I would like to use a python program, which I can call in the command line, from within an R script and then call this program for every row in a data frame, add the result to the data frame and then export the data frame as .tsv data.
My example is this: I have a data frame where in column 2 there is a string (amino_acid) and I would like to call the program "olga-compute_pgen" on every string in column 2 and add the resulting numerical value from "olga-compute_pgen" to a new column in the data frame.
my command in the terminal (I use MacOS) would be
olga-compute_pgen --humanTRB (and here would be the string)
An example with output would be:
olga-compute_pgen --humanTRB CASSLGRDGGHEQYF
which leads to this output:
Pgen of the amino acid sequence CASSLGRDGGHEQYF: 7.253421763151433e-10
Completed pgen computation in: 0.05 seconds.
The line of code, where I want to add a column to my dataframe and fill it with the python results is right now
df <- cbind(df,NewColumn=(system('olga-compute_pgen --humanTRB') df[,2], wait=FALSE))
but this obviously does not work at all.
I appreciate any help, thanks in advance.
Edit:
after some help, I have gotten some of it to work and I'll post the progress here, because the code is easier to read:
system2 seems to be a good start, because I can give a command with an argument and then output it into a dataframe to edit further:
I now have two problems left:
this line cannot use a value of a previous dataframe as additional input for the command, but rather uses strings
olga <- system2('olga-compute_pgen' , args="--humanTRB ", input=x, wait=TRUE, stdout=TRUE)
The output (once I have gotten the input to work) should then be appended to the original data frame in a new column
Thanks for the help already!
Edit2: Solution
I have gotten this to work with a combination of "system2" and "foreach", here is what I did in the end:
#dataframe is df
#first define the function to use (in this case olga from python)
olgafunction <- function(y) {
olga <- system2('olga-compute_pgen', args=c("--humanTRB ",
as.character(y[,2])), wait=TRUE, stdout=TRUE)
y <- cbind(y, pgen=olga)
}
#now use this function for every row of the dataframe
dfoutput <- foreach(dfrow=iter(df, by='row'), .combine=rbind) %do%
olgafunction(dfrow)
Apologies for the late reply and harsh tone in the previous comment. I was trying to help you learn to debug your issues, which you successfully are now. system returns the exit status of the executable. You can read this from the documentation for the system-function (https://stat.ethz.ch/R-manual/R-devel/library/base/html/system.html). All programmes returns an exit status (usually with 0 for successful termination), and this is quite different from what it outputs to the screen.
I would however advice you to take a look at the function system2, which should be a bit more sturdy and flexible. Take a look at the use of the stdout arguments, this would be key to solving your issue.
I'm starting a work to analyse data from Stats Institutions like Eurostat using python, and so pandas. I found out there are two methods to get data from Eurostat.
pandas_datareader: it seems very easy to use but I found some problems to get some specific data
pandasdmx: I've found it a bit complicated but it seems a promising solution, but documentation is poor
I use a free Azure notebook, online service, but I don't think it will complicate more my situation.
Let me explain the problems for pandas_datareader. According to the pandas documentation, in the section API, there is this short documented package and it works. Apart from the shown example, that nicely works, a problem arises about other tables. For example, I can get data about European house price, which ID table is prc_hpi_a with this simple code:
import pandas_datareader.data as web
import datetime
df = web.DataReader('prc_hpi_a', 'eurostat')
But the table has three types of data about dwellings: TOTAL, EXISTING and NEW. I got only Existing dwellings and I don't know how to get the other ones. Do you have a solution for these types of filtering.
