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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 tabublar data
we are going to use a LIBRARY called numpy
Step1:  Variables
Step2:  Task
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Python Code:
import numpy
numpy.loadtxt(fname='data/weather-01.csv', delimiter = ',')
Explanation: Analysing tabublar 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 ('Weight in pounds:', 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 upt to But not including index 4
# We don`t neet to start slicing at 0
print (data[5:10, 7:15])
# We dont 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.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 temperatura for each recording period
print (numpy.mean (data, axis = 0))
# Axis = 1 gets the mean Across each row, so the mean temperatura 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 temperature over time
avg_temperature = numpy.mean (data, axis = 0)
avg_plot = matplotlib.pyplot.plot(avg_temperature)
Explanation: Variables
End of explanation
Max_plot = matplotlib.pyplot.plot (numpy.max(data, axis =0))
Min_plot = matplotlib.pyplot.plot (numpy.min(data, axis =0))
max_temp = numpy.max (data, axis=0)
min_temp = numpy.min (data, axis=0)
max_plot = matplotlib.pyplot.plot (max_temp)
min_plot = matplotlib.pyplot.plot (min_temp)
Explanation: Task:
    - Produce maximum and minimum plots of this data
    - What do you think?
End of explanation
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