beatorizu

reduce(lambda _, __: _ + __, valores)

beatorizu

from collections.abc import Mapping


# Recursive function to apply a func to nested dict
def map_nested_dicts(ob, func):
    if isinstance(ob, Mapping):
        return {key: map_nested_dicts(value, func) for key, value in ob.items()}
    return func(ob)

beatorizu

" Specify a directory for plugins
call plug#begin('~/.local/share/nvim/plugged')                
                                                              
" Colorizer A plugin to color colornames and codes            
Plug 'chrisbra/Colorizer'

" vim-markdown-composer An asynchronous markdown preview plugin for Vim and Neovim
function! BuildComposer(info)
  if a:info.status != 'unchanged' || a:info.force
    if has('nvim')

beatorizu

def nan_if(data, missing_value):
    return np.where(data == missing_value, np.nan, data)

beatorizu

[run]
branch = True
[report]
exclude_lines=
    if response.status_code == 404:
    elif response.status_code == 503:
    pragma: no cover
    class ServiceUnavailable(Exception):

beatorizu

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import facebook


class SimpleFacebook(object):

    def __init__(self, oauth_token):
        self.graph = facebook.GraphAPI(oauth_token)

beatorizu

#!/usr/bin/env python
# -*- coding: utf-8 -*-

from controller import simple_facebook

from unittest import mock, TestCase


class SimpleFacebookTestCase(TestCase):

beatorizu

# Aplicando formatos diferentes para cada coluna no DataFrame
df = DataFrame({'a':[7.7885], 'b': [1.989878968657576e-10]})
# out
#         a             b
# 0  7.7885  1.989879e-10

df.loc[:, 'b'] = df.loc[:, 'b'].map(lambda x: float('%.11f' % x))
df.loc[:, 'a'] = df.loc[:, 'a'].map(lambda x: float('%.2f' % x))

# out

beatorizu

import numpy as np


data = np.random.rand(7, 3, 2)

op_dict = {'sum': np.sum, 'mean': np.mean, 'max': np.max, 'min': np.min}

def apply_function(func, data, step, axis=0, slice_=slice(None)):
    sections = np.arange(step, data.shape[axis], step)
    return np.array([func(d[slice_], axis=axis) for d in np.split(data, sections, axis=axis)])

beatorizu

# Para somar no eixo 1 de três em três
txprec[::3].sum(axis=1)