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What is XML?

XML stands for eXtensible Markup Language. It was designed to store and transport small to medium amounts of data and is widely used for sharing structured information.

Python enables you to parse and modify XML documents. In order to parse XML document, you need to have the entire XML document in memory. In this tutorial, we will see how we can use XML minidom class in Python to load and parse XML files.

How to Parse XML using minidom

We have created a sample XML file that we are going to parse.

Step 1) Create Sample XML file

Inside the file, we can see the first name, last name, home, and the area of expertise (SQL, Python, Testing and Business)

Step 2) Use the parse function to load and parse the XML file

Once we have parsed the document, we will print out the “node name” of the root of the document and the “firstchild tagname”. Tagname and nodename are the standard properties of the XML file.

Import the xml.dom.minidom module and declare file that has to be parsed (myxml.xml)

This file carries some basic information about an employee like first name, last name, home, expertise, etc.

We use the parse function on the XML minidom to load and parse the XML file

We have variable doc and doc gets the result of the parse function

We want to print the nodename and child tagname from the file, so we declare it in print function

Run the code- It prints out the nodename (#document) from the XML file and the first child tagname (employee) from the XML file

Note:

Nodename and child tagname are the standard names or properties of an XML dom.

Step 3) Call the list of XML tags from the XML document and printed out

Next, We can also call the list of XML tags from the XML document and printed out. Here we printed out the set of skills like SQL, Python, Testing and Business.

Declare the variable expertise, from which we going to extract all the expertise name employee is having

Use the dom standard function called “getElementsByTagName”

This will get all the elements named skill

Declare loop over each one of the skill tags

Run the code- It will give list of four skills

How to Write XML Node

We can create a new attribute by using the “createElement” function and then append this new attribute or tag to the existing XML tags. We added a new tag “BigData” in our XML file.

You have to code to add the new attribute (BigData) to the existing XML tag

Then, you have to print out the XML tag with new attributes appended with the existing XML tag

To add a new XML and add it to the document, we use code “doc.create elements”

This code will create a new skill tag for our new attribute “Big-data”

Add this skill tag into the document first child (employee)

Run the code- the new tag “big data” will appear with the other list of expertise

XML Parser Example

Python 2 Example

import xml.dom.minidom def main(): # use the parse() function to load and parse an XML file doc = xml.dom.minidom.parse("Myxml.xml"); # print out the document node and the name of the first child tag print doc.nodeName print doc.firstChild.tagName # get a list of XML tags from the document and print each one expertise = doc.getElementsByTagName("expertise") print "%d expertise:" % expertise.length for skill in expertise: print skill.getAttribute("name") #Write a new XML tag and add it into the document newexpertise = doc.createElement("expertise") newexpertise.setAttribute("name", "BigData") doc.firstChild.appendChild(newexpertise) print " " expertise = doc.getElementsByTagName("expertise") print "%d expertise:" % expertise.length for skill in expertise: print skill.getAttribute("name") if name == "__main__": main();

Python 3 Example

import xml.dom.minidom def main(): # use the parse() function to load and parse an XML file doc = xml.dom.minidom.parse("Myxml.xml"); # print out the document node and the name of the first child tag print (doc.nodeName) print (doc.firstChild.tagName) # get a list of XML tags from the document and print each one expertise = doc.getElementsByTagName("expertise") print ("%d expertise:" % expertise.length) for skill in expertise: print (skill.getAttribute("name")) # Write a new XML tag and add it into the document newexpertise = doc.createElement("expertise") newexpertise.setAttribute("name", "BigData") doc.firstChild.appendChild(newexpertise) print (" ") expertise = doc.getElementsByTagName("expertise") print ("%d expertise:" % expertise.length) for skill in expertise: print (skill.getAttribute("name")) if __name__ == "__main__": main(); How to Parse XML using ElementTree

ElementTree is an API for manipulating XML. ElementTree is the easy way to process XML files.

