Reading JSON (read_json) in Data Analysis Python - Mini Project: Build & Apply

Reading JSON Data with pandas

📖 Scenario: You work at a small online store. The store keeps its product data in JSON format. You want to load this data into Python to analyze it easily.

🎯 Goal: Load a JSON string into a pandas DataFrame, then filter products by price.

📋 What You'll Learn

Create a JSON string variable with product data

Create a price threshold variable

Use pandas read_json to load the JSON string into a DataFrame

Filter the DataFrame to keep only products with price above the threshold

Print the filtered DataFrame

💡 Why This Matters

🌍 Real World

Many companies store data in JSON format. Loading JSON into DataFrames helps analyze and visualize data easily.

💼 Career

Data analysts and scientists often read JSON data from APIs or files to prepare data for analysis.

1

Create JSON string with product data

Create a variable called json_data that holds this exact JSON string: [{"product": "Pen", "price": 1.5}, {"product": "Notebook", "price": 3.0}, {"product": "Eraser", "price": 0.5}]

Data Analysis Python

# Create the variable json_data with the exact JSON string
# Your code here

Hint

Use single quotes outside and double quotes inside for the JSON string.

2

Set price threshold

Create a variable called price_threshold and set it to 1.0

Data Analysis Python

json_data = '[{"product": "Pen", "price": 1.5}, {"product": "Notebook", "price": 3.0}, {"product": "Eraser", "price": 0.5}]'
# Create price_threshold variable and set it to 1.0
# Your code here

Hint

Just assign 1.0 to price_threshold.

3

Load JSON and filter products

Import pandas as pd. Use pd.read_json to load json_data into a DataFrame called df. Then create a new DataFrame called filtered_df that contains only rows where the price column is greater than price_threshold.

Data Analysis Python

json_data = '[{"product": "Pen", "price": 1.5}, {"product": "Notebook", "price": 3.0}, {"product": "Eraser", "price": 0.5}]'
price_threshold = 1.0
# Import pandas as pd
# Load json_data into df using pd.read_json
# Create filtered_df with rows where price > price_threshold
# Your code here

Hint

Use square brackets to filter the DataFrame by condition.

4

Print filtered DataFrame

Print the filtered_df DataFrame to show products with price above the threshold.

Data Analysis Python

import pandas as pd

json_data = '[{"product": "Pen", "price": 1.5}, {"product": "Notebook", "price": 3.0}, {"product": "Eraser", "price": 0.5}]'
price_threshold = 1.0

df = pd.read_json(json_data)
filtered_df = df[df['price'] > price_threshold]
# Print filtered_df
# Your code here

Hint

Use print(filtered_df) to display the filtered products.

Practice

1. What does the pandas.read_json() function do in data analysis?

easy

A. It saves a DataFrame to a CSV file.

B. It converts a DataFrame into a JSON string.

C. It loads JSON data into a DataFrame for easy analysis.

D. It visualizes JSON data as a chart.

Solution

1. Step 1: Understand the purpose of read_json

   The function pandas.read_json() is designed to read JSON formatted data and convert it into a DataFrame.

2. Step 2: Compare options with function behavior

   Only It loads JSON data into a DataFrame for easy analysis. correctly describes this behavior. Options B, C, and D describe other unrelated functions.

3. Final Answer:

   It loads JSON data into a DataFrame for easy analysis. -> Option C

4. Quick Check:

   read_json loads JSON to DataFrame [OK]

Hint: Remember: read_json reads JSON into DataFrame [OK]

Common Mistakes:

• Confusing read_json with to_json (writing JSON)
• Thinking it saves data instead of loading
• Assuming it creates visualizations

2. Which of the following is the correct syntax to read a JSON file named data.json into a DataFrame using pandas?

easy

A. df = pd.read_json(data.json)

B. df = pd.read_json('data.json')

C. df = pd.read_json("data.json", header=None)

D. df = pd.read_json[data.json]

Solution

1. Step 1: Check correct function call syntax

   The function pd.read_json() requires the filename as a string argument inside parentheses.

2. Step 2: Identify correct string usage

   df = pd.read_json('data.json') uses single quotes correctly. df = pd.read_json(data.json) misses quotes, causing a NameError. df = pd.read_json("data.json", header=None) uses an invalid header parameter (not supported by read_json). df = pd.read_json[data.json] uses square brackets which is invalid syntax.

3. Final Answer:

   df = pd.read_json('data.json') -> Option B

4. Quick Check:

   Use parentheses and quotes for filename [OK]

Hint: Use parentheses and quotes for file name in read_json [OK]

Common Mistakes:

• Forgetting quotes around filename
• Using square brackets instead of parentheses
• Passing variable without defining it
• Using invalid parameters like header= (confusing with read_csv)

3. Given the JSON string '{"name": ["Alice", "Bob"], "age": [25, 30]}', what will be the output of pd.read_json(json_str)?

medium

A. A DataFrame with columns 'name' and 'age' and two rows for Alice and Bob.

B. A DataFrame with two rows and columns named 0 and 1.

C. A Series object with keys 'name' and 'age'.

D. An error because JSON string is invalid.

Solution

1. Step 1: Understand JSON structure

   The JSON string has keys 'name' and 'age' with lists as values, representing columns with two entries each.

2. Step 2: Behavior of pd.read_json on this JSON

   By default, pd.read_json() interprets this as columns with lists of values, creating a DataFrame with columns 'name' and 'age' and two rows.

3. Final Answer:

   A DataFrame with columns 'name' and 'age' and two rows for Alice and Bob. -> Option A

4. Quick Check:

   JSON keys become columns, lists become rows [OK]

Hint: Keys become columns, lists become rows in DataFrame [OK]

Common Mistakes:

• Thinking JSON keys become rows
• Expecting a Series instead of DataFrame
• Assuming JSON string is invalid

4. What is wrong with this code snippet?

import pandas as pd
json_str = '{"0": {"name": "Alice", "age": 25}, "1": {"name": "Bob", "age": 30}}'
df = pd.read_json(json_str)
print(df)

medium

A. The JSON string is missing quotes around keys.

B. The print statement is incorrect syntax.

C. pd.read_json cannot read JSON strings, only files.

D. The JSON string format requires orient='index' to read correctly.

Solution

1. Step 1: Analyze JSON structure

   The JSON string has numeric keys '0' and '1' with nested dictionaries representing rows.

2. Step 2: Understand read_json orient parameter

   By default, read_json expects columns orientation. Here, the data is oriented by index, so orient='index' is needed to parse correctly.

3. Final Answer:

   The JSON string format requires orient='index' to read correctly. -> Option D

4. Quick Check:

   Use orient='index' for nested dicts keyed by row labels [OK]

Hint: Use orient='index' for JSON keyed by row labels [OK]

Common Mistakes:

• Ignoring orient parameter causing wrong DataFrame
• Assuming JSON keys need quotes (they have quotes)
• Thinking read_json can't read strings
• Misreading print syntax

5. You have a JSON file where each entry is a list of values representing rows, like this:

[ ["Alice", 25], ["Bob", 30] ]

How do you read this JSON into a DataFrame with columns 'name' and 'age'?

hard

A. Use pd.read_json('file.json', orient='records') then assign columns manually.

B. Use pd.read_json('file.json', orient='values', columns=['name', 'age'])

C. Use pd.read_json('file.json', orient='split') with columns parameter.

D. Use pd.read_json('file.json', orient='records', names=['name', 'age'])

Solution

1. Step 1: Understand JSON structure and orient options

   The JSON is a list of lists, which matches the 'records' orient but without keys for columns.

2. Step 2: Read JSON and assign columns

   Using orient='records' reads the data as rows but columns are unnamed, so columns must be assigned manually after reading.

3. Final Answer:

   Use pd.read_json('file.json', orient='records') then assign columns manually. -> Option A

4. Quick Check:

   Assign columns manually when JSON has no keys [OK]

Hint: Assign columns after reading list-of-lists JSON [OK]

Common Mistakes:

• Using non-existent orient values like 'values'
• Expecting read_json to assign columns automatically
• Confusing 'split' orient with list-of-lists JSON