In [1]:
import numpy as np
np.random.seed(12345)
import matplotlib.pyplot as plt
plt.rc("figure", figsize=(10, 6))
np.set_printoptions(precision=4, suppress=True)
In [2]:
import numpy as np my_arr = np.arange(1_000_000) my_list = list(range(1_000_000))
In [3]:
%timeit my_arr2 = my_arr * 2 %timeit my_list2 = [x * 2 for x in my_list]
In [4]:
import numpy as np data = np.array([[1.5, -0.1, 3], [0, -3, 6.5]]) data
In [7]:
data1 = [6, 7.5, 8, 0, 1] arr1 = np.array(data1) arr1
In [8]:
data2 = [[1, 2, 3, 4], [5, 6, 7, 8]] arr2 = np.array(data2) arr2
In [11]:
np.zeros(10) np.zeros((3, 6)) np.empty((2, 3, 2))
In [13]:
arr1 = np.array([1, 2, 3], dtype=np.float64) arr2 = np.array([1, 2, 3], dtype=np.int32) arr1.dtype arr2.dtype
In [14]:
arr = np.array([1, 2, 3, 4, 5]) arr.dtype float_arr = arr.astype(np.float64) float_arr float_arr.dtype
In [15]:
arr = np.array([3.7, -1.2, -2.6, 0.5, 12.9, 10.1]) arr arr.astype(np.int32)
In [16]:
numeric_strings = np.array(["1.25", "-9.6", "42"], dtype=np.string_) numeric_strings.astype(float)
In [17]:
int_array = np.arange(10) calibers = np.array([.22, .270, .357, .380, .44, .50], dtype=np.float64) int_array.astype(calibers.dtype)
In [18]:
zeros_uint32 = np.zeros(8, dtype="u4") zeros_uint32
In [19]:
arr = np.array([[1., 2., 3.], [4., 5., 6.]]) arr arr * arr arr - arr
In [21]:
arr2 = np.array([[0., 4., 1.], [7., 2., 12.]]) arr2 arr2 > arr
In [22]:
arr = np.arange(10) arr arr[5] arr[5:8] arr[5:8] = 12 arr
In [23]:
arr_slice = arr[5:8] arr_slice
In [26]:
arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) arr2d[2]
In [28]:
arr3d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]) arr3d
In [30]:
old_values = arr3d[0].copy() arr3d[0] = 42 arr3d arr3d[0] = old_values arr3d
In [36]:
lower_dim_slice = arr2d[1, :2]
In [41]:
names = np.array(["Bob", "Joe", "Will", "Bob", "Will", "Joe", "Joe"])
data = np.array([[4, 7], [0, 2], [-5, 6], [0, 0], [1, 2],
[-12, -4], [3, 4]])
names
data
In [44]:
data[names == "Bob", 1:] data[names == "Bob", 1]
In [45]:
names != "Bob" ~(names == "Bob") data[~(names == "Bob")]
In [46]:
cond = names == "Bob" data[~cond]
In [47]:
mask = (names == "Bob") | (names == "Will") mask data[mask]
In [49]:
data[names != "Joe"] = 7 data
In [50]:
arr = np.zeros((8, 4))
for i in range(8):
arr[i] = i
arr
In [53]:
arr = np.arange(32).reshape((8, 4)) arr arr[[1, 5, 7, 2], [0, 3, 1, 2]]
In [54]:
arr[[1, 5, 7, 2]][:, [0, 3, 1, 2]]
In [55]:
arr[[1, 5, 7, 2], [0, 3, 1, 2]] arr[[1, 5, 7, 2], [0, 3, 1, 2]] = 0 arr
In [56]:
arr = np.arange(15).reshape((3, 5)) arr arr.T
In [57]:
arr = np.array([[0, 1, 0], [1, 2, -2], [6, 3, 2], [-1, 0, -1], [1, 0, 1]]) arr np.dot(arr.T, arr)
In [60]:
samples = np.random.standard_normal(size=(4, 4)) samples
In [61]:
from random import normalvariate N = 1_000_000 %timeit samples = [normalvariate(0, 1) for _ in range(N)] %timeit np.random.standard_normal(N)
In [62]:
rng = np.random.default_rng(seed=12345) data = rng.standard_normal((2, 3))
In [64]:
arr = np.arange(10) arr np.sqrt(arr) np.exp(arr)
In [65]:
x = rng.standard_normal(8) y = rng.standard_normal(8) x y np.maximum(x, y)
In [66]:
arr = rng.standard_normal(7) * 5 arr remainder, whole_part = np.modf(arr) remainder whole_part
In [67]:
arr out = np.zeros_like(arr) np.add(arr, 1) np.add(arr, 1, out=out) out
In [68]:
points = np.arange(-5, 5, 0.01) # 100 equally spaced points xs, ys = np.meshgrid(points, points) ys
In [69]:
z = np.sqrt(xs ** 2 + ys ** 2) z
In [70]:
import matplotlib.pyplot as plt
plt.imshow(z, cmap=plt.cm.gray, extent=[-5, 5, -5, 5])
plt.colorbar()
plt.title("Image plot of $\sqrt{x^2 + y^2}$ for a grid of values")
In [73]:
xarr = np.array([1.1, 1.2, 1.3, 1.4, 1.5]) yarr = np.array([2.1, 2.2, 2.3, 2.4, 2.5]) cond = np.array([True, False, True, True, False])
In [74]:
result = [(x if c else y)
for x, y, c in zip(xarr, yarr, cond)]
result
In [75]:
result = np.where(cond, xarr, yarr) result
In [76]:
arr = rng.standard_normal((4, 4)) arr arr > 0 np.where(arr > 0, 2, -2)
In [77]:
np.where(arr > 0, 2, arr) # set only positive values to 2
In [78]:
arr = rng.standard_normal((5, 4)) arr arr.mean() np.mean(arr) arr.sum()
In [79]:
arr.mean(axis=1) arr.sum(axis=0)
In [80]:
arr = np.array([0, 1, 2, 3, 4, 5, 6, 7]) arr.cumsum()
In [81]:
arr = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) arr
In [82]:
arr.cumsum(axis=0) arr.cumsum(axis=1)
In [83]:
arr = rng.standard_normal(100) (arr > 0).sum() # Number of positive values (arr <= 0).sum() # Number of non-positive values
In [84]:
bools = np.array([False, False, True, False]) bools.any() bools.all()
In [85]:
arr = rng.standard_normal(6) arr arr.sort() arr
In [86]:
arr = rng.standard_normal((5, 3)) arr
In [87]:
arr.sort(axis=0) arr arr.sort(axis=1) arr
In [88]:
arr2 = np.array([5, -10, 7, 1, 0, -3]) sorted_arr2 = np.sort(arr2) sorted_arr2
In [89]:
names = np.array(["Bob", "Will", "Joe", "Bob", "Will", "Joe", "Joe"]) np.unique(names) ints = np.array([3, 3, 3, 2, 2, 1, 1, 4, 4]) np.unique(ints)
In [91]:
values = np.array([6, 0, 0, 3, 2, 5, 6]) np.in1d(values, [2, 3, 6])
In [92]:
arr = np.arange(10)
np.save("some_array", arr)
In [93]:
np.load("some_array.npy")
In [94]:
np.savez("array_archive.npz", a=arr, b=arr)
In [95]:
arch = np.load("array_archive.npz")
arch["b"]
In [96]:
np.savez_compressed("arrays_compressed.npz", a=arr, b=arr)
In [97]:
!rm some_array.npy !rm array_archive.npz !rm arrays_compressed.npz
In [98]:
x = np.array([[1., 2., 3.], [4., 5., 6.]]) y = np.array([[6., 23.], [-1, 7], [8, 9]]) x y x.dot(y)
In [101]:
from numpy.linalg import inv, qr X = rng.standard_normal((5, 5)) mat = X.T @ X inv(mat) mat @ inv(mat)
In [102]:
import random
position = 0
walk = [position]
nsteps = 1000
for _ in range(nsteps):
step = 1 if random.randint(0, 1) else -1
position += step
walk.append(position)
In [105]:
nsteps = 1000 rng = np.random.default_rng(seed=12345) # fresh random generator draws = rng.integers(0, 2, size=nsteps) steps = np.where(draws == 0, 1, -1) walk = steps.cumsum()
In [107]:
(np.abs(walk) >= 10).argmax()
In [108]:
nwalks = 5000 nsteps = 1000 draws = rng.integers(0, 2, size=(nwalks, nsteps)) # 0 or 1 steps = np.where(draws > 0, 1, -1) walks = steps.cumsum(axis=1) walks
In [110]:
hits30 = (np.abs(walks) >= 30).any(axis=1) hits30 hits30.sum() # Number that hit 30 or -30
In [111]:
crossing_times = (np.abs(walks[hits30]) >= 30).argmax(axis=1) crossing_times
In [113]:
draws = 0.25 * rng.standard_normal((nwalks, nsteps))
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