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Python Requests Tutorial Guide for Beginner

Python is a standout amongst the most well known and broadly utilized programming dialects and has supplanted many programming dialects in the business. There is plenty of reasons why Python is well known among engineers, and one of them is that it has an incredibly huge gathering of libraries that clients can work with. The effortlessness of Python has pulled in numerous engineers to make new libraries for AI. As a result of the enormous gathering of libraries, Python is winding up being extremely among AI specialists. Today we shall discuss Python requests in detail. The blog covers the following topics-

What is the Requests Resource?

Requests is an Apache2 Licensed HTTP library, written in Python. It is intended to be utilized by people to interface with the language. This implies you don't need to add inquiry strings to URLs physically, or structure encodes your POST information. Try not to stress if that looks bad to you. It will in due time.

What can Requests do?

Requests will enable you to send HTTP/1.1 solicitations utilizing Python. With it, you can include substance like headers, structure information, multipart records, and parameters through basic Python libraries. It likewise enables you to get to the reaction information of Python similarly. Solicitations enable you to send natural, grass-sustained HTTP/1.1 solicitations, without the requirement for physical work. There's no compelling reason to physically add inquiry strings to your URLs or to shape encode your POST information. Keep-alive and HTTP association pooling are 100% programmed, on account of urllib3.

In programming, a library is a gathering or pre-arranged choice of schedules, capacities, and activities that a program can utilize. These components are frequently alluded to as modules and put away in object format.

Libraries are significant because you load a module and exploit all that it offers without expressly connecting to each program that depends on them. They are independent so that you can construct your very own projects with them, but then they stay separate from different projects. 

The Python Standard Library

While The Python Language Reference portrays the careful sentence structure and semantics of the Python language, this library instructional pamphlet depicts the standard library that is conveyed with Python. It likewise portrays a portion of the discretionary segments that are generally incorporated into Python distributions.

Python's standard library is broad, offering a wide scope of offices as shown by the long chapter by chapter guide recorded underneath. The library contains worked in modules (written in C) that give access to framework usefulness, for example, a record I/O that would somehow be unavailable to Python software engineers, just as modules written in Python that give institutionalized answers for some issues that happen in ordinary programming. A portion of these modules is expressly intended to support and upgrade the versatility of Python programs by abstracting without end-stage points of interest into platform impartial APIs.

The Python installers for the Windows platform, for the most part, incorporate the whole standard library and regularly likewise incorporate numerous extra segments. For Unix-like working frameworks Python is ordinarily given as a gathering of bundles, so it might be important to utilize the bundling apparatuses gave the working framework to get a few or the majority of the discretionary segments.

Notwithstanding the standard library, there is a developing gathering of a few thousand segments (from individual projects and modules to bundles and whole application improvement systems), accessible from the Python Package Index. 

How to Install Requests?

Fortunately, there are a couple of approaches to introduce the Requests library. To see the full rundown of alternatives available to you, you can see the authority introduce documentation for Requests here.

You can easily make use of options such as pip, easy_install, or tarball.

In case that you'd preferably work with source code, you can get that on GitHub, also.

With the end goal of this guide, we are going to utilize pip to introduce the library.

Type the following, in your Python interpreter,

pip install requests 

Making a GET Request

It is genuinely direct to send an HTTP solicitation utilizing Requests. You begin by bringing in the module and after that, create the solicitation. Look at the model:

import requests
req = requests.get('')

All in all, all the data is put away in some place, right?

Truly, it is put away in a Response item called as req.

Suppose, for instance; you need the encoding of a page with the goal that you can check it or use it elsewhere. This should be possible utilizing the req.encoding property.

An additional in addition to is that you can likewise separate numerous highlights like the status code, for instance (of the solicitation). This should be possible utilizing the req.status_code property.

req.encoding # returns 'utf-8'
req.status_code # returns 200

We can likewise get to the treats that the server sent back. This is finished utilizing req.cookies, as direct as that! Additionally, you can get the reaction headers also. This is finished by utilizing req.headers.

Do take note of that the req.headers property will restore a case-obtuse word reference of the reaction headers. Things being what they are, what does this suggest?

