Comments (4)
datadwerg
commented on May 19, 2024
Hello, I hope it is OK for another user to reply instead of the package creator?
In the vignette there is an example which uses has_substring() to filter. I also think it is better to filter on a dimension column and not a measure column. I think this works:
cbs_get_data(id = "84583NED", WijkenEnBuurten = has_substring("GM"))
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ayoub-abdessadak
commented on May 19, 2024
CODE in python:
from logging import exception
import pandas
import cbsodata
from openpyxl import Workbook
# Filter met cbsodata api
def cbs_filter(ned, soort_regio=None, gemeente=None):
# NED = bijvoorbeeld 84583NED, table wordt gedownload en omgezet naar pandas tabel (https://pandas.pydata.org/docs/reference/frame.html)
try:
data = pandas.DataFrame(cbsodata.get_data(ned))
except exception as E:
print(E)
return 'Hmmm, er is iets fout gegaan bekijk je logs'
#Indien er een soort_regio is doorgegeven dan wordt er gefilterd op soort_regio
if soort_regio:
data = data[data.SoortRegio_2.str.contains(soort_regio[0].upper() + soort_regio[1::].lower())]
#Indien gemeente is doorgegeven dan wordt er gefilterd op gemeente
if gemeente:
data = data[data.Gemeentenaam_1.str.contains(gemeente[0].upper() + gemeente[1::].lower())]
#Verder kunnen er nog meerdere filters worden toegevoegd aan de hand van de beschikbare kolommen
return data
# Filter zonder cbsodata api
def cbs_filter_dt(data, soort_regio=None, gemeente=None):
#Indien er een soort_regio is doorgegeven dan wordt er gefilterd op soort_regio
if soort_regio:
data = data[data.SoortRegio_2.str.contains(soort_regio[0].upper() + soort_regio[1::].lower())]
#Indien gemeente is doorgegeven dan wordt er gefilterd op gemeente
if gemeente:
data = data[data.Gemeentenaam_1.str.contains(gemeente[0].upper() + gemeente[1::].lower())]
#Verder kunnen er nog meerdere filters worden toegevoegd aan de hand van de beschikbare kolommen
return data
Uitvoering:
>>> from cbs_data import *
>>> data = cbs_filter('84583NED', 'wijk', 'amsterdam')
>>> data
ID WijkenEnBuurten ... MateVanStedelijkheid_115 Omgevingsadressendichtheid_116
921 921 Burgwallen-Oude Zijde ... 1.0 7622.0
927 927 Burgwallen-Nieuwe Zijde ... 1.0 9222.0
936 936 Grachtengordel-West ... 1.0 10638.0
941 941 Grachtengordel-Zuid ... 1.0 9212.0
949 949 Nieuwmarkt/Lastage ... 1.0 6995.0
... ... ... ... ... ...
1464 1464 Bijlmer Oost (E,G,K) ... 1.0 3329.0
1479 1479 Nellestein ... 2.0 2021.0
1483 1483 Holendrecht/Reigersbos ... 1.0 2539.0
1491 1491 Gein ... 2.0 2081.0
1496 1496 Driemond ... 5.0 453.0
[99 rows x 118 columns]
>>> data.to_excel("amsterdam_wijk.xlsx")
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edwindj
commented on May 19, 2024
@datadwerg thx for answering the question: seems indeed the intention of the question.
Another option of course is to do post-filtering: download the data and then filter on this column (but that will take a lot more memory)
data_all <- cbs_get_data(id = "84583NED")
data_gm <- subset(data_all, grepl("GM", WijkenEnBuurten))
# or using tidyverse
library(tidyverse)
data_gm <- data_all %>% filter(str_detect(WijkenEnBuurten, "GM"))from cbsodatar.
victorhartman
commented on May 19, 2024
Thanks for the responses. The solution by @datadwerg answers my question and works.
Kerncijfers wijken en buurten 2020 contains ~17.000 rows and the gemeentes only take 355 of those. So although post-filtering works, it is much slower/requires more resources.
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Related Issues (20)
- More informative error message when request too long
- problems loading 70747ned HOT 19
- cbs_get_toc for a particular city HOT 3
- error bij `cbs_add_columns()` HOT 3
- please document output values HOT 3
- cbs_get_meta() gives error: fixed but not yet on CRAN HOT 2
- filter data with geq, neq and not in HOT 3
- week periods are not converted with `cbs_add_date_column` HOT 4
- cbsodataR in Remoteacces environment HOT 7
- setwd() in package limits cronjobs HOT 4
- `cbs_get_data` with `base_url="http://dataderden.cbs.nl` fails HOT 2
- Solution for no connection with Windows 7/8 and IE11
- cbs_get_data() no response error
- error cbs_download_data HOT 1
- HTTP versus HTTPS
- method to keep track of table updates? HOT 1
- `cbs_download_data`: catalog error
- `cbs_get_data` argument `typed=FALSE` not working correctly
- `cbs_get_meta`: default selection which includes a substring of is not parsed correctly
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