In [1]:

    

from __future__ import division
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline


    

In [2]:

    

x = np.array([2,7,5])    # explicit vector creation
x


    

    
Out[2]:





array([2, 7, 5])

In [3]:

    

y = np.arange(4, 13, 3)  # vector creation from a sequence (start, stop, step)
y


    

    
Out[3]:





array([ 4,  7, 10])

In [4]:

    

x + y    # vectors can be added


    

    
Out[4]:





array([ 6, 14, 15])

In [5]:

    

x / y    # divided


    

    
Out[5]:





array([ 0.5,  1. ,  0.5])

In [6]:

    

x ** y    # exponentiated


    

    
Out[6]:





array([     16,  823543, 9765625])

In [7]:

    

x[1]    # vector elements can be selected by position


    

    
Out[7]:





7

In [8]:

    

x[1:3]  # multiple elements can be selected using slices


    

    
Out[8]:





array([7, 5])

In [9]:

    

x[-2]  # elements can be specified as offset from end


    

    
Out[9]:





7

In [10]:

    

x[np.array([0,1])]  # elements can be specified as an array


    

    
Out[10]:





array([2, 7])

In [11]:

    

Z = np.matrix(np.arange(1,13)).reshape((4, 3))
Z                 # note: R arranges the elements column-wise


    

    
Out[11]:





matrix([[ 1,  2,  3],
        [ 4,  5,  6],
        [ 7,  8,  9],
        [10, 11, 12]])

In [12]:

    

Z[2:4, 1:3]    # R is 1-based and includes ending index, Python is 0 based and does not.


    

    
Out[12]:





matrix([[ 8,  9],
        [11, 12]])

In [13]:

    

Z[:, 1:3]    # column slice


    

    
Out[13]:





matrix([[ 2,  3],
        [ 5,  6],
        [ 8,  9],
        [11, 12]])

In [14]:

    

Z.shape


    

    
Out[14]:





(4, 3)

In [15]:

    

x = np.random.uniform(0.0, 1.0, 50)
x


    

    
Out[15]:





array([ 0.65268288,  0.55438063,  0.12332376,  0.73656161,  0.172588  ,
        0.6952661 ,  0.36758793,  0.42034067,  0.54835434,  0.85493252,
        0.36666796,  0.14029571,  0.16910043,  0.18956811,  0.77016189,
        0.1914512 ,  0.48202498,  0.98936099,  0.43339457,  0.3690124 ,
        0.91976653,  0.42354589,  0.23428098,  0.24846295,  0.20409963,
        0.71024602,  0.57361799,  0.12510961,  0.70550836,  0.62120022,
        0.7089459 ,  0.67670746,  0.39836928,  0.17109468,  0.57367169,
        0.60584339,  0.72445482,  0.67991423,  0.94658844,  0.91068048,
        0.65489981,  0.05985463,  0.56797862,  0.67604148,  0.989085  ,
        0.79619792,  0.46406902,  0.91227664,  0.38043679,  0.04246386])

In [16]:

    

y = np.random.normal(0.0, 1.0, 50)
y


    

    
Out[16]:





array([ -5.95648785e-01,   1.54445571e+00,   1.35312925e+00,
         1.88123098e+00,  -3.80353324e-01,  -9.67551972e-04,
         5.97422145e-01,   3.76114342e-02,  -1.33779968e+00,
        -5.95407697e-01,   1.27206236e+00,  -1.70114410e+00,
        -1.84147571e+00,   7.16611666e-01,   2.33006825e+00,
        -6.56709283e-01,   8.26951067e-01,   4.64854930e-01,
         8.79290232e-01,  -7.42861574e-02,   4.88797828e-01,
         1.50860241e+00,   1.07467313e+00,  -7.99843607e-01,
         6.81719603e-02,   5.06424430e-01,   2.15770333e-01,
         1.67843398e+00,  -5.01086886e-01,   1.51407152e-01,
        -3.30648417e-02,  -8.50683506e-01,  -1.56531920e+00,
        -6.43438018e-01,  -9.55514605e-01,   1.51696803e-01,
        -9.87417666e-01,  -4.44820480e-01,   1.21902188e-01,
        -5.20474443e-01,   8.31544982e-01,  -6.75527626e-01,
         1.00537660e+00,   4.87948062e-01,   3.43123002e-01,
         3.95408496e-01,   4.27046930e-01,   3.69945068e-01,
         1.36728064e+00,  -3.15495180e-01])

