unutbu

import numpy as np
import pandas as pd
import guppy

def array_equivalent(a1, a2):
     h.setrelheap()
     try:
         a1, a2 = np.asarray(a1), np.asarray(a2)
     except (TypeError, ValueError):
         return False

unutbu

import numpy as np
import pandas as pd
import timeit

def array_equivalent(a1, a2):
     try:
         a1, a2 = np.asarray(a1), np.asarray(a2)
     except (TypeError, ValueError):
         return False
     a1_mask = pd.isnull(a1)

unutbu

import numpy as np
import pandas as pd
nan = np.nan

def array_equivalent(a1, a2):
     try:
         a1, a2 = np.asarray(a1), np.asarray(a2)
     except (TypeError, ValueError):
         return False
     a1_mask = pd.isnull(a1)

unutbu

Invoked with :
--ncalls: 3
--repeats: 3


-------------------------------------------------------------------------------
Test name                                    | head[ms] | base[ms] |  ratio   |
-------------------------------------------------------------------------------
frame_reindex_columns                        |   0.4276 |   0.5569 |   0.7678 |

unutbu

Invoked with :
--ncalls: 3
--repeats: 6


-------------------------------------------------------------------------------
Test name                                    | head[ms] | base[ms] |  ratio   |
-------------------------------------------------------------------------------
mask_bools                                   |  33.1504 |  46.0804 |   0.7194 |

unutbu

Invoked with :
--ncalls: 3
--repeats: 6


-------------------------------------------------------------------------------
Test name                                    | head[ms] | base[ms] |  ratio   |
-------------------------------------------------------------------------------
groupby_first_float32                        |   5.1910 |   6.4894 |   0.7999 |

unutbu

import numpy as np
from scipy import ndimage
import matplotlib.pyplot as plt

#Data:
x = np.linspace(0, 2*np.pi,100)
f = np.sin(x) + 0.002*(np.random.rand(100)-.5)

#Normalization:
dx = x[1]-x[0]

unutbu

import numpy as np
import timeit

def random_data(N):
    # Generate some random data.
    return np.random.uniform(0., 10., N)

# Data lists.
array1 = np.array([random_data(4) for _ in range(1000)])
array2 = np.array([random_data(3) for _ in range(2000)])

unutbu

import numpy as np
import timeit

def random_data(N):
    # Generate some random data.
    return np.random.uniform(0., 10., N)

# Data lists.
array1 = np.array([random_data(4) for _ in range(10000)])   # pump up the number of iterations in optfunc
array2 = np.array([random_data(3) for _ in range(100)])

unutbu

import networkx as nx
import matplotlib.pyplot as plt
G = nx.Graph()
G.add_edges_from(
    [(7, 11), (7, 8), (5, 11), (3, 8), (3, 10), (11, 2), (11, 9),
     (11, 10), (8, 9)])

pos = nx.spring_layout(G, iterations=100)
for node, loc in pos.iteritems():
    print('{n}: {l}'.format(n=node, l=loc))