There are four major ways to install the Solidity compiler:

1. Install Solidity Compiler via npm
2. Install Solidity Compiler via Docker on Ubuntu
3. Install Solidity Compiler via Source Code Compilation
4. Install Solidity Compiler via Static Binary and Linux Packages

In this tutorial, we’ll have a quick look at each of them and give you a link to a more detailed resource so you can set up your Solidity compiler as quickly and efficiently as possible.

Video: For your convenience, I embedded the video tutorial provided by our Solidity expert Matija so you don’t even need to leave this page.

Without further ado, let’s get started!

Method 1: Install Solidity Compiler via npm

As you watch the video or go through this tutorial, feel free to download the following slides as well — for your convenience:

Full Tutorial: How to Install the Solidity Compiler via npm?

Method 2: Install Solidity Compiler via Docker on Ubuntu

Before we go into details about the Docker installation of solc, let’s first get introduced to what Docker is.

Docker is an open platform for developing, shipping, and running applications… Docker provides the ability to package and run an application in a loosely isolated environment called a container… Containers are lightweight and contain everything needed to run the application, so you do not need to rely on what is currently installed on the host.

Source: https://docs.docker.com/get-started/overview/

There are some parts of the description I’ve deliberately left out (separated by the symbol …) because they’re not essential to our understanding of the technology.

Full Tutorial: How to Install the Solidity Compiler via Docker on Ubuntu?

Method 3: Install Solidity Compiler via Source Code Compilation

This is a very complex way to install the Solidity compiler and I wouldn’t recommend it for most people. Due to the complexity, I’ll only give a quick overview of the associated article (tutorial).

Feel free to dive into it after scanning through these three contributions:

1. First, we listed and explained the software prerequisites needed for compiling a Solidity compiler. In some cases, we reached a complete explanation, and in others, we just gave a brief introductory explanation and announced an entire topic, such as in the case of the Satisfiability Modulo Theorem, SMT.
2. Second, we installed the prerequisites by following the first part of a step-by-step tutorial. All the examples have been checked and validated at the time of writing the article, so I expect that we’ll be able to follow them without issues. We also concluded that a compilation process can in some cases take a substantial amount of time; it took almost 40 minutes to compile the z3 SMT solver on my machine.
3. Third, we compiled a Solidity compiler following a step-by-step tutorial. I explained for each command example to broaden our learning process even outside of the strict scope of Solidity, to Linux (as far as we needed to go). Finally, when the compilation ended, we confirmed that our home-compiled Solidity compiler works at least as charming as the ones we’ve simply downloaded or installed in a precompiled state.

Full Tutorial: How to Install the Solidity Compiler via Source Code Compilation?

Method 4: Install Solidity Compiler via Static Binary and Linux Packages

You’ll just download the compiler’s static binary, or in short, binary, and simply run it, without any additional prerequisites or preparations required.

First, downloading the file solc-static-linux and giving it an executable privilege:

$ cd ~ && wget https://github.com/ethereum/solidity/rel...atic-linux

$ chmod +x ~/solc-static-linux

Second, running solc:

$ ~/solc-static-linux 1_Storage.sol -o output – abi – bin

Compiler run successful. Artifact(s) can be found in directory "output".

When checking our solidity_src directory, we’ll discover a new directory output, created by the Solidity compiler, containing both .abi and .bin files.

Full Tutorial: How to Install the Solidity Compiler via Static Binary and Linux Packages?

---

Learn Solidity Course

Solidity is the programming language of the future.

It gives you the rare and sought-after superpower to program against the “Internet Computer”, i.e., against decentralized Blockchains such as Ethereum, Binance Smart Chain, Ethereum Classic, Tron, and Avalanche – to mention just a few Blockchain infrastructures that support Solidity.

In particular, Solidity allows you to create smart contracts, i.e., pieces of code that automatically execute on specific conditions in a completely decentralized environment. For example, smart contracts empower you to create your own decentralized autonomous organizations (DAOs) that run on Blockchains without being subject to centralized control.

NFTs, DeFi, DAOs, and Blockchain-based games are all based on smart contracts.

This course is a simple, low-friction introduction to creating your first smart contract using the Remix IDE on the Ethereum testnet – without fluff, significant upfront costs to purchase ETH, or unnecessary complexity.

Problem Formulation

Programming Challenge: Given a list of lists (nested list). Find and return the shortest inner list from the outer list of lists.

Here are some examples:

• [[1], [2, 3], [4, 5, 6]] [1]
• [[1, [2, 3], 4], [5, 6], [7]] [7]
• [[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]] [[1], [2], [3]]

Also, you’ll learn how to solve a variant of this challenge.

Bonus challenge: Find only the length of the shortest list in the list of lists.

Here are some examples:

• [[1], [2, 3], [4, 5, 6]] 1
• [[1, [2, 3], 4], [5, 6], [7]] 1
• [[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]] 3

So without further ado, let’s get started!

Method 1: min(lst, key=len)

Use Python’s built-in min() function with a key argument to find the shortest list in a list of lists. Call min(lst, key=len) to return the shortest list in lst using the built-in len() function to associate the weight of each list, so that the shortest inner list will be the minimum.

Here’s an example:

def get_shortest_list(lst): return min(lst, key=len) print(get_shortest_list([[1], [2, 3], [4, 5, 6]]))

# [1] print(get_shortest_list([[1, [2, 3], 4], [5, 6], [7]]))

# [7] print(get_shortest_list([[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]]))

# [[1], [2], [3]]

A beautiful one-liner solution, isn’t it? Let’s have a look at a slight variant to check the length of the shortest list instead.

Method 2: len(min(lst, key=len))

To get the length of the shortest list in a nested list, use the len(min(lst, key=len)) function. First, you determine the shortest inner list using the min() function with the key argument set to the len() function. Second, you pass this shortest list into the len() function itself to determine the minimum.

Here’s an analogous example:

def get_length_of_shortest_list(lst): return len(min(lst, key=len)) print(get_length_of_shortest_list([[1], [2, 3], [4, 5, 6]]))

# 1 print(get_length_of_shortest_list([[1, [2, 3], 4], [5, 6], [7]]))

# 1 print(get_length_of_shortest_list([[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]]))

# 3

Method 3: min(len(x) for x in lst)

A Pythonic way to check the length of the shortest list is to combine a generator expression or list comprehension with the min() function without key. For instance, min(len(x) for x in lst) first turns all inner list into length integer numbers and passes this iterable into the min() function to get the result.

Here’s this approach on the same examples as before:

def get_length_of_shortest_list(lst): return min(len(x) for x in lst) print(get_length_of_shortest_list([[1], [2, 3], [4, 5, 6]]))

# 1 print(get_length_of_shortest_list([[1, [2, 3], 4], [5, 6], [7]]))

# 1 print(get_length_of_shortest_list([[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]]))

# 3

A good training effect can be obtained by studying the following tutorial on the topic—feel free to do so!

Training: Understanding List Comprehension in Python

Method 4: Naive For Loop

A not so Pythonic but still fine approach is to iterate over all lists in a for loop, check their length using the len() function, and compare it against the currently shortest list stored in a separate variable. After the termination of the loop, the variable contains the shortest list.

Here’s a simple example:

def get_shortest_list(lst): shortest = lst[0] if lst else None for x in lst: if len(x) < len(shortest): shortest = x return shortest print(get_shortest_list([[1], [2, 3], [4, 5, 6]]))

# [1] print(get_shortest_list([[1, [2, 3], 4], [5, 6], [7]]))

# [7] print(get_shortest_list([[[1], [2], [3]], [4, 5, [6]], [7, 8, 9, 10]]))

# [[1], [2], [3]] print(get_shortest_list([]))

# None

So many lines of code! At least does the approach also work when passing in an empty list due to the ternary operator used in the first line.

lst[0] if lst else None

If you need a refresher on the ternary operator, you should check out our blog tutorial.

Training Tutorial: The Ternary Operator — A Powerful Python Device

Note: If you need the length of the shortest list, you could simply replace the last line of the function with return len(shortest) , and you’re done!

Summary

You have learned about four ways to find the shortest list and its length from a Python list of lists (nested list):

• Method 1: min(lst, key=len)
• Method 2: len(min(lst, key=len))
• Method 3: min(len(x) for x in lst)
• Method 4: Naive For Loop

I hope you found the tutorial helpful, if you did, feel free to consider joining our community of likeminded coders—we do have lots of free training material!

Also, check out our tutorial on How to Find the Minimum of a List of Lists in Python?—it’s a slight variation!

Recommended Tutorial: Python Find Longest List in Dict of Lists

Problem Formulation and Solution Overview

This article will show you how to delete a line from a file in Python.

To make it more interesting, we have the following running scenario:

Rivers Clothing has a flat text file, rivers_emps.txt containing employee data. What happens if an employee leaves? They would like you to write code to resolve this issue.

Contents of rivers_emps.txt

100:Jane Smith 101:Daniel Williams 102:Steve Markham 103:Howie Manson 104:Wendy Wilson 105:Anne McEvans 106:Bev Doyle 107:Hal Holden 108:Mich Matthews 109:Paul Paulson

---

Question: How would we write code to remove this line?

We can accomplish this task by one of the following options:

• Method 1: Use List Comprehension
• Method 2: Use List Comprehension and Slicing
• Method 3: Use List Comprehension, Slicing and np.savetxt(),
• Method 4: Use pop()
• Method 5: Use remove()
• Method 6: Remove row(s) from a DataFrame

---

Method 1: Use List Comprehension

This example uses List Comprehension to remove a specific line from a flat text file.

orig_lines = [line.strip() for line in open('rivers_emps.txt')]

new_lines = [l for l in orig_lines if not l.startswith('102')] with open('rivers_01.txt', 'w') as fp: print(*new_lines, sep='\n', file=fp)

The above code uses List Comprehension to read in the contents of a flat text file to a List, orig_lines. If output to the terminal, the following displays.

['100:Jane Smith', '101:Daniel Williams', '102:Steve Markham', '103:Howie Manson', '104:Wendy Wilson', '105:Anne McEvans', '106:Bev Doyle', '107:Hal Holden', '108:Mich Matthews', '109:Paul Paulson']

Then, List Comprehension is used again to append each element to a new List only if the element does not start with 102. If output to the terminal, the following displays.

['100:Jane Smith', '101:Daniel Williams', '103:Howie Manson', '104:Wendy Wilson', '105:Anne McEvans', '106:Bev Doyle', '107:Hal Holden', '108:Mich Matthews', '109:Paul Paulson']

As you can see, the element starting with 102 has been removed.

Next, a new file, rivers_01.txt, is opened in write (w) mode and the List created above is written to the file with a newline (\n) character appended to each line. The contents of the file are shown below.

100:Jane Smith 101:Daniel Williams 103:Howie Manson 104:Wendy Wilson 105:Anne McEvans 106:Bev Doyle 107:Hal Holden 108:Mich Matthews 109:Paul Paulson

---

Method 2: Use List Comprehension and Slicing

This example uses List Comprehension and Slicing to remove a specific line from a flat text file.

orig_lines = [line.strip() for line in open('rivers_emps.txt')]

new_lines = orig_lines[0:2] + orig_lines[3:] with open('rivers_02.txt', 'w') as fp: fp.write('\n'.join(new_lines))

The above code uses List Comprehension to read in the contents of a flat text file to a List, orig_lines. If output to the terminal, the following displays.

['100:Jane Smith', '101:Daniel Williams', '102:Steve Markham', '103:Howie Manson', '104:Wendy Wilson', '105:Anne McEvans', '106:Bev Doyle', '107:Hal Holden', '108:Mich Matthews', '109:Paul Paulson']

Then Slicing is used to extract all elements, except element two (2). The results save to new_lines. If output to the terminal, the following displays.

100:Jane Smith 101:Daniel Williams 103:Howie Manson 104:Wendy Wilson 105:Anne McEvans 106:Bev Doyle 107:Hal Holden 108:Mich Matthews 109:Paul Paulson

As you can see, element two (2) has been removed.

Next, a new file, rivers_02.txt, is opened in write (w) mode and the List created above is written to the file with a newline (\n) character appended to each line. The contents of the file are shown below.

100:Jane Smith 101:Daniel Williams 103:Howie Manson 104:Wendy Wilson 105:Anne McEvans 106:Bev Doyle 107:Hal Holden 108:Mich Matthews 109:Paul Paulson

---

Method 3: Use Slicing and np.savetxt()

This example uses List Comprehension, Slicing and NumPy’s np.savetxt() function to remove a specific line from a flat text file.

Before moving forward, please ensure that the NumPy library is installed to ensure this code runs error-free.

import numpy as np orig_lines = [line.strip() for line in open('rivers_emps.txt')]

new_lines = orig_lines[0:2] + orig_lines[3:] np.savetxt('rivers_03.txt', new_lines, delimiter='\n', fmt='%s')

The first line imports the NumPy library.

The following line uses List Comprehension to read the contents of a flat text file to the List, orig_lines. If output to the terminal, the following displays.

['100:Jane Smith', '101:Daniel Williams', '102:Steve Markham', '103:Howie Manson', '104:Wendy Wilson', '105:Anne McEvans', '106:Bev Doyle', '107:Hal Holden', '108:Mich Matthews', '109:Paul Paulson']

Then Slicing is applied to extract all elements, except element two (2). The results save to new_lines. If output to the terminal, the following displays.

100:Jane Smith 101:Daniel Williams 103:Howie Manson 104:Wendy Wilson 105:Anne McEvans 106:Bev Doyle 107:Hal Holden 108:Mich Matthews 109:Paul Paulson

As you can see, element two (2) has been removed.

The last code line calls np.savetxt() and passes it three (3) arguments:

• The filename (‘rivers_03.txt‘).
• An iterable, in this case, a List (new_lines).
• A delimiter (appended to each line) – a newline character (\n).
• The format. Strings are defined as %s.

The contents of rivers_03.txt displays below.

100:Jane Smith 101:Daniel Williams 103:Howie Manson 104:Wendy Wilson 105:Anne McEvans 106:Bev Doyle 107:Hal Holden 108:Mich Matthews 109:Paul Paulson

---

Method 4: Use pop()

This example uses the pop() function to remove a specific line from a flat text file.

import numpy as np orig_lines = [line.strip() for line in open('rivers_emps.txt')]

orig_lines.pop(2)

np.savetxt('rivers_04.txt', orig_lines, delimiter='\n', fmt='%s')

The first line imports the NumPy library.

The following line uses List Comprehension to read in the contents of a flat text file to the List, orig_lines. If output to the terminal, the following displays.

['100:Jane Smith', '101:Daniel Williams', '102:Steve Markham', '103:Howie Manson', '104:Wendy Wilson', '105:Anne McEvans', '106:Bev Doyle', '107:Hal Holden', '108:Mich Matthews', '109:Paul Paulson']

Then, the pop() method is called and passed one (1) argument, the element’s index to remove.

In this case, it is the second element.

If this List was output to the terminal, the following would display.

100:Jane Smith 101:Daniel Williams 103:Howie Manson 104:Wendy Wilson 105:Anne McEvans 106:Bev Doyle 107:Hal Holden 108:Mich Matthews 109:Paul Paulson

As shown in Method 3, the results save to a flat text file. In this case, rivers_04.txt. The contents are the same as in the previous examples.

Note: The pop() function removes the appropriate index and returns the contents to capture if necessary.

---

Method 5: Use remove()

This example uses the remove() function to remove a specific line from a flat text file.

import numpy as np orig_lines = [line.strip() for line in open('rivers_emps.txt')]

orig_lines.remove('102:Steve Markham')

np.savetxt('rivers_05.txt', orig_lines, delimiter='\n', fmt='%s')

This code works exactly like the code in Method 4. However, instead of passing a location of the element to remove, this function requires the contents of the entire line you to remove.

Then, the remove() function is called and passed one (1) argument, the index to remove. In this case, it is the second element. If this List was output to the terminal, the following would display.

100:Jane Smith 101:Daniel Williams 103:Howie Manson 104:Wendy Wilson 105:Anne McEvans 106:Bev Doyle 107:Hal Holden 108:Mich Matthews 109:Paul Paulson

As shown in the previous examples, the results save to a flat text file. In this case, rivers_05.txt.

---

Bonus: Remove row(s) from a DataFrame

CSV files are also known as flat-text files. This code shows you how to easily remove single or multiple rows from a CSV file

import pandas as pd

import numpy as np staff = { 'First' : ['Alice', 'Micah', 'James', 'Mark'], 'Last' : ['Smith', 'Jones', 'Watts', 'Hunter'], 'Rate' : [30, 40, 50, 37], 'Age' : [23, 29, 19, 45]} indexes=['FName', 'LName', 'Rate', 'Age']

df = pd.DataFrame(staff, index=indexes) df1 = df.drop(index=['Age'])

df.to_csv('staff.csv', index=False)

Finxter Challenge
Find 2 Additional Ways to Remove Lines

---

Summary

This article has provided five (5) ways to delete a line from a file to select the best fit for your coding requirements.

Good Luck & Happy Coding!

---

Programmer Humor – Blockchain

Export data to an excel file is mainly used for taking a backup. When taking database backup, excel format is a convenient one to read and manage easily. For some applications exporting data is important to take a backup or an offline copy of the server database.

This article shows how to export data to excel using PHP. There are many ways to implement this functionality. We have already seen an example of data export from MySQL.

This article uses the PHPSpreadSheet library for implementing PHP excel export.

It is a popular library that supports reading, and writing excel files. It will smoothen the excel import-export operations through its built-in functions.

The complete example in this article will let create your own export tool or your application.

About this Example

It will show a minimal interface with the list of database records and an “Export to Excel” button. By clicking this button, it will call the custom ExportService created for this example.

This service instantiates the PHPSpreadsheet library class and sets the column header and values. Then it creates a writer object by setting the PHPSpreadsheet instance to output the data to excel.

Follow the below steps to let this example run in your environment.

1. Create and set up the database with data exported to excel.
2. Download the code at the end of this article and configure the database.
3. Add PHPSpreadSheet library and other dependencies into the application.

We have already used the PHPSpreadsheet library to store extracted image URLs.

1) Create and set up the database with data exported to excel

Create a database named “db_excel_export” and import the below SQL script into it.

structure.sql

--

-- Table structure for table `tbl_products`

-- CREATE TABLE `tbl_products` ( `id` int(8) NOT NULL, `name` varchar(255) NOT NULL, `price` double(10,2) NOT NULL, `category` varchar(255) NOT NULL, `product_image` text NOT NULL, `average_rating` float(3,1) NOT NULL

); --

-- Dumping data for table `tbl_products`

-- INSERT INTO `tbl_products` (`id`, `name`, `price`, `category`, `product_image`, `average_rating`) VALUES

(1, 'Tiny Handbags', 100.00, 'Fashion', 'gallery/handbag.jpeg', 5.0),

(2, 'Men\'s Watch', 300.00, 'Generic', 'gallery/watch.jpeg', 4.0),

(3, 'Trendy Watch', 550.00, 'Generic', 'gallery/trendy-watch.jpeg', 4.0),

(4, 'Travel Bag', 820.00, 'Travel', 'gallery/travel-bag.jpeg', 5.0),

(5, 'Plastic Ducklings', 200.00, 'Toys', 'gallery/ducklings.jpeg', 4.0),

(6, 'Wooden Dolls', 290.00, 'Toys', 'gallery/wooden-dolls.jpeg', 5.0),

(7, 'Advanced Camera', 600.00, 'Gadget', 'gallery/camera.jpeg', 4.0),

(8, 'Jewel Box', 180.00, 'Fashion', 'gallery/jewel-box.jpeg', 5.0),

(9, 'Perl Jewellery', 940.00, 'Fashion', 'gallery/perls.jpeg', 5.0); --

-- Indexes for dumped tables

-- --

-- Indexes for table `tbl_products`

--

ALTER TABLE `tbl_products` ADD PRIMARY KEY (`id`); --

-- AUTO_INCREMENT for dumped tables

-- --

-- AUTO_INCREMENT for table `tbl_products`

--

ALTER TABLE `tbl_products` MODIFY `id` int(8) NOT NULL AUTO_INCREMENT, AUTO_INCREMENT=10;

2) Download the code and configure the database

The source code contains the following files. This section explains the database configuration.

Once you download the excel export code from this page, you can find DataSource.php file in the lib folder. Open it and configure the database details in it as below.

<?php class DataSource

{ const HOST = 'localhost'; const USERNAME = 'root'; const PASSWORD = ''; const DATABASENAME = 'db_excel_export'; ... ...

?>

3) Add PHPSpreadSheet library and other dependencies into the application

When you see the PHPSpreadsheet documentation, it provides an easy to follow installation steps.

It gives the composer command to add the PHPSpreadsheet and related dependencies into the application.

composer require phpoffice/phpspreadsheet

For PHP version 7

Add the below specification to the composer.json file.

{ "require": { "phpoffice/phpspreadsheet": "^1.23" }, "config": { "platform": { "php": "7.3" } }
}

then run

composer update

Note: PHPSpreadsheet requires at least PHP 7.3 version.

How it works

Simple interface with export option

This page fetches the data from the MySQL database and displays it in a grid form. Below the data grid, this page shows an “Excel Export” button.

By clicking this button the action parameter is sent to the URL to call the excel export service in PHP.

index.php

<?php

require_once __DIR__ . '/lib/Post.php';

$post = new post();

$postResult = $post->getAllPost();

$columnResult = $post->getColumnName();

if (! empty($_GET["action"])) { require_once __DIR__ . '/lib/ExportService.php'; $exportService = new ExportService(); $result = $exportService->exportExcel($postResult, $columnResult);

}

?>

<html>

<head>

<meta name="viewport" content="width=device-width, initial-scale=1">

<link href="./style.css" type="text/css" rel="stylesheet" />

</head>

<body> <div id="table-container"> <table id="tab"> <thead> <tr> <th width="5%">Id</th> <th width="35%">Name</th> <th width="20%">Price</th> <th width="25%">Category</th> <th width="25%">product Image</th> <th width="20%">Average Rating</th> </tr> </thead> <tbody> <?php if (! empty($postResult)) { foreach ($postResult as $key => $value) { ?> <tr> <td><?php echo $postResult[$key]["id"]; ?></td> <td><?php echo $postResult[$key]["name"]; ?></td> <td><?php echo $postResult[$key]["price"]; ?></td> <td><?php echo $postResult[$key]["category"]; ?></td> <td><?php echo $postResult[$key]["product_image"]; ?></td> <td><?php echo $postResult[$key]["average_rating"]; ?></td> </tr> <?php } } ?> </tbody> </table> <div class="btn"> <form action="" method="POST"> <a href="<?php echo strtok($_SERVER["REQUEST_URI"]);?><?php echo $_SERVER["QUERY_STRING"];?>?action=export"><button type="button" id="btnExport" name="Export" value="Export to Excel" class="btn btn-info">Export to Excel</button></a> </form> </div> </div>

</body>

</html>

PHP model calls prepare queries to fetch data to export

This is a PHP model class that is called to read data from the database. The data array will be sent to the export service to build the excel sheet object.

The getColumnName() reads the database table column name array. This array will supply data to form the first row in excel to create a column header.

The getAllPost() reads the data rows that will be iterated and set the data cells with the values.

lib/Post.php

<?php

class Post

{ private $ds; public function __construct() { require_once __DIR__ . '/DataSource.php'; $this->ds = new DataSource(); } public function getAllPost() { $query = "select * from tbl_products"; $result = $this->ds->select($query); return $result; } public function getColumnName() { $query = "select * from INFORMATION_SCHEMA.COLUMNS where TABLE_NAME=N'tbl_products'"; $result = $this->ds->select($query); return $result; }

}

?>

PHP excel export service

This service helps to export data to the excel sheet. The resultant file will be downloaded to the browser by setting the PHP header() properties.

The $postResult has the row data and the $columnResult has the column data.

This example instantiates the PHPSpreadSheet library class and sets the column header and values. Then it creates a writer object by setting the spreadsheet instance to output the data to excel.

lib/ExportService.php

<?php

use PhpOffice\PhpSpreadsheet\IOFactory;

use PhpOffice\PhpSpreadsheet\Spreadsheet;

use PhpOffice\PhpSpreadsheet\Writer\Xlsx;

use PhpOffice\PhpSpreadsheet\Calculation\TextData\Replace;

require_once __DIR__ . '/../vendor/autoload.php'; class ExportService

{ public function exportExcel($postResult, $columnResult) { $spreadsheet = new Spreadsheet(); $spreadsheet->getProperties()->setTitle("excelsheet"); $spreadsheet->setActiveSheetIndex(0); $spreadsheet->getActiveSheet()->SetCellValue('A1', ucwords($columnResult[0]["COLUMN_NAME"])); $spreadsheet->getActiveSheet()->SetCellValue('B1', ucwords($columnResult[1]["COLUMN_NAME"])); $spreadsheet->getActiveSheet()->SetCellValue('C1', ucwords($columnResult[2]["COLUMN_NAME"])); $spreadsheet->getActiveSheet()->SetCellValue('D1', ucwords($columnResult[3]["COLUMN_NAME"])); $spreadsheet->getActiveSheet()->SetCellValue('E1', str_replace('_', ' ', ucwords($columnResult[4]["COLUMN_NAME"], '_'))); $spreadsheet->getActiveSheet()->SetCellValue('F1', str_replace('_', ' ', ucwords($columnResult[5]["COLUMN_NAME"], '_'))); $spreadsheet->getActiveSheet() ->getStyle("A1:F1") ->getFont() ->setBold(true); $rowCount = 2; if (! empty($postResult)) { foreach ($postResult as $k => $v) { $spreadsheet->getActiveSheet()->setCellValue("A" . $rowCount, $postResult[$k]["id"]); $spreadsheet->getActiveSheet()->setCellValue("B" . $rowCount, $postResult[$k]["name"]); $spreadsheet->getActiveSheet()->setCellValue("C" . $rowCount, $postResult[$k]["price"]); $spreadsheet->getActiveSheet()->setCellValue("D" . $rowCount, $postResult[$k]["category"]); $spreadsheet->getActiveSheet()->setCellValue("E" . $rowCount, $postResult[$k]["product_image"]); $spreadsheet->getActiveSheet()->setCellValue("F" . $rowCount, $postResult[$k]["average_rating"]); $rowCount ++; } $spreadsheet->getActiveSheet() ->getStyle('A:F') ->getAlignment() ->setWrapText(true); $spreadsheet->getActiveSheet() ->getRowDimension($rowCount) ->setRowHeight(- 1); } $writer = IOFactory::createWriter($spreadsheet, 'Xls'); header('Content-Type: text/xls'); $fileName = 'exported_excel_' . time() . '.xls'; $headerContent = 'Content-Disposition: attachment;filename="' . $fileName . '"'; header($headerContent); $writer->save('php://output'); }

}

?>

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