Import matplotlib pyplot as plt
[DOCX File]INFORMATICS PRACTICES NEW (065) - CLASS XII - …
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import matplotlib.pyplot as plt . x=['JAN', 'FEB', 'MAR’, 'APR',’MAY’,’JUN’] y=[30,20,30,50,10,60] _____ Statement 1 _____ Statement 2. OR. Ms. Shalu wants to plot a Line Chart for the given set of values of months on x-axis and number of participants who attended workshop in particular month on y-axis. Complete the code to perform ...
[DOCX File]fenyolab.org
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Proteomics Informatics (Spring 2014): Week 9. Introductory Matplotlib hands-on > import matplotlib.pyplot as plt > plt.plot(range(1,11), range(11,21))
[DOCX File]matplotlib - GitHub Pages
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import matplotlib.pyplot as pltimport numpy as np ## we almost always use matplotlib with numpy. plotting basics (“hello, world” for plots) x = np.arange(5)plt.plot(x) showing/saving plots. if using Spyder (or PyCharm), plots might just show up. in Jupyter notebooks, put the magic %matplotlib inline in a code chunk to display plots. use plt ...
[DOC File]WordPress.com
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import matplotlib.pyplot as plt. import pandas as pd # Read Dataset. dataset=pd.read_csv("hours.csv") X=dataset.iloc[:,:-1].values. y=dataset.iloc[:,1].values # Import the Linear Regression and Create object of it. from sklearn.linear_model import LinearRegression. regressor=LinearRegression() regressor.fit(X,y) Accuracy=regressor.score(X, y)*100
[DOC File]dsrajnor.files.wordpress.com
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#import packages. import numpy as np. import matplotlib.pyplot as plt. import pandas as pd. #create dataset using DataFrame. df=pd.DataFrame({'X':[0.1,0.15,0.08,0.16 ...
[DOC File]Instructables
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import matplotlib.pyplot as plt. import RPi.GPIO as GPIO. import time. GPIO.setmode(GPIO.BCM) TRIG = 23 . ECHO = 24. print "Distance measurement in progress" GPIO.setup(TRIG,GPIO.OUT) GPIO.setup(ECHO,GPIO.IN) fig = plt.figure() plt.xlim([-400, 400]) plt.ylim([-400, 400]) while True: GPIO.output(TRIG, False) print "Waitng For Sensor To Settle ...
[DOC File]Assignment No
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import matplotlib.pyplot as plt . import seaborn as sns # use to set style of background of plot . sns.set(style ="whitegrid") # loading data-set . iris = sns.load_dataset('iris'); # plotting strip plot with seaborn # deciding the attributes of dataset on which plot should be made
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