Feb-14-2022, 07:48 PM
This is just an Exercise and I did it but I have some errors. I attached my file. Could you help me, please?
Write a Python program through the following steps:
1. Create the variables x and y like this:
3. Create two array of random numbers using np.random package:
data_rnd_1 : shape=100-by-2
data_rnd_2 : shape=120-by-2
4. Create a figure of size 6x9 with two subplots, top and bottom.
5. Consider the rows of data_rnd_1 and data_rnd_2 as two-dimensional points, and daw the scatter plot of them (first column versus the second column) in the first and the second subplot, respectively. The markers of the subplots should be "*" and ".", respectively. The colors of the data points in the subplots should be green and red, respectively.
6. Add labels for both x and y axes of the subplots as "dimension 1" and "dimension 2", respectively.
7. Show the plot on the screen.
Write a Python program through the following steps:
1. Create the variables x and y like this:
import numpy as np # Create 100 evenly-spaced points between -4 and 10 x = np.linspace(-4,10,100) y = -0.5*x**2 + 2*x - 32. Draw the curve of y versus x . The curve should be in red color, with a line width of 2, and in dashed-line style.
3. Create two array of random numbers using np.random package:
data_rnd_1 : shape=100-by-2
data_rnd_2 : shape=120-by-2
4. Create a figure of size 6x9 with two subplots, top and bottom.
5. Consider the rows of data_rnd_1 and data_rnd_2 as two-dimensional points, and daw the scatter plot of them (first column versus the second column) in the first and the second subplot, respectively. The markers of the subplots should be "*" and ".", respectively. The colors of the data points in the subplots should be green and red, respectively.
6. Add labels for both x and y axes of the subplots as "dimension 1" and "dimension 2", respectively.
7. Show the plot on the screen.
import numpy as np x = np.linspace(-5, 5, 100) y = 4 * (x**3) + 2 * (x**2) + 5 * x import matplotlib.pyplot as plt plt.plot(x, y, color='red', linestyle='--', linewidth=2) plt.show()
Output:import numpy as np
rand_int_1 = np.random.randn(100,2)
print("First array", rand_int_1)
rand_int_2 = np.random.randn(120,2)
print("Second array", rand_int_2)Output:First array [[ 1.49640185 -0.66136227]
[ 1.60592569 -1.00050129]
[ 0.3692072 -0.65355689]
[ 1.17201832 -0.64600418]
[-0.0502125 -0.52215024]
[-2.00133328 0.37088889]
[-1.35206998 -0.82313078]
[-0.80944694 0.77050384]
[-1.45875423 0.88411917]
[ 0.41081153 -0.37732382]
[ 1.43853755 0.25879336]
[-0.16880245 0.65282008]
[ 0.83117115 1.45340928]
[ 0.748778 0.45377505]
[ 0.58926665 0.25232723]
[ 0.53539355 0.41047299]
[ 0.56356845 -0.07005123]
[-1.49576535 -0.21530849]
[ 0.11826929 0.04476837]
[-0.99748351 0.08577806]
[ 1.74663997 -0.91651593]
[-0.08709894 -0.54249125]
[ 1.51059814 -0.7595974 ]
[ 0.44316279 -0.25325591]
[ 0.59857887 0.76817626]
[ 0.82461033 1.49741473]
[ 0.84859489 -0.34372826]
[ 0.49569524 0.23631009]
[ 0.63617553 0.37383413]
[-0.70796829 -1.35339274]
[ 0.4191391 1.08997109]
[-1.52265186 -0.69733364]
[-1.43614787 -1.28475393]
[-1.7990606 -0.96242155]
[-1.23909522 -0.85365844]
[ 0.97573238 -0.25087961]
[ 0.07665907 1.59519797]
[ 0.48247256 0.33283678]
[-1.97240187 1.60911192]
[ 0.11598151 -0.66755312]
[-0.42564187 0.94075559]
[ 0.28498309 0.43170389]
[-1.27016193 1.61724424]
[-0.85347036 0.66329169]
[-0.17967041 0.52705415]
[-1.24546743 -0.4993504 ]
[-2.27932601 -0.5873414 ]
[ 2.05634685 0.73010786]
[ 0.26655388 -0.57670283]
