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A small demo of some Statistical Functions in Python Pandas
Nimish Khurana
a year ago
Table of Content
- Percent_change
- Covariance
- Cov Series
- Correlation
- Data Ranking
Pandas is a well-known and widely used Python library for data
manipulation and analysis. It provides numerous methods and functions that
expedite data analysis and preprocessing steps. On top of that, pandas also provide
statistical functions that can be used to further understand the data.
Statistical
methods help in understanding and analyzing the behavior of data.
In
this article we will try to learn about a few statistical functions, which are
commonly used on Pandas objects.
Percent_change
Percent_change can be used with Series, DataFrames, and Panel. It
compares every element with its prior element and computes the change
percentage.
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()
Its the output is as follows −
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 -1.860434By
default, the pct_change() operates on columns; if you want to apply the same
row-wise, then use axis=1() argument.
Covariance
Covariance is applied
on series data. The Series object has a method cov to compute covariance
between series objects. NA will be excluded automatically.
Cov Series
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)Its the output is as follows −
-0.12978405324
Covariance method when
applied on a DataFrame, computes cov between all the columns.
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()Its the output is as follows −
-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 0.960761Note − Observe the cov
between a and b column in the first statement and the same is the value
returned by cov on DataFrame.
Correlation
Correlation
gives the linear relationship between any two arrays of values (series). There
are multiple methods to compute the correlation like Pearson(default), spearman, and Kendall.
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()Its the output is as follows −
-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 1.000000If
any non-numeric column is present in the DataFrame, it is excluded
automatically.
Data Ranking
Data Ranking produces a ranking for each element in the array of elements. In case of ties, assigns the
mean rank.
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()Its
output is as follows −
a 1.0
b 3.5
c 2.0
d 3.5
e 5.0
dtype: float64Rank
optionally takes a parameter ascending which by default is true; when false,
data is reverse-ranked, with larger values assigned a smaller rank.
Rank
supports different tie-breaking methods, specified with the method parameter −
- average − average the rank of tied group
- min − lowest rank in the group
- max − highest rank in the group
- first − ranks assigned in the order they appear in the array
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