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Nimish Khurana
a year ago
import pandas as pd
import numpy as np
s = pd.Series([1,2,3,4,5,4])
print s.pct_change()
df = pd.DataFrame(np.random.randn(5, 2))
print df.pct_change()
0 NaN
1 1.000000
2 0.500000
3 0.333333
4 0.250000
5 -0.200000
dtype: float64
0 1
0 NaN NaN
1 -15.151902 0.174730
2 -0.746374 -1.449088
3 -3.582229 -3.165836
4 15.601150 -1.860434
import pandas as pd
import numpy as np
s1 = pd.Series(np.random.randn(10))
s2 = pd.Series(np.random.randn(10))
print s1.cov(s2)
import pandas as pd
import numpy as np
frame = pd.DataFrame(np.random.randn(10, 5), columns=['a', 'b', 'c', 'd', 'e'])
print frame['a'].cov(frame['b'])
print frame.cov()
-0.58312921152741437
a b c d e
a 1.780628 -0.583129 -0.185575 0.003679 -0.136558
b -0.583129 1.297011 0.136530 -0.523719 0.251064
c -0.185575 0.136530 0.915227 -0.053881 -0.058926
d 0.003679 -0.523719 -0.053881 1.521426 -0.487694
e -0.136558 0.251064 -0.058926 -0.487694 0.960761
import pandas as pd
import numpy as np
frame = pd.DataFrame(np.random.randn(10, 5), columns=['a', 'b', 'c', 'd', 'e'])
print frame['a'].corr(frame['b'])
print frame.corr()
-0.383712785514
a b c d e
a 1.000000 -0.383713 -0.145368 0.002235 -0.104405
b -0.383713 1.000000 0.125311 -0.372821 0.224908
c -0.145368 0.125311 1.000000 -0.045661 -0.062840
d 0.002235 -0.372821 -0.045661 1.000000 -0.403380
e -0.104405 0.224908 -0.062840 -0.403380 1.000000
import pandas as pd
import numpy as np
s = pd.Series(np.random.np.random.randn(5), index=list('abcde'))
s['d'] = s['b'] # so there's a tie
print s.rank()
a 1.0
b 3.5
c 2.0
d 3.5
e 5.0
dtype: float64