We are using the following XML document as the sample data:

Reading XML using ElementTree:

we must first import the xml.etree.ElementTree module.

import xml.etree.ElementTree as ET

Now let’s fetch the root element:

root = tree.getroot()

Following is the complete code for reading above xml data

import xml.etree.ElementTree as ET tree = ET.parse('items.xml') root = tree.getroot() # all items data print('Expertise Data:') for elem in root: for subelem in elem: print(subelem.text)

Following is the complete code for reading above xml data

output:

Expertise Data: SQL Python Summary:

Python enables you to parse the entire XML document at one go and not just one line at a time. In order to parse XML document you need to have the entire document in memory.

To parse XML document

Import xml.dom.minidom

Use the function “parse” to parse the document ( doc=xml.dom.minidom.parse (file name);

Call the list of XML tags from the XML document using code (=doc.getElementsByTagName( “name of xml tags”)

To create and add new attribute in XML document

Use function “createElement”

You're reading Python Xml File – How To Read, Write & Parse

How To Read Common File Formats In Python – Csv, Excel, Json, And More!

I have recently come across a lot of aspiring data scientists wondering why it’s so difficult to import different file formats in Python. Most of you might be familiar with the read_csv() function in Pandas but things get tricky from there.

How to read a JSON file in Python? How about an image file? How about multiple files all at once? These are questions you should know the answer to – but might find it difficult to grasp initially.

And mastering these file formats is critical to your success in the data science industry. You’ll be working with all sorts of file formats collected from multiple data sources – that’s the reality of the modern digital age we live in.

So in this article, I will introduce you to some of the most common file formats that a data scientist should know. We will learn how to read them in Python so that you are well prepared before you enter the battlefield!

I highly recommend taking our popular ‘Python for Data Science‘ course if you’re new to the Python programming language. It’s free and acts as the perfect starting point in your journey.

Table of Contents

Extracting from Zip files in Python

Reading Text files in Python

Import CSV file in Python using Pandas

Reading Excel file in Python

Importing Data from Database using Python

Working with JSON files in Python

Reading data from Pickle files in Python

Web Scraping with Python

Reading Image files using PIL

Read multiple files using Glob

Extracting from Zip Files in Python

Zip files are a gift from the coding gods. It is like they have fallen from heaven to save our storage space and time. Old school programmers and computer users will certainly relate to how we used to copy gigantic installation files in Zip format!

But technically speaking, ZIP is an archive file format that supports lossless data compression. This means you don’t have to worry about your data being lost in the compression-decompression process (Silicon Valley, anyone?).

Here, let’s look at how you can open a ZIP folder with Python. For this, you will need the zip file library in Python.

I have zipped all the files required for this article in a separate ZIP folder, so let’s extract them!

Once you run the above code, you can view the extracted files in the same folder as your Python script:

Reading Text Files in Python

Text files are one of the most common file formats to store data. Python makes it very easy to read data from text files.

Python provides the open() function to read files that take in the file path and the file access mode as its parameters. For reading a text file, the file access mode is ‘r’. I have mentioned the other access modes below:

‘w’ – writing to a file

‘r+’ or ‘w+’ – read and write to a file

‘a’ – appending to an already existing file

‘a+’ – append to a file after reading

Python provides us with three functions to read data from a text file:

readline(n) – This function allows you to read n bytes from the file but not more than one line of information

Let us see how these functions differ in reading a text file:

The read() function imported all the data in the file in the correct structured form.

By providing a number in the read() function, we were able to extract the specified amount of bytes from the file.

Using readline(), only a single line from the text file was extracted.

View the code on Gist.

Here, the readline() function extracted all the text file data in a list format.

Reading CSV Files in Python

Ah, the good old CSV format. A CSV (or Comma Separated Value) file is the most common type of file that a data scientist will ever work with. These files use a “,” as a delimiter to separate the values and each row in a CSV file is a data record.

These are useful to transfer data from one application to another and is probably the reason why they are so commonplace in the world of data science.

If you look at them in the Notepad, you will notice that the values are separated by commas:

The Pandas library makes it very easy to read CSV files using the read_csv() function:



But CSV can run into problems if the values contain commas. This can be overcome by using different delimiters to separate information in the file, like ‘t’ or ‘;’, etc. These can also be imported with the read_csv() function by specifying the delimiter in the parameter value as shown below while reading a TSV (Tab Separated Values) file:

View the code on Gist.

Reading Excel Files in Python

Most of you will be quite familiar with Excel files and why they are so widely used to store tabular data. So I’m going to jump right to the code and import an Excel file in Python using Pandas.

Pandas has a very handy function called read_excel() to read Excel files:

But an Excel file can contain multiple sheets, right? So how can we access them?

For this, we can use the Pandas’ ExcelFile() function to print the names of all the sheets in the file:

After doing that, we can easily read data from any sheet we wish by providing its name in the sheet_name parameter in the read_excel() function:

View the code on Gist.

And voila!

Importing Data from a Database using Python

When you are working on a real-world project, you would need to connect your program to a database to retrieve data. There is no way around it (that’s why learning SQL is an important part of your data science journey).

Data in databases is stored in the form of tables and these systems are known as Relational database management systems (RDBMS). However, connecting to RDBMS and retrieving the data from it can prove to be quite a challenging task. Here’s the good news – we can easily do this using Python’s built-in modules!

One of the most popular RDBMS is SQLite. It has many plus points:

Lightweight database and hence it is easy to use in embedded software

35% faster reading and writing compared to the File System

No intermediary server required. Reading and writing are done directly from the database files on the disk

Cross-platform database file format. This means a file written on one machine can be copied to and used on a different machine with a different architecture

There are many more reasons for its popularity. But for now, let’s connect with an SQLite database and retrieve our data!

You will need to import the sqlite3 module to use SQLite. Then, you need to work through the following steps to access your data:

Create a connection with the database connect(). You need to pass the name of your database to access it. It returns a Connection object

Once you have done that, you need to create a cursor object using the cursor() function. This will allow you to implement SQL commands with which you can manipulate your data

You can execute the commands in SQL by calling the execute() function on the cursor object. Since we are retrieving data from the database, we will use the SELECT statement and store the query in an object

Store the data from the object into a dataframe by either calling fetchone(), for one row, or fecthall(), for all the rows, function on the object

And just like that, you have retrieved the data from the database into a Pandas dataframe!

A good practice is to save/commit your transactions using the commit() function even if you are only reading the data.

You can read more about SQLite in Python from the official documentation.

Working with JSON Files in Python

JSON (JavaScript Object Notation) files are lightweight and human-readable to store and exchange data. It is easy for machines to parse and generate these files and are based on the JavaScript programming language.

JSON files store data within {} similar to how a dictionary stores it in Python. But their major benefit is that they are language-independent, meaning they can be used with any programming language – be it Python, C or even Java!

This is how a JSON file looks:

Python provides a json module to read JSON files. You can read JSON files just like simple text files. However, the read function, in this case, is replaced by json.load() function that returns a JSON dictionary.

Once you have done that, you can easily convert it into a Pandas dataframe using the pandas.DataFrame() function:

But you can even load the JSON file directly into a dataframe using the pandas.read_json() function as shown below:

View the code on Gist.

Reading Data from Pickle Files in Python

Pickle files are used to store the serialized form of Python objects. This means objects like list, set, tuple, dict, etc. are converted to a character stream before being stored on the disk. This allows you to continue working with the objects later on. These are particularly useful when you have trained your machine learning model and want to save them to make predictions later on.

So, if you serialized the files before saving them, you need to de-serialize them before you use them in your Python programs. This is done using the pickle.load() function in the pickle module. But when you open the pickle file with Python’s open() function, you need to provide the ‘rb’ parameter to read the binary file.

Web Scraping using Python

Web Scraping refers to extracting large amounts of data from the web. This is important for a data scientist who has to analyze large amounts of data.

Python provides a very handy module called requests to retrieve data from any website. The requests.get() function takes in a URL as its parameter and returns the HTML response as its output. The way it works is summarized in the following steps:

It packages the Get request to retrieve data from webpage

Sends the request to the server

Receives the HTML response and stores in a response object

For this example, I want to show you a bit about my city – Delhi. So, I will retrieve data from the Wikipedia page on Delhi:

But as you can see, the data is not very readable. The tree-like structure of the HTML content retrieved by our request is not very comprehensible. To improve this readability, Python has another wonderful library called BeautifulSoup.

BeautifulSoup is a Python library for parsing the tree-like structure of HTML and extracting data from the HTML document.

Find more about BeautifulSoup in this here.

Right, let’s see the wonder of BeautifulSoup.

To make it work, we need to pass the text response from the request object to BeautifulSoup() which creates its own object – “soup” in this case. Calling prettify() on BeautifulSoup object parses the tree-like structure of the HTML document:

You must have noticed the difference in the output. We have a more structured output in this case!

Now, we can extract the title of the webpage by calling the title() function of our soup object:

The webpage has a lot of pictures of the famous monuments in Delhi and other things related to Delhi. Let’s try and store these in a local folder.

We will need the Python urllib library to retrieve the URL of the images that we want to store. It has a urllib.request() function that is used for opening and reading URLs. Calling the urlretrieve() function on this object allows us to download objects denoted by the URL to a local file:

View the code on Gist.

The images are stored in the “img” tag in HTML. These can be found by calling find_all() on the soup object. After this, we can iterate over the image and get its source by calling the get() function on the image object. The rest is handled by our download function:

View the code on Gist.

Excellent! Now its time to read those images. We’ll cover that in the next section.

Reading Image Files using PIL

But before you jump on to working with these problems, you need to know how to open your images in Python. Let’s see how we can do that by retrieving images from the webpage that we stored in our local folder.

You will need the Python PIL (Python Image Library) for this job.

Simply call the open() function in the Image module of PIL and pass in the path to your image:

Voila! We have our image to work with! And isn’t my Delhi just beautiful?

Read Multiple Files using Glob

And now, what if you want to read multiple files in one go? That’s quite a common challenge in data science projects.

Python’s Glob module lets you traverse through multiple files in the same location. Using glob.glob(), we can import all the files from our local folder that match a special pattern.

These filename patterns can be made using different wildcards like  “*” (for matching multiple characters), “?” (for matching any single character), or ‘[0-9]’ (for matching any number). Let’s see glob in action below.

When importing multiple .py files from the same directory as your Python script, we can use the “*” wildcard:

When importing only a 5 character long Python file, we can use the “?” wildcard:

When importing an image file containing a number in the filename, we can use the “[0-9]” wildcard:

Earlier, we imported a few images from the Wikipedia page on Delhi and saved them in a local folder. I will retrieve these images using the glob module and then display them using the PIL library:

View the code on Gist.

Also Read: The Evolution and Future of Data Science Innovation

End Notes

If you are looking to kickstart your journey in data science, I recommend going through some of our amazingly curated courses on Python and Machine Learning.

How To Write An Iso File To A Usb Drive Using Dd Command

If you’ve ever needed to create a bootable USB drive for a new operating system or to install software on a computer without an optical drive, you may have heard of the dd command. This is a powerful command-line tool that allows you to write an ISO file to a USB drive. In this article, we’ll take a closer look at how to use the dd command to write an ISO file to a USB drive on Linux.

Prerequisites

Before you can start writing an ISO file to a USB drive, you’ll need to make sure you have the following:

A USB drive with enough space to hold the contents of the ISO file.

An ISO file containing the operating system or software you want to install.

A Linux machine with the dd command installed.

Writing an ISO File to a USB Drive

To write an ISO file to a USB drive using the dd command, follow these steps:

Insert the USB drive into your Linux machine.

Open a terminal window and type the following command to find the device name of the USB drive:

$ sudo fdisk -l

Unmount the USB drive by typing the following command:

$ sudo umount /dev/sdb1

Make sure to replace /dev/sdb1 with the device name of your USB drive.

Write the ISO file to the USB drive by typing the following command:

$ sudo dd bs=4M if=/path/to/iso/file of=/dev/sdb status=progress && sync

Make sure to replace /path/to/iso/file with the path to your ISO file and /dev/sdb with the device name of your USB drive.

The dd command will start writing the ISO file to the USB drive. This may take some time depending on the size of the ISO file and the speed of your USB drive.

