Analysing tabular data

we are going to use a LIBRARY called numpy



In [1]:

    
import numpy



In [2]:

    
numpy.loadtxt(fname='data/weather-01.csv', delimiter = ',')









    Out[2]:





array([[ 0.,  0.,  1., ...,  3.,  0.,  0.],
       [ 0.,  1.,  2., ...,  1.,  0.,  1.],
       [ 0.,  1.,  1., ...,  2.,  1.,  1.],
       ..., 
       [ 0.,  1.,  1., ...,  1.,  1.,  1.],
       [ 0.,  0.,  0., ...,  0.,  2.,  0.],
       [ 0.,  0.,  1., ...,  1.,  1.,  0.]])

Variables



In [5]:

    
Weight_kg = 55



In [8]:

    
print (Weight_kg)



In [9]:

    
print('Weight in pounds:', Weight_kg * 2.2)









    



Weight in pounds: 121.00000000000001



In [10]:

    
Weight_kg = 57.5



In [12]:

    
print ('New weight: ', Weight_kg * 2.2)









    



New weight:  126.50000000000001



In [13]:

    
%whos









    



Variable    Type      Data/Info
-------------------------------
Weight_kg   float     57.5
Wight_kg    int       55
numpy       module    <module 'numpy' from '//a<...>kages/numpy/__init__.py'>



In [14]:

    
data = numpy.loadtxt(fname='data/weather-01.csv', delimiter = ',')



In [15]:

    
print (data)









    



[[ 0.  0.  1. ...,  3.  0.  0.]
 [ 0.  1.  2. ...,  1.  0.  1.]
 [ 0.  1.  1. ...,  2.  1.  1.]
 ..., 
 [ 0.  1.  1. ...,  1.  1.  1.]
 [ 0.  0.  0. ...,  0.  2.  0.]
 [ 0.  0.  1. ...,  1.  1.  0.]]



In [16]:

    
print (type(data))









    



<class 'numpy.ndarray'>



In [17]:

    
%whos









    



Variable    Type       Data/Info
--------------------------------
Weight_kg   float      57.5
Wight_kg    int        55
data        ndarray    60x40: 2400 elems, type `float64`, 19200 bytes
numpy       module     <module 'numpy' from '//a<...>kages/numpy/__init__.py'>



In [18]:

    
# Finding out the data type
print (data.dtype)









    



float64



In [19]:

    
# Find out the shape
print (data.shape)



In [20]:

    
# This is 60 rows * 40 columns



In [22]:

    
# Getting a single number out of the array
print ("First value in data: ", data [0, 0])









    



First value in data:  0.0

print ('A middle value: ', data[30, 30])



In [24]:

    
print ('A middle value: ', data[30, 20])









    



A middle value:  13.0



In [26]:

    
# Lets get the first 10 columns for the first 4 rows
print (data[0:4, 0:10])
# Start at index 0 and go up to BUT NOT INCLUDING index 4









    



[[ 0.  0.  1.  3.  1.  2.  4.  7.  8.  3.]
 [ 0.  1.  2.  1.  2.  1.  3.  2.  2.  6.]
 [ 0.  1.  1.  3.  3.  2.  6.  2.  5.  9.]
 [ 0.  0.  2.  0.  4.  2.  2.  1.  6.  7.]]



In [27]:

    
# We don't need to start slicing at 0
print (data [5:10, 7:15])









    



[[  1.   6.   4.   7.   6.   6.   9.   9.]
 [  5.   5.   8.   6.   5.  11.   9.   4.]
 [  3.   5.   3.   7.   8.   8.   5.  10.]
 [  5.   5.   8.   2.   4.  11.  12.  10.]
 [  3.   5.   8.   6.   8.  12.   5.  13.]]



In [28]:

    
# We don't even need to include the UPPER and LOWER bounds
smallchunk = data [:3, 36:]
print (smallchunk)









    



[[ 2.  3.  0.  0.]
 [ 1.  1.  0.  1.]
 [ 2.  2.  1.  1.]]