[ 1.99989822 0.03111458]
[ 0.24318088 -0.84504788]
[-2.30511512 -1.4931346 ]
[ 0.29199263 -1.08535811]
[-2.53109714 0.43907014]
[ 0.16005339 1.08404753]
[-0.6402196 0.52055492]
[-0.65396027 -0.00411722]
[-0.67409229 0.76800164]
[ 1.33194302 -0.38805457]
[ 0.6037552 0.33065125]
[-0.44111077 -1.17897769]
[-1.3827073 -0.5541841 ]
[-0.66629365 0.8548909 ]
[ 1.0789364 -0.43969718]
[-1.34002559 -0.76229719]
[ 1.62977397 0.36395259]
[ 0.87560937 -1.37508597]
[-0.91214153 0.5192275 ]
[-0.1313885 -1.99834874]
[-0.80740125 0.33509786]
[ 0.90573347 -1.15800689]
[ 0.73091749 -0.11494194]
[-0.95207499 1.70390183]
[-0.7807826 1.18661921]
[-0.08465479 -0.80179412]
[ 0.9801296 -0.08572493]
[-1.75606103 1.86116148]
[ 0.76398521 -1.5602368 ]
[ 1.62196017 -1.43663579]
[ 0.48218422 1.98186682]
[-0.59215991 -0.68954956]
[-1.62845565 0.87886794]
[-0.59446473 -0.53331269]
[ 0.18880219 0.63195542]
[ 0.56976069 -0.25107048]
[ 0.54849514 1.30694834]
[-0.52600658 1.91312649]
[ 0.25563035 2.56027139]
[ 0.33194719 0.57458993]
[-1.94070096 -0.31473919]
[ 0.26501133 -0.33709175]
[ 0.12532544 -0.20618611]
[-0.48312128 -1.25242019]
[ 1.30900889 -1.59669083]
[-0.01568461 -0.60254162]
[-0.04270708 2.54644392]
[ 1.38063447 -1.76322973]
[ 0.09442477 0.51040657]
[ 0.59917315 -0.21247956]
[ 0.1541758 -0.11325782]]
Second array [[ 7.48099918e-01 1.18264847e+00]
[-2.23133766e+00 -1.55762057e+00]
[-1.04462058e+00 -5.49467922e-01]
[-1.53862283e+00 -3.64846910e-01]
[-7.01715860e-01 -1.42459432e+00]
[-8.19690224e-01 -2.91201820e-01]
[-5.03466555e-01 -2.58572249e-01]
[ 7.62518615e-01 -3.56013289e-01]
[ 6.10867429e-01 -1.07932615e+00]
[-6.21378241e-01 2.03929061e+00]
[-1.15158641e+00 1.29730015e+00]
[-2.29112373e-01 1.28870394e+00]
[-1.77705956e+00 6.85668134e-01]
[ 1.69873754e+00 -8.06340949e-02]
[ 8.47773387e-01 8.19588115e-01]
[-7.90721724e-01 -1.22740598e+00]
[ 1.43057966e+00 1.03234732e+00]
[-2.65905077e-01 2.04453714e+00]
[ 1.29153883e+00 1.23231405e+00]
[ 1.11357029e+00 3.85680576e-02]
[-1.28873166e+00 7.99790974e-01]
[ 1.10954953e-01 -7.59981814e-02]
[-2.08343205e-01 7.76529485e-01]
[ 1.05367293e+00 1.30709057e+00]
[-1.67357727e+00 -4.38357319e-01]
[-5.99401237e-01 -1.20915798e+00]
[-1.45236570e+00 -2.41843817e-01]
[ 1.52202091e+00 1.20424557e+00]
[-2.03248245e-01 9.22083018e-01]
[ 7.49849753e-01 7.70107253e-01]
[-3.62262805e-01 -4.93539605e-02]
[ 5.92774845e-01 1.71538503e-01]
[-2.66668020e+00 -7.07788807e-01]
[ 6.48773495e-01 5.08520679e-01]
[-1.49729586e+00 1.51709189e-02]
[ 1.37079326e+00 2.04452656e+00]
[-1.50460691e+00 -5.03481521e-01]
[ 3.21198281e-02 -5.62401088e-01]
[-7.33982834e-02 -1.42651619e+00]
[ 1.51023944e-01 7.19347571e-01]
[ 1.07714394e+00 1.21259936e+00]
[ 1.11437253e+00 -1.04650749e+00]
[ 1.60128628e-02 -7.42315320e-02]
[ 8.87893818e-01 -9.15525007e-01]
[-2.02196405e+00 7.76996294e-01]
[-7.56922975e-01 -1.79488795e+00]
[-3.89765080e-01 8.43555054e-01]
[ 8.56078990e-01 -2.51927290e+00]
[-3.57355256e-01 2.24793154e-01]
[-1.33983095e+00 7.72415983e-01]
[-7.69958902e-01 2.43881809e-02]
[ 1.23317128e+00 2.66524847e-01]
[-1.11911996e+00 -1.00941556e+00]
[ 1.06888481e+00 -7.52488916e-01]
[ 1.60011708e+00 -2.34582115e-01]
[-1.43613752e+00 2.32089575e-01]
[ 2.76827686e+00 4.64236811e-02]
[-5.27535636e-01 -7.75962187e-01]
[-1.12495302e+00 -3.48897817e-01]
[-5.97493581e-01 2.84369327e-01]
[ 3.45347914e-01 1.16839513e+00]
[-1.54558688e+00 2.49992131e-01]
[-2.11190805e+00 5.67828308e-01]
[-5.00570911e-01 1.34624679e-01]
[ 7.30058096e-01 -1.38506744e+00]
[ 2.00731322e-02 1.13366903e-03]
