Eight common data science tools for data visualization

Eight common data science tools for data visualization are Matplotlib, Seaborn, Plotly, Bokeh, ggplot, Tableau, Power BI, and D3.js. Each of these tools has its own strengths and weaknesses, making them suitable for different types of data visualization tasks. Let's dive into each of these tools and see how they can be used for data visualization. 1. Matplotlib is a widely used plotting library in Python that is known for its flexibility and customization options. It allows you to create a wide variety of visualizations, from simple line plots to complex heatmaps. 
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y)
plt.xlabel('x')
plt.ylabel('sin(x)')
plt.title('Sine Wave')
plt.show()

2. Seaborn is built on top of Matplotlib and provides a high-level interface for creating attractive and informative statistical graphics. It simplifies many common visualization tasks and offers a wide range of built-in themes and color palettes. 
import seaborn as sns
import pandas as pd

data = pd.read_csv('data.csv')
sns.scatterplot(x='x', y='y', data=data)
plt.show()

3. Plotly is a powerful interactive visualization library that allows you to create interactive plots that can be easily shared online. It supports a wide range of chart types and provides tools for creating dashboards and web applications. 
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species')
fig.show()

4. Bokeh is another interactive visualization library that is designed for creating web-ready plots. It allows you to create interactive plots with tools like zoom, pan, and hover, making it ideal for exploring large datasets. 
from bokeh.plotting import figure, show
from bokeh.io import output_notebook

output_notebook()
p = figure()
p.line([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], line_width=2)
show(p)

5. ggplot is a plotting system for Python based on the Grammar of Graphics, which allows you to create complex and customized plots using a simple and consistent syntax. It is inspired by the ggplot2 package in R. 
from plotnine import ggplot, geom_point, aes
from plotnine.data import mpg

(ggplot(mpg) + aes(x='displ', y='hwy') + geom_point())

6. Tableau is a popular data visualization tool that allows you to create interactive dashboards and reports without writing any code. It offers a wide range of visualizations and data connection options. 7. Power BI is a business analytics tool that provides interactive data visualization and business intelligence capabilities. It allows you to connect to a wide range of data sources and create interactive reports and dashboards. 8. D3.js is a JavaScript library for creating interactive and dynamic data visualizations in web browsers. It allows you to bind data to the Document Object Model (DOM) and apply data-driven transformations to create custom visualizations.

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