In HighCharts, bar graphs use inverted axes, so the bottom axis is really the Y axis. (See also “column” graphs where the graphic is rotated 90 degrees, in which case the bottom axis is the X axis.) You need to add the following to the yAxis config yAxis: { labels: { enabled: false } } … Read more
For anyone coming across this question: Since pandas 0.14, plotting with bars has a ‘width’ command: https://github.com/pydata/pandas/pull/6644 The example above can now be solved simply by using df.plot(kind=’bar’, stacked=True, width=1) See pandas.DataFrame.plot.bar or pandas.DataFrame.plot with kind=’bar’. When changing the width of the bars, it might also be appropriate to change the figure size by specifying … Read more
There is an example in the matplotlib site. Basically, you just shift the x values by width. Here is the relevant bit: import numpy as np import matplotlib.pyplot as plt N = 5 menMeans = (20, 35, 30, 35, 27) menStd = (2, 3, 4, 1, 2) ind = np.arange(N) # the x locations for … Read more
I think you’re confused on a few points about how matplotlib handles dates. You’re not actually plotting dates, at the moment. You’re plotting things on the x-axis with [0,1,2,…] and then manually labeling every point with a string representation of the date. Matplotlib will automatically position ticks. However, you’re over-riding matplotlib’s tick positioning functionality (Using … Read more
You need the bottom of each dataset to be the sum of all the datasets that came before. you may also need to convert the datasets to numpy arrays to add them together. p1 = plt.bar(ind, dataset[1], width, color=”r”) p2 = plt.bar(ind, dataset[2], width, bottom=dataset[1], color=”b”) p3 = plt.bar(ind, dataset[3], width, bottom=np.array(dataset[1])+np.array(dataset[2]), color=”g”) p4 = … Read more
This functionality is not built into seaborn.countplot as far as I know – the order parameter only accepts a list of strings for the categories, and leaves the ordering logic to the user. This is not hard to do with value_counts() provided you have a DataFrame though. For example, import pandas as pd import seaborn … Read more
Tested in python 3.11, pandas 1.5.1, matplotlib 3.6.2 Sample Data and Imports import pandas as pd import matplotlib.pyplot as plt import numpy as np np.random.seed(2022) # creates a consistent sample y = np.random.rand(10,4) y[:,0]= np.arange(10) df = pd.DataFrame(y, columns=[“X”, “A”, “B”, “C”]) X A B C 0 0.0 0.499058 0.113384 0.049974 1 1.0 0.486988 0.897657 … Read more
I eventually found a trick (edit: see below for using seaborn and longform dataframe): Solution with pandas and matplotlib Here it is with a more complete example : import pandas as pd import matplotlib.cm as cm import numpy as np import matplotlib.pyplot as plt def plot_clustered_stacked(dfall, labels=None, title=”multiple stacked bar plot”, H=”/”, **kwargs): “””Given a … Read more