Convert Dictionary to DataFrame

Using Python and pandas, convert the following dictionary into a DataFrame. Each top-level key is a target column name, and each value is a list of [row_id, cell_value] pairs. Use the first element of each pair as the row identifier, align rows by row_id across all keys, and produce a wide DataFrame with one row per row_id and one column per top-level key.

Input:

data = {
    'text1': [[123, '2'], [345, '3']],
    'text2': [[123, '4'], [345, '4']]
}

Expected structure:

• row_id = 123 -> text1 = '2', text2 = '4'
• row_id = 345 -> text1 = '3', text2 = '4'

Write Python code to create the DataFrame.

Convert Dictionary to DataFrame

Given a key-value style table kv_cells with columns (key_name, row_id, cell_value), pivot the data to produce one row per row_id and one column per key_name (text1, text2).

Table kv_cells

column	type	constraints
key_name	VARCHAR	NOT NULL
row_id	INTEGER	NOT NULL
cell_value	VARCHAR	NOT NULL

Sample data:

cell_value	key_name	row_id
2	text1	123
3	text1	345
4	text2	123
4	text2	345

Expected output

A wide result with one row per row_id and separate columns for text1 and text2, aligned by row_id. Cell values remain strings as in the source.

row_id	text1	text2
123	2	4
345	3	4

Hints

1. Use conditional aggregation with CASE expressions to pivot key_name values into columns.
2. GROUP BY row_id and compute each target column with MAX(CASE WHEN key_name = 'textX' THEN cell_value END).