Como imputar ou ler Séries Trimestrais no Python 3.7.3 ou 3.6.6

Olá.

Não estou conseguindo gerar um código que leia a primeira coluna do arquivo que apresento como amostra em formato de data Ano.Trimestre. Se ao menos conseguir imputar o trimestre inicial e depois gerar incrementos unitários, seria o ideal. Mas mesmo assim não consegui entender a lógica da criação de trimestres no python. Aí ‘YEARQUARTER’ será o índice da série temporal. Tenho quase certeza que meu erro está na hora de se carregar os dados, por que não sei a instrução para ler e transformar a coluna ‘YEARQUARTER’ em tempo.

Agradeço a ajuda. inserir a descrição da imagem aqui

Contas Nacionais Trimestrais.

Solve errors in a series of FILTER functions within array brackets?

I have been working on a budget project on Google Sheets. My goal is for a range of data to be filtered by date (which is represented as an integer between 1 and 31 for the day of the month). All entries in the first two columns are returned if the entry in the third column is equal to 1 (or whatever day of the month for which I am looking). And I require that on ~25 categories, and I want them to all be in one function, so I combined 25 filter functions in an array and they all combined nicely.

My only problem is, what if I don’t have any values in one of the categories that correspond to that date. The FILTER function returns an error and then the ENTIRE array function fails. I tried using IFERROR to return a string, but the array function does not like having strings as an argument.

={iferror(sort(FILTER(Sheet1!$ AH$ 13:$ AI$ 62,Sheet1!$ AJ$ 13:$ AJ$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ AK$ 13:$ AL$ 62,Sheet1!$ AM$ 13:$ AM$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ AN$ 13:$ AO$ 62,Sheet1!$ AP$ 13:$ AP$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ AQ$ 13:$ AR$ 62,Sheet1!$ AS$ 13:$ AS$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ AT$ 13:$ AU$ 62,Sheet1!$ AV$ 13:$ AV$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ AW$ 13:$ AX$ 62,Sheet1!$ AY$ 13:$ AY$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ AZ$ 13:$ BA$ 62,Sheet1!$ BB$ 13:$ BB$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ BC$ 13:$ BD$ 62,Sheet1!$ BE$ 13:$ BE$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ BF$ 13:$ BG$ 62,Sheet1!$ BH$ 13:$ BH$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ BI$ 13:$ BJ$ 62,Sheet1!$ BK$ 13:$ BK$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ BL$ 13:$ BM$ 62,Sheet1!$ BN$ 13:$ BN$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ BO$ 13:$ BP$ 62,Sheet1!$ BQ$ 13:$ BQ$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ BR$ 13:$ BS$ 62,Sheet1!$ BT$ 13:$ BT$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ BU$ 13:$ BV$ 62,Sheet1!$ BW$ 13:$ BW$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ BX$ 13:$ BY$ 62,Sheet1!$ BZ$ 13:$ BZ$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ CA$ 13:$ CB$ 62,Sheet1!$ CC$ 13:$ CC$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ CD$ 13:$ CE$ 62,Sheet1!$ CF$ 13:$ CF$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ CG$ 13:$ CH$ 62,Sheet1!$ CI$ 13:$ CI$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ CJ$ 13:$ CK$ 62,Sheet1!$ CL$ 13:$ CL$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ CM$ 13:$ CN$ 62,Sheet1!$ CO$ 13:$ CO$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ CP$ 13:$ CQ$ 62,Sheet1!$ CR$ 13:$ CR$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ CS$ 13:$ CT$ 62,Sheet1!$ CU$ 13:$ CU$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ CV$ 13:$ CW$ 62,Sheet1!$ CX$ 13:$ CX$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ CY$ 13:$ CZ$ 62,Sheet1!$ DA$ 13:$ DA$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ DB$ 13:$ DC$ 62,Sheet1!$ DD$ 13:$ DD$ 62=B5),2,TRUE),"None Today"); iferror(sort(FILTER(Sheet1!$ DE$ 13:$ DF$ 62,Sheet1!$ DG$ 13:$ DG$ 62=B5),2,TRUE),"None Today")}

Each FILTER’s first argument refers to two columns that contain the text I want to be returned based on the second argument as a condition (in this case equaling B5 or 1). The sort function organizes this alphabetically. However, if one of these FILTERs does not filter ANY results that equal B5 (or 1), the entire array function fails. How can I resolve this?