Secondly there is the path using pandasdmx. Here it is more complicated. My idea is to upload all data to a pandas DataFrame, and then I can analyse as I want. Easy to say, but I've not find many tutorials that explain this passage: upload data to pandas structures. For example, I found this tutorial, but I'm stuck to the first step, that is instantiate a client:
import pandasdmx
from pandasdmx import client
#estat=client('Eurostat', 'milk.db')
and it returns:
--------------------------------------------------------------------------- ImportError Traceback (most recent call
last) in ()
1 import pandasdmx
----> 2 from pandasdmx import client
3 estat=client('Eurostat', 'milk.db')
ImportError: cannot import name 'client'
What's the problem here? I've looked around but no answer to this problem
I also followed this tutorial:
from pandasdmx import Request
estat = Request('ESTAT')
metadata = estat.datastructure('DSD_une_rt_a').write()
metadata.codelist.iloc[8:18]
resp = estat.data('une_rt_a', key={'GEO': 'EL+ES+IE'}, params={'startPeriod': '2007'})
data = resp.write(s for s in resp.data.series if s.key.AGE == 'TOTAL')
data.columns.names
data.columns.levels
data.loc[:, ('PC_ACT', 'TOTAL', 'T')]
I got the data, but my purpose is to upload them to a pandas structure (Series, DataFrame, etc..), so I can handle easily according to my work. How to do that?
Actually I did with this working line (below the previous ones):
s=pd.DataFrame(data)
But it doesn't work if I try to get other data tables. Let me explain with another example about the Harmonized Index Current Price table:
estat = Request('ESTAT')
metadata = estat.datastructure('DSD_prc_hicp_midx').write()
resp = estat.data('prc_hicp_midx')
data = resp.write(s for s in resp.data.series if s.key.COICOP == 'CP00')
It returns an error here, that is:
--------------------------------------------------------------------------- AttributeError Traceback (most recent call
last) in ()
2 metadata = estat.datastructure('DSD_prc_hicp_midx').write()
3 resp = estat.data('prc_hicp_midx')
----> 4 data = resp.write(s for s in resp.data.series if s.key.COICOP == 'CP00')
5 #metadata.codelist
6 #data.loc[:, ('TOTAL', 'INX_Q','EA', 'Q')]
~/anaconda3_501/lib/python3.6/site-packages/pandasdmx/api.py in
getattr(self, name)
622 Make Message attributes directly readable from Response instance
623 '''
--> 624 return getattr(self.msg, name)
625
626 def _init_writer(self, writer):
AttributeError: 'DataMessage' object has no attribute 'data'
Why does it do not get data now? What's wrong now?
I lost almost a day looking around for some clear examples and explanations. Do you have some to propose? Is there a full and clear documentation? I found also this page with other examples, explaining the use of categorical schemes, but it is not for Eurostat (as explained at some point)
Both methods could work, apart from some explained issues, but I need also a suggestion to have a definitely method to use, to query Eurostat but also many other institutions like OECD, World Bank, etc...
Could you guide me to a definitive and working solution, even if it is different for each institution?
That's my definitive answer to my question that works for each type of data collected from Eurostat. I post here because it can be useful for many.
Let me propose some examples. They produce three pandas series (EU_unempl,EU_GDP,EU_intRates) with data and correct time indexes
#----Unemployment Rate---------
dataEU_unempl=pd.read_json('http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/ei_lmhr_m?geo=EA&indic=LM-UN-T-TOT&s_adj=NSA&unit=PC_ACT',typ='series',orient='table',numpy=True) #,typ='DataFrame',orient='table'
x=[]
for i in range(int(sorted(dataEU_unempl['value'].keys())[0]),1+int(sorted(dataEU_unempl['value'].keys(),reverse=True)[0])):
x=numpy.append(x,dataEU_unempl['value'][str(i)])
EU_unempl=pd.Series(x,index=pd.date_range((pd.to_datetime((sorted(dataEU_unempl['dimension']['time']['category']['index'].keys())[(sorted(int(v) for v in dataEU_unempl['value'].keys())[0])]),format='%YM%M')), periods=len(x), freq='M')) #'1/1993'
#----GDP---------
dataEU_GDP=pd.read_json('http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/namq_10_gdp?geo=EA&na_item=B1GQ&s_adj=NSA&unit=CP_MEUR',typ='series',orient='table',numpy=True) #,typ='DataFrame',orient='table'
x=[]
for i in range((sorted(int(v) for v in dataEU_GDP['value'].keys())[0]),1+(sorted((int(v) for v in dataEU_GDP['value'].keys()),reverse=True))[0]):
x=numpy.append(x,dataEU_GDP['value'][str(i)])
EU_GDP=pd.Series(x,index=pd.date_range((pd.Timestamp(sorted(dataEU_GDP['dimension']['time']['category']['index'].keys())[(sorted(int(v) for v in dataEU_GDP['value'].keys())[0])])), periods=len(x), freq='Q'))
#----Money market interest rates---------
dataEU_intRates=pd.read_json('http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/irt_st_m?geo=EA&intrt=MAT_ON',typ='series',orient='table',numpy=True) #,typ='DataFrame',orient='table'
x=[]
for i in range((sorted(int(v) for v in dataEU_intRates['value'].keys())[0]),1+(sorted((int(v) for v in dataEU_intRates['value'].keys()),reverse=True))[0]):
x=numpy.append(x,dataEU_intRates['value'][str(i)])
EU_intRates=pd.Series(x,index=pd.date_range((pd.to_datetime((sorted(dataEU_intRates['dimension']['time']['category']['index'].keys())[(sorted(int(v) for v in dataEU_intRates['value'].keys())[0])]),format='%YM%M')), periods=len(x), freq='M'))
The general solution is to not rely on overly-specific APIs like datareader and instead go to the source. You can use datareader's source code as inspiration and as a guide for how to do it. But ultimately when you need to get data from a source, you may want to directly access that source and load the data.
One very popular tool for HTTP APIs is requests. You can easily use it to load JSON data from any website or HTTP(S) service. Once you have the JSON, you can load it into Pandas. Because this solution is based on general-purpose building blocks, it is applicable to virtually any data source on the Web (as opposed to e.g. pandaSDMX, which is only applicable to SDMX data sources).
Load with read_csv and multiple separators
The problem with eurostat data from the bulk download repository is that they are tab separated files where the first 3 columns are separated by commas. Pandas read_csv() can deal with mulitple separators as a regex if you specify engine="python". This works for some data sets, but the OP's dataset also contains flags, which cannot be ignored in the last column.
# Load the house price index from the Eurostat bulk download facility
import pandas
code = "prc_hpi_a"
url = f"https://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=1&file=data%2F{code}.tsv.gz" # Pandas.read_csv could almost read it directly with a multiple separator
df = pandas.read_csv(url, sep=",|\t| [^ ]?\t", na_values=":", engine="python")
# But the last column is a character column instead of a numeric because of the
# presence of a flag ": c" illustrated in the last line of the table extract
# below
# purchase,unit,geo\time\t 2006\t 2005
# DW_EXST,I10_A_AVG,AT\t :\t :
# DW_EXST,I10_A_AVG,BE\t 83.86\t 75.16
# DW_EXST,I10_A_AVG,BG\t 87.81\t 76.56
# DW_EXST,I10_A_AVG,CY\t :\t :
# DW_EXST,I10_A_AVG,CZ\t :\t :
# DW_EXST,I10_A_AVG,DE\t100.80\t101.10
# DW_EXST,I10_A_AVG,DK\t113.85\t 91.79
# DW_EXST,I10_A_AVG,EE\t156.23\t 98.69
# DW_EXST,I10_A_AVG,ES\t109.68\t :
# DW_EXST,I10_A_AVG,FI\t : c\t : c
Load with the eurostat package
There is also a python package called eurostat which makes it possible to search and load data set from the bulk facility into pandas data frames.
