Reading JSON with read_json in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a JSON file into a pandas DataFrame.

Pandas

import pandas as pd

df = pd.[1]('data.json')

Drag options to blanks, or click blank then click option'

Aread_excel

Bread_csv

Cread_table

Dread_json

Attempts:

3 left

💡 Hint

Common Mistakes

Using read_csv instead of read_json

Forgetting to import pandas

Using read_excel for JSON files

The read_json function reads JSON files into a pandas DataFrame.

2fill in blank

medium

Complete the code to read JSON data from a string using pandas.

Pandas

import pandas as pd

json_str = '{"name": ["Alice", "Bob"], "age": [25, 30]}'
df = pd.read_json([1])

Drag options to blanks, or click blank then click option'

A'data.json'

Bjson_file

Cjson_str

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a filename string instead of the JSON string variable

Not using quotes correctly around the JSON string

Using a variable name that does not exist

To read JSON from a string, pass the string variable to read_json.

3fill in blank

hard

Fix the error in the code to correctly read a JSON file with pandas.

Pandas

import pandas as pd

df = pd.read_json('data.json', [1]='records')

Drag options to blanks, or click blank then click option'

Aorient

Bformat

Cstyle

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect parameter names like 'format' or 'style'

Misspelling 'orient'

Passing wrong values to the parameter

The orient parameter specifies the expected JSON string format.

4fill in blank

hard

Fill both blanks to create a DataFrame from JSON data and display the first rows.

Pandas

import pandas as pd

json_path = 'data.json'
df = pd.[1](json_path)
print(df.[2]())

Drag options to blanks, or click blank then click option'

Aread_json

Bread_csv

Chead

Dtail

Attempts:

3 left

💡 Hint

Common Mistakes

Using read_csv instead of read_json

Using tail() instead of head() to preview data

Forgetting parentheses after head or tail

Use read_json to load JSON data and head() to show the first rows.

5fill in blank

hard

Fill all three blanks to read JSON data, select a column, and convert it to a list.

Pandas

import pandas as pd

df = pd.[1]('data.json')
names = df[[2]].[3]()

Drag options to blanks, or click blank then click option'

Aread_json

B'name'

Ctolist

D'age'

Attempts:

3 left

💡 Hint

Common Mistakes

Using read_csv instead of read_json

Selecting the wrong column name

Using to_list() instead of tolist()

Read JSON with read_json, select the 'name' column, and convert it to a list with tolist().

Practice

1. What does the pandas function read_json do?

easy

A. It loads JSON data into a pandas DataFrame for analysis.

B. It converts a DataFrame into a JSON string.

C. It saves a DataFrame as a CSV file.

D. It reads CSV files into a DataFrame.

Solution

1. Step 1: Understand the purpose of read_json

   The function read_json is designed to load JSON formatted data into a pandas DataFrame.

2. Step 2: Compare with other pandas functions

   Functions like to_json convert DataFrames to JSON, and read_csv reads CSV files, so they do not match the description.

3. Final Answer:

   It loads JSON data into a pandas DataFrame for analysis. -> Option A

4. Quick Check:

   read_json loads JSON to DataFrame = C [OK]

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

Common Mistakes:

• Confusing read_json with to_json
• Thinking it reads CSV files
• Assuming it saves data instead of loading

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

easy

A. pd.read_json(data.json)

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

C. pd.readjson('data.json')

D. pd.read_json['data.json']

Solution

1. Step 1: Check the function name and usage

   The correct pandas function is read_json with an underscore, called as a function with parentheses.

2. Step 2: Validate the argument format

   The file name must be a string inside quotes, so 'data.json' is correct.

3. Final Answer:

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

4. Quick Check:

   Correct function call with string filename = B [OK]

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

Common Mistakes:

• Missing quotes around filename
• Using wrong function name like readjson
• Using square brackets instead of parentheses

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

medium

A. An error because the JSON string is not a file path.

B. A Series object with the JSON string as a single entry.

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

D. A DataFrame with one column containing the entire JSON string.

Solution

1. Step 1: Understand input format

   The JSON string is a list of objects, each with 'name' and 'age' keys, suitable for conversion to a DataFrame.

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

   Passing a JSON string with list of dicts creates a DataFrame with columns matching keys and rows for each dict.

3. Final Answer:

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

4. Quick Check:

   List of dicts JSON string -> DataFrame rows and columns = A [OK]

Hint: List of dicts JSON string becomes DataFrame rows [OK]

Common Mistakes:

• Expecting error for string input
• Thinking it returns Series instead of DataFrame
• Assuming entire JSON is one column

4. What is wrong with this code snippet?

import pandas as pd
json_data = '{"name": "Alice", "age": 30}\n{"name": "Bob", "age": 25}'
df = pd.read_json(json_data)

medium

A. The function read_json cannot read JSON strings.

B. The JSON string is invalid because it uses double quotes.

C. The variable json_data should be a file path, not a string.

D. The JSON string is multiline but lines=True is missing in read_json.

Solution

1. Step 1: Identify JSON format

   The JSON string contains multiple JSON objects separated by newlines, which is a JSON lines format.

2. Step 2: Check pandas parameter for JSON lines

   To read JSON lines, read_json requires lines=True to parse each line as a JSON object.

3. Final Answer:

   The JSON string is multiline but lines=True is missing in read_json. -> Option D

4. Quick Check:

   Multiline JSON needs lines=True in read_json = D [OK]

Hint: Use lines=True for newline-separated JSON objects [OK]

Common Mistakes:

• Ignoring the need for lines=True with JSON lines
• Thinking double quotes are invalid in JSON
• Assuming read_json only reads files

5. You have a JSON file where data is stored as a dictionary of lists like this:

{"name": ["Alice", "Bob"], "age": [30, 25]}

Which read_json parameter should you use to correctly load this into a DataFrame?

hard

A. orient='columns'

B. orient='index'

C. orient='records'

D. orient='split'

Solution

1. Step 1: Identify JSON structure

   The JSON is a dictionary with keys as column names and values as lists of column data, matching the 'columns' orientation.

2. Step 2: Match pandas orient parameter

   Using orient='columns' tells pandas to interpret keys as columns and values as column data lists.

3. Final Answer:

   orient='columns' -> Option A

4. Quick Check:

   Dict of lists JSON needs orient='columns' = A [OK]

Hint: Use orient='columns' for dict of lists JSON format [OK]

Common Mistakes:

• Using orient='records' which expects list of dicts
• Using orient='index' which expects dict of dicts
• Not specifying orient and getting wrong DataFrame