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Commit57a38c7

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ENH: Add grouped_bar() method
This is a WIP to implement#24313. It will be updated incrementally. Asa first step, I've designed the data and label input API. Feedback iswelcome.
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"""
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=================
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Grouped bar chart
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=================
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This is an ex
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Case 1: multiple separate datasets
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----------------------------------
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"""
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importpandasaspd
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importmatplotlib.pyplotasplt
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importnumpyasnp
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x= ['A','B']
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data1= [1,1.2]
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data2= [2,2.4]
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data3= [3,3.6]
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fig,axs=plt.subplots(1,2)
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# current solution: manual positioning with multiple bar)= calls
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label_pos=np.array([0,1])
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bar_width=0.8/3
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data_shift=-1*bar_width+np.array([0,bar_width,2*bar_width])
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axs[0].bar(label_pos+data_shift[0],data1,width=bar_width,label="data1")
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axs[0].bar(label_pos+data_shift[1],data2,width=bar_width,label="data2")
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axs[0].bar(label_pos+data_shift[2],data3,width=bar_width,label="data3")
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axs[0].set_xticks(label_pos,x)
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axs[0].legend()
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# grouped_bar() with list of datasets
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# note also that this is a straight-forward generalization of the single-dataset case:
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# bar(x, data1, label="data1")
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axs[1].grouped_bar(x, [data1,data2,data3],dataset_labels=["data1","data2","data3"])
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# %%
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# Case 1b: multiple datasets as dict
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# ----------------------------------
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# instead of carrying a list of datasets and a list of dataset labels, users may
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# want to organized their datasets in a dict.
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datasets= {
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'data1':data1,
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'data2':data2,
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'data3':data3,
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}
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# %%
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# While you can feed keys and values into the above API, it may be convenient to pass
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# the whole dict as "data" and automatically extract the labels from the keys:
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fig,axs=plt.subplots(1,2)
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# explicitly extract values and labels from a dict and feed to grouped_bar():
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axs[0].grouped_bar(x,datasets.values(),dataset_labels=datasets.keys())
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# accepting a dict as input
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axs[1].grouped_bar(x,datasets)
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# %%
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# Case 2: 2D array data
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# ---------------------
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# When receiving a 2D array, we interpret the data as
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#
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# .. code-block:: none
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#
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# dataset_0 dataset_1 dataset_2
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# x[0]='A' ds0_a ds1_a ds2_a
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# x[1]='B' ds0_b ds1_b ds2_b
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#
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# This is consistent with the standard data science interpretation of instances
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# on the vertical and features on the horizontal. And also matches how pandas is
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# interpreting rows and columns.
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#
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# Note that a list of individual datasets and a 2D array behave structurally different,
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# i.e. hen turning a list into a numpy array, you have to transpose that array to get
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# the correct representation. Those two behave the same::
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#
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# grouped_bar(x, [data1, data2])
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# grouped_bar(x, np.array([data1, data2]).T)
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#
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# This is a conscious decision, because the commonly understood dimension ordering
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# semantics of "list of datasets" and 2D array of datasets is different.
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x= ['A','B']
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data=np.array([
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[1,2,3],
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[1.2,2.4,3.6],
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])
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columns= ["data1","data2","data3"]
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fig,axs=plt.subplots(1,2)
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axs[0].grouped_bar(x,data,dataset_labels=columns)
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df=pd.DataFrame(data,index=x,columns=columns)
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df.plot.bar(ax=axs[1])

‎lib/matplotlib/axes/_axes.py

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returncol
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defgrouped_bar(self,x,heights,dataset_labels=None):
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"""
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Parameters
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-----------
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x : array-like of str
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The labels.
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heights : list of array-like or dict of array-like or 2D array
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The heights for all x and groups. One of:
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- list of array-like: A list of datasets, each dataset must have
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``len(x)`` elements.
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.. code-block:: none
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x = ['a', 'b']
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group_labels = ['ds0', 'ds1', 'ds2']
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# group_labels: ds0 ds1 dw2
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heights = [dataset_0, dataset_1, dataset_2]
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# x[0] x[1]
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dataset_0 = [ds0_a, ds0_b]
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# x[0] x[1]
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heights = [[ds0_a, ds0_b], # dataset_0
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[ds1_a, ds1_b], # dataset_1
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[ds2_a, ds2_b], # dataset_2
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]
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- dict of array-like: A names to datasets, each dataset (dict value)
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must have ``len(x)`` elements.
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group_labels = heights.keys()
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heights = heights.values()
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- a 2D array: columns map to *x*, columns are the different datasets.
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.. code-block:: none
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dataset_0 dataset_1 dataset_2
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x[0]='a' ds0_a ds1_a ds2_a
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x[1]='b' ds0_b ds1_b ds2_b
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Note that this is consistent with pandas. These two calls produce
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the same bar plot structure::
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grouped_bar(x, array, group_labels=group_labels)
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pd.DataFrame(array, index=x, columns=group_labels).plot.bar()
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An iterable of array-like: The iteration runs over the groups.
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Each individual array-like is the list of label values for that group.
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dataset_labels : array-like of str, optional
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The labels of the datasets.
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"""
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ifhasattr(heights,'keys'):
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ifdataset_labelsisnotNone:
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raiseValueError(
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"'dataset_labels' cannot be used if 'heights' are a mapping")
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dataset_labels=heights.keys()
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heights=heights.values()
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elifhasattr(heights,'shape'):
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heights=heights.T
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num_labels=len(x)
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num_datasets=len(heights)
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fordatasetinheights:
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assertlen(dataset)==num_labels
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margin=0.1
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bar_width= (1-2*margin)/num_datasets
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block_centers=np.arange(num_labels)
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ifdataset_labelsisNone:
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dataset_labels= [None]*num_datasets
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else:
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assertlen(dataset_labels)==num_datasets
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fori, (hs,dataset_label)inenumerate(zip(heights,dataset_labels)):
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lefts=block_centers-0.5+margin+i*bar_width
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print(i,x,lefts,hs,dataset_label)
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self.bar(lefts,hs,width=bar_width,align="edge",label=dataset_label)
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self.xaxis.set_ticks(block_centers,labels=x)
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# TODO: does not return anything for now
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@_preprocess_data()
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defstem(self,*args,linefmt=None,markerfmt=None,basefmt=None,bottom=0,
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label=None,orientation='vertical'):

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