[-7.94813873e-01 -1.46685836e+00]
[-8.49770854e-01 -1.94732677e+00]
[ 8.68067635e-02 -7.13487927e-01]
[ 1.01843956e+00 -1.62252298e-01]
[-2.62704850e-04 -8.52242843e-01]
[ 4.52246355e-01 8.36870515e-01]
[ 9.50844740e-01 1.46941955e+00]
[ 1.90165032e-02 3.00832009e-01]
[-2.62871813e-01 -4.76389989e-01]
[-5.45010309e-01 5.07698362e-01]
[-2.14740421e+00 1.74494787e-01]
[-6.58151595e-01 -1.02180818e-03]
[-1.09019280e+00 -1.11610784e+00]
[ 3.31202122e-01 1.75803925e-03]
[ 9.27351637e-01 -3.54472878e-01]
[ 1.85478899e+00 -8.68877729e-01]
[-9.51952893e-01 -7.13536855e-01]
[ 6.93507185e-01 -7.70291182e-02]
[-1.32085755e-01 5.72530157e-01]
[ 5.92167545e-01 7.65998597e-01]
[ 6.53578237e-03 2.91517827e-01]
[-1.66554376e+00 4.16894866e-01]
[ 1.70570182e+00 -4.39818760e-01]
[ 9.93463550e-01 1.80889097e-01]
[-1.54201625e+00 3.60513419e-01]
[ 6.19968519e-02 6.84228241e-01]
[ 8.40418524e-01 4.45707454e-02]
[ 7.77167702e-01 -4.71110263e-01]
[-1.28971360e+00 -8.92542205e-02]
[ 7.79037778e-01 4.24205626e-01]
[ 1.36277668e+00 1.64887445e+00]
[-6.02546972e-02 5.91612964e-01]
[ 1.12119654e+00 2.18273688e-01]
[ 6.08112346e-01 1.57850160e+00]
[-3.67369089e-02 1.59761060e+00]
[-2.99789258e-01 3.80572874e-01]
[ 1.77598813e+00 -2.82818418e-01]
[-8.06582150e-01 4.00439731e-01]
[-8.00358430e-01 -1.11907538e+00]
[-1.22356349e+00 2.02790064e+00]
[ 4.73654124e-01 3.28665793e-01]
[-3.96535159e-01 -3.15869916e+00]
[ 2.24847589e-01 -3.27497295e-01]
[-3.85249076e-01 -6.02969593e-01]
[-9.06580248e-01 -5.77946901e-01]
[ 5.30289533e-01 6.27977318e-01]
[-9.97598790e-02 1.80817464e+00]
[ 5.46139734e-01 3.01609168e-01]
[ 4.08523590e-01 -4.94147030e-01]
[-9.44716490e-01 3.01617405e-01]
[ 3.52401157e-01 -8.57517147e-01]
[-9.81752356e-01 2.47824413e+00]
[-1.60518987e+00 1.20014542e+00]
[-5.60320176e-02 -4.19654462e-01]]import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(6, 9))
X = np.arange(-5, 5.0, 0.05)
fig, ax = plt.subplots(2,
sharex='col', sharey='row')
ax[0].plot(X, f(X), 'bo', X, f(X), 'k')
ax[0].set(title='The function f')
ax[1].plot(X, fp(X), 'go', X, fp(X), 'k')
ax[1].set(xlabel='X Values', ylabel='Y Values',
title='Derivative Function of f')
plt.show()Output:import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(6, 9))
X = np.arange(-5, 5.0, 0.05)
fig, ax = plt.subplots(2,
sharex='col', sharey='row')
ax[0].plot(X, f(X), 'r', X, f(X), '*')
ax[0].set(title='The function f')
ax[1].plot(X, fp(X), 'g', X, fp(X), '.')
ax[1].set(xlabel='X Values', ylabel='Y Values',
title='Derivative Function of f')
plt.scatter()
plt.show()Error:NameError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_124/2710991408.py in <module>
8 sharex='col', sharey='row')
9
---> 10 ax[0].plot(X, f(X), 'r', X, f(X), '*')
11 ax[0].set(title='The function f')
12
NameError: name 'f' is not defined
<Figure size 432x648 with 0 Axes>import numpy as np import matplotlib.pyplot as plt x , y = np.random.rand(100,2) fig,axes=plt.subplots(ncols=2) sc1 = axes[0].scatter(x,y, marker="*", color="r") sc2 = axes[1].scatter(x,y, marker=".", color="g") axes[0].set(xlabel="dimension 1", ylabel="dimension 2") axes[1].set(xlabel="dimension 1", ylabel="dimension 2") axes[0].legend([sc1], ["Admitted"]) axes[1].legend([sc2], ["Not-Admitted"]) plt.show()
Error:ValueError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_124/997060509.py in <module>
2 import matplotlib.pyplot as plt
3
----> 4 x , y = np.random.rand(100,2)
5
6 fig,axes=plt.subplots(ncols=2)
ValueError: too many values to unpack (expected 2)