I'm facing a challenging issue with converting JSON strings to Python data dictionaries for analysis in Pandas. Despite researching other solutions, I haven't found one that works for my specific case.
In the past, I relied on CSVs and the read_csv function in Pandas for my analysis. However, I've now transitioned to extracting data directly from PostgreSQL.
Using SQLAlchemy to connect to my engine and execute queries is not a problem. My entire script functions smoothly until it reaches the point where I need to convert a column (specifically, the 'config' column in the example below) from JSON to a Python dictionary. The objective of this conversion is to count the number of responses under the "options" field within the "config" column.
df = pd.read_sql_query('SELECT questions.id, config from questions ', engine)
df = df['config'].apply(json.loads)
df = pd.DataFrame(df.tolist())
df['num_options'] = np.array([len(row) for row in df.options])
Executing this code results in the error message "TypeError: expected string or buffer". Attempting to convert the 'config' column data from object to string did not resolve the issue (another error occurred, such as "ValueError: Expecting property name...").
For reference, here's a snippet of data from a single cell in the 'config' column (the code should return '6' in this instance as there are 6 options):
{"graph_by":"series","options":["Strongbow Case Card/Price Card","Strongbow Case Stacker","Strongbow Pole Topper","Strongbow Base wrap","Other Strongbow POS","None"]}
My suspicion is that SQLAlchemy manipulates JSON strings in a way different from when handling CSV data. This discrepancy could explain the unexpected errors I encounter.