..: In [4]: def integrate_f(a, b, N): ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f(a + i * dx) ...: return s * dx ...: 411 pandas: powerful Python data analysis toolkit ...: def integrate_f_plain(a, b, N): ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f_plain(a + i * dx) ...: return s * dx ...: Note: If you’re having trouble pasting the above N): ....: cdef int i ....: cdef double s, dx ....: s = 0 ....: dx = (b - a) / N ....: for i in range(N): ....: s += f_typed(a + i * dx) ....: return s * dx ....: In [11]: %timeit df.apply(lambda

..: In [4]: def integrate_f(a, b, N): ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f(a + i * dx) ...: return s * dx ...: We achieve our result by by using apply (row-wise): ...: def integrate_f_plain(a, b, N): ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f_plain(a + i * dx) ...: return s * dx ...: 536 Chapter 21. Enhancing Performance pandas: N): ....: cdef int i ....: cdef double s, dx ....: s = 0 ....: dx = (b - a) / N ....: for i in range(N): ....: s += f_typed(a + i * dx) ....: return s * dx ....: In [11]: %timeit df.apply(lambda

..: In [4]: def integrate_f(a, b, N): ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f(a + i * dx) ...: return s * dx ...: We achieve our result by by using apply (row-wise): ...: def integrate_f_plain(a, b, N): ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f_plain(a + i * dx) ...: return s * dx ...: 596 Chapter 21. Enhancing Performance pandas: N): ....: cdef int i ....: cdef double s, dx ....: s = 0 ....: dx = (b - a) / N ....: for i in range(N): ....: s += f_typed(a + i * dx) ....: return s * dx ....: In [11]: %timeit df.apply(lambda

..: In [4]: def integrate_f(a, b, N): ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f(a + i * dx) ...: return s * dx ...: We achieve our result by using apply (row-wise): .: dx = (b - a) / N ...: for i in range(N): ...: s += f_plain(a + i * dx) 862 Chapter 26. Enhancing Performance pandas: powerful Python data analysis toolkit, Release 0.17.0 ...: return s * dx . int N): ...: cdef int i ...: cdef double s, dx ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f_typed(a + i * dx) ...: return s * dx ...: In [4]: %timeit df.apply(lambda x: i

..: In [4]: def integrate_f(a, b, N): ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f(a + i * dx) ...: return s * dx ...: We achieve our result by by using apply (row-wise): ...: def integrate_f_plain(a, b, N): ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f_plain(a + i * dx) ...: return s * dx ...: 746 Chapter 25. Enhancing Performance pandas: N): ....: cdef int i ....: cdef double s, dx ....: s = 0 ....: dx = (b - a) / N ....: for i in range(N): ....: s += f_typed(a + i * dx) ....: return s * dx ....: In [11]: %timeit df.apply(lambda

..: In [4]: def integrate_f(a, b, N): ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f(a + i * dx) ...: return s * dx ...: We achieve our result by by using apply (row-wise): ...: def integrate_f_plain(a, b, N): ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f_plain(a + i * dx) ...: return s * dx ...: 732 Chapter 25. Enhancing Performance pandas: N): ....: cdef int i ....: cdef double s, dx ....: s = 0 ....: dx = (b - a) / N ....: for i in range(N): ....: s += f_typed(a + i * dx) ....: return s * dx ....: In [11]: %timeit df.apply(lambda

..: In [4]: def integrate_f(a, b, N): ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f(a + i * dx) ...: return s * dx ...: We achieve our result by using apply (row-wise): powerful Python data analysis toolkit, Release 0.19.0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f_plain(a + i * dx) ...: return s * dx ...: Note: If you’re having trouble pasting the above int N): ...: cdef int i ...: cdef double s, dx ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f_typed(a + i * dx) ...: return s * dx ...: In [4]: %timeit df.apply(lambda x: i

..: In [4]: def integrate_f(a, b, N): ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f(a + i * dx) ...: return s * dx ...: We achieve our result by using apply (row-wise): powerful Python data analysis toolkit, Release 0.19.1 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f_plain(a + i * dx) ...: return s * dx ...: Note: If you’re having trouble pasting the above int N): ...: cdef int i ...: cdef double s, dx ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f_typed(a + i * dx) ...: return s * dx ...: In [4]: %timeit df.apply(lambda x: i

..: In [4]: def integrate_f(a, b, N): ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f(a + i * dx) ...: return s * dx ...: We achieve our result by using apply (row-wise): powerful Python data analysis toolkit, Release 0.20.3 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f_plain(a + i * dx) ...: return s * dx ...: Note: If you’re having trouble pasting the above int N): ...: cdef int i ...: cdef double s, dx ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f_typed(a + i * dx) ...: return s * dx ...: In [4]: %timeit df.apply(lambda x: i

..: In [4]: def integrate_f(a, b, N): ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f(a + i * dx) ...: return s * dx ...: We achieve our result by using apply (row-wise): powerful Python data analysis toolkit, Release 0.20.2 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f_plain(a + i * dx) ...: return s * dx ...: Note: If you’re having trouble pasting the above int N): ...: cdef int i ...: cdef double s, dx ...: s = 0 ...: dx = (b - a) / N ...: for i in range(N): ...: s += f_typed(a + i * dx) ...: return s * dx ...: In [4]: %timeit df.apply(lambda x: i