This implies req.headers['Content-Length'], req.headers[content-length'] and req.headers[CONTENT-LENGTH'] will all arrival the estimation of the simply the 'Content-Length' reaction header

Read: Top 10 Python Libraries For Machine Learning

We can also check if the response obtained is a well-formed HTTP redirect (or not) that could have been processed automatically using the req.is_redirect property. This will return True or False based on the response obtained.

You can also get the time elapsed between sending the request and getting back a response using another property. Take a guess? Yes, it is the req.elapsed property.

Remember the URL that you initially passed to the get() function? Well, it can be different than the final URL of the response for any reason, and this includes redirects as well.

And to see the actual response URL, you can use the req.url property.

# importing the requests library 
import requests 
# api-endpoint 
URL = ""
# location given here 
location = "delhi technological university"
# defining a params dict for the parameters to be sent to the API 
PARAMS = {'address':location} 
# sending get request and saving the response as response object 
r = requests.get(url = URL, params = PARAMS) 
# extracting data in json format 
data = r.json() 
# extracting latitude, longitude and formatted address  
# of the first matching location 
latitude = data['results'][0]['geometry']['location']['lat'] 
longitude = data['results'][0]['geometry']['location']['lng'] 
formatted_address = data['results'][0]['formatted_address'] 
# printing the output 
print("Latitude:%s\nLongitude:%s\nFormatted Address:%s"
      %(latitude, longitude,formatted_address))

Wouldn't you say that getting this data about the page is decent? Yet, indeed you most presumably need to get to the genuine substance, right?

If the substance you are getting to is content, you can generally utilize the req.text property to get to it. Do take note of what the substance is then parsed as Unicode as it were. You can pass this encoding with which to decipher this content utilizing the req.encoding property as we examined before.

In the case of non-text responses, you can access them very easily. It's done in binary format when you use req.content. This module will automatically decode gzip and deflate transfer-encodings for us. This can be very helpful when you are dealing directly with media files. Also, you can access the JSON-encoded content of the response as well, that is if it exists, using the req.json()function.

On account of non-content reactions, you can get to them all around effectively. It's done in the parallel organization when you use req.content. This module will consequently interpret gzip and flatten move encodings for us. This can be extremely useful when you are managing media documents. Additionally, you can get to the JSON-encoded substance of the reaction too, that is on the off chance that it exists, utilizing the req.json()function.

Important points to infer :

PARAMS = {'address':location}

The URL for a GET request, by and large, conveys a few parameters with it. For solicitations library, parameters can be characterized as a lexicon. These parameters are later parsed down and added to the base URL or the programming interface endpoint.

To comprehend the parameters job, attempt to print r.url after the reaction article is made. You will see something like this:

This is the real URL on which GET requests are usually made

r = requests.get(URL = URL, params = PARAMS)

Here we make a reaction object 'r' which will store the solicitation reaction. We use requests. Get () strategy since we are sending a GET demand. The two contentions we pass our URL and the parameters word reference.

data = r.json()

Presently, to recover the information from the reaction object, we have to change over the crude reaction content into a JSON type information structure. This is accomplished by utilizing JSON() strategy. At long last, we extricate the required data by parsing down the JSON type object.

If it is necessary, you can likewise get the crude reaction from the server just by utilizing req.raw. Do remember that you should pass stream=True in the solicitation to get the crude reaction according to require.

However, a few documents that you download from the web utilizing the Requests module may have tremendous size, right? All things considered, in such cases, it won't be savvy to stack the entire reaction or document in the memory without a moment's delay. In any case, it is prescribed that you download a record in pieces or lumps utilizing the iter_content(chunk_size = 1, decode_unicode = False) method.

In this way, this strategy repeats over the reaction information in chunk_size number of bytes immediately. What's more, when the stream=True has been determined to the solicitation, this technique will abstain from perusing the entire record into memory without a moment's delay for simply the large responses.

Do take note of that the chunk_size parameter can be either a number or None. In any case, when set to a whole number worth, chunk_size decides the number of bytes that ought to be perfused into the memory without a moment's delay.

At the point when chunk_size is set to None and stream is set to True, the information will be perused as it lands in whatever size of lumps are gotten as and when they seem to be. When chunk_size is set to None and stream is set to False, every one of the information will be returned as a solitary piece of information as it were.

Read: Python vs Java : Which Programming Language is Best for Your Career?

Passing Parameters in Request URLs

You frequently need to send a type of information in the URL's inquiry string. If you were building the URL by hand, this information would be given as key/esteem combines in the URL after a question mark, for example, Solicitations enable you to give these contentions as a lexicon of strings, utilizing the params catchphrase contention. For instance, in the event that you needed to pass key1=value1and key2=value2 to, you would utilize the accompanying code:

payload = {'key1': 'value1', 'key2': 'value2'}
r = requests.get('', params=payload)

You would see that the  given URL has been correctly encoded by correctly printing the URL:


Please make a Note that any of the required dictionary keys whose value is None is not going to be added to the URL’s query string.

You can additionally pass on a list of items as a value:

payload = {'key1': 'value1', 'key2': ['value2', 'value3']}
r = requests.get('', params=payload)

Response Content

We can also read the delivery content of the given server’s response. For this, consider the GitHub timeline again:

import requests
r = requests.get('')

Requests will naturally disentangle content from the server. Most Unicode charsets are consistently decoded.

When you make a solicitation, Requests makes instructed surmises about the encoding of the reaction depends on the HTTP headers. The content encoding speculated by Requests is utilized when you get to r.text. You can discover what encoding Requests are utilizing, and transform it, utilizing the r.encodingproperty:

r.encoding = 'ISO-8859-1'

If you change the encoding, Requests will utilize the new benefit of r.encoding at whatever point you call r.text. You should need to do this in any circumstance where you can apply unique rationale to work out what the encoding of the substance will be. For instance, HTML and XML can determine their encoding in their body. In the circumstances like this, you should utilize r.content to discover the encoding, and after that set r.encoding. This will give you a chance to utilize r.text with the right encoding.

Requests will likewise utilize custom encodings if you need them. On the off chance that you have made your very own encoding and unrolled it with the codecs module, you can essentially utilize the codec name as the benefit of r.encoding and Requests will deal with the disentangling for you.

Binary Response Content

You can likewise get to the reaction body as bytes, for non-content requests:


The gzip and flatten move encodings are consequently decoded for you.

For instance, to make a picture from parallel information returned by a solicitation, you can utilize the accompanying code:

from PIL import Image
from io import BytesIO
i =

JSON Response Content

There’s also a built-in JSON decoder, in case you’re dealing with JSON data:

import requests
 r = requests.get('')
[{u'repository': {u'open_issues': 0, u'url': '

If the JSON disentangling fizzles, r.json() raises a special case. For instance, if the reaction gets a 204 (No Content), or if the reaction contains invalid JSON, endeavoring r.json() raises ValueError: No JSON article could be decoded.

It ought to be noticed that the accomplishment of the call to r.json() does not demonstrate the achievement of the reaction. A few servers may restore a JSON object in a bombed reaction (for example mistake subtleties with HTTP 500). Such JSON will be decoded and returned. To watch that a solicitation is fruitful, user.raise_for_status() or check r.status_code is the thing that you anticipate.

Raw Response Content

In the uncommon case that you'd like to get the crude attachment reaction from the server, you can get to r.raw. On the off chance that you need to do this, ensure you set stream=True in your underlying solicitation. When you do, you can do this:

r = requests.get('', stream=True)

When all is said in done, be that as it may, you should utilize an example like this to spare what is being streamed to a record:

with open(filename, 'wb') as fd:
    for chunk in r.iter_content(chunk_size=128):

Utilizing Response.iter_content will deal with a great deal of what you would somehow or another need to deal with when utilizing Response.raw straightforwardly. When gushing a download, the above is liked and prescribed an approach to recover the substance. Note that chunk_size can be unreservedly changed by a number that may better accommodate your use cases.

Custom Headers

On the off chance that you'd like to add HTTP headers to a solicitation, just go in a direction to the headers parameter.

For instance, we haven’t yet specified our user-agent in the last example:

url = ''
 headers = {'user-agent': 'my-app/0.0.1'}
 r = requests.get(url, headers=headers)

Note: Custom headers are given less priority than progressively explicit wellsprings of data. For example:

  • Authorization headers set with headers= will be superseded if qualifications are determined in .netrc, which thusly will be abrogated by the auth= parameter.
  • Authorization headers will be evacuated on the off chance that you get diverted off-have.
  • Proxy-Authorization headers will be superseded as a substitute qualification gave in the URL.

