Sometimes could be tricky to find the right bar width. I usually use this np.diff to find the right dimension.

import numpy as np
import matplotlib.pyplot as plt

#The data
womenMeans = (25, 32, 34, 20, 25)
menMeans = (20, 35, 30, 35, 27)
indices = [5.5,6,7,8.5,8.9]
#Calculate optimal width
width = np.min(np.diff(indices))/3


fig = plt.figure()
ax = fig.add_subplot(111)
# matplotlib 3.0 you have to use align
ax.bar(indices-width,womenMeans,width,color='b',label='-Ymin',align='edge')
ax.bar(indices,menMeans,width,color='r',label='Ymax',align='edge')


ax.set_xlabel('Test histogram')
plt.show()
# matplotlib 2.0 (you could avoid using align)
# ax.bar(indices-width,womenMeans,width,color='b',label='-Ymin')
# ax.bar(indices,menMeans,width,color='r',label='Ymax')

This is the result:

What if my indices on my x axis are nominal values like names:

#
import numpy as np
import matplotlib.pyplot as plt

# The data
womenMeans = (25, 32, 34, 20, 25)
menMeans = (20, 35, 30, 35, 27)
indices = range(len(womenMeans))
names = ['Asian','European','North Amercian','African','Austrailian','Martian']
# Calculate optimal width
width = np.min(np.diff(indices))/3.

fig = plt.figure()
ax = fig.add_subplot(111)
ax.bar(indices-width/2.,womenMeans,width,color='b',label='-Ymin')
ax.bar(indices+width/2.,menMeans,width,color='r',label='Ymax')
#tiks = ax.get_xticks().tolist()
ax.axes.set_xticklabels(names)
ax.set_xlabel('Test histogram')
plt.show()

Below answer will explain each and every line of code in the simplest manner possible:

# Numbers of pairs of bars you want
N = 3

# Data on X-axis

# Specify the values of blue bars (height)
blue_bar = (23, 25, 17)
# Specify the values of orange bars (height)
orange_bar = (19, 18, 14)

# Position of bars on x-axis
ind = np.arange(N)

# Figure size
plt.figure(figsize=(10,5))

# Width of a bar 
width = 0.3       

# Plotting
plt.bar(ind, blue_bar , width, label='Blue bar label')
plt.bar(ind + width, orange_bar, width, label='Orange bar label')

plt.xlabel('Here goes x-axis label')
plt.ylabel('Here goes y-axis label')
plt.title('Here goes title of the plot')

# xticks()
# First argument - A list of positions at which ticks should be placed
# Second argument -  A list of labels to place at the given locations
plt.xticks(ind + width / 2, ('Xtick1', 'Xtick3', 'Xtick3'))

# Finding the best position for legends and putting it
plt.legend(loc='best')
plt.show()

Here are two examples of creating a side-by-side bar chart when you have more than two "categories" in a group.

Manual Method

Manually set the position and width of each bar.

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import ticker

coins = ['penny', 'nickle', 'dime', 'quarter']
worth = np.array([.01, .05, .10, .25])

# Coin values times *n* coins
#    This controls how many bars we get in each group
values = [worth*i for i in range(1,6)]

n = len(values)                # Number of bars to plot
w = .15                        # With of each column
x = np.arange(0, len(coins))   # Center position of group on x axis

for i, value in enumerate(values):
    position = x + (w*(1-n)/2) + i*w
    plt.bar(position, value, width=w, label=f'{i+1}x')

plt.xticks(x, coins);

plt.ylabel('Monetary Value')
plt.gca().yaxis.set_major_formatter(ticker.FormatStrFormatter('$%.2f'))

plt.legend()

Pandas Method

If you put the data into a pandas DataFrame, pandas will do the hard stuff for you.

import pandas as pd
coins = ['penny', 'nickle', 'dime', 'quarter']
worth = [0.01, 0.05, 0.10, 0.25]
df = pd.DataFrame(worth, columns=['1x'], index=coins)
df['2x'] = df['1x'] * 2 
df['3x'] = df['1x'] * 3 
df['4x'] = df['1x'] * 4 
df['5x'] = df['1x'] * 5 

from matplotlib import ticker
import matplotlib.pyplot as plt

df.plot(kind='bar')

plt.ylabel('Monetary Value')
plt.gca().yaxis.set_major_formatter(ticker.FormatStrFormatter('$%.2f'))
plt.gca().xaxis.set_tick_params(rotation=0)

Pandas creates a similar figure…