Numerical Computing with Python

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Appending a single row to a DataFrame is a fairly expensive operation and if you find yourself writing a loop to append single rows of data to a DataFrame, then you are doing it wrong. Let's first create 1,000 rows of new data as a list of Series:

>>> random_data = []>>> for i in range(1000):        d = dict()        for k, v in data_dict.items():            if isinstance(v, str):                d[k] = np.random.choice(list('abcde'))            else:                d[k] = np.random.randint(10)        random_data.append(pd.Series(d, name=i + len(bball_16)))>>> random_data[0].head()2B    3
3B    9
AB    3
BB    9
CS    4
Name: 16, dtype: object

Let's time how long it takes to loop through each item making one append at a time:

>>> %%timeit>>> bball_16_copy = bball_16.copy()>>> for row in random_data: bball_16_copy ...