Exploring the Untapped Potential of Your Data: Uncover Insights with Seaborn
Seaborn:
Developed on top of Matplotlib, Seaborn is a Python data visualization library. It offers a sophisticated interface for making eye-catching and educational statistical visuals. When it comes to examining correlations between variables and displaying intricate datasets, Seaborn is very helpful.
For data scientists, analysts, and researchers who want to efficiently analyze and understand data, Seaborn is a potent tool. It is a well-liked option for Python data visualization projects because of its ability to blend elegance, practicality, and simplicity of usage.
import seaborn as sns
iris = sns.load_dataset("iris")
sns.histplot(data=iris)
use iris.head() in Python with Pandas to display the first few rows of the Iris dataset:
iris.head()
Scatter Plot
In Seaborn, a scatter plot is a kind of plot that’s used to show the relationship between two numerical variables. A two-dimensional plane with one variable shown along the x-axis and the other along the y-axis is used to display individual data points as markers. Data patterns, trends, correlations, and outliers are frequently found in scatter plots.
sns.scatterplot(x="sepal_length", y="sepal_width", data=iris)
import matplotlib.pyplot as plt
sns.scatterplot(x="sepal_length", y="sepal_width",hue="species",size="petal_length", sizes=(20, 200), data=iris)
plt.xlabel("Sepal Length (cm)")
plt.ylabel("Sepal Width (cm)")
plt.title("Scatter Plot of Sepal Length vs Sepal Width ")
plt.legend(title="Species")
# Display the plot
plt.show()
lineplot
In Seaborn, a line plot is a kind of plot that shows the relationship across a continuous interval between two numerical variables. It can be used to depict trends, patterns, and changes in data over time or another continuous variable since it joins data points with straight lines.
sns.lineplot(x="sepal_length", y='sepal_width', data = iris)
sns.lineplot(x="species", y="sepal_length", hue="species", style="species",markers=True,
dashes=False, data=iris)
Bar Plots
The distribution or link between a categorical variable and a numerical variable can be seen and categorical data can be visualized using bar graphs in Seaborn. They work well when comparing the values of various groupings or categories.
sns.barplot(x="sepal_length", y="sepal_width", data=iris)
Histograms
Using bins to hold the data and a count or frequency of data points within each bin, histograms in Seaborn are used to show the distribution of numerical data. Understanding the underlying distribution, seeing trends, and spotting outliers in the data may all be accomplished with the help of histograms.
sns.histplot(data=iris, x="sepal_length", bins=25, kde=True, color="pink")
Density Plots
Kernel density estimate (KDE) plots, commonly referred to as density plots in Seaborn, are used to show the distribution of a continuous numerical variable. Density plots estimate the probability density function of the data, giving a smooth representation of the distribution of the data, in contrast to histograms that bin the data into intervals.
sns.kdeplot(x="sepal_length", data=iris)
sns.kdeplot(data=iris, x="sepal_length",hue="species", fill=True, alpha=0.6,linewidth=1.5)
Box plots
Box plots, sometimes referred to as box-and-whisker plots, are a kind of graphical representation that shows important statistical metrics including outliers, median, and quartiles while summarizing the distribution of a numerical variable. They are especially helpful for comparing how a variable is distributed among various groups or categories.
sns.boxplot(x="sepal_length", y="sepal_width", data=iris)
Violin plots
Violin plots in Seaborn are similar to box plots, but they provide a more thorough perspective of how a numerical variable is distributed throughout several categories or groups. They represent the data density at various values, providing information on the distribution’s shape, spread, and central tendency.
sns.violinplot(data = iris, x= "sepal_length", y="sepal_width")
Heatmaps
Heatmaps in Seaborn are used to visually represent the associations between two variables in a dataset by presenting a color matrix. They are very effective at detecting patterns, correlations, and trends in massive datasets.
sns.pairplot(data=iris)
numeric_iris = iris.drop(columns=['species'])
# Compute correlation matrix
corr = numeric_iris.corr()
print(corr)
sns.heatmap(corr, annot=True, cmap="coolwarm")
plt.title("Heatmap")
plt.show()
Joint plot
In Seaborn, a joint plot is a collection of several plots that allow you to visualize the relationship between two variables. It is often a scatter plot with marginal distributions (histograms or kernel density estimates) for each variable. Joint plots are excellent for determining correlations, patterns, and trends in data.
sns.jointplot(data=iris)
Facet grids
Facet grids in Seaborn are an effective approach to generate many plots (such as scatter plots, histograms, or any other plot type) from subsets of your data. They let you to visualize links and patterns across multiple categories or groups of your dataset by assembling plots in a grid manner.
g = sns.FacetGrid(iris, col="species")
# Plot histogram for sepal_length in each species
g.map(sns.histplot, "sepal_length")