In [29]:

    
# Arithmetic on arrays
doublesmallchunk = smallchunk * 2.0



In [30]:

    
print (doublesmallchunk)









    



[[ 4.  6.  0.  0.]
 [ 2.  2.  0.  2.]
 [ 4.  4.  2.  2.]]



In [31]:

    
triplesmallchunk = smallchunk + doublesmallchunk



In [32]:

    
print (triplesmallchunk)









    



[[ 6.  9.  0.  0.]
 [ 3.  3.  0.  3.]
 [ 6.  6.  3.  3.]]



In [33]:

    
print (numpy.mean(data))



In [34]:

    
print (numpy.transpose(data))









    



[[ 0.  0.  0. ...,  0.  0.  0.]
 [ 0.  1.  1. ...,  1.  0.  0.]
 [ 1.  2.  1. ...,  1.  0.  1.]
 ..., 
 [ 3.  1.  2. ...,  1.  0.  1.]
 [ 0.  0.  1. ...,  1.  2.  1.]
 [ 0.  1.  1. ...,  1.  0.  0.]]



In [35]:

    
print (numpy.max(data))



In [36]:

    
print (numpy.min(data))

0.0



In [39]:

    
# Get a set of data for the first station
station_0 = data [0, :]



In [40]:

    
print (numpy.max(station_0))



In [41]:

    
# We don't need to create 'temporary' array slices
# We can refer to what we call array axes



In [47]:

    
# axis = 0 gets the mean DOWN each column, so the mean temperature for each recording period
print (numpy.mean(data, axis = 0))









    



[  0.           0.45         1.11666667   1.75         2.43333333   3.15
   3.8          3.88333333   5.23333333   5.51666667   5.95         5.9
   8.35         7.73333333   8.36666667   9.5          9.58333333
  10.63333333  11.56666667  12.35        13.25        11.96666667
  11.03333333  10.16666667  10.           8.66666667   9.15         7.25
   7.33333333   6.58333333   6.06666667   5.95         5.11666667   3.6
   3.3          3.56666667   2.48333333   1.5          1.13333333
   0.56666667]



In [48]:

    
# axis = 1 gets the mean ACROSS each row, so the mean temperature for each recording period
print (numpy.mean(data, axis = 1))









    



[ 5.45   5.425  6.1    5.9    5.55   6.225  5.975  6.65   6.625  6.525
  6.775  5.8    6.225  5.75   5.225  6.3    6.55   5.7    5.85   6.55
  5.775  5.825  6.175  6.1    5.8    6.425  6.05   6.025  6.175  6.55
  6.175  6.35   6.725  6.125  7.075  5.725  5.925  6.15   6.075  5.75
  5.975  5.725  6.3    5.9    6.75   5.925  7.225  6.15   5.95   6.275  5.7
  6.1    6.825  5.975  6.725  5.7    6.25   6.4    7.05   5.9  ]



In [49]:

    
# do some simple visualisations



In [50]:

    
import matplotlib.pyplot









    



//anaconda/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
//anaconda/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')



In [51]:

    
%matplotlib inline



In [52]:

    
image = matplotlib.pyplot.imshow(data)



In [53]:

    
# Let's look at the average temprature over time
avg_temperature = numpy.mean(data, axis = 0)



In [54]:

    
avg_plot = matplotlib.pyplot.plot(avg_temperature)

Tasks

Produce maximum and minimum plots of this data
What do you think?



In [59]:

    
max_temprature = numpy.max(data, axis = 0)
min_temprature = numpy.min(data, axis = 0)
max_plot = matplotlib.pyplot.plot(max_temprature)
min_plot = matplotlib.pyplot.plot(min_temprature)



In [58]:

    
min_p = numpy.min(data, axis = 0)
min_plot = matplotlib.pyplot.plot(min_p)



In [ ]: