In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rc('figure', figsize=(10, 6))
PREVIOUS_MAX_ROWS = pd.options.display.max_rows
pd.options.display.max_columns = 20
pd.options.display.max_rows = 20
pd.options.display.max_colwidth = 80
np.set_printoptions(precision=4, suppress=True)
In [2]:
rng = np.random.default_rng(seed=12345)
In [4]:
np.ones((3, 4, 5), dtype=np.float64).strides
In [5]:
ints = np.ones(10, dtype=np.uint16) floats = np.ones(10, dtype=np.float32) np.issubdtype(ints.dtype, np.integer) np.issubdtype(floats.dtype, np.floating)
In [7]:
np.issubdtype(ints.dtype, np.number)
In [8]:
arr = np.arange(8) arr arr.reshape((4, 2))
In [9]:
arr.reshape((4, 2)).reshape((2, 4))
In [10]:
arr = np.arange(15) arr.reshape((5, -1))
In [11]:
other_arr = np.ones((3, 5)) other_arr.shape arr.reshape(other_arr.shape)
In [12]:
arr = np.arange(15).reshape((5, 3)) arr arr.ravel()
In [14]:
arr = np.arange(12).reshape((3, 4))
arr
arr.ravel()
arr.ravel('F')
In [15]:
arr1 = np.array([[1, 2, 3], [4, 5, 6]]) arr2 = np.array([[7, 8, 9], [10, 11, 12]]) np.concatenate([arr1, arr2], axis=0) np.concatenate([arr1, arr2], axis=1)
In [16]:
np.vstack((arr1, arr2)) np.hstack((arr1, arr2))
In [17]:
arr = rng.standard_normal((5, 2)) arr first, second, third = np.split(arr, [1, 3]) first second third
In [18]:
arr = np.arange(6) arr1 = arr.reshape((3, 2)) arr2 = rng.standard_normal((3, 2)) np.r_[arr1, arr2] np.c_[np.r_[arr1, arr2], arr]
In [20]:
arr = np.arange(3) arr arr.repeat(3)
In [22]:
arr = rng.standard_normal((2, 2)) arr arr.repeat(2, axis=0)
In [23]:
arr.repeat([2, 3], axis=0) arr.repeat([2, 3], axis=1)
In [25]:
arr np.tile(arr, (2, 1)) np.tile(arr, (3, 2))
In [26]:
arr = np.arange(10) * 100 inds = [7, 1, 2, 6] arr[inds]
In [27]:
arr.take(inds) arr.put(inds, 42) arr arr.put(inds, [40, 41, 42, 43]) arr
In [28]:
inds = [2, 0, 2, 1] arr = rng.standard_normal((2, 4)) arr arr.take(inds, axis=1)
In [29]:
arr = np.arange(5) arr arr * 4
In [30]:
arr = rng.standard_normal((4, 3)) arr.mean(0) demeaned = arr - arr.mean(0) demeaned demeaned.mean(0)
In [31]:
arr row_means = arr.mean(1) row_means.shape row_means.reshape((4, 1)) demeaned = arr - row_means.reshape((4, 1)) demeaned.mean(1)
In [33]:
arr - arr.mean(1).reshape((4, 1))
In [34]:
arr = np.zeros((4, 4)) arr_3d = arr[:, np.newaxis, :] arr_3d.shape arr_1d = rng.standard_normal(3) arr_1d[:, np.newaxis] arr_1d[np.newaxis, :]
In [35]:
arr = rng.standard_normal((3, 4, 5)) depth_means = arr.mean(2) depth_means depth_means.shape demeaned = arr - depth_means[:, :, np.newaxis] demeaned.mean(2)
In [36]:
arr = np.zeros((4, 3)) arr[:] = 5 arr
In [37]:
col = np.array([1.28, -0.42, 0.44, 1.6]) arr[:] = col[:, np.newaxis] arr arr[:2] = [[-1.37], [0.509]] arr
In [38]:
arr = np.arange(10) np.add.reduce(arr) arr.sum()
In [39]:
my_rng = np.random.default_rng(12346) # for reproducibility arr = my_rng.standard_normal((5, 5)) arr arr[::2].sort(1) # sort a few rows arr[:, :-1] < arr[:, 1:] np.logical_and.reduce(arr[:, :-1] < arr[:, 1:], axis=1)
In [40]:
arr = np.arange(15).reshape((3, 5)) np.add.accumulate(arr, axis=1)
In [41]:
arr = np.arange(3).repeat([1, 2, 2]) arr np.multiply.outer(arr, np.arange(5))
In [42]:
x, y = rng.standard_normal((3, 4)), rng.standard_normal(5) result = np.subtract.outer(x, y) result.shape
In [43]:
arr = np.arange(10) np.add.reduceat(arr, [0, 5, 8])
In [44]:
arr = np.multiply.outer(np.arange(4), np.arange(5)) arr np.add.reduceat(arr, [0, 2, 4], axis=1)
In [45]:
def add_elements(x, y):
return x + y
add_them = np.frompyfunc(add_elements, 2, 1)
add_them(np.arange(8), np.arange(8))
In [46]:
add_them = np.vectorize(add_elements, otypes=[np.float64]) add_them(np.arange(8), np.arange(8))
In [47]:
arr = rng.standard_normal(10000) %timeit add_them(arr, arr) %timeit np.add(arr, arr)
In [48]:
dtype = [('x', np.float64), ('y', np.int32)]
sarr = np.array([(1.5, 6), (np.pi, -2)], dtype=dtype)
sarr
In [51]:
dtype = [('x', np.int64, 3), ('y', np.int32)]
arr = np.zeros(4, dtype=dtype)
arr
In [54]:
dtype = [('x', [('a', 'f8'), ('b', 'f4')]), ('y', np.int32)]
data = np.array([((1, 2), 5), ((3, 4), 6)], dtype=dtype)
data['x']
data['y']
data['x']['a']
In [55]:
arr = rng.standard_normal(6) arr.sort() arr
In [56]:
arr = rng.standard_normal((3, 5)) arr arr[:, 0].sort() # Sort first column values in place arr
In [57]:
arr = rng.standard_normal(5) arr np.sort(arr) arr
In [58]:
arr = rng.standard_normal((3, 5)) arr arr.sort(axis=1) arr
In [60]:
values = np.array([5, 0, 1, 3, 2]) indexer = values.argsort() indexer values[indexer]
In [61]:
arr = rng.standard_normal((3, 5)) arr[0] = values arr arr[:, arr[0].argsort()]
In [62]:
first_name = np.array(['Bob', 'Jane', 'Steve', 'Bill', 'Barbara']) last_name = np.array(['Jones', 'Arnold', 'Arnold', 'Jones', 'Walters']) sorter = np.lexsort((first_name, last_name)) sorter list(zip(last_name[sorter], first_name[sorter]))
In [63]:
values = np.array(['2:first', '2:second', '1:first', '1:second',
'1:third'])
key = np.array([2, 2, 1, 1, 1])
indexer = key.argsort(kind='mergesort')
indexer
values.take(indexer)
In [64]:
rng = np.random.default_rng(12345) arr = rng.standard_normal(20) arr np.partition(arr, 3)
In [65]:
indices = np.argpartition(arr, 3) indices arr.take(indices)
In [66]:
arr = np.array([0, 1, 7, 12, 15]) arr.searchsorted(9)
In [67]:
arr.searchsorted([0, 8, 11, 16])
In [68]:
arr = np.array([0, 0, 0, 1, 1, 1, 1]) arr.searchsorted([0, 1]) arr.searchsorted([0, 1], side='right')
In [69]:
data = np.floor(rng.uniform(0, 10000, size=50)) bins = np.array([0, 100, 1000, 5000, 10000]) data
In [70]:
labels = bins.searchsorted(data) labels
In [71]:
pd.Series(data).groupby(labels).mean()
In [72]:
import numpy as np
def mean_distance(x, y):
nx = len(x)
result = 0.0
count = 0
for i in range(nx):
result += x[i] - y[i]
count += 1
return result / count
In [73]:
mmap = np.memmap('mymmap', dtype='float64', mode='w+',
shape=(10000, 10000))
mmap
In [75]:
section[:] = rng.standard_normal((5, 10000)) mmap.flush() mmap del mmap
In [76]:
mmap = np.memmap('mymmap', dtype='float64', shape=(10000, 10000))
mmap
In [78]:
arr_c = np.ones((100, 10000), order='C') arr_f = np.ones((100, 10000), order='F') arr_c.flags arr_f.flags arr_f.flags.f_contiguous
In [79]:
%timeit arr_c.sum(1) %timeit arr_f.sum(1)
In [81]:
arr_c[:50].flags.contiguous arr_c[:, :50].flags
In [84]:
pd.options.display.max_rows = PREVIOUS_MAX_ROWS
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