In this question I got help with flatten of each row in a column of a dataframe.

The code is

    df = pd.DataFrame()

for d in data:
    df_tmp = pd.json_normalize(d)
    row = pd.DataFrame(df_tmp.to_numpy().flatten()).T.dropna(axis=1)
    row.columns = df_tmp.columns
    df = pd.concat([df, row])

print(df.reset_index(drop=True))

This code works good but takes enormous amount of time. To go over 1 column of 1 day of data (660k rows) it took about 10 hours. And there are 2 such columns. Means python should work 24/7.

Is there a way to rewrite it to make more time efficient?