Python Matplotlib Multiple Bars

Python matplotlib multiple bars

import matplotlib.pyplot as plt
from matplotlib.dates import date2num
import datetime

x = [
    datetime.datetime(2011, 1, 4, 0, 0),
    datetime.datetime(2011, 1, 5, 0, 0),
    datetime.datetime(2011, 1, 6, 0, 0)
]
x = date2num(x)

y = [4, 9, 2]
z = [1, 2, 3]
k = [11, 12, 13]

ax = plt.subplot(111)
ax.bar(x-0.2, y, width=0.2, color='b', align='center')
ax.bar(x, z, width=0.2, color='g', align='center')
ax.bar(x+0.2, k, width=0.2, color='r', align='center')
ax.xaxis_date()

plt.show()

I don't know what's the "y values are also overlapping" means, does the following code solve your problem?

ax = plt.subplot(111)
w = 0.3
ax.bar(x-w, y, width=w, color='b', align='center')
ax.bar(x, z, width=w, color='g', align='center')
ax.bar(x+w, k, width=w, color='r', align='center')
ax.xaxis_date()
ax.autoscale(tight=True)

plt.show()

Multiple multiple-bar graphs using matplotlib

The easiest way to simulate a two-level x-axis is via subplots and adapting the x-label. Erasing the intermediate spines and minimizing the distance helps to get a view similar to the linked example.

import matplotlib.pyplot as plt
import seaborn as sns

titanic = sns.load_dataset("titanic")
sns.set_style('whitegrid')
g = sns.catplot(x="sex", hue="alive", col="deck",
                data=titanic[titanic.deck.notnull()],
                kind="count", height=3, aspect=.4, palette='Set1')
for ax in g.axes.flat[1:]:
    sns.despine(ax=ax, left=True)
for ax in g.axes.flat:
    ax.set_xlabel(ax.get_title())
    ax.set_title('')
    ax.margins(x=0.1) # slightly more margin as a separation
plt.subplots_adjust(wspace=0, bottom=0.18, left=0.06)
plt.show()

Here is another example, with rotated x-tick labels:

import matplotlib.pyplot as plt
import seaborn as sns

flights = sns.load_dataset("flights")
sns.set_style('whitegrid')
g = sns.catplot(x="month", y="passengers", col="year",
                data=flights[flights.year % 2 == 0],
                kind="bar", height=3, aspect=.7, palette='turbo')
for ax in g.axes.flat[1:]:
    sns.despine(ax=ax, left=True)
for ax in g.axes.flat:
    ax.set_xlabel(ax.get_title())
    ax.set_title('')
    ax.margins(x=0.03)
    ax.tick_params(axis='x', labelrotation=90)
plt.tight_layout()
plt.subplots_adjust(wspace=0)
plt.show()

Plot multiple bars for categorical data

There is no automatic support of subcategories in matplotlib.

Placing bars with matplotlib

You may go the way of placing the bars numerically, like you propose yourself in the question. You can of course let the code manage the unknown number of subcategories.

import numpy as np
import matplotlib.pyplot as plt

X = ['A','B','C']
Y = [1,2,3]
Z = [2,3,4]

def subcategorybar(X, vals, width=0.8):
    n = len(vals)
    _X = np.arange(len(X))
    for i in range(n):
        plt.bar(_X - width/2. + i/float(n)*width, vals[i], 
                width=width/float(n), align="edge")   
    plt.xticks(_X, X)
    
subcategorybar(X, [Y,Z,Y])

plt.show()

Using pandas

You may also use pandas plotting wrapper, which does the work of figuring out the number of subcategories. It will plot one group per column of a dataframe.

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

X = ['A','B','C']
Y = [1,2,3]
Z = [2,3,4]

df = pd.DataFrame(np.c_[Y,Z,Y], index=X)
df.plot.bar()

plt.show()

Issue in Plotting multiple bars in one graph in python

df2.melt("URL").\
groupby("variable").\
agg(sums=("value", "sum"),
    nz=("value", lambda x: sum(x != 0))).\
plot(kind="bar")

Matplotlib: histogram with multiple bars

It looks like you want to create a bar plot, not a histogram.

