Learn to use PHP and Jupyter Notebook for data analysis and visualization

With the popularization of the Internet and the rapid development of Internet applications, the demand for data analysis and visualization is gradually heating up. Programming languages ​​such as Python, R and MATLAB have become the first choice for data science practitioners. However, as a popular programming language, PHP's application areas are also constantly expanding. In this article, I will introduce readers to how to use PHP and Jupyter Notebook for data analysis and visualization.

First, let’s talk about Jupyter Notebook. Jupyter Notebook is a web-based open source interactive Jupyter Notebook environment that supports more than 40 programming languages, including Python, R, Julia, and PHP. Using Jupyter Notebook, we can create and share documents that include code, equations, visualizations, and narrative text. Jupyter Notebook supports real-time calculations and data analysis, and can provide a convenient way for data analysis and visualization.

Next, let’s take a look at how to integrate the PHP language into Jupyter Notebook. First, you need to install Jupyter Notebook. Jupyter Notebook can be distributed via Anaconda (a Python distribution for data science) or installed using pip in a Python environment.

After completing the installation, we need to install the PHP kernel. The PHP kernel is a plug-in in Jupyter Notebook that allows us to use the PHP language for interactive programming and data analysis. Enter the following command in the terminal to install the PHP kernel:

pip3 install IPython[all] After completion, enter the following command to install the PHP kernel:

python3 -m ipykernel install --user -- name=php_kernel
After the installation is complete, we can start using Jupyter Notebook and PHP language for data analysis and visualization.

Next we need some data for analysis. Here we will use a small dataset called "data.csv". The data set contains the sales data of a certain company for a certain month. The data set contains information such as product name, price, sales quantity, and sales volume.

We will use PHP's CSV processing function to read the data set. CSV (Comma-Separated Values) is a commonly used data exchange format that uses commas to separate rows of data. Here is PHP sample code on how to read a CSV file:

ccadc724e03c16560e2d282238c38549Top Selling Products2e9b454fa8428549ca2e64dfac4625cd';
foreach ($top_selling_products as $product => $quantity) {

echo "$product: $quantity<br/>";

}

// Calculate and draw histogram
$quantities = array();
foreach ($data as $row) {

if (!isset($quantities[$row['product']])) {
    $quantities[$row['product']] = 0;
}
$quantities[$row['product']] += $row['quantity'];

}
$products = array_keys($quantities);
$values ​​= array_values($quantities);
$plot = new PHPlot(500, 500);
$plot->SetDataType('text -data');
$plot->SetDataValues(array(array_values($products), array_values($values)));
$plot->SetTitle('Sales by Product');
$plot->SetXTitle('Products');
$plot->SetYTitle('Sales Quantity');
$plot->SetPlotType('bars');
$plot- >DrawGraph();

This sample code loads the "data.csv" data set, calculates the top five products by sales volume, and draws a histogram grouped by product and sorted by sales volume. .

In this article, we introduce how to use Jupyter Notebook and PHP language for data analysis and visualization. We demonstrated how to use PHP's CSV processing functions to read a dataset, use PHP kernels to build data analysis and visualization code in a Jupyter Notebook environment, and use PHPlot to plot histograms. By using Jupyter Notebook and PHP language, we can achieve a fast and easy-to-use way of data analysis and visualization, providing a new choice for data science practitioners.

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