In [17]:

    

fig, ax = plt.subplots()
plt.scatter(x, y)


    

    
Out[17]:





<matplotlib.collections.PathCollection at 0x420b410>






    

In [18]:

    

fig, ax = plt.subplots()
plt.xlabel("Random Uniform")
plt.ylabel("Random Normal")
plt.scatter(x, y, marker='o', color='red')  # plot customizations


    

    
Out[18]:





<matplotlib.collections.PathCollection at 0x4241f50>






    

In [19]:

    

plt.subplot(121)    # parameter indicates 1 rows, 2 col, first figure
plt.scatter(x, y)
plt.subplot(122)
plt.hist(y)


    

    
Out[19]:





(array([ 3.,  1.,  9.,  5.,  9.,  9.,  6.,  3.,  4.,  1.]),
 array([-1.84147571, -1.42432131, -1.00716692, -0.59001252, -0.17285812,
        0.24429627,  0.66145067,  1.07860506,  1.49575946,  1.91291385,
        2.33006825]),
 <a list of 10 Patch objects>)






    

In [20]:

    

# data comes from ISL book site: http://www-bcf.usc.edu/~gareth/ISL/data.html
auto_df = pd.read_csv("../data/Auto.csv")
auto_df.columns     # column names


    

    
Out[20]:





Index([u'mpg', u'cylinders', u'displacement', u'horsepower', u'weight', u'acceleration', u'year', u'origin', u'name'], dtype='object')

In [21]:

    

auto_df.shape    # number of rows, number of columns


    

    
Out[21]:





(397, 9)

In [22]:

    

type(auto_df)


    

    
Out[22]:





pandas.core.frame.DataFrame

In [23]:

    

auto_df.describe()  # equivalent of R's DataFrame.summary()


    

    
Out[23]:






  

    

      

      
mpg
      
cylinders
      
displacement
      
weight
      
acceleration
      
year
      
origin
    
  
  

    

      
count
      
 397.000000
      
 397.000000
      
 397.000000
      
  397.000000
      
 397.000000
      
 397.000000
      
 397.000000
    
    

      
mean
      
  23.515869
      
   5.458438
      
 193.532746
      
 2970.261965
      
  15.555668
      
  75.994962
      
   1.574307
    
    

      
std
      
   7.825804
      
   1.701577
      
 104.379583
      
  847.904119
      
   2.749995
      
   3.690005
      
   0.802549
    
    

      
min
      
   9.000000
      
   3.000000
      
  68.000000
      
 1613.000000
      
   8.000000
      
  70.000000
      
   1.000000
    
    

      
25%
      
  17.500000
      
   4.000000
      
 104.000000
      
 2223.000000
      
  13.800000
      
  73.000000
      
   1.000000
    
    

      
50%
      
  23.000000
      
   4.000000
      
 146.000000
      
 2800.000000
      
  15.500000
      
  76.000000
      
   1.000000
    
    

      
75%
      
  29.000000
      
   8.000000
      
 262.000000
      
 3609.000000
      
  17.100000
      
  79.000000
      
   2.000000
    
    

      
max
      
  46.600000
      
   8.000000
      
 455.000000
      
 5140.000000
      
  24.800000
      
  82.000000
      
   3.000000
    
  

8 rows × 7 columns

In [24]:

    

plt.ylabel("MPG")
auto_df.plot(x="cylinders", y="mpg", style='o')


    

    
Out[24]:





<matplotlib.axes.AxesSubplot at 0x46ef650>






    

In [25]:

    

auto_df.boxplot(column="mpg", by="cylinders") # MPG distribution by number of cylinders


    

    
Out[25]:





<matplotlib.axes.AxesSubplot at 0x499f510>






    

In [26]:

    

# similar to R pairs, shows correlation scatter plots between columns and distribution for each 
# column along the diagonal.
# The R version that uses formulas does not seem to have a Python equivalent (and doesn't seem
# to be very useful for exploratory analysis IMO).
axes = pd.tools.plotting.scatter_matrix(auto_df, color="brown")