This is my first jupyter notebook

In [2]:
import matplotlib.pyplot as plt
import numpy as np
In [7]:
x = np.linspace (0,2,100)
#y=x**2
y=2*x
fig, ax = plt.subplots()
#ax.plot (x,y)
ax.plot(x, y, color='red')  # Change 'red' to any color you want
ax.set_xlabel("x axis")
ax.set_ylabel("y axis")
plt.show()
No description has been provided for this image
In [4]:
x
Out[4]:
array([0.        , 0.02020202, 0.04040404, 0.06060606, 0.08080808,
       0.1010101 , 0.12121212, 0.14141414, 0.16161616, 0.18181818,
       0.2020202 , 0.22222222, 0.24242424, 0.26262626, 0.28282828,
       0.3030303 , 0.32323232, 0.34343434, 0.36363636, 0.38383838,
       0.4040404 , 0.42424242, 0.44444444, 0.46464646, 0.48484848,
       0.50505051, 0.52525253, 0.54545455, 0.56565657, 0.58585859,
       0.60606061, 0.62626263, 0.64646465, 0.66666667, 0.68686869,
       0.70707071, 0.72727273, 0.74747475, 0.76767677, 0.78787879,
       0.80808081, 0.82828283, 0.84848485, 0.86868687, 0.88888889,
       0.90909091, 0.92929293, 0.94949495, 0.96969697, 0.98989899,
       1.01010101, 1.03030303, 1.05050505, 1.07070707, 1.09090909,
       1.11111111, 1.13131313, 1.15151515, 1.17171717, 1.19191919,
       1.21212121, 1.23232323, 1.25252525, 1.27272727, 1.29292929,
       1.31313131, 1.33333333, 1.35353535, 1.37373737, 1.39393939,
       1.41414141, 1.43434343, 1.45454545, 1.47474747, 1.49494949,
       1.51515152, 1.53535354, 1.55555556, 1.57575758, 1.5959596 ,
       1.61616162, 1.63636364, 1.65656566, 1.67676768, 1.6969697 ,
       1.71717172, 1.73737374, 1.75757576, 1.77777778, 1.7979798 ,
       1.81818182, 1.83838384, 1.85858586, 1.87878788, 1.8989899 ,
       1.91919192, 1.93939394, 1.95959596, 1.97979798, 2.        ])
In [20]:
#calculate costs of sequencing 245Mbp at 2001 price, $10,000 per 1MPbg
#cost=10000.00
cost=0.001
bp1=248  #Mpbs fo chromosome 1
bp2 = 242
total_cost = cost*bp1 + cost*bp2
print(total_cost)

0.49
In [41]:
import pandas as pd

# Read the Excel file
data = pd.read_excel("CHrompose.xltx")  # For .xls, make sure 'xlrd' is installed
In [42]:
data
Out[42]:
chrmoosomes baspepiars
0 1 248956422
1 2 242193529
2 3 198295559
3 4 190214555
4 5 181538259
5 6 170805979
6 7 159345973
7 8 145138636
8 9 138394717
9 10 133797422
10 11 135086622
11 12 133275309
12 13 114364328
13 14 107043718
14 15 101991189
15 16 90338345
16 17 83257441
17 18 80373285
18 19 58617616
19 20 64444167
20 21 46709983
21 22 50818468
22 X 156040895
23 Y 57227415
In [55]:
#cost in $ to sequence 1Mega bps.
cost_2001 = 10000.00
cost_2011 = 0.10
cost_2021 = 0.01
In [46]:
#df['Sequencing_Cost'] = df['Length_Mbps'] * 10000 
data['squencing_cost_2001']=data['baspepiars']*cost_2001/1000000
In [47]:
data
Out[47]:
chrmoosomes baspepiars squencing_cost_2001
0 1 248956422 2489564.22
1 2 242193529 2421935.29
2 3 198295559 1982955.59
3 4 190214555 1902145.55
4 5 181538259 1815382.59
5 6 170805979 1708059.79
6 7 159345973 1593459.73
7 8 145138636 1451386.36
8 9 138394717 1383947.17
9 10 133797422 1337974.22
10 11 135086622 1350866.22
11 12 133275309 1332753.09
12 13 114364328 1143643.28
13 14 107043718 1070437.18
14 15 101991189 1019911.89
15 16 90338345 903383.45
16 17 83257441 832574.41
17 18 80373285 803732.85
18 19 58617616 586176.16
19 20 64444167 644441.67
20 21 46709983 467099.83
21 22 50818468 508184.68
22 X 156040895 1560408.95
23 Y 57227415 572274.15
In [48]:
data['squencing_cost_2011']=data['baspepiars']*cost_2011/1000000
In [49]:
data['squencing_cost_2021']=data['baspepiars']*cost_2021/1000000
In [50]:
data
Out[50]:
chrmoosomes baspepiars squencing_cost_2001 squencing_cost_2011 squencing_cost_2021
0 1 248956422 2489564.22 24.895642 2.489564
1 2 242193529 2421935.29 24.219353 2.421935
2 3 198295559 1982955.59 19.829556 1.982956
3 4 190214555 1902145.55 19.021455 1.902146
4 5 181538259 1815382.59 18.153826 1.815383
5 6 170805979 1708059.79 17.080598 1.708060
6 7 159345973 1593459.73 15.934597 1.593460
7 8 145138636 1451386.36 14.513864 1.451386
8 9 138394717 1383947.17 13.839472 1.383947
9 10 133797422 1337974.22 13.379742 1.337974
10 11 135086622 1350866.22 13.508662 1.350866
11 12 133275309 1332753.09 13.327531 1.332753
12 13 114364328 1143643.28 11.436433 1.143643
13 14 107043718 1070437.18 10.704372 1.070437
14 15 101991189 1019911.89 10.199119 1.019912
15 16 90338345 903383.45 9.033834 0.903383
16 17 83257441 832574.41 8.325744 0.832574
17 18 80373285 803732.85 8.037328 0.803733
18 19 58617616 586176.16 5.861762 0.586176
19 20 64444167 644441.67 6.444417 0.644442
20 21 46709983 467099.83 4.670998 0.467100
21 22 50818468 508184.68 5.081847 0.508185
22 X 156040895 1560408.95 15.604090 1.560409
23 Y 57227415 572274.15 5.722741 0.572274
In [51]:
total_cost_2001=data['squencing_cost_2001'].sum()
In [52]:
print(total_cost_2001)

30882698.32
In [53]:
total_cost_2011=data['squencing_cost_2011'].sum()
print(total_cost_2011)

308.8269832
In [54]:
total_cost_2021=data['squencing_cost_2021'].sum()
print(total_cost_2021)

30.882698320000003
In [ ]: