Reading JSON (read_json) in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of the read_json function in pandas?

The read_json function in pandas is used to load data from a JSON file or JSON string into a pandas DataFrame, making it easy to analyze and manipulate structured data.

intermediate

Which parameter in read_json helps to specify the orientation of the JSON data?

The orient parameter specifies the expected format of the JSON string or file, such as 'records', 'split', 'index', 'columns', or 'values'. This helps pandas correctly parse the JSON structure into a DataFrame.

beginner

True or False: read_json can read JSON data directly from a URL.

True. read_json can read JSON data from a local file path, a JSON string, or directly from a URL, making it flexible for different data sources.

intermediate

What happens if the JSON data is not in the expected format when using read_json?

If the JSON data format does not match the specified or default orient, pandas may raise a parsing error or produce an incorrect DataFrame. It's important to know the JSON structure or adjust the orient parameter accordingly.

beginner

Example: How do you read a JSON file named 'data.json' into a DataFrame using pandas?

Use the code: <br><pre>import pandas as pd<br>df = pd.read_json('data.json')</pre><br>This loads the JSON data from the file into the DataFrame <code>df</code>.

What does the read_json function return?

A A JSON string

B A Python dictionary

C A pandas DataFrame

D A list of JSON objects

read_json loads JSON data into a pandas DataFrame for easy data analysis.

Which orient option treats each JSON object as a row in the DataFrame?

A 'split'

B 'records'

C 'index'

D 'columns'

The 'records' orient treats each JSON object as a row, which is common for JSON arrays of objects.

Can read_json read JSON data from a web URL?

A No, only local files

B Only if the JSON is compressed

C Only if the URL is HTTPS

D Yes, it can read from URLs

read_json supports reading JSON data directly from URLs.

If JSON data is an array of arrays, which orient should you use?

A 'values'

B 'records'

C 'index'

D 'columns'

'values' orient treats JSON as an array of arrays, mapping directly to DataFrame values.

What will happen if you do not specify the correct orient for your JSON data?

A You might get a parsing error or wrong DataFrame

B The JSON file will be deleted

C pandas will guess correctly every time

D The function will return None

Incorrect orient can cause parsing errors or incorrect DataFrames.

Explain how you would use pandas to load JSON data from a URL into a DataFrame.

Think about how read_json can accept a URL string.

Describe the role of the orient parameter in read_json and why it matters.

Consider how JSON structure matches DataFrame layout.

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