There the different types of visualization techniques are used in machine learning, in this blog we will learn 5 top important visualization libraries that are mostly used by a data scientist to visualizing the data.
Matplotlib
Seaborn
ggplot
pyplot
pygal
Visualization with Matplotlib
Matplotlib is a free open source plotting library for creating static, animated, and interactive visualizations in Python.
It also supports the mathematics extension using NumPy.
Installing matplotlib using "pip"
pip install matplotlib
After this need to import this using:
import matplotlib.pyplot as plt
Examples:
Plotting line graph using matplotlib: plot line using one variable(y coordinate):
#plotting the line using one variable
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4])
plt.show()
Here the value of y is given and x is generated automatically and it starts from 0 with interval 0.5.
If both coordinates are given then:
Style formating
This is other arguments except for x, y, which used for styling the graph: "ro", "-b", etc
Here "ro", means "red: o" shape, "-b", show "-" in blue color.
Histogram using matplotlib
You may use the below syntax to plot histogram using matplotlib.
Syntax:
import matplotlib.pyplot as plt
x = [value1, value2, value3,....]
plt.hist(x, bins = number of bins)
plt.show()
Example:
import matplotlib.pyplot as plt
x = [1,1,2,3,3,5,7,8,9,10,
10,11,11,13,13,15,16,17,18,18,
18,19,20,21,21,23,24,24,25,25
]
plt.hist(x, bins=10)
plt.show()
Creating a Bar Chart Using Matplotlib:
Below the syntax which is used to creating the bar chart
#ploting histogram using matplotlib
import matplotlib.pyplot as plt
plt.bar(xAxis,yAxis)
plt.title('title name')
plt.xlabel('xAxis name')
plt.ylabel('yAxis name')
plt.show()
Example:
import matplotlib.pyplot as plt
Country = ['USA','Canada','Germany','UK','France']
GDP_Per_Capita = [45000,42000,52000,49000,47000]
plt.bar(Country, GDP_Per_Capita)
plt.title('Country Vs GDP Per Capita')
plt.xlabel('Country')
plt.ylabel('GDP Per Capita')
plt.show()
Visualization with Seaborn
Seaborn as a library is used in Data visualizations from the models built over the dataset to predict the outcome and analyse the variations in the data.
Seaborn Line Plots depict the relationship between continuous as well as categorical values in a continuous data point format.
first, need to install seaborn using pip:
pip install seaborn
then after import, it using:
import seaborn
Syntax to creating a line chart using seaborn:
seaborn.lineplot(x, y, data)
Where:
x: variable for the x-axis
y: variable for the y-axis
data: data values
Example:
import pandas as pd
import seaborn as sns
Year = [1990, 1994, 1998, 2002, 2006, 2010, 2014]
Profit = [10, 62.02, 48.0, 75, 97.5, 25, 66.6]
data_plot = pd.DataFrame({"Year":Year, "Profit":Profit})
sns.lineplot(x = "Year", y = "Profit", data=data_plot)
plt.show()
you can also create more other visualization like bar, histogram, scatter plot, etc using seaborn.
Visualization with "ggplot"
This is a visualization package that is used in R programming.
Let suppose data is like:
## baby_wt income mother_age smoke gestation mother_wt ## 1 120 level_1 27 nonsmoker 284 100 ## 2 113 level_4 33 nonsmoker 282 135 ## 3 128 level_2 28 smoker 279 115 ## 4 108 level_1 23 smoker 282 125 ## 5 132 level_2 23 nonsmoker 245 140 ## 6 120 level_2 25 nonsmoker 289 125
Plot1: Simple Bar-plot (Showing distribution of baby’s weight)
ggplot(data = Birth_weight,aes(x=baby_wt))+geom_bar()
The above code has three parts:
data: It is the name of the data-set
aes: This is where we provide the aesthetics, i.e. the “x-scale” which will be showing the distribution of “baby_wt”(baby weight)
geometry: The geometry which we are using is bar plot and it can be invoked by using geom_bar() function.
Plot2: Simple Bar-plot (Showing distribution of mother’s age)
ggplot(data = Birth_weight,aes(x=mother_age))+geom_bar()
Visualization Using "Pygal"
Pygal specializes in allowing the user to create SVGs. Besides the scalability of an SVG, you can edit them in any editor and print them in very high-quality resolution.
Installing "pygal" using "pip"
pip install Pygal
Import "pygal"
import pygal
Creating the variable to create the graph:
import pygal
bar_chart=pygal.Bar()()
Adding some values like "title", etc.
import pygal
bar_chart = pygal.Bar()
bar_chart.title = " ratio"
Example:
import pygal
bar_chart = pygal.Bar()
bar_chart.title = "Ratio"
bar_chart.add("add1", [0.94])
bar_chart.add("add2", [1.05])
bar_chart.add("add3", [1.10])
bar_chart.render_in_browser()
where render_in_browser() is used to render the graph in the web - browser.
Contact us to get help related to data visualization in machine learning using below contact details: contact@codersarts.com
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