beatorizu

from PIL import Image

# The main image
img_b = Image.open("tumblr_nv07r7pHRh1txa2z2o1_500.png")
# The potato seal image
img_l = Image.open("avatar_3ee88102fcff_128.png")

# Area to paste potato seal
area = (img_b.size[0] - img_l.size[0],
        img_b.size[1] - img_l.size[1],

beatorizu

# OBMEP 2015 - 2ª Fase - Data

def sum_digit(number):
    result = 0
    while number:
        result, number = result + number % 10, number // 10
    return result


def reverse_number(number):

beatorizu

/* OBMEP 2015 - 2ª Fase - Data */

function sum_digit(number) {
  var result = 0;
  while (number) {
    result += number % 10;
    number = Math.floor(number / 10);
  }
  return result;
}

beatorizu

from pandas import DatetimeIndex, read_excel

# Essa planilha só tem um folha então vai retorna o DataFrame
sheets = read_excel('./BulletinSearch.xlsx')

# Pode agrupar para fazer max, min, sum e count
count_impact = sheets.groupby(by='Impact').count()

# Para agrupar datas tem que criar um obj DatetimeIndex
# Nesse DataFrame os dados já são diários, mas se fossem dados horários

beatorizu

"""Clean downloaded files

Usage:
    download_cleaner.py <ftpconfig_dir>

Arguments:
    <ftpconfig_dir>   Path to ftpconfig directories

Options:
    -h, --help      Show this screen

beatorizu

def find_nearest_point(latitude, longitude, l_latitude, l_longitude):
    """
    This function find the index of the nearest value to the latitude and longitude values int the l_latitude and
    l_longitude lists.

    Parameters
    ----------
    latitude: float
    longitude: float
    l_latitude: list

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

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

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)])