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Python 2022-03-02 12:20:01
significant figures on axes plot matplotlib
import matplotlib.pyplot as plt import numpy as np from matplotlib.ticker import FormatStrFormatter fig, ax = plt.subplots() ax.yaxis.set_major_formatter(FormatStrFormatter('%g')) ax.yaxis.set_ticks(np.arange(-2, 2, 0.25)) x = np.arange(-1, 1, 0.1) plt... Add solution -
Python 2022-02-16 01:10:02
matplotlib plot two graphs side by side
import matplotlib.pyplot as plt import numpy as np # Simple data to display x = np.linspace(0, 2 * np.pi, 400) y = np.sin(x ** 2) # the container holding the two Axes have already been unpacked # useful if just few Axes have been created f, (ax1, ax2) ... Add solution -
Python 2022-02-15 20:00:08
how to plot side by side bar horizontal bar graph in python
import pandas import matplotlib.pyplot as plt import numpy as np df = pandas.DataFrame(dict(graph=['Item one', 'Item two', 'Item three'], n=[3, 5, 2], m=[6, 1, 3])) ind = np.arange(len(df)) width = 0.4 fig, ax = plt.subplots... Add solution -
Python 2022-02-09 19:35:06
pyplot second y axis
import numpy as np import matplotlib.pyplot as plt # Create some mock data t = np.arange(0.01, 10.0, 0.01) data1 = np.exp(t) data2 = np.sin(2 * np.pi * t) fig, ax1 = plt.subplots() color = 'tab:red' ax1.set_xlabel('time (s)') ax1.set_ylabel('exp', colo... Add solution -
Other 2022-02-02 19:45:38
matplotlib join axes
import matplotlib.pyplot as plt fig, axs = plt.subplots(ncols=3, nrows=3) gs = axs[1, 2].get_gridspec() # remove the underlying axes for ax in axs[1:, -1]: ax.remove() axbig = fig.add_subplot(gs[1:, -1]) axbig.annotate('Big Axes \nGridSpec[1:, -1]', ... Add solution -
Python 2022-01-23 08:15:31
pyplot rectangle over image
import matplotlib.pyplot as plt import matplotlib.patches as patches from PIL import Image im = Image.open('stinkbug.png') # Create figure and axes fig, ax = plt.subplots() # Display the image ax.imshow(im) # Create a Rectangle patch rect = patches.Re... Add solution -
Other 2021-11-21 21:57:18
PCA with covariance
import numpy as npimport pandas as pdimport seaborn as snsimport matplotlib.pyplot as plt# Load datasetdataset = pd.read_csv('src/dataset.csv')pca = PCA(dataset, standardize=True, method='eig')normalized_dataset = pca.transformed_data# Covariance Matrix# ... Add solution
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