data_selectionOctober 19, 2022[1] # Data Selection.docx

data_selection
October 19, 2022
[1]: # Data Selection
[2]: import numpy as np
[3]: # This is weather data recorded in Memphis during summer
(June to September).
# Column 0: month
# Column 1: temperature in Farenheit
# Column 2: precipitation in inches
data = np.array([
[6, 70, 3],
[7, 75, 3],
[6, 85, 4],
[7, 90, 4],
[7, 91, 5],
[8, 85, 2],
[8, 87, 4],
[6, 83, 5],
[8, 77, 3],
[6, 69, 6],
[9, 68, 1],
[6, 80, 6],
[9, 65, 3],
[9, 75, 4],
[9, 80, 5]])
[4]: data.shape

[4]: (15, 3)
[5]: # Select the data for the row 0:
data[0, :]
# row_selection: 0
# column_selection: all
[5]: array([ 6, 70, 3])
1
[6]: # Select the data of column 2:
data[:, 2]
# row_selection: all
# column_selection: 2
[6]: array([3, 3, 4, 4, 5, 2, 4, 5, 3, 6, 1, 6, 3, 4, 5])
[7]: # Get the data for the first five rows.
data[0:5, :]
[7]: array([[ 6, 70, 3],
[ 7, 75, 3],
[ 6, 85, 4],
[ 7, 90, 4],
[ 7, 91, 5]])
[8]: # Get the data for the first five rows,
# and the first two columns.
data[0:5, 0:2]
[8]: array([[ 6, 70],
[ 7, 75],

[ 6, 85],
[ 7, 90],
[ 7, 91]])
[9]: # Get the data for the last two columns,
# and the first five rows.
data[0:5, 1:3]
[9]: array([[70, 3],
[75, 3],
[85, 4],
[90, 4],
[91, 5]])
[10]: # or can be written as
data[:5, 1:]
[10]: array([[70, 3],
[75, 3],
[85, 4],
[90, 4],
[91, 5]])
[11]: # or can be written as
data[:5, -2:]
2
[11]: array([[70, 3],
[75, 3],
[85, 4],
[90, 4],
[91, 5]])

[12]: # Get the last 4 rows
data[-4:, :]
[12]: array([[ 6, 80, 6],
[ 9, 65, 3],
[ 9, 75, 4],
[ 9, 80, 5]])
[13]: # Find the temperature values, and store them in a variable
temp = data[:, 1]
[14]: temp
[14]: array([70, 75, 85, 90, 91, 85, 87, 83, 77, 69, 68, 80, 65,
75, 80])
[15]: # Find the month values, and store them in a variable
month = data[:, 0]
[16]: month
[16]: array([6, 7, 6, 7, 7, 8, 8, 6, 8, 6, 9, 6, 9, 9, 9])
[17]: # Find the maximum temperature
np.max(temp)
[17]: 91
[18]: # Find the index (or position) of the maximum temperature
np.argmax(temp)
[18]: 4
[19]: # Find the month that corresponds to the maximum
temperature
data[np.argmax(temp), 0]

[19]: 7
[20]: m = np.argmax(temp)
data[m, 0]
[20]: 7
3
[21]: # boolean selection
[22]: # Find all the temperatures below 70 degrees
data[temp < 70, 1]
[22]: array([69, 68, 65])
[23]: # Find the months with temperatures below 70 degrees
data[temp < 70, 0]
[23]: array([6, 9, 9])
[24]: np.unique(data[temp < 70, 0])
[24]: array([6, 9])
[25]: # Find all the temperatures for the month of August
data[month == 8, 1]
[25]: array([85, 87, 77])
[26]: # Find the average temperature for August
np.average(data[month == 8, 1])

