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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Analysing tabular data
we are going to use a LIBRARY called numpy
Step1:  Variables
Step2:  Tasks
  * Produce maximum and minimum plots of this data
  * What do you think?
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Python Code:
import numpy
numpy.loadtxt(fname='data/weather-01.csv', delimiter = ',')
Explanation: Analysing tabular data
we are going to use a LIBRARY called numpy
End of explanation
Weight_kg = 55
print (Weight_kg)
print('Weight in pounds:', Weight_kg * 2.2)
Weight_kg = 57.5
print ('New weight: ', Weight_kg * 2.2)
%whos
data = numpy.loadtxt(fname='data/weather-01.csv', delimiter = ',')
print (data)
print (type(data))
%whos
# Finding out the data type
print (data.dtype)
# Find out the shape
print (data.shape)
# This is 60 rows * 40 columns
# Getting a single number out of the array
print ("First value in data: ", data [0, 0])
print ('A middle value: ', data[30, 20])
# 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
# We don't need to start slicing at 0
print (data [5:10, 7:15])
# We don't even need to include the UPPER and LOWER bounds
smallchunk = data [:3, 36:]
print (smallchunk)
# Arithmetic on arrays
doublesmallchunk = smallchunk * 2.0
print (doublesmallchunk)
triplesmallchunk = smallchunk + doublesmallchunk 
print (triplesmallchunk)
print (numpy.mean(data))
print (numpy.transpose(data))
print (numpy.max(data))
print (numpy.min(data))
# Get a set of data for the first station
station_0 = data [0, :]
print (numpy.max(station_0))
# We don't need to create 'temporary' array slices
# We can refer to what we call array axes
# axis = 0 gets the mean DOWN each column, so the mean temperature for each recording period
print (numpy.mean(data, axis = 0))
# axis = 1 gets the mean ACROSS each row, so the mean temperature for each recording period
print (numpy.mean(data, axis = 1))
# do some simple visualisations
import matplotlib.pyplot
%matplotlib inline
image = matplotlib.pyplot.imshow(data)
# Let's look at the average temprature over time
avg_temperature = numpy.mean(data, axis = 0)
avg_plot = matplotlib.pyplot.plot(avg_temperature)
Explanation: Variables
End of explanation
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)
min_p = numpy.min(data, axis = 0)
min_plot = matplotlib.pyplot.plot(min_p)
Explanation: Tasks
  * Produce maximum and minimum plots of this data
  * What do you think?
End of explanation
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