<jupyter_start><jupyter_text>This is text* This is a list* More text<jupyter_code>import numpy
data = numpy.loadtxt(fname='data/inflammation-01.csv',delimiter=',')
print(data)
print(type(data))
print(data.shape)
print('other value in data:',data[30,20])
small = data[:3,36:]
print(small)
print(data)
doubledata = data * 2.0
print(doubledata)
tripledata = data + doubledata
print(tripledata)
print(numpy.mean(data))
maxval, minval, stdev = numpy.max(data), numpy.min(data), numpy.std(data)

print('Max inflammation:',maxval)
print('Min inflammation:',minval)
print('Standard Deviation:',stdev)
patient_2 = data[2,:]
print('max inflammation for patient 2:', numpy.max(patient_2))
print(numpy.max(data,axis=1))
print(numpy.max(data,1).shape)
help(numpy.max)
import matplotlib.pyplot
image = matplotlib.pyplot.imshow(data)
matplotlib.pyplot.show()
ave_inflammation = numpy.mean(data,axis=0)
ave_plot = matplotlib.pyplot.plot(ave_inflammation)
matplotlib.pyplot.show()
max_plot = matplotlib.pyplot.plot(numpy.max(data,axis=0))
matplotlib.pyplot.show()
min_plot = matplotlib.pyplot.plot(numpy.min(data,axis=0))
matplotlib.pyplot.show()
import numpy
import matplotlib.pyplot

#Comment
fig = matplotlib.pyplot.figure(figsize=(10.0,3.0))

axes1 = fig.add_subplot(1,3,1)
axes2 = fig.add_subplot(1,3,2)
axes3 = fig.add_subplot(1,3,3)

axes1.set_ylabel("average")
axes1.plot(numpy.mean(data,axis=0))

axes2.set_ylabel("min")
axes2.plot(numpy.min(data,axis=0))

axes3.set_ylabel("max")
axes3.plot(numpy.max(data,axis=0))

fig.tight_layout()

matplotlib.pyplot.show()
word = "oxygen"
word[0]
word[-1]
word[10]
for e in word:
    print(e)
for letter in word:
    print("hi")
vowels = 'aeiou'

no_of_vowels = 0
for char in vowels:
    no_of_vowels += 1
print(no_of_vowels)

for e in range(3,33,4):
    print(e)
ans = 1
for e in range(3):
    ans *= 5 
print(ans)

a = 'aei'
b = 'ou'
a+b
s = ''
w = "Newton"
w[5]
s = s + w[4]
s
s = ''
for e in 'Newton':
    s = e + s
print(s)
for i, x in enumerate(xs):
xs = 'oxygen'
for i, x in enumerate(xs):
    print(i, x)
x = 5
cc = [2, 4, 3]
x = 5
cc = [2, 4, 3]
y = cc[0] * x**0 + cc[1] * x**1 + cc[2] * x**2
y

odds = [1,3,5,7]
letters = ['a','b']
hetero = ['d', 2]
list_for_slicing = ["fluorine", "F", 
                    "chlorine", "Cl", 
                    "bromine", "Br", 
                    "iodine", "I", 
                    "astatine", "At"]
for e in range(1,len(list_for_slicing),2):
    print(list_for_slicing[e])
    
list_for_slicing
new_list = list_for_slicing
new_list
new_list[0]
new_list[1]
new_list[0] = 'lead'
new_list[1] = 'Pb'
new_list
print(list_for_slicing)
newest_list =list(new_list)
lst = [['lead','Pb'], ['chlorine', 'Cl'],['bromine','Br'],['iodine',
 'I'],['astatine','At']]
len(lst)
lst[3]
lst[3][0]
lst[3][1]
a = ['a']
a.append('b')
a

a = "hello"
l = []
for e in a:
    l.append(e)
print(l)
a = [1,2,3]
b = [4,5,6]
c=a+b
c

a
b
c
d = a*2
d<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18777739842949812, 'fraction_numerical': 0.033458518265619665, 'mean_word_length': 4.656370656370656, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '379194', 'license': 'CC-BY-4.0', 'n_tokens_mistral': 1363, 'n_tokens_neox': 1292, 'n_words': 259, 'path': 'files/day1.ipynb', 'repo_name': 'caltechlibrary/2019-01-22-Python-Workshop', 'size': '2929'}