Unleashing the Power of Python Pandas: Extracting CSV Columns Containing JSON Data

Extracting JSON Data from CSV Files using Python Pandas

To extract JSON data from a CSV file using Python Pandas, follow these steps:

1. Import necessary libraries

   import pandas as pd
   import json

2. Read the CSV file

   df = pd.read_csv('example.csv')

3. Identify the column containing JSON data

   In our example, the “metadata” column contains JSON data.

4. Apply the json.loads() function

   Use the `json.loads()` function to parse the JSON data in the “metadata” column:

   df['metadata'] = df['metadata'].apply(json.loads)

5. Extract specific JSON data

   Now you can extract specific JSON data using the `apply()` function with a lambda function:

   df['age'] = df['metadata'].apply(lambda x: x['age'])

   This creates a new column “age” with the extracted values.

Example Code

import pandas as pd
import json

# Read the CSV file
df = pd.read_csv('example.csv')

# Apply the json.loads() function
df['metadata'] = df['metadata'].apply(json.loads)

# Extract specific JSON data
df['age'] = df['metadata'].apply(lambda x: x['age'])
df['occupation'] = df['metadata'].apply(lambda x: x['occupation'])

# Print the resulting DataFrame
print(df)

Tips and Variations

• Handling malformed JSON data**: Use the `try-except` block to handle malformed JSON data:

df['metadata'] = df['metadata'].apply(lambda x: json.loads(x) if x else {})

• Extracting nested JSON data**: Use the `apply()` function with a lambda function to extract nested JSON data:

df['nested_data'] = df['metadata'].apply(lambda x: x['nested_key']['value'])

• Converting JSON data to separate columns**: Use the `pd.json_normalize()` function to convert JSON data into separate columns:

df_json = pd.json_normalize(df['metadata'])

• Handling large datasets**: Use the `dask` library to handle large datasets and improve performance:

import dask.dataframe as dd

ddf = dd.from_pandas(df, npartitions=2)