[26]: 83.0
[27]: # Find the temperatures above 80 for June
data[(month == 6) & (temp > 80), 1]
# & means and
[27]: array([85, 83])
[28]: # Find the temperatures for the months of June, July, and
August
data[month != 9, 1]
[28]: array([70, 75, 85, 90, 91, 85, 87, 83, 77, 69, 80])
[29]: data[(month == 6) | (month == 7) | (month == 8), 1]
[29]: array([70, 75, 85, 90, 91, 85, 87, 83, 77, 69, 80])
[30]: data[(month >= 6) & (month <= 8), 1]
[30]: array([70, 75, 85, 90, 91, 85, 87, 83, 77, 69, 80])
[31]: # Print the average temperature for each month:
for x in [6, 7, 8, 9]:
print(np.average(data[month == x, 1]))
4
77.4
85.33333333333333
83.0
72.0

[32]: # Find the average temperature for each month, and store
it in a list:
[np.average(data[month == x, 1]) for x in [6, 7, 8, 9]]
[32]: [77.4, 85.33333333333333, 83.0, 72.0]
[33]: month
[33]: array([6, 7, 6, 7, 7, 8, 8, 6, 8, 6, 9, 6, 9, 9, 9])
[34]: # Display the the data set sorted by month
data[month.argsort()]
[34]: array([[ 6, 70, 3],
[ 6, 85, 4],
[ 6, 83, 5],
[ 6, 69, 6],
[ 6, 80, 6],
[ 7, 75, 3],
[ 7, 90, 4],
[ 7, 91, 5],
[ 8, 85, 2],
[ 8, 87, 4],
[ 8, 77, 3],
[ 9, 68, 1],
[ 9, 65, 3],
[ 9, 75, 4],
[ 9, 80, 5]])
[35]: month.argsort?
Docstring:
a.argsort(axis=-1, kind=None, order=None)
Returns the indices that would sort this array.

Refer to `numpy.argsort` for full documentation.
See Also
--------
numpy.argsort : equivalent function
Type: builtin_function_or_method
5
[ ]:
6
vis_matplotlib
October 25, 2022
[1]: # data visualization
[2]: import numpy as np
[3]: weather = np.genfromtxt('weather_madrid.csv',
delimiter=',', skip_header=True,␣
↪filling_values=0.0)
[4]: weather
[4]: array([[1.997e+03, 1.000e+00, 1.000e+00, …, 6.000e+00,
0.000e+00,
2.290e+02],

[1.997e+03, 1.000e+00, 2.000e+00, …, 5.000e+00, 0.000e+00,
1.430e+02],
[1.997e+03, 1.000e+00, 3.000e+00, …, 6.000e+00, 0.000e+00,
2.560e+02],
…,
[2.015e+03, 1.200e+01, 2.900e+01, …, 5.000e+00, 0.000e+00,
1.900e+02],
[2.015e+03, 1.200e+01, 3.000e+01, …, 6.000e+00, 0.000e+00,
2.560e+02],
[2.015e+03, 1.200e+01, 3.100e+01, …, 6.000e+00, 0.000e+00,
3.130e+02]])
[5]: np.set_printoptions(suppress=True)
[6]: weather
[6]: array([[1997., 1., 1., …, 6., 0., 229.],
[1997., 1., 2., …, 5., 0., 143.],
[1997., 1., 3., …, 6., 0., 256.],
…,
[2015., 12., 29., …, 5., 0., 190.],
[2015., 12., 30., …, 6., 0., 256.],
[2015., 12., 31., …, 6., 0., 313.]])
[7]: weather[1, :] # row1
1
[7]: array([1997., 1., 2., 7., 3., 0., 6., 3., 0.,
100., 92., 71., 1007., 1003., 997., 10., 9., 4.,

26., 8., 47., 0., 5., 0., 143.])
[8]: import matplotlib.pyplot as plt
[9]: # Plot 1: Plot of monthly average temperatures.
[10]: month = weather[:, 1]
[11]: # step1: find average temperature for january
[12]: np.average(weather[month == 1, 4])
# since column 4 has the mean temperatures.
[12]: 5.688729874776387
[13]: # step 2: find the average temperature for all months, store
them in array Y.
[14]: for x in range(1, 13): # 1 in included in the range, 13 is
excluded.
print(x)
1
2
3
4
5
6
7
8
9
10
11
12
[15]: X = [x for x in range(1, 13)]

