![]() Resmap = ax2.scatter(X,Y, c=data, cmap="YlGnBu",edgecolors='none',alpha=0.5)Ĭax1 = divider1.append_axes("right", size="5%", pad=0.05)Ĭax2 = divider2.append_axes("right", size="5%", pad=0.05)Īnd btw, fig = plt.figure(figsize=(10,5)) produces a rectangle, while fig = plt.figure(figsize=(20,20)) produces a square. import matplotlib.pyplot as pltįrom mpl_toolkits.axes_grid1 import make_axes_locatableĭata = np.sin(X/2.3)*np.cos(Y/2.7)*np.cos(X*Y/20.) One step at a time, until you find the piece of code that causes problems. So start from there and add the stuff you might need accordingly. The following code shows how you would do that and it acutally shows no difference in size of the two plots. If you apply everything to both plots simultaneously, how can they be different after all? You are looking for the difference between two things - so make them as equal as possible. What you need to do is break down the problem. You will find out that producing such a minimal working example, almost always makes you find the problem and a corresponding solution yourself.Īs we do not have the necessary knowledge of your data and the variables you are using, it is almost impossible to come up with a solution. ![]() In order to get help, you need to provide a minimal working example. Si this makes me think there is some margin around the scatter.Īlso, is there a way I could make the whole PNG file not so square? Could it be a rectangle? Actually, it's supposed to be as big as its colorbar, but that doesn't work either. Resmap = plt.scatter(xs,ys, c=data,cmap=cm,edgecolors='none',alpha=0.5,s=data)īut I find no way of making the scatter plot as big as the heatmap. Then the ax is passed over to another function to plot the little blue lines on the map: def plot_chosen(edges,endnodes,side,ax):Īx.plot(,, 'k-', lw=4, color='blue',alpha=0.5)įinally, I plot the scatter like this def plot_satter(edges,endnodes,side,xs,ys,data):Īx.set_yticks(np.arange(side) + 0.5, minor=False)Īx.set_xticks(np.arange(side) + 0.5, minor=False)Īx.set_xticklabels(range(0,side), minor=False)Īx.set_yticklabels(range(0,side), minor=False) # note I could have used lumns but made "labels" insteadĪx.set_xticklabels(range(0,matrix.shape), minor=False)Īx.set_yticklabels(range(0,matrix.shape), minor=False)Ĭax = divider.append_axes("right", size="5%", pad=0.05) # want a more natural, table-like display # put the major ticks at the middle of each cellĪx.set_yticks(np.arange(matrix.shape) + 0.5, minor=False)Īx.set_xticks(np.arange(matrix.shape) + 0.5, minor=False) The way I generate the heatmap is: def plot_map(matrix):Ĭm = make_colormap() Instead, my right one (which is a scatter plot) has some margins that make it appear slightly smaller than the left (a heatmap). I managed to make them stay next to each other, but I need them to have the exact same size: each point in the right one should be easily mapped to a location on the left one with the naked eye. If you are just starting out with Data Visualization, you might also want to look into our tutorial on defining axis limits in Seaborn.I am having trouble with matplotlib in Python trying to create two plots side by side. If setting your figsize correctly as per one of the methods above doesn’t do the tricvk, i would recommend that you save, close and re-open Jupyter Notebooks. The above mentioned procedures work for other Seaborn charts such as line, barplots etc’. Heat = sns.heatmap(subset, annot=True, fmt= '.2f' ) Here’s a simple snippet of the code you might want to use: fig, heat = plt.subplots(figsize = (11,7)) ![]() Using similar technique, you can also reset an heatmap Plt.rcParams = (11,7) Changing Seaborn heatmap size Otherwise, you’ll get the name ‘plt’ is not defined error. Make sure that you have imported Matplotlib beforehahnd. Instead of setting the size of your individual plots, you can simply use the runtime configuration of Matplotlib to set a common figsize value for all your notebooks charts. Here’s the much nicer scatter chart in our Jupyter notebook (note i have tweaked the axes labels font size and the legend fonts and location). Scatter.legend(loc='upper right', fontsize = 15) t_ylabel ('Tip Amount ($)', fontsize = 18) t_xlabel('Delivery Time (min)', fontsize = 18) Scatter = sns.scatterplot(x = 'time_to_deliver', y ='del_tip_amount', data=deliveries, hue='type', ) fig, scatter = plt.subplots(figsize = (11,7)) It provides 3 different methods using which we can create different subplots of different sizes. Let’s re-do this chart, this time, we’ll use the object oriented approach to create also a figure, that we can later resize. In this article, we will learn different ways to create subplots of different sizes using Matplotlib. Resize scatter chart using figsize parameter ![]()
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