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Analysing tabublar data

we are going to use a LIBRARY called numpy





In [14]:



    


import numpy



    











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



    


















    
Out[15]:








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



    











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print (weight_kg)



    


















    






57.5

















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



    


















    






Weight in pounds: 121.00000000000001

















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weight_kg = 57.5



    











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



    


















    






Weight in pounds: 126.50000000000001

















In [16]:



    


%whos



    


















    






Variable          Type       Data/Info
--------------------------------------
Max_plot          list       n=1
avg_plot          list       n=1
avg_temperature   ndarray    40: 40 elems, type `float64`, 320 bytes
data              ndarray    60x40: 2400 elems, type `float64`, 19200 bytes
matplotlib        module     <module 'matplotlib' from<...>/matplotlib/__init__.py'>
numpy             module     <module 'numpy' from '/Us<...>kages/numpy/__init__.py'>
station_0         ndarray    40: 40 elems, type `float64`, 320 bytes

















In [6]:



    


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



    











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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.]]

















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print (type(data))



    


















    






<class 'numpy.ndarray'>

















In [15]:



    


%whos



    


















    






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

















In [16]:



    


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



    


















    






float64

















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# Find out the shape
print (data.shape)



    


















    






(60, 40)

















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# This is 60 rows *40 columns



    











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



    


















    






First value in data: 0.0

















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



    


















    






a middle value: 13.0

















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# Lets get the first 10 columns for the first 4 rows
print (data [0:4, 0:10])
# start at index 0 and go upt 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 [22]:



    


# We don`t neet 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.]]

















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# We dont 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.]]

















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# Arithmetic on arrays
doublesmallchunk = smallchunk * 2.0



    











In [25]:



    


print (doublesmallchunk)



    


















    






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

















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triplesmallchunk = smallchunk + doublesmallchunk



    











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print (triplesmallchunk)



    


















    






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

















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print (numpy.mean (data))



    


















    






6.14875

















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print (numpy.max(data))



    


















    






20.0

















In [30]:



    


print (numpy.min(data))



    


















    






0.0

















In [20]:



    


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



    











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print (numpy.max (station_0))



    


















    






18.0

















In [33]:



    


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



    











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# Axis = 0 gets the mean Down each column, so the mean temperatura 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 [8]:



    


# Axis = 1 gets the mean Across each row, so the mean temperatura 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 [37]:



    


# Do some simple visualisations



    











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import matplotlib.pyplot



    











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%matplotlib inline



    











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image = matplotlib.pyplot.imshow(data)



    


















    
























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



    











In [25]:



    


avg_plot = matplotlib.pyplot.plot(avg_temperature)

Task:

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




In [27]:



    


Max_plot = matplotlib.pyplot.plot (numpy.max(data, axis =0))



    


















    
























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



    


















    
























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max_temp = numpy.max (data, axis=0)
min_temp = numpy.min (data, axis=0)



    











In [31]:



    


max_plot = matplotlib.pyplot.plot (max_temp)
min_plot = matplotlib.pyplot.plot (min_temp)



    


















    
























In [ ]:

Content source: chanitacr/sc-python

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