Data lies on Sheet1

The array is placed in B6 on Sheet2 Data Being Filtered Filtered Data

Divergent Series & Continued Fraction (from Gauss’ Mathematical Diary)

I’ve asked that question before on History of Science and Mathematics but haven’t received an answer

Does someone have a reference or further explanation on Gauß’ entry from May 24, 1796 in his mathematical diary (Mathematisches Tagebuch, full scan available via https://gdz.sub.uni-goettingen.de/id/DE-611-HS-3382323) on page 3 regarding the divergent series $ $ 1-2+8-64…$ $ in relation to the continued fraction $ $ \frac{1}{1+\frac{2}{1+\frac{2}{1+\frac{8}{1+\frac{12}{1+\frac{32}{1+\frac{56}{1+128}}}}}}}$ $

He states also – if I read it correctly – Transformatio seriei which could mean series transformation, but I don’t see how he transforms from the series to the continued fraction resp. which transformation or rule he applied.

The OEIS has an entry (https://oeis.org/A014236) for the sequence $ 2,2,8,12,32,56,128$ , but I don’t see the connection either.

My question: Can anyone help or clarify the relationship that Gauss’ used?

Torsten Schoeneberg remarked rightfully in the original question that the term in the series are $ (-1)^n\cdot 2^{\frac{1}{2}n(n+1)}$ and Gerald Edgar conjectures it might be related to Gauss’ Continued Fraction.

Time series on a small dataset. Also application of ADF is not happening

Hi I am new in R and was tring to convert dataframe into a time series object but after applying groupby on a certain index the datatype gets changed to “tbl_df” “tbl” “data.frame” format. Also i am trying to make a another dataframe subset out of an existing dataframe which is returning null. Also after converting the dataframe into a time series object its converting to ts matrix. Can you please let me know why all this issues are happening?

I have tried all the basic operations but somehow missing the background interpretation of all the codes used in the code. Kindly help

data <- read.csv("Time_Series_Data_Peak2.csv") head(data) class(data) #Groupby library(dplyr) Dates_class = data %>%    group_by(Date) %>%    summarise(Dates_class= sum(Calls_Handled)) View(Dates_class) head(Dates_class) plot(Dates_class$  Date,Dates_class$  Dates_class) lines(Dates_class$  Date,Dates_class$  Dates_class) class(Dates_class) Dates_class1 <- ts(Dates_class,start=c(2019,3),end=c(2019,5),frequency=1)  I want the data to be ready for checking stationarity. 

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Webscrapper code to download a manga series

I wrote a program to download a manga series fron www.mangapanda.com

Here it is:

import os  import requests from lxml import html  from cleanname import clean_filename  dir_loc = r'' website_url = r'https://www.mangapanda.com' manga_url = r'https://www.mangapanda.com/one-piece'   def check_url(url):     url_status = requests.head(url)     if url_status.status_code < 400:         return True     return False   def scrap_chapter_list(url, respose):     dic = {'chapter': '', 'name': '', 'link': ''}      # start scrapping     # soup  = BeautifulSoup(respose.text,'html.parser')     tree = html.fromstring(respose.content)     return None   def get_list_of_chapers(url):     if check_url(url):         response = requests.get(url).content         tree = html.fromstring(response)         path = r'//*/div[@id="chapterlist"]/table[@id="listing"]/tr/td/a'         res = tree.xpath(path)         dic = {'chapter': '', 'url': '', 'name': ''}         result = []         for i in res:             dic['chapter'] = i.text             dic['url'] = website_url + i.attrib['href']             dic['name'] = i.tail             result.append(dic)             dic = {'chapter': '', 'url': '', 'name': ''}         return result     return None   def get_page_list(chapter_url):     res = requests.get(chapter_url).content     path = r'//*/div[@id="selectpage"]/select[@id="pageMenu"]'     tree = html.fromstring(res)     data = tree.xpath(path)[0]     page_links = ['{}'.format(i.attrib['value']) for i in data]     return page_links   def get_image_from_page(url):     """      :param url:  url of the given manga page eg. /one-piece/1/1     :return: name of the page(manga name, link to the image file     """     dic = {'page_name': '', 'source': ''}     page_url = r'{}{}'.format(website_url, url)     res = requests.get(page_url).content     path = r'//*/img[@id="img"]'     tree = html.fromstring(res)     result = tree.xpath(path)     dic['page_name'], dic['source'] = result[0].attrib['alt'], result[0].attrib['src']     return dic   def download_image(image_url):     image_file = requests.get(image_url).content     return image_file   def save_file(image_file, location, filename, img_format):     image_loc = os.path.join(location, filename)+img_format     with open(image_loc, 'wb') as file:         file.write(image_file)     return True if os.path.isfile(image_loc) else False   def get_page_details(chapter_url):     dic = {'page_link': '', 'page_name': '', 'source': ''}     page_details = get_page_list(chapter_url)     result = []     for page in page_details:         details = get_image_from_page(page)         dic['page_link'] = page         dic['page_name'], dic['source'] = details['page_name'], details['source']         result.append(dic)         dic = {'page_link': '', 'page_name': '', 'source': ''}     return result   # if __name__ == '__main__': #     from .cleanname import clean_filename manga_url = r'https://www.mangapanda.com/akame-ga-kiru' storing_location = r'C:\Users\prashra\Pictures\mangascrapper' manga_name = manga_url.split('/')[-1] location = os.path.join(storing_location, clean_filename(manga_name)) chapter_list = get_list_of_chapers(manga_url)[:6]  if not os.path.exists(location):     print('creating the folder {}'.format(manga_name))     os.makedirs(location)  for chapter in chapter_list:     name = r'{} {}'.format(chapter['chapter'], chapter['name'])     chapter_path = os.path.join(location, clean_filename(name))     print(chapter_path)     if not os.path.exists(chapter_path):         os.makedirs(chapter_path)     chapter_details = get_page_details(chapter['url'])     for _page in chapter_details:         name, src = _page['page_name'], _page['source']         img_format = '.' + src.split('.')[-1]         print('saving image {} in path {}'.format(name, chapter_path))         image_data = requests.get(src).content         save_file(image_data, chapter_path, name, img_format) 

and in cleanname.py file

import unicodedata import string  valid_filename_chars = "-_ %s%s" % (string.ascii_letters, string.digits) char_limit = 255   def clean_filename(filename, whitelist=valid_filename_chars, replace='_'):     # replace spaces     for r in replace:         filename = filename.replace(r, '_')      # keep only valid ascii chars     cleaned_filename = unicodedata.normalize('NFKD', filename).encode('ASCII', 'ignore').decode()      # keep only whitelisted chars     cleaned_filename = ''.join(c for c in cleaned_filename if c in whitelist)     if len(cleaned_filename) > char_limit:         print(             "Warning, filename truncated because it was over {}. Filenames may no longer be unique".format(char_limit))     return cleaned_filename[:char_limit] 

I want to ask :

  1. review on this code
  2. is it better to convert the code in classes form
  3. how to make it scaleable, like to download a chapter only not entire list

Computing the sum of an infinite series as a variant of a geometric series

I came across the following series when computing the covariance of a transform of a bivariate Gaussian random vector via Hermite polynomials and Mehler’s expansion:

$ $ S = \sum_{n=1}^{\infty} \frac{\rho^n}{n^{1/6}} $ $ for $ \vert \rho \vert < 1$ . We know that $ S$ must be finite and satisfy $ $ S \le \rho (1-\rho)^{-1} $ $ since the original series is dominated by $ \sum_{n=1}^{\infty} \rho^n$ .

However, there is a catch if we use for $ S$ the upper bound $ \rho (1-\rho)^{-1}$ , which tends to $ \infty$ when $ \rho \to 1-$ . This happens when the two marginal random variables in the Gaussian vector are almost surely, positively linearly dependent (asymptotically).

So, the target is to obtain a good upper bound, much better than $ \rho (1-\rho)^{-1}$ when we restrict $ \rho$ to be away from $ 1$ , to reduce the effect of $ \rho \to 1-$ . In other words, let $ 1-\rho = \delta$ for some fixed $ \delta \in (0,1)$ , what is a better upper bound for $ S$ ?

Because of the scaling term $ n^{-1/6}$ that induces a divergent series $ \sum_{n=1}^{\infty} n^{-1/6}$ , probably not much improvement should be expected. I have Googled but did not find an illuminating technique for this. Any pointer or help is appreciated. Thank you.