.csv',
         'Python201107-12.csv',
         'Python201201-07.csv',
         'Python201207-12.csv',
         'Python201301.csv']

tables = []
for path in files:
    tables.append(pd.read_csv(path))

posts = pd.concat(tables, ignore_index=True)

posts.ix[0]

posts.Tags

import re

regex = re.compile('<([^>]*)>')

ids = []
tags = []
for id, val in zip(posts.Id, posts.Tags):
    for tag in regex.findall(val):
        ids.append(id)
        tags.append(tag)
        
tag_table = pd.DataFrame({'subtag': tags, 'Id': ids})

tag_table.head()

merged = pd.merge(tag_table, posts)

merged.ix[0]

top = merged.groupby('subtag').size().order(ascending=False)[:500]

merged.CreationDate = pd.to_datetime(merged.CreationDate)

filtered = merged[merged.subtag.isin(top.index)]
grouped = filtered.groupby('subtag')

def agg_monthly(group):
    return group.set_index('CreationDate').Score.resample('M', how='count')
results = grouped.apply(agg_monthly).unstack('subtag')

results = results[:'2013-05-31']

[x for x in top.index if 'meta' in x]

normed = results.div(results['python'], axis=0)
normed['metaprogramming'].plot()

results['python'].plot()

normed = results.div(results['python'], axis=0)
to_analyze = normed[:'2013-05-31']
to_analyze['pandas'].plot()

to_analyze['django'].plot()

to_analyze['flask'].plot()

to_analyze['google-app-engine'].plot()

to_analyze['python-3.x'].plot()

to_analyze['matplotlib'].plot()

to_analyze['regex'].plot()

top

to_analyze['ironpython'].plot()

to_analyze['twisted'].plot()

to_analyze['tornado'].plot()

filtered = merged[merged.subtag.isin(top.index)]
grouped = filtered.groupby('subtag')

def agg_monthly(group):
    return group.set_index('CreationDate').Score.resample('A', how='count')
results = grouped.apply(agg_monthly).unstack('subtag')


n
results['django'].plot(kind='bar')

normed = results.div(results['python'], axis=0)

normed['django'].plot(kind='bar')

normed['django'].pct_change()

whats_happening2013 = normed.pct_change().ix[-1]

[x for x in whats_happening2013.index if 'sublime' in x]

whats_happening2013['sublimetext2']

downtrends = whats_happening2013.order()[:50]
uptrends = whats_happening2013.order()[-50:]


uptrends[::-1]

downtrends