In [1]:
import pandas as pd
In [2]:
import numpy as np
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from pandas import DataFrame, Series
In [4]:
s1 = Series([7.3, -2.5, 3.4, 1.5], index=['a', 'c', 'd', 'e'])
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s2 = Series([-2.1, 3.6, -1.5, 4, 3.1], index=['a', 'c', 'e', 'f', 'g'])
In [6]:
s1
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In [7]:
s2
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In [8]:
s1 + s2
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In [9]:
df1 = DataFrame(np.arange(9.).reshape((3, 3)),
columns=list('bcd'), index=['Ohio', 'Texas', 'Colorado'])
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df2 = DataFrame(np.arange(12.).reshape((4, 3)),
columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])
In [11]:
df1
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In [12]:
df2
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In [13]:
df1 + df2
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In [14]:
df1 = DataFrame(np.arange(12.).reshape((3, 4)), columns=list('abcd'))
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df2 = DataFrame(np.arange(20.).reshape((4, 5)), columns=list('abcde'))
In [16]:
df1
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In [17]:
df2
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In [18]:
df1 + df2
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In [19]:
df1.add(df2, fill_value=0)
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In [20]:
df1.reindex(columns=df2.columns, fill_value=0)
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In [21]:
arr = np.arange(12.).reshape((3,4))
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arr
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In [23]:
arr[0]
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In [25]:
arr - arr[0]
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In [26]:
frame = DataFrame(np.arange(12.).reshape((4, 3)),
columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])
In [27]:
frame
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In [29]:
series = frame.ix[0]
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series
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In [31]:
frame - series
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In [32]:
series2 = Series(range(3), index=['b', 'e', 'f'])
In [38]:
series2
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In [40]:
frame
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In [39]:
frame + series2
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In [34]:
series3 = frame['d']
In [35]:
frame
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In [36]:
series3
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In [37]:
frame.sub(series3, axis=0)
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