Once the dd command has finished writing the ISO file to the USB drive, you can eject the USB drive by typing the following command:

$ sudo eject /dev/sdb Understanding the DD Command

The dd command is a powerful tool that can be used for a variety of disk-related tasks. It stands for “data duplicator” and is often used for copying and converting data between disks, partitions, and files.

Here’s a breakdown of the different parts of the dd command we used to write an ISO file to a USB drive:

bs=4M: This sets the block size to 4 megabytes. This can help improve the speed of the write operation.

if=/path/to/iso/file: This specifies the input file, which in this case is the ISO file we want to write to the USB drive.

of=/dev/sdb: This specifies the output file, which in this case is the USB drive we want to write the ISO file to.

status=progress: This displays the progress of the write operation in real-time.

sync: This ensures that all data has been written to the USB drive before the command completes.

Conclusion

In this article, we’ve covered how to use the dd command to write an ISO file to a USB drive on Linux. This can be a useful skill to have if you need to create a bootable USB drive for a new operating system or install software on a computer without an optical drive. Remember to always double-check the device names and paths before running the dd command to avoid data loss.

How To Open A File In Append Mode With Python?

File handling in Python, among others, involves the task of opening a file in append mode which has its own importance in that scheme of things. Append mode makes it possible for you to add new content to a file without deleting or overwriting the existing data. Here, in this article, we will explore several different ways how you can open a file in append mode using Python; we make provision for some code examples with easy-to-follow detailed stepwise explanations.

Opening a File in Append Mode for Text Writing

In order to open a file in append mode for writing text, you can follow these steps:

Step 1: You make use of the open() function to open the file in append mode. You give the file path as the first argument and use the mode ‘a’ to indicate append mode.

Step 2: You then assign the returned file object to a variable for further operations, such as writing or reading.

Example # Open the file in append mode for text writing file = open('myfile.txt', 'a') # Perform operations on the file (e.g., write or read) # Close the file file.close() Opening a File in Append Mode for Binary Writing

In order that you can open a file in append mode for writing binary data, you can follow similar steps:

Step 1: You make use of the open() function to open the file in append mode. You give the file path as the first argument and use the mode ‘ab’ to indicate append mode for binary writing.

Step 2: You then assign the returned file object to a variable for further operations, such as writing or reading binary data.

Example

Let us suppose you have a binary file chúng tôi You can carry out or execute following operations on that file as follows.

# Open the file in append mode for binary writing file = open('myfile.bin', 'ab') # Perform operations on the file (e.g., write or read binary data) # Close the file file.close() Opening a File in Append Mode with Context Managers

It is important to note that Python also provides an efficient way to work with files using context managers. Context managers have this functionality that they automatically handle file closing, even if an exception occurs. Here is the way how you can open a file in append mode using a context manager:

Step 1: You make use of the ‘with’ statement and the open() function to open the file in append mode.

Step 2: You then assign the returned file object to a variable within the with block.

Example

# Open the file in append mode using a context manager with open('myfile.txt', 'a') as file: # Perform operations on the file (e.g., write or read) # The file is automatically closed outside the context manager Opening a File in Append Mode to Read Existing Content

In order that a file to be opened in append mode to read the existing content, you can follow these steps:

Step 1: You use the open() function to open the file in append mode. You give the file path as the first argument and use the mode ‘a+’ to indicate append mode for both reading and writing.

Step 2: You assign the returned file object to a variable for further operations, such as reading the existing content.

Example

Suppose we have a text file as shown below

#myfile.txt This is a test file # Open the file in append mode for reading and writing file = open('myfile.txt', 'a+') # Read the existing content from the file content = file.read() # Perform operations with the existing content print("Existing content:", content) # Close the file file.close()

When we run the above code we get the following output when the chúng tôi is opened and existing content is read.

Output #myfile.txt This is a test file Opening a File in Append Mode and Appending New Lines

If you want to open a file in append mode and add or append new lines to it, you can use the following steps:

Step 1: You use the open() function to open the file in append mode. You give the file path as the first argument and use the mode ‘a’ to indicate append mode.

Step 2: You make use of the write() method to add new lines to the file. Each line should be added separately using multiple write() statements.

Step 3: Finally, you can close the file without fail to make sure proper handling of system resources takes place.

Example

Suppose we have a text file as follows.

#myfile.txt This is a test file # Open the file in append mode file = open('myfile.txt', 'a') # Append new lines to the file file.write("Line 4n") file.write("Line 5n") file.write("Line 6n") # Close the file file.close()

When we run the above code we get the following output when the chúng tôi is opened.

Output #myfile.txt This is a test file Line 4 Line 5 Line 6

The routing task or operation of opening a file in append mode makes it possible for you to add new content to a file without overwriting the existing data. In this article, we have covered some examples with detailed explanations, that gave a demonstration of how to open a file in append mode for text writing, binary writing, and how to use a context manager for file handling. We also made available two additional examples that again showcased opening a file in append mode to read existing content and appending new lines to a file. By following the stepwise and easy-to-follow explanations and code examples, you can certainly gain a solid understanding of how to open files in append mode using Python. It should be kept in mind to close the file after your operations are done to maintain proper resource management.

How To Read Smartphone Specs – Part 2

Smartphones began as slow and weak pieces of hardware, but they have since evolved into very powerful machines. As a result, there are so many aspects to consider when reading up on a new phone. What do all of those numbers mean? Last week, in Part 1 , I broke down screen displays, processors, storage space, and battery life. Now it’s time to tackle much of what’s left.

Just like last time, we’ll start by pulling up the HTC One’s specs.

Let’s take a look at the camera first.

Camera

Much of the information here is more than you need to know. First, the most useful bit of information is what’s left off – megapixels (MP). The HTC One has a 4MP camera. Many of its competitors these days ship with 8 or 13MP. The Nokia Lumia 1020 has surprised people by shipping with a staggering 41MP. This spec determines how large the pictures you take are and how far you can digitally zoom before the picture looks fuzzy.

The rest of these specs are aimed towards people who have a strong understanding of cameras in general, and that’s an area that could take up an entire post on its own. Just know that a smartphone with optical image stabilization should have a more stable picture when you’re taking shots or recording videos. With the latter, 1080p means the videos you capture should look fine on a modern computer or TV.

Network

The first thing you want to know is whether your device is GSM or CDMA. This will determine which networks you can run your phone on. In the US, AT&T and T-Mobile use GSM, while Verizon and Sprint rely on CDMA. If you pick up a phone that only works with GSM carriers, it won’t work with the latter two. If your phone is GSM, though, you have the freedom to pop out the SIM card and switch around between various providers. CDMA carriers are typically more restrictive, requiring you to get a new phone entirely when you’re looking to switch. Keep that in mind before picking up a device.

After that, it helps to see which frequency bands your phone supports. If you’re buying it straight from a carrier, you’re already covered, as you know they’re giving you a device that will work on their network. But if you’re buying a phone online, be sure to check if your phone’s supported frequency bands match those that your wireless provider relies on.

Then there are network speeds. Most smartphones will connect to a 3G network just fine, but some budget devices won’t connect to one that’s 4G. This means that such a device won’t be able to access the faster data speeds that a 4G LTE network provides. This could be a problem if you plan on streaming music and video or downloading apps using your mobile network.

Other Essentials

GPS: You need GPS in order to use in-car navigation or most apps that want to pinpoint your precise location. This information is often used to suggest local stores, restaurants, events, and the like. It can also be used to provide accurate weather forecasts. Chances are your phone comes with this as well.

Accelerometer/Gyro sensor: These determine how fast your device is moving and how you’re holding it. Many games require these, such as racing games that simulate a steering wheel using the phone. Many devices also come with a digital compass, which can detect which cardinal direction it’s pointing in, just like a physical compass.

Conclusion

This isn’t a list of everything you could possibly want to know about smartphones, but hopefully it’s enough information to hold your hand through your next purchase. These things can be intimidating, especially if you’re dealing with a contract that will lock you in for two years. It’s worth taking your time and doing some research before making a commitment. After all, this device will go with you practically everywhere.

Bertel King, Jr.

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How To Read Someone’s Text Messages Without Their Phone

How to Read Someone’s Text Messages without Installing Software on their Phone

mSpy is the best tool for the job. Here are steps for tracking SMS from targeted Android Phones:

Step 1) Visit chúng tôi Enter your email address and purchase a plan based on your requirement. Once you have done that, you will receive the login credentials in your email.

Step 2) Next, you need to select the type of device you want to monitor. However, you must ensure you have full physical access to the device you want to spy on. Physical access is required ONLY during the installation. Later everything works remotely without access.

Step 3) Select your device manufacturer.

You have a wide range of Android options such as Samsung, Huawei, Xiaomi, Moto, Google Pixel, LG, or any other manufacturer.

Here, we have selected Samsung.

Step 4) Next, you need to select your Android Version and Disable the PlayProtect option.

Step 5) Download the app on your Mobile. Follow on-screen instructions and configure mSpy.

Once your mSpy account is set up, allow some time for the mSpy app to record the acidity and send it back to the server.

Step 7) Start tracking targeted devise SMS messages

After you have finished the initial app setup, visit the mSpy Dashboard and select SMS Messages.

Step 8) Read text messages of the targeted Phone

You can now read text messages without the target phone access.

Best Tools to See and Read Someone’s Message

Many spying tools are available in the market that allows reading someone’s text message without the target phone access. Following is a handpicked list of Top spy apps that allow you to access someone’s phone without accessing their phone.

1) mSpy – Best for sending, receiving, and deleting messages

mSpy is a phone tracker software that allows you to check the GPS location of the device. It also helps you to block inappropriate websites on the target cell phone.

After installing this spying app, you can get access through a web-based control panel. It is also possible to access your target’s text messages, shared media content, and GPS coordinates with this app.

#1 Top Pick

mSpy

5.0

Supported Phones: Android, iPhone, iPad

24/7 Support: Yes – Offers a toll-free helpline as well

Refund Policy: 14-Days Money-Back Guarantee

Visit mSpy

Features:

You can send, receive, and delete messages in real time and still remains undetected.

mSpy offers complete tech support and requires no jailbreaking or rooting of the target phone to work

mSpy app works in background mode.

Provides quick updates regarding their location within a specified time frame.

It offers GPS Tracking and a GEO Fencing feature

It supports Android and iOS devices.

Live Demo is available

Provide 24/7 Customer support via phone, chatbot, and email.

Key Specs:

Pricing: Plan starts at $11.66 for one month plan.

14-Days Money-Back Guarantee

2) uMobix – Best for reading texts without access of a phone

uMobix is a mobile spy app that is capable of monitoring a wide range of target phone activity, including phone calls, SMS, GPS location, social media, etc. This phone tracker app offers easy access to the target device messages instantly. Umboix app helps you to capture screenshots on the target phone.

#2

uMobix

4.9

Supported Phones: Android, iOS

24/7 Support: Yes

Refund Policy: 14-Days Money-Back Guarantee

Visit uMobix

Features:

You can read texts without access to a phone

Offers feature for live phone call tracking

Text message monitoring.

Provides an accurate real-time location of the user and with visited places’ history.

It supports Android and iOS devices

You can explore the Live Demo of the app

👍 Pros 👎 Cons

You will get timestamps and contact info. It offers limited features on the basic version.

It allows you to monitor call logs, intercept text messages, detect deleted messages and calls, restrict calls, etc. Provides one subscription for a single device.

You can have to access the photo gallery of a particular device.

Key Specs:

Pricing: Plans starting at $12.49/month. Discounts Offer on Yearly payments.

14-Days Money-Back Guarantee

3) Hoverwatch – Best spy app that always remains invisible

Hoverwatch is a mobile phone spy app that helps you spy SMS messages and calls on the targeted device’s phone. Further, it is invisible to the users of the target Android devices. This spying app also helps you check all the information received and sent by the device user.

#3

Hoverwatch

4.8

Supported Platforms: Windows, Mac, Android, iOS

24/7 Support: Yes

Free Trial: 3-Days Free Trial

Visit Hoverwatch

Features:

Once installed, this spy app can be downloaded and used from your online account.

