In [1]:
import pandas as pd
years = range(1950,2013)
values = []
for year in years:
names1986 = pd.read_csv('data/yob%s.txt' % year, names=['name','sex','births'])
values.append(names1986.births[names1986.name=='Dmitri'].sum())
In [2]:
names1986.plot()
Out[2]:
In [3]:
Dmitri = pd.Series(values,index=years)
In [4]:
Dmitri.plot()
Out[4]:
In [50]:
Dmitri.head(10)
Out[50]:
In [11]:
type(names1986.name[names1986.name == 'Dmitri'])
Out[11]:
In [23]:
names1986.head()
Out[23]:
In [24]:
names1986.ix[10]
Out[24]:
In [26]:
pieces = []
for year in range(1880,2011):
path = 'data/yob%s.txt' % year
frame = pd.read_csv(path, names = ['name','sex','births'])
frame['year'] = year
pieces.append(frame)
names = pd.concat(pieces, ignore_index=True)
In [27]:
names.count()
Out[27]:
In [29]:
glory = names[names.name=='Dmitri']
In [32]:
total_births = names.pivot_table('births',rows = 'year',cols = 'sex',aggfunc = sum)
In [33]:
total_births
Out[33]:
In [34]:
total_births.head()
Out[34]:
In [47]:
geo = pd.Series(['Uchaly','Ufa','Moskva','Vancouver','Ottawa'], index = [1986,1986,2003,2005,2014])
In [48]:
geo
Out[48]:
In [43]:
geo.values
Out[43]:
In [44]:
geo.index
Out[44]:
In [53]:
geo[geo == 'Ufa']
Out[53]:
In [ ]:
names.