Pandas hist()

import pandas as pd
import matplotlib.pyplot as plt

# sample DataFrame
data = {'A': [1, 2, 3, 4, 5],
        'B': [6, 7, 8, 9, 10]}

df = pd.DataFrame(data)

# plot histogram for all columns
hist_plot = df.hist()
plt.show()

hist() Syntax

The syntax of the hist() method in Pandas is:

df.hist(column=None, by=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, sharey=False, figsize=None, layout=None, bins=10, **kwargs)

hist() Arguments

The hist() method has the following arguments:

• column (optional): specifies which columns to plot
• by (optional): allows grouping by the specified column
• grid (optional): adds a grid to the histogram
• xlabelsize and ylabelsize (optional): control the font size of the x-axis and y-axis labels, respectively
• xrot and yrot (optional): rotation of x-axis and y-axis labels
• ax (optional): matplotlib axes object where the histogram is plotted
• sharex and sharey (optional): control sharing of properties among x (sharex) or y (sharey) axes
• figsize (optional): a tuple to control the figure size
• layout (optional): controls the layout of the histograms
• bins (optional): specifies the number of bins or the specific bin edges
• **kwargs (optional): additional keyword arguments

hist() Return Value

The hist() method returns a matplotlib Axes object or a numpy array of them.

Example 1: Basic Histogram

import pandas as pd
import matplotlib.pyplot as plt

data = {'A': [12, 13, 14, 27, 29, 41, 43, 45],
        'B': [20, 35, 30, 35, 27, 28, 32, 44]}
df = pd.DataFrame(data)

# plot a basic histogram of one column
hist_plot = df['A'].hist(bins=5)
plt.show()

Output

In this example, we displayed a histogram for column A with 5 bins.

Here, the minimum value is 12 and the maximum value is 45, the width of a bin is:

(45-12)/5 = 6.6

So the bin ranges are:

• Bin1: 12 to 18.6
• Bin2: 18.6 to 25.2
• Bin3: 25.2 to 31.8
• Bin4: 31.8 to 38.4
• Bin5: 38.4 to 45

Example 2: Customize a Histogram

import pandas as pd
import matplotlib.pyplot as plt

data = {'A': [12, 13, 14, 27, 29, 41, 43, 45],
        'B': [20, 35, 30, 35, 27, 28, 32, 44]}
df = pd.DataFrame(data)

# plot histogram with additional customizations
hist_plot = df.hist(bins=3, grid=False, figsize=(8,6), color='#86bf91', zorder=2, rwidth=0.9)
plt.show()

Output

In this example, we customized the histogram in many ways. We changed the number of bins to 3, turned off the grid for a cleaner look, chose a specific color for the bars, and adjusted the size of the figure to make it larger.

Here,

• bins=3: sets the number of bins to 3
• grid=False: turns off the grid lines
• figsize=(8,6): adjusts the figure size to 8x6
• color='#86bf91': sets the color according to the hex code
• rwidth=0.9: sets the relative bar width
• zorder=2: controls the order of drawing

Example 3: Group Histograms by a Column

import pandas as pd
import matplotlib.pyplot as plt

data = {'Scores': [90, 85, 92, 88, 91],
        'Class': ['A', 'B', 'A', 'B', 'B']}
df = pd.DataFrame(data)

# plot a histogram of scores grouped by class
hist_plot = df.hist(column='Scores', by='Class')
plt.show()

Output

In this example, we created histograms for the Scores column and grouped the data by the Class category. This generated a separate histogram for each class.