Top 15 functions for Analytics in Python #python #rstats #analytics

Here is a list of top ten  fifteen functions for analysis in Python

  1. import (imports a particular package library in Python)
  2. getcwd (from os library) – get current working directory
  3. chdir (from os) -change directory
  4. listdir (from os ) -list files in the specified directory
  5. read_csv(from pandas) reads in a csv file
  6. objectname.info (like proc contents in SAS or str in R , it describes the object called objectname)
  7. objectname.columns (like proc contents in SAS or names in R , it describes the object variable names of the object called objectname)
  8. objectname.head (like head in R , it prints the first few rows in the object called objectname)
  9. objectname.tail (like tail in R , it prints the last few rows in the object called objectname)
  10. len (length)
  11. objectname.ix[rows] (here if rows is a list of numbers this     will give those rows (or index) for the object called objectname)
  12. groupby -group by a categorical variable
  13. crosstab -cross tab between two categorical variables
  14. describe – data analysis exploratory of numerical variables
  15. corr – correlation between numerical variables
In [1]:
import pandas as pd #importing packages
import os as os
In [2]:
os.getcwd() #current working directory
Out[2]:
'/home/ajay/Desktop'
In [3]:
os.chdir('/home/ajay/Downloads') #changes the working directory
In [4]:
os.getcwd()
Out[4]:
'/home/ajay/Downloads'
In [5]:
a=os.getcwd()
os.listdir(a) #lists all the files in a directory

