The problem with your question has nothing to do with programming, or JavaScript, or D3… the problem is a basic dataviz concept (that’s why I added the data-visualization tag in your question): What you’re trying to do is not correct! You should not use bars with a time scale. Time scales are for time series … Read more
I find this hybrid boxplot very, very lovely so I wanted to recreate it too. I wrote a geom_boxjitter that inherits from geom_boxplot and only adds minor changes: It draws the geom_rect only on the left half. It jitters the points with default width of the right half, default height 0.4*resolution and can also take … Read more
You can do this by making a twinx axes for the frequencies. You can switch the two y axes around so the frequencies stay on the left and the counts on the right, but without having to recalculate the counts axis (here we use tick_left() and tick_right() to move the ticks and set_label_position to move … Read more
You can use pyplot.matshow() from matplotlib: import matplotlib.pyplot as plt plt.matshow(dataframe.corr()) plt.show() Edit: In the comments was a request for how to change the axis tick labels. Here’s a deluxe version that is drawn on a bigger figure size, has axis labels to match the dataframe, and a colorbar legend to interpret the color scale. … Read more
If you want a histogram, you don’t need to attach any ‘names’ to x-values because: on x-axis you will have data bins on y-axis counts (by default) or frequencies (density=True) import matplotlib.pyplot as plt import numpy as np %matplotlib inline np.random.seed(42) x = np.random.normal(size=1000) plt.hist(x, density=True, bins=30) # density=False would make counts plt.ylabel(‘Probability’) plt.xlabel(‘Data’); Note, … Read more
Edit: added spline interpolation for smoother transitions, without making rank changes happen too fast. Code at bottom. I’ve adapted an answer of mine to a related question. I like to use geom_tile for animated bars, since it allows you to slide positions. I worked on this prior to your addition of data, but as it … Read more
Try this, monthly_mean.plot(y=’A’, use_index=True)
You can create this sort of plot yourself pretty easily using the built-in functions imagesc and text and adjusting a number of parameters for the graphics objects. Here’s an example: mat = rand(5); % A 5-by-5 matrix of random values from 0 to 1 imagesc(mat); % Create a colored plot of the matrix values colormap(flipud(gray)); … Read more
Use ax.xaxis.tick_top() to place the tick marks at the top of the image. The command ax.set_xlabel(‘X LABEL’) ax.xaxis.set_label_position(‘top’) affects the label, not the tick marks. import matplotlib.pyplot as plt import numpy as np column_labels = list(‘ABCD’) row_labels = list(‘WXYZ’) data = np.random.rand(4, 4) fig, ax = plt.subplots() heatmap = ax.pcolor(data, cmap=plt.cm.Blues) # put the major … Read more
From the documentation for seaborn.distplot, which has been DEPRECATED in seaborn 0.11. .distplot is replaced with the following: displot(), a figure-level function with a similar flexibility over the kind of plot to draw. This is a FacetGrid, and does not have the ax parameter. histplot(), an axes-level function for plotting histograms, including with kernel density … Read more