How to Extract a Column from JSON in Python?

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In order to extract a column from a JSON object in Python, you need to first load the JSON data into a Python object (such as a dictionary or a list) using the json module. Once you have the JSON data loaded, you can access the desired column by indexing or iterating through the JSON object.

Here’s an example of extracting a column from a JSON object:

import json

# Example JSON data
json_data = '''
    "data": [
        {"name": "John", "age": 30, "city": "New York"},
        {"name": "Alice", "age": 25, "city": "Los Angeles"},
        {"name": "Bob", "age": 35, "city": "Chicago"}

# Load JSON data
data = json.loads(json_data)

# Extract "name" column
names = [item["name"] for item in data["data"]]

# Extract "age" column
ages = [item["age"] for item in data["data"]]

# Extract "city" column
cities = [item["city"] for item in data["data"]]

# Print extracted columns
print("Names:", names)
print("Ages:", ages)
print("Cities:", cities)

In this example, we have an example JSON object represented as a string in json_data. We load the JSON data using json.loads() to convert it into a Python dictionary called data. We then extract the desired columns from the “data” key in the data dictionary using list comprehension, which iterates through the list of items in the “data” key and extracts the corresponding values for the specified keys (“name”, “age”, and “city”) into separate lists (names, ages, and cities).

Note that the actual approach to extracting columns from JSON data may vary depending on the specific structure of your JSON object. You may need to adjust the code to match the structure of your JSON data. Additionally, you should handle exceptions appropriately to account for cases where the specified keys may not exist in the JSON data or the JSON data structure may not be as expected. You can use techniques like dict.get() or dict.setdefault() to provide default values or handle missing keys gracefully.

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