In [1]:
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
In [2]:
animals = DataFrame(np.arange(16).reshape(4,4), columns = list('WXYZ'), index=['Dog', 'Cat', 'Bird', 'Mouse'])
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animals
Out[3]:
In [4]:
animals.ix[1:2, ['W','Y']] = np.nan
animals
Out[4]:
In [6]:
behavior_map = {'W': 'good', 'X': 'bad', 'Y': 'good', 'Z':'bad'}
In [8]:
animal_col = animals.groupby(behavior_map, axis=1)
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animal_col.sum()
Out[10]:
In [11]:
behave_series = Series(behavior_map)
behave_series
Out[11]:
In [13]:
animals.groupby(behave_series, axis=1).count()
Out[13]:
In [14]:
animals.groupby(len).sum()
Out[14]:
In [15]:
keys = list('ABAB')
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animals.groupby([len, keys]).max()
Out[16]:
In [17]:
hier_col = pd.MultiIndex.from_arrays([['NY', 'NY', 'NY', 'SF', 'SF'], [1,2,3,1,2]], names=['City', 'sub_value'])
In [18]:
dframe_hr = DataFrame(np.arange(25).reshape(5,5), columns = hier_col)
In [19]:
dframe_hr = dframe_hr * 100
In [20]:
dframe_hr
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