Working on API technique using IBM learning platform

[peter2680]

import numpy as np
import pandas as pd

# You can also use this section to suppress warnings generated by your code:
def warn(*args, **kwargs):
    pass
import warnings
warnings.warn = warn
warnings.filterwarnings('ignore')

URL="https://web.archive.org/web/20230902185326/https://en.wikipedia.org/wiki/List_of_countries_by_GDP_%28nominal%29"

# Extract tables from webpage using Pandas. Retain table number 3 as the required dataframe.

# Replace the column headers with column numbers
#df.columns = range(df.shape[1])

# Extract tables from webpage using Pandas. Retain table number 3 as the required dataframe.
tables = pd.read_html(URL)
df = tables[3]

# Replace the column headers with column numbers
df.columns = range(df.shape[1])

# Retain columns with index 0 and 2 (name of country and value of GDP quoted by IMF)
df = df[[0,2]]

# Retain the Rows with index 1 to 10, indicating the top 10 economies of the world.
df = df.iloc[1:11,:]

# Assign column names as "Country" and "GDP (Million USD)"
df.columns = ['Country','GDP (Million USD)']

df

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# Change the data type of the 'GDP (Million USD)' column to integer. Use astype() method.
df['GDP (Million USD)'] = df['GDP (Million USD)'].astype(int)

# Convert the GDP value in Million USD to Billion USD
df[['GDP (Million USD)']] = df[['GDP (Million USD)']]/1000

# Use numpy.round() method to round the value to 2 decimal places.
df[['GDP (Million USD)']] = np.round(df[['GDP (Million USD)']], 2)

# Rename the column header from 'GDP (Million USD)' to 'GDP (Billion USD)'
df.rename(columns = {'GDP (Million USD)' : 'GDP (Billion USD)'})

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# Load the DataFrame to the CSV file named "Largest_economies.csv"
df.to_csv('./Largest_economies.csv')

[peter2680]

## HTTP and Requests

import requests
import os 
from PIL import Image
from IPython.display import IFrame

url='https://www.ibm.com/'
r=requests.get(url)

r.status_code
print(r.request.headers)
print("request body:", r.request.body)

header=r.headers
print(r.headers)

# Use single quotation marks for defining string
url='https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/IDSNlogo.png'

path=os.path.join(os.getcwd(),'image.png')

with open(path,'wb') as f:
    f.write(r.content)

Image.open(path)

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url='https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/data/Example1.txt'
path=os.path.join(os.getcwd(),'example1.txt')
r=requests.get(url)
with open(path,'wb') as f:
    f.write(r.content)

url_get='http://httpbin.org/get'
r=requests.get(url_get,params=payload)

print("request body:", r.request.body)
Post Requests
Like a GET request, a POST is used to send data to a server, but the POST request sends the data in a request body. In order to send the Post Request in Python, in the URL we change the route to POST:

url_post='http://httpbin.org/post'
This endpoint will expect data as a file or as a form. A form is convenient way to configure an HTTP request to send data to a server.

To make a POST request we use the post() function, the variable payload is passed to the parameter  data :

r_post=requests.post(url_post,data=payload)
Comparing the URL from the response object of the GET and POST request we see the POST request has no name or value pairs.

print("POST request URL:",r_post.url )
print("GET request URL:",r.url)
We can compare the POST and GET request body, we see only the POST request has a body:

print("POST request body:",r_post.request.body)
print("GET request body:",r.request.body)
We can view the form as well:

r_post.json()['form']