Content-Length headers will be abrogated when we can decide the length of the substance.

Besides, Requests does not change its conduct at all dependent on which custom headers are determined. The headers are basically passed on into the last solicitation.

Read: What is the Average Salary of a Python Developer in the USA?

Note: All header esteems must be a string, byte string, or Unicode. While allowed, it's encouraged to abstain from passing Unicode header values.

Session Objects

Now and then it is helpful to safeguard certain parameters over different solicitations. The Session item does precisely that. For instance, it will endure treating overall information solicitations made utilizing a similar session.

The Session article utilizes urllib3's association pooling. This implies the hidden TCP association will be reused for every one of the solicitations made to a similar host.

This can essentially support the exhibition. You can likewise utilize strategies for the Requests object with the Session object.

Sessions are additionally useful when you need to send similar information overall solicitations. For instance, on the off chance that you choose to send a treat or a client operator header with every one of the solicitations to a given space, you can utilize Session objects. Here is an example of that:

import requests
ssn = requests.Session()
ssn.cookies.update({'visit-month': 'February'})
reqOne = ssn.get('')
# prints information about "visit-month" cookie
reqTwo = ssn.get('', cookies={'visit-year': '2017'})
# prints information about "visit-month" and "visit-year" cookie
reqThree = ssn.get('')
# prints information about "visit-month" cookie

As should be obvious, the "visit-month" session treat is sent with every one of the three solicitations. In any case, the "visit-year" treat is sent uniquely during the subsequent solicitation. There is no notice of the "visit-year" treat in the third solicitation as well. This affirms the way that treats or another informational index on individual solicitations won't be sent with other session demands.


Requests will need consequently decade any substance pulled from a server. Yet, most Unicode character sets are consistently decoded at any rate.

When you make a solicitation to a server, the Requests library make an informed theory about the encoding for the reaction, and it does this dependent on the HTTP headers. The encoding that is speculated will be utilized when you get to the r.text record.

Through this document, you can perceive what encoding the Requests library is utilizing, and transform it if need be. This is conceivable gratitude to the r.encoding property you'll discover in the record.

If and when you change the encoding esteem, Requests will utilize the new sort since you call r.text in your code.

print r.encoding
 r.encoding = ‘ISO-8859-1’

Making a POST request

# importing the requests library 
import requests 
# defining the api-endpoint  
# your API key here 
# your source code here 
source_code = ''' 
print("Hello, world!") 
a = 1 
b = 2 
print(a + b) 
# data to be sent to api 
data = {'api_dev_key':API_KEY, 
# sending post request and saving response as response object 
r = = API_ENDPOINT, data = data) 
# extracting response text  
pastebin_url = r.text 
print("The pastebin URL is:%s"%pastebin_url) 

This model discloses how to glue your source_code to by sending POST solicitation to the PASTEBIN API.

Above all else, you should produce an API key by joining here and afterwards get to your API key here.

Important features of this code:

data = {'api_dev_key':API_KEY,

Here once more, we should pass a few information to the API server. We store this information as a word reference.

r = = API_ENDPOINT, data = data)

Here we make a reaction object 'r' which will store the solicitation reaction. We use strategy since we are sending a POST demand. The two contentions we pass our URL and the information word reference.

pastebin_url = r.text

Accordingly, the server forms the information sent to it and sends the pastebin URL of your source_code, which can be just gotten to by r.text. strategy could be utilized for some different errands too like filling and presenting the web shapes, posting on your FB course of events utilizing the Facebook Graph API, and so forth.

Here are some important points to ponder upon:

  • When the technique is GET, all structure information is encoded into the URL, annexed to the activity URL as inquiry string parameters. With POST, structure information shows up inside the message body of the HTTP demand.
  • In GET strategy, the parameter information is constrained to what we can stuff into the solicitation line (URL). Most secure to utilize under 2K of parameters, a few servers handle up to 64K.No such issue in POST technique since we send information in the message body of the HTTP demand, not the URL.
  • Only ASCII characters are taken into consideration information to be sent in the GET method. There is no such confinement in POST technique.
  • GET is less secure contrasted with POST since information sent is a piece of the URL. Along these lines, GET technique ought not to be utilized when sending passwords or other delicate information.

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