[](https://wesmckinney.com/blog/high-perf-arrow-to-pandas/#cb1-1)import numpy as np
[](https://wesmckinney.com/blog/high-perf-arrow-to-pandas/#cb1-2)import pandas as pd
[](https://wesmckinney.com/blog/high-perf-arrow-to-pandas/#cb1-3)import pyarrow as pa
[](https://wesmckinney.com/blog/high-perf-arrow-to-pandas/#cb1-4)
[](https://wesmckinney.com/blog/high-perf-arrow-to-pandas/#cb1-5)type_ = np.dtype('float64')
[](https://wesmckinney.com/blog/high-perf-arrow-to-pandas/#cb1-6)DATA_SIZE = (1 << 30)
[](https://wesmckinney.com/blog/high-perf-arrow-to-pandas/#cb1-7)NCOLS = 100
[](https://wesmckinney.com/blog/high-perf-arrow-to-pandas/#cb1-8)NROWS = DATA_SIZE / NCOLS / np.dtype(type_).itemsize
[](https://wesmckinney.com/blog/high-perf-arrow-to-pandas/#cb1-9)
[](https://wesmckinney.com/blog/high-perf-arrow-to-pandas/#cb1-10)data = {
[](https://wesmckinney.com/blog/high-perf-arrow-to-pandas/#cb1-11)    'c' + str(i): np.random.randn(NROWS)
[](https://wesmckinney.com/blog/high-perf-arrow-to-pandas/#cb1-12)    for i in range(NCOLS)
[](https://wesmckinney.com/blog/high-perf-arrow-to-pandas/#cb1-13)}