In this case, the grouping, the labels and the legend are easiest if you create a pandas dataframe, and use pandas plotting (pandas uses matplotlib for plotting):

import matplotlib.pyplot as plt
import pandas as pd

tags = ('manual (4 serv.)', 'Cromlech (average, 4 serv.)', 'Cromlech (0.925, 4 serv.)',
        'Cromlech (0.925 improved, 7 serv.)', 'Cromlech (15 serv.)', 'Pangaea', 'ServiceCutter (4 serv.)')
# insert some newlines in the tags to better fit into the plot
tags = [tag.replace(' (', '\n(') for tag in tags]
a = (0.385, 0.4128, 0.406, 0.5394, 0.7674, 0.306, 0.3505)
b = (0.4025, 0.1935, 0.189, 0.189, 0.415, 0.238, 0.1714)
c = (1, 0.3619, 0.5149, 1, 0.4851, 0.4092, 0.4407)
d = (1, 0.3495, 0.4888, 1, 0.4861, 0.4985, 0.5213)
# create a dataframe
df = pd.DataFrame({"decoupling": a, "op. cost": b, "op. similarity": c, "data similarity": d}, index=tags)
df.plot.bar(rot=0, figsize=(12, 5))
plt.tight_layout() # fit labels etc. nicely into the plot
plt.show()

Optionally, you can modify visual aspects. Here is an example:

ax = df.plot.bar(rot=0, figsize=(12, 5), width=0.75, cmap='turbo')
for spine in ['top', 'right']:
    ax.spines[spine].set_visible(False)
ax.set_axisbelow(True)
ax.grid(axis='y', color='grey', ls=':')
ax.set_facecolor('beige')
ax.set_ylim(0, 1)
plt.tight_layout()

Add ONLY the total values on top of stacked bars in matplotlib

I'm not sure there's a way to do this with the new bar_label method, but you can label the totals manually.

Compute the row totals via piv.sum(axis=1) and annotate the totals:

for x, y in enumerate(piv.sum(axis=1)):
    ax.annotate(y, (x, y), ha='center')

Matplotlib plot multiple bars in one graph

You want to plot the data column-wise. Hence it makes sense to convert the lists to arrays and select the respective column to plot.

import numpy as np
import matplotlib.pyplot as plt

data = np.array([[20, 35, 30, 40], [25, 40, 45, 30], 
                 [15, 20, 35, 45], [10, 25, 40, 15], 
                 [50, 20, 45, 55], [10, 55, 60, 20]])
data_std = np.array([[1, 2, 1, 2], [1, 2, 1, 2], [1, 2, 1, 2], 
                     [1, 2, 1, 2], [1, 2, 1, 2], [1, 2, 1, 2]])    

length = len(data)
x_labels = ['A', 'B', 'C', 'D', 'E', 'F']

# Set plot parameters
fig, ax = plt.subplots()
width = 0.2 # width of bar
x = np.arange(length)

ax.bar(x, data[:,0], width, color='#000080', label='Case-1', yerr=data_std[:,0])
ax.bar(x + width, data[:,1], width, color='#0F52BA', label='Case-2', yerr=data_std[:,1])
ax.bar(x + (2 * width), data[:,2], width, color='#6593F5', label='Case-3', yerr=data_std[:,2])
ax.bar(x + (3 * width), data[:,3], width, color='#73C2FB', label='Case-4', yerr=data_std[:,3])

ax.set_ylabel('Metric')
ax.set_ylim(0,75)
ax.set_xticks(x + width + width/2)
ax.set_xticklabels(x_labels)
ax.set_xlabel('Scenario')
ax.set_title('Title')
ax.legend()
plt.grid(True, 'major', 'y', ls='--', lw=.5, c='k', alpha=.3)

fig.tight_layout()
plt.show()

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