In [105]:
diamonds=pd.read_csv("diamonds.csv")
#note header =0 means we take the first row as a header (default) else we can specify header=None
In [106]:
diamonds.info()
<class 'pandas.core.frame.dataframe'="">
Int64Index: 53940 entries, 0 to 53939
Data columns (total 10 columns):
carat      53940 non-null float64
cut        53940 non-null object
color      53940 non-null object
clarity    53940 non-null object
depth      53940 non-null float64
table      53940 non-null float64
price      53940 non-null int64
x          53940 non-null float64
y          53940 non-null float64
z          53940 non-null float64
dtypes: float64(6), int64(1), object(3)
memory usage: 3.9+ MB
In [36]:
diamonds.head()
Out[36]:
carat cut color clarity depth table price x y z
0 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
1 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
2 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
3 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
4 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
In [37]:
diamonds.tail(10)
Out[37]:
carat cut color clarity depth table price x y z
53930 0.71 Premium E SI1 60.5 55 2756 5.79 5.74 3.49
53931 0.71 Premium F SI1 59.8 62 2756 5.74 5.73 3.43
53932 0.70 Very Good E VS2 60.5 59 2757 5.71 5.76 3.47
53933 0.70 Very Good E VS2 61.2 59 2757 5.69 5.72 3.49
53934 0.72 Premium D SI1 62.7 59 2757 5.69 5.73 3.58
53935 0.72 Ideal D SI1 60.8 57 2757 5.75 5.76 3.50
53936 0.72 Good D SI1 63.1 55 2757 5.69 5.75 3.61
53937 0.70 Very Good D SI1 62.8 60 2757 5.66 5.68 3.56
53938 0.86 Premium H SI2 61.0 58 2757 6.15 6.12 3.74
53939 0.75 Ideal D SI2 62.2 55 2757 5.83 5.87 3.64
In [38]:
diamonds.columns
Out[38]:
Index([u'carat', u'cut', u'color', u'clarity', u'depth', u'table', u'price', u'x', u'y', u'z'], dtype='object')
In [92]:
b=len(diamonds) #this is the total population size
print(b)
53940
In [93]:
import numpy as np
In [98]:
rows = np.random.choice(diamonds.index.values, 0.0001*b)
print(rows)
sampled_df = diamonds.ix[rows]
[45653  7503 47794 12017 46125]
In [99]:
sampled_df
Out[99]:
carat cut color clarity depth table price x y z
45653 0.25 Ideal H IF 61.4 57 525 4.05 4.08 2.49
7503 1.05 Premium G SI2 61.3 58 4241 6.55 6.60 4.03
47794 0.71 Ideal J VS2 62.4 54 1899 5.72 5.76 3.58
12017 1.00 Premium F SI1 59.8 59 5151 6.55 6.49 3.90
46125 0.51 Ideal F VS1 61.7 54 1744 5.14 5.17 3.18
In [108]:
diamonds.describe()
Out[108]:
carat depth table price x y z
count 53940.000000 53940.000000 53940.000000 53940.000000 53940.000000 53940.000000 53940.000000
mean 0.797940 61.749405 57.457184 3932.799722 5.731157 5.734526 3.538734
std 0.474011 1.432621 2.234491 3989.439738 1.121761 1.142135 0.705699
min 0.200000 43.000000 43.000000 326.000000 0.000000 0.000000 0.000000
25% 0.400000 61.000000 56.000000 950.000000 4.710000 4.720000 2.910000
50% 0.700000 61.800000 57.000000 2401.000000 5.700000 5.710000 3.530000
75% 1.040000 62.500000 59.000000 5324.250000 6.540000 6.540000 4.040000
max 5.010000 79.000000 95.000000 18823.000000 10.740000 58.900000 31.800000
In [109]:
cut=diamonds.groupby("cut")
In [110]:
cut.count()
Out[110]:
carat color clarity depth table price x y z
cut
Fair 1610 1610 1610 1610 1610 1610 1610 1610 1610
Good 4906 4906 4906 4906 4906 4906 4906 4906 4906
Ideal 21551 21551 21551 21551 21551 21551 21551 21551 21551
Premium 13791 13791 13791 13791 13791 13791 13791 13791 13791
Very Good 12082 12082 12082 12082 12082 12082 12082 12082 12082
In [114]:
cut.mean()
Out[114]:
carat depth table price x y z
cut
Fair 1.046137 64.041677 59.053789 4358.757764 6.246894 6.182652 3.982770
Good 0.849185 62.365879 58.694639 3928.864452 5.838785 5.850744 3.639507
Ideal 0.702837 61.709401 55.951668 3457.541970 5.507451 5.520080 3.401448
Premium 0.891955 61.264673 58.746095 4584.257704 5.973887 5.944879 3.647124
Very Good 0.806381 61.818275 57.956150 3981.759891 5.740696 5.770026 3.559801
In [115]:
cut.median()
Out[115]:
carat depth table price x y z
cut
Fair 1.00 65.0 58 3282.0 6.175 6.10 3.97
Good 0.82 63.4 58 3050.5 5.980 5.99 3.70
Ideal 0.54 61.8 56 1810.0 5.250 5.26 3.23
Premium 0.86 61.4 59 3185.0 6.110 6.06 3.72
Very Good 0.71 62.1 58 2648.0 5.740 5.77 3.56
In [117]:
pd.crosstab(diamonds.cut, diamonds.color)
Out[117]:
color D E F G H I J
cut
Fair 163 224 312 314 303 175 119
Good 662 933 909 871 702 522 307
Ideal 2834 3903 3826 4884 3115 2093 896
Premium 1603 2337 2331 2924 2360 1428 808
Very Good 1513 2400 2164 2299 1824 1204 678
In [121]:
diamonds.corr()
Out[121]:
carat depth table price x y z
carat 1.000000 0.028224 0.181618 0.921591 0.975094 0.951722 0.953387
depth 0.028224 1.000000 -0.295779 -0.010647 -0.025289 -0.029341 0.094924
table 0.181618 -0.295779 1.000000 0.127134 0.195344 0.183760 0.150929
price 0.921591 -0.010647 0.127134 1.000000 0.884435 0.865421 0.861249
x 0.975094 -0.025289 0.195344 0.884435 1.000000 0.974701 0.970772
y 0.951722 -0.029341 0.183760 0.865421 0.974701 1.000000 0.952006
z 0.953387 0.094924 0.150929 0.861249 0.970772 0.952006 1.000000
 

Polyglots for Data Science #python #sas #r #stats #spss #matlab #julia #octave

In the future I think analysts need to be polyglots- you will need to know more than one language for crunching data.

SAS, Python, R, Julia,SPSS,Matlab- Pick Any Two ;) or Any Three.

No, you can’t count C or Java as a statistical  language :) :)

Efforts to promote Polyglots in Statistical Software are-

1) R for SAS and SPSS Users (free or book)

2) R for Stata Users (book)

3) SAS and R (blog and book)

4) Using Python and R together

Probably we need a Python and R for Data Analysis book- just like we have for SAS and R books.

5) Matlab   and R

Reference (http://mathesaurus.sourceforge.net/matlab-python-xref.pdf ) includes Python

5) Octave and R

package http://cran.r-project.org/web/packages/RcppOctave/vignettes/RcppOctave.pdf includes Matlab

reference http://cran.r-project.org/doc/contrib/R-and-octave.txt

6) Julia and python

  • PyPlot uses the Julia PyCall package to call Python’s matplotlib directly from Julia

7) SPSS and Python is here

8) SPSS and R is as below

  • The Essentials for R for Statistics versions 22, 21, 20, and 19 are available here.
  • This link will take you to the SourceForge site where the Version 18 Essentials and Plugins are hosted.

     

9) Using R from Clojure – Incanter

Use embedded R from Clojure and Incanter http://github.com/jolby/rincanter