PYTHON

python pip install matplotlib

pip install matplotlib

matplotlib install

pip3 install matplotlib

install matplotlib pip

// install matplotlib

pip install matplotlib

// using conda

conda install -c conda-forge matplotlib 

Use matplotlib in python

import matplotlib.pyplot as plt # Import the matplotlib module
from matplotlib import style # Optionally you dont need to use style
style.use('ggplot') # Just for style

x = [10, 20, 30, 40, 50] # Get points to plot on graph

plt.plot(x) # We want to plot x on our graph
plt.show()

matplotlib.pyplot

import matplotlib.pyplot as plt 
x=[0,10,20,30,60,90]
y=[-4.39,-4.69,-4.99,-5.30,-6.21,-7.13]
fig=plt.figure()
ax=fig.add_axes([0,0,1,1]) #grand
plt.plot(x,y)
plt.show()

python matplotlib

#install matplotlib
pip install matplotlib

matplotlib.pyplot

import numpy as np
import matplotlib.pyplot as plt

x = np.arange(0, 5, 0.1);
y = np.sin(x)
plt.plot(x, y)

python matplotlib

import matplotlib.pyplot as plt
import numpy as np

# Simple data to display in various forms
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)

fig, axarr = plt.subplots(2, 2)
fig.suptitle("This Main Title is Nicely Formatted", fontsize=16)

axarr[0, 0].plot(x, y)
axarr[0, 0].set_title('Axis [0,0] Subtitle')
axarr[0, 1].scatter(x, y)
axarr[0, 1].set_title('Axis [0,1] Subtitle')
axarr[1, 0].plot(x, y ** 2)
axarr[1, 0].set_title('Axis [1,0] Subtitle')
axarr[1, 1].scatter(x, y ** 2)
axarr[1, 1].set_title('Axis [1,1] Subtitle')

# Fine-tune figure;
# hide x ticks for top plots and y ticks for right plots

plt.setp([a.get_xticklabels() for a in axarr[0, :]], visible=False)
plt.setp([a.get_yticklabels() for a in axarr[:, 1]], visible=False)


# Tight layout often produces nice results
# but requires the title to be spaced accordingly

fig.tight_layout()
fig.subplots_adjust(top=0.88)

plt.show()

pip matplotlib

! pip install matplotlib

matplotlib.pyplot

import matplotlib.pyplot as plt 
import numpy as np 

# Generate pseudo-random numbers:
np.random.seed(0) 

# Sampling interval:    
dt = 0.01 

# Sampling Frequency:
Fs = 1 / dt  # ex[;aom Fs] 

# Generate noise:
t = np.arange(0, 10, dt) 
res = np.random.randn(len(t)) 
r = np.exp(-t / 0.05) 

# Convolve 2 signals (functions):
conv_res = np.convolve(res, r)*dt
conv_res = conv_res[:len(t)] 
s = 0.5 * np.sin(1.5 * np.pi * t) + conv_res

# Create the plot: 
fig, (ax) = plt.subplots() 
ax.plot(t, s) 
# Function plots phase spectrum:
ax.phase_spectrum(s, Fs = Fs)

plt.title(“Phase Spectrum Plot”)
plt.show()

matplotlib.pyplot

plt.plot([1, 2, 3, 4], [1, 4, 9, 16]) # plot x against y