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import matplotlib pyplot as plt

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badman answered on March 15, 2020 Popularity 10/10 Helpfulness 3/10

answer import matplotlib pyplot as plt

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import matplotlib pyplot as plt

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from matplotlib import pyplot as plt
df.plot()

Popularity 10/10 Helpfulness 3/10 Language python

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Contributed on Dec 15 2020

badman

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import matplotlib.pyplot as plt

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from matplotlib import pyplot as plt

import matplotlib.pyplot as plt

Popularity 10/10 Helpfulness 10/10 Language python

Source: stackoverflow.com

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Contributed on Mar 15 2020

Foolish Flamingo

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import pyplot python

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import matplotlib.pyplot as plt

Popularity 10/10 Helpfulness 6/10 Language python

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Contributed on Apr 01 2020

Tremendous Enceladus

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import matplotlib.pyplot as plt

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import matplotlib.pyplot as plt
%matplotlib inline

Popularity 9/10 Helpfulness 4/10 Language python

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Contributed on Apr 10 2023

Manish Bhusal

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import matplotlib.pyplot as plt

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from matplotlib import pyplot as plt

import matplotlib.pyplot as plt1

print(dir(plt) == dir(plt1))
True

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Source: stackoverflow.com

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Contributed on Mar 15 2020

Foolish Flamingo

0 Answers Avg Quality 2/10

matplotlib.pyplot

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import numpy as np
import matplotlib.pyplot as plt

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

Popularity 10/10 Helpfulness 3/10 Language python

Source: matplotlib.org

Tags: python py

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Contributed on Jun 04 2020

Friendly Flatworm

0 Answers Avg Quality 2/10

matplotlib.pyplot

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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()

Popularity 10/10 Helpfulness 3/10 Language python

Source: Grepper

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Contributed on May 04 2022

Mouad En-naciry

0 Answers Avg Quality 2/10

matplotlib.pyplot

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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()

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Source: www.activestate.com

Tags: python py

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Contributed on Jun 04 2022

Funny Fly

0 Answers Avg Quality 2/10

matplotlib.pyplot

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

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Contributed on Apr 03 2020

Open Opossum

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import matplotlib.pyplot as plt

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The research team determined that polar bears existed on the southwestern margin of the Scandinavian Ice Sheet through the Late Glacial, surviving into the earliest Holocene (the present interglacial over the last 12,000 years). Paleoenvironmental proxies from marine sediments, such as single-celled foraminifera and diatomaceous algae, suggest summer temperatures in the region were 6–10°C.

Fossil polar bears survived last global warming deglaciation in Siberian and Canadian refugia 
Polar bear fossils across Scandinavia and the Bering Sea dating from 11,000-15,000 years ago during the Late Glacial and Holocene deglaciation, as well as modeled sea ice extent for a key period of reduced fossil occurrence coinciding with peak warmth. Credit: Seppä et al, 2023.
Continuing into the interglacial, sea ice cover in the arctic 8,000–10,000 years ago is modeled to be approximately comparable to present day. During the Holocene, this supported an exceptional assemblage of polar bears on Zhokhov Island off the coast of Russia, where at least 130 polar bear remains have been found, and is inferred to represent the maximum perennial sea ice extent at this time.

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Contributed on May 09 2024

Dawn Miller

0 Answers Avg Quality 2/10

import matplotlib pyplot as plt

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