Web Scraping using JSON and API

JSON–from the web to Python

Wow, congrats! You've just queried your first API programmatically in Python and printed the text of the response to the shell. However, as you know, your response is actually a JSON, so you can do one step better and decode the JSON. You can then print the key-value pairs of the resulting dictionary. That's what you're going to do now!


  • Pass the variable url to the requests.get() function in order to send the relevant request and catch the response, assigning the resultant response message to the variable r.
  • Apply the json() method to the response object r and store the resulting dictionary in the variable json_data.
  • # Import package
    import requests

    # Assign URL to variable: url
    url = 'http://www.omdbapi.com/?apikey=72bc447a&t=social+network'

    # Package the request, send the request and catch the response: r
    r = requests.get(url)

    # Decode the JSON data into a dictionary: json_data
    json_data = r.json()


    # Print each key-value pair in json_data
    for k in json_data.keys():
        print(k + ': ', json_data[k])

 

Comments

Post a Comment

Popular posts from this blog

Binomial Test in Python

  Binomial Test Let’s imagine that we are analyzing the percentage of customers who make a purchase after visiting a website. 1000 customers visited the site this month, and 58 of them made a purchase. The marketing department reports that historical data suggests about 72 of every 1000 visitors make a purchase. Thus, they estimate that the probability of any particular customer making a purchase is 7.2%. We would like to know if this month’s number, 58 purchases, is significantly different from normal or a reasonable fluctuation due to random chance. In previous exercises, we collected samples of numerical information (eg. order price) and then used the mean and standard deviation of those samples to make comparisons. In contrast, we now have a sample where each unit (a visitor) falls into one of two discrete categories: “made a purchase” “did not make a purchase” Instead of comparing sample means, we want to compare the percent in the “made a purchase” category to some expectatio...
Read more

Python Syntax and Functions Part2 (Summary Statistics)

The  .agg()  method allows you to apply your own custom functions to a DataFrame, as well as apply functions to more than one column of a DataFrame at once, making your aggregations super efficient. In the custom function for this exercise, "IQR" is short for inter-quartile range, which is the 75th percentile minus the 25th percentile. It's an alternative to standard deviation that is helpful if your data contains   import numpy as np def iqr(column):     return column.quantile(0.75) - column.quantile(0.25) # Update to print IQR and median of temperature_c, fuel_price_usd_per_l, & unemployment print(sales[["temperature_c", "fuel_price_usd_per_l", "unemployment"]].agg([iqr, np.median])) Calulating Cumulative Statistics Syntax = df.drop_duplicates sales_1_1 = sales_1_1.sort_values("date") # Get the cumulative sum of weekly_sales, add as cum_weekly_sales col sales_1_1["cum_weekly_sales"] = sales_1_1["weekly_sales...
Read more

Slicing and Indexing in Python Pandas

 # Make a list of cities to subset on cities = ["Moscow", "Saint Petersburg"] # Subset temperatures using square brackets print(temperatures[temperatures["city"].isin(cities)]) # Subset temperatures_ind using .loc[] print(temperatures_ind.loc[cities]) temperatures_ind = temperatures.set_index(['country','city']) # List of tuples: Brazil, Rio De Janeiro & Pakistan, Lahore rows_to_keep = [('Brazil','Rio De Janeiro'),('Pakistan','Lahore')] # Subset for rows to keep print(temperatures_ind.loc[rows_to_keep]) # Sort temperatures_ind by index values print(temperatures_ind.sort_index()) # Sort temperatures_ind by index values at the city level print(temperatures_ind.sort_index(level="city")) # Sort temperatures_ind by country then descending city print(temperatures_ind.sort_index(level=["country", "city"], ascending = [True, False])) Slice rows with code like  df.loc[("a", ...
Read more