In [2]:
import pandas as pd
In [3]:
import numpy as np
In [4]:
from pandas import DataFrame, Series
In [5]:
obj = Series(range(4), index=['d', 'a', 'b', 'c'])
In [6]:
obj.sort_index()
Out[6]:
In [7]:
frame = DataFrame(np.arange(8).reshape((2, 4)),
index=['three', 'one'], columns=['d', 'a', 'b', 'c'])
In [8]:
frame
Out[8]:
In [9]:
frame.sort_index(axis=1, ascending=False)
Out[9]:
In [10]:
obj = Series([4,7,-3,2])
In [13]:
obj.sort_values()
Out[13]:
In [14]:
obj = Series([4, np.nan, 7, np.nan, -3, 2])
In [16]:
obj.sort_values()
Out[16]:
In [17]:
frame = DataFrame({'b': [4, 7, -3, 2], 'a': [0, 1, 0, 1]})
In [19]:
frame.sort_values(by=['a', 'b'])
Out[19]:
In [22]:
obj = Series([7, -5, 7, 4, 2, 0, 4])
In [23]:
obj.rank()
Out[23]:
In [24]:
obj.rank(method='first')
Out[24]:
In [25]:
obj.rank(ascending=False, method='max')
Out[25]:
In [26]:
frame = DataFrame({'b': [4.3, 7, -3, 2],
'a': [0, 1, 0, 1], 'c': [-2, 5, 8, -2.5]})
In [27]:
frame
Out[27]:
In [28]:
frame.rank(axis=1)
Out[28]:
In [30]:
obj
Out[30]:
In [29]:
obj = Series(range(5), index=['a', 'a', 'b', 'b', 'c'])
In [30]:
obj
Out[30]:
In [31]:
obj.index.is_unique
Out[31]:
In [32]:
obj['a']
Out[32]:
In [33]:
obj['c']
Out[33]:
In [34]:
df = DataFrame(np.random.randn(4, 3), index=['a', 'a', 'b', 'b'])
In [35]:
df
Out[35]:
In [36]:
df.ix['b']
Out[36]:
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