You can keep track of your child’s or spouse’s browsing habits.

Offers GPS Location Tracking and 24/7 live customer support.

You can record and monitor browsing history.

You can explore the Live Demo of the app

It supports Windows, Mac, Android, iOS platforms.

👍 Pros 👎 Cons

You can record calls and SMS, tracks incoming/outgoing calls, and access phone book information. Provides geolocation tracking feature.

It supports social media like Facebook, WhatsApp, Snapchat, etc. Manual and complex installation process.

Key Specs:

Pricing: Plan starts $12.49 per month for a personal plan

3-Days Free Trial

4) KidGuard – Best for capturing screen shots remotely

KidGuard is a phone tracking app that helps you track your target device user’s activity remotely. You can access phone files from anywhere using this phone spying app. It is one of the best mobile phone spy apps that provide live alerts when your kids enter or exits the boundary.

#4

KidGuard

4.7

Supported Phones: Android, iOS, Windows

24/7 Support: Yes

Refund Policy: 30-Days Money-Back Guarantee

Visit KidGuard

Features:

With Kidguard, you can track your targeted device’s live GPS location.

It allows you to capture screenshots remotely.

You can monitor your browsing history

It provides real-time data sync via a 3G/4G network or Wi-Fi.

You can explore the Live Demo of the app

KidGuard offers GPS Location Tracking

It supported iOS, Android, and Windows devices.

👍 Pros 👎 Cons

You can monitor call logs, contacts, and messages, record call logs It does not allow monitor more than one device at a time.

KidGuard supports multiple languages. Does not offer any free trial version

It provides support 24/7 via email and chat.

Key Specs:

Refund Policy: 30-Days Money-Back Guarantee

30-Days Money-Back Guarantee

5) Flexispy – Best for recording surroundings using an ambient recording.

Flexispy is one of the best spying apps to read someone’s text message without access to the target phone for computers, mobile phones, and tablets. You can also uninstall or deactivate this app remotely. Flexispy lets you view all the sent and received messages with their contact and timestamp details.

#5

FlexiSPY

4.6

Supported Platforms: Android, iPhone, iPad, and PC

24/7 Support: Yes

Free Trial: 1-Day Free Trial

Visit FlexiSPY

Features:

You can record the surroundings using the ambient recording option.

Offers free remote installation service that runs in hidden or stealth mode.

You will get automatic remote updates.

Flexispy allows you to send SMS from your target phone remotely to any number.

Supported Platforms: Android, iOS, and Windows PC.

You can have access to a live demo

It offers support for GPS Location Tracking

👍 Pros 👎 Cons

You can monitor incoming/outgoing calls, contact list SMS, MMS, etc. Not a cheap spy app.

Supports social media apps like WhatsApp, Facebook, Instagram, Skype, Google, Hangouts, etc.

Key Specs:

Free Trial: 1-Day Free Trial

1-Day Free Trial

FAQs

Here are the Best apps to read someone’s text:

mSpy

uMobix

Hoverwatch

KidGuard

Flexispy

Yes, it is legal to hack text messages from someone else’s phone in the following conditions:

You own the phone and want to hack your children’s phone activities.

You are notifying the phone’s owner that their phone is being monitored

Moreover, using Free Phone spying app software on a spouse or another adult is NOT legal unless you notify them.

You can spy on an employee’s phone if he/she is indulged in any suspicious activities. It should be the company phone

Easy Interface: It should have an easy interface, and the app should be user-friendly and easy to use.

Runs in Stealth mode: You should make sure that your selected app should runs in stealth mode so that you can spy on someone’s text messages.

Offers Maximum features: Your selected Phone tracking app must offer features like remote tracking, remote camera control, and message interception.

Easy Setup Process: You need to make sure that your select mobile spy tool should have a minimum setup process. It allows you to save your time in installing.

Auto-detection: The app will automatically detect when a new message arrives and will start reading it aloud. It should also allow you to keep a copy of the message if you want.

Cost-effective: Your selected spying app should not be very costly and should be able to provide value for your money.

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