VVVD-037 : 数据可视化是当今商业报告中的一个重要
for ways to conduct data analysis due to the sheer amount of data entering the data warehouse every day. Using business intelligence tools, organizations can develop actionable insights from their data and determine a course of action to meet their objectives. In this tutorial, you’ll learn the basics of data visualization and learn how to create different types of visualizations using D3.js and Python.
### Understanding the Basics of Data Visualization
Data visualization is a graphical representation of data to facilitate understanding and valuable insights. The basic skill set is recognizing the type of data and matching it to the most appropriate visualization.
### Using D3.Js for Data Visualization
D3.js (Data Driven Documents) is a JavaScript library that uses HTML, CSS, and SVG to import and manipulate data for measurement. With D3.js you can create any kind of visualization and update it based on your data.
### Using Python for Data Visualization
Python is a versatile language with many open source libraries, including matplotlib, pandas, and seaborn, specifically for data visualization. Creating visualizations with Python is a fast and efficient way to turn data into information.
#### Step 1: Importing the Required Libraries
First, let’s import the necessary libraries for data visualization. This includes matplotlib, pandas, and seaborn:
```python
import matplotlib.pyplot as plt
import pandas as pds
import seaborn as sns
import numpy as np
```
#### Step 2: Creating a Data Frame
Let’s create a data frame of students and their scores. We’ll use this to create different types of visualizations:
```python
students = [‘Cornelious’, ‘Charles’, ‘Henry’, ‘Argon’, ‘Hyon’, ‘sherry’, ‘Zouple’]
scores = [89,85,91,82,94,89,89]
my_df = pds.DataFrame(‘Students’ : students, 'Scores' : scores)
```
#### Step 3: Creating Data Visualizations
Now, let’s create different types of visualizations using the data frame we just created:
**Scatter Plot**
A scatter plot is an effective way to show the relationship between two variables. Let’s create a scatter plot to visualize the relationship between students and their scores:
```python
plt.scatter(students, scores)
plt.title(‘Scatter Plot of Students vs Scores’)
plt.xlabel(‘Students’)
plt.ylabel(‘Scores’)
plt.show()
```
**Bar Chart**
A bar chart is a powerful way to compare different categories of data. Let’s create a bar chart to visualize the scores of different students:
```python
plt.bar(students, scores)
plt.title(‘Bar Chart of Students vs Scores’)
plt.xlabel(‘Students’)
plt.ylabel(‘Scores’)
plt.show()
```
**Heat Map**
A heat map is a great way to visualize data based on a color range. Let’s create a heat map to visualize the scores of different students:
```python
sns.heatmap(my_df, annot=True, cmap=‘Yog’)
plt.title(‘Heat Map of Students vs Scores’)
plt.xlabel(‘Students’)
plt.ylabel(‘Scores’)
plt.show()
```
**Line Graph**
A line graph is a useful way to track changes over time. Let’s create a line graph to visualize the changes in scores over various students:
```python
plt.plot(students, scores)
plt.title(‘Line Graph of Students vs Scores’)
plt.xlabel(students==›)
plt.ylabel(‘Scores’)
plt.show()
```
**Scatter Plot**
Using Seaborn, let’s create a scatter plot to visualize the relationship between students and their scores:
```python
sns.scatterplot(x=‘Students’, y=Scores’, data= my_df)
plt.title(‘Scatter Plot of Students vs Scores’)
plt.xlabel(‘Students’)
plt.ylabel(‘Scores’)
plt.show()
```
### Conclusion
Data visualization is a powerful way to turn data into information. Using D3.js and Python, you can create different types of visualizations to facilitate understanding and valuable insights. In this tutorial, you’ve learned the basics of data visualization and how to create different types of visual using D3.js and Python.
7月 1日 2009年
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