unutbu

import pandas as pd

df = pd.read_excel('2016_Bike_Share_Toronto_Ridership_Q4.xlsx')

for col in ['trip_start_time', 'trip_stop_time']:
    df[col] = pd.to_datetime(df[col])
for col in ['trip_start_time', 'trip_stop_time']:
    swapped = pd.to_datetime({'year':df[col].dt.year, 
                              'month':df[col].dt.day, 
                              'day':df[col].dt.month,

unutbu

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.ticker as mticker

img = np.random.randn(300,300)*10**-6
myplot = plt.imshow(img)

class FixedOrderFormatter(mticker.ScalarFormatter):
    # http://stackoverflow.com/questions/3677368/#3679918 (Joe Kington)
    """Formats axis ticks using scientific notation with a constant order of

unutbu

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.ticker as ticker

img = np.random.randn(300,300)*10**-6
myplot = plt.imshow(img)

def fmt(x, pos):
    a, b = '{:.2e}'.format(x).split('e')
    # x = a * 10**b = a * 10**(b-1) * 10**1

unutbu

class MyInt(int):
    def __add__(self, other):
        print('__add__({}, {})'.format(self, other))
        return MyInt(other+self)
    def __radd__(self, other):
        print('__radd__({}, {})'.format(self, other))
        return MyInt(int(self)+other)

c = MyInt(5)

unutbu

import errno
import os
import pty
import select
from subprocess import Popen, STDOUT

master_fd, slave_fd = pty.openpty()  # provide tty to enable
                                     # line-buffering on ruby's side
                                   
proc = Popen(['python', 'test.py'],

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

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


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

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: 3


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

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