X
[15]: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
[16]: Y = [np.average(weather[month == x, 4]) for x in X]
Y
[16]: [5.688729874776387,
6.947069943289225,
10.14874551971326,
12.85925925925926,
16.667235494880547,
2
22.037366548042705,
25.074702886247877,
24.742556917688265,
20.49473684210526,
15.152801358234296,
9.196491228070176,
5.859083191850594]
[17]: # step 3: create a plot
[18]: plt.plot(X, Y)
[18]: [<matplotlib.lines.Line2D at 0x125d1dbbe80>]
[19]: plt.bar(X, Y)
[19]: <BarContainer object of 12 artists>
3

[20]: # Plot 2: Monthly minimum (blue), mean (green), and
maximum (red) recorded␣
↪temperatures.
[21]: X = [x for x in range(1, 13)]
X
[21]: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
[22]: Y_min = [np.min(weather[month == x, 5]) for x in X]
Y_mean = [np.average(weather[month == x, 4]) for x in X]
Y_max = [np.max(weather[month == x, 3]) for x in X]
[23]: plt.plot(X, Y_min, 'b')
plt.plot(X, Y_mean, 'g')
plt.plot(X, Y_max, 'r')
[23]: [<matplotlib.lines.Line2D at 0x125d27a1190>]
4
[24]: Y_min = [np.average(weather[month == x, 5]) for x in X]
Y_mean = [np.average(weather[month == x, 4]) for x in X]
Y_max = [np.average(weather[month == x, 3]) for x in X]
[25]: plt.plot(X, Y_min, 'b')
plt.plot(X, Y_mean, 'g')
plt.plot(X, Y_max, 'r')
[25]: [<matplotlib.lines.Line2D at 0x125d27fbe80>]

5
[26]: # Plot 3: Average yearly temperatures
[27]: # for the months:
# month = weather[:, 1]
# X = [x for x in range(1, 13)]
# Y = [np.average(weather[month == x, 4]) for x in X]
[28]: # for the years?
year = weather[:, 0]
# X = [x for x in range(1997, 2016)]
X = np.unique(year)
X.sort()
Y = [np.average(weather[year == x, 4]) for x in X]
[29]: plt.bar(X, Y)
[29]: <BarContainer object of 19 artists>
6
[30]: # Plot 4: Histogram of number of data points per
temperature interval
# visualize the distribution of the data
[31]: plt.hist(weather[:, 4], bins=5, ec='black')
[31]: (array([ 414., 2013., 1825., 1808., 752.]),
array([-3., 4., 11., 18., 25., 32.]),
<BarContainer object of 5 artists>)

7
[32]: # for example, we have about 2000 temperatures between
4 and 11 degrees (second␣
↪bar).
[ ]:
8
Let data be a dataset having 4 columns.
For example data =
87, 29, 10, 88
93, 95, 40, 67
55, 98, 27, 96
15, 34, 78, 23
12, 58, 11, 20
68, 65, 93, 11
...
Answer the following questions using Python commands.
Your answers must work on any dataset that has 4 columns and
not only on the one provided above.
(a) Select all of row 0: [87, 29, 10, 88].
I am giving you the answer to the first question:
data[0, :]
(b) Select all of column 3: [88, 67, 96, 23, 20, 11].
(c) Select all of rows 2, 3, and 4:
55, 98, 27, 96
15, 34, 78, 23
12, 58, 11, 20.
(d) Find the maximum value in column 3. For the given
example, the value is 96.
(e) Find the row index of the maximum value in column 3. For

the given example the output is 2.
(f) Select the entire row that contains the maximum value for
column 3. For the given example, the row is [55, 98, 27, 96].
(g) Store the values of column 1 in a variable named col1.
(h) Using the variable col1, select all the values in column 1
that are greater than 50: [95, 98, 58, 65].
(i) Using the variable col1, select all the values in column 1 that
are greater than 50 and smaller than 90: [58, 65].
(j) Display the entire data set sorted on column 1. For the given
example, the output is
87, 29, 10, 88
15, 34, 78, 23
12, 58, 11, 20
68, 65, 93, 11
93, 95, 40, 67
55, 98, 27, 96

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