Plotting Histogram in Python using Matplotlib

import matplotlib.pyplot as plt
import numpy as np

# Generate random data for the histogram
data = np.random.randn(1000)

# Plotting a basic histogram
plt.hist(data, bins=30, color='skyblue', edgecolor='black')

# Adding labels and title
plt.xlabel('Values')
plt.ylabel('Frequency')
plt.title('Basic Histogram')

# Display the plot
plt.show()

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# Generate random data for the histogram
data = np.random.randn(1000)

# Creating a customized histogram with a density plot
sns.histplot(data, bins=30, kde=True, color='lightgreen', edgecolor='red')

# Adding labels and title
plt.xlabel('Values')
plt.ylabel('Density')
plt.title('Customized Histogram with Density Plot')

# Display the plot
plt.show()

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


# Creating dataset
np.random.seed(23685752)
N_points = 10000
n_bins = 20

# Creating distribution
x = np.random.randn(N_points)
y = .8 ** x + np.random.randn(10000) + 25
legend = ['distribution']

# Creating histogram
fig, axs = plt.subplots(1, 1,
                        figsize =(10, 7), 
                        tight_layout = True)


# Remove axes splines 
for s in ['top', 'bottom', 'left', 'right']: 
    axs.spines[s].set_visible(False) 

# Remove x, y ticks
axs.xaxis.set_ticks_position('none') 
axs.yaxis.set_ticks_position('none') 
  
# Add padding between axes and labels 
axs.xaxis.set_tick_params(pad = 5) 
axs.yaxis.set_tick_params(pad = 10) 

# Add x, y gridlines 
axs.grid(b = True, color ='grey', 
        linestyle ='-.', linewidth = 0.5, 
        alpha = 0.6) 

# Add Text watermark 
fig.text(0.9, 0.15, 'Jeeteshgavande30', 
         fontsize = 12, 
         color ='red',
         ha ='right',
         va ='bottom', 
         alpha = 0.7) 

# Creating histogram
N, bins, patches = axs.hist(x, bins = n_bins)

# Setting color
fracs = ((N**(1 / 5)) / N.max())
norm = colors.Normalize(fracs.min(), fracs.max())

for thisfrac, thispatch in zip(fracs, patches):
    color = plt.cm.viridis(norm(thisfrac))
    thispatch.set_facecolor(color)

# Adding extra features    
plt.xlabel("X-axis")
plt.ylabel("y-axis")
plt.legend(legend)
plt.title('Customized histogram')

# Show plot
plt.show()

import matplotlib.pyplot as plt
import numpy as np

# Generate random data for multiple histograms
data1 = np.random.randn(1000)
data2 = np.random.normal(loc=3, scale=1, size=1000)

# Creating subplots with multiple histograms
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 4))

axes[0].hist(data1, bins=30, color='Yellow', edgecolor='black')
axes[0].set_title('Histogram 1')

axes[1].hist(data2, bins=30, color='Pink', edgecolor='black')
axes[1].set_title('Histogram 2')

# Adding labels and title
for ax in axes:
    ax.set_xlabel('Values')
    ax.set_ylabel('Frequency')

# Adjusting layout for better spacing
plt.tight_layout()

# Display the figure
plt.show()

import matplotlib.pyplot as plt
import numpy as np

# Generate random data for stacked histograms
data1 = np.random.randn(1000)
data2 = np.random.normal(loc=3, scale=1, size=1000)

# Creating a stacked histogram
plt.hist([data1, data2], bins=30, stacked=True, color=['cyan', 'Purple'], edgecolor='black')

# Adding labels and title
plt.xlabel('Values')
plt.ylabel('Frequency')
plt.title('Stacked Histogram')

# Adding legend
plt.legend(['Dataset 1', 'Dataset 2'])

# Display the plot
plt.show()

import matplotlib.pyplot as plt
import numpy as np

# Generate random 2D data for hexbin plot
x = np.random.randn(1000)
y = 2 * x + np.random.normal(size=1000)

# Creating a 2D histogram (hexbin plot)
plt.hexbin(x, y, gridsize=30, cmap='Blues')

# Adding labels and title
plt.xlabel('X values')
plt.ylabel('Y values')
plt.title('2D Histogram (Hexbin Plot)')

# Adding colorbar
plt.colorbar()

# Display the plot
plt.show()