Load 2 different monthly exchange rate data sets:
import eurostat
df1 = eurostat.get_data_df(code)
The table of content of the bulk download facility can be read with
toc_url = "https://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=1&file=table_of_contents_en.txt"
toc2 = pandas.read_csv(toc_url, sep="\t")
# Remove white spaces at the beginning and end of strings
toc2 = toc2.applymap(lambda x: x.strip() if isinstance(x, str) else x)
or with
toc = eurostat.get_toc_df()
toc0 = (eurostat.subset_toc_df(toc, "exchange"))
The last line searches for the datasets that have "exchange" in their title
Reshape to long format
It might be useful to reshape the eurostat data to long format
with
if any(df.columns.str.contains("time")):
time_column = df.columns[df.columns.str.contains("time")][-1]
# Id columns are before the time columns
id_columns = df.loc[:, :time_column].columns
df = df.melt(id_vars=id_columns, var_name="period", value_name="value")
# Remove "\time" from the rightmost column of the index
df = df.rename(columns=lambda x: re.sub(r"\\time", "", x))
How to get/extract number of lines added and deleted?
(Just like we do using git diff --numstat).
repo_ = Repo('git-repo-path')
git_ = repo_.git
log_ = g.diff('--numstat','HEAD~1')
print(log_)
prints the entire output (lines added/deleted and file-names) as a single string. Can this output format be modified or changed so as to extract useful information?
Output format: num(added) num(deleted) file-name
For all files modified.
If I understand you correctly, you want to extract data from your log_ variable and then re-format it and print it? If that's the case, then I think the simplest way to fix it, is with a regular expression:
import re
for line in log_.split('\n'):
m = re.match(r"(\d+)\s+(\d+)\s+(.+)", line)
if m:
print("{}: rows added {}, rows deleted {}".format(m[3], m[1], m[2]))
The exact output, you can of course modify any way you want, once you have the data in a match m. Getting the hang of regular expressions may take a while but it can be very helpful for small scripts.
However, be adviced, reg exps tend to be write-only code and can be very hard to debug. However, for extracting small parts like this, it is very helpful.
I am currently writing a program which uses the ComapaniesHouse API to return a json file containing information about a certain company.
I am able to retrieve the data easily using the following commands:
r = requests.get('https://api.companieshouse.gov.uk/company/COMPANY-NO/filing-history', auth=('API-KEY', ''))
data = r.json()
With that information I can do an awful lot, however I've ran into a problem which I was hoping you guys could possible help me with. What I aim to do is go through every nested entry in the json file and check if the value of certain keys matches certain criteria, if the values of 2 keys match a certain criteria then other code is executed.
One of the keys is the date of an entry, and I would like to ignore results that are older than a certain date, I have attempted to do this with the following:
date_threshold = datetime.date.today() - datetime.timedelta(days=30)``
for each in data["items"]:
date = ['date']
type = ['type']
if date < date_threshold and type is "RM01":
print("wwwwww")
In case it isn't clear, what I'm attempting to do (albeit very badly) is assign each of the entries to a variable, which then gets tested against certain criteria.
Although this doesn't work, python spits out a variable mismatch error:
TypeError: unorderable types: list() < datetime.date()
Which makes me think the date is being stored as a string, and so I can't compare it to the datetime value set earlier, but when I check the API documentation (https://developer.companieshouse.gov.uk/api/docs/company/company_number/filing-history/filingHistoryItem-resource.html), it says clearly that the 'date' entry is returned as a date type.
What am I doing wrong, its very clear that I'm extremely new to python given what I presume is the atrocity of my code, but in my head it seems to make at least a little sense. In case none of this clear, I basically want to go through all the entries in the json file, and the if the date and type match a certain description, then other code can be executed (in this case I have just used random text).
Any help is greatly appreciated! Let me know if you need anything cleared up.
:)
EDIT
After tweaking my code to the below:
for each in data["items"]:
date = each['date']
type = each['type']
if date is '2016-09-15' and type is "RM01":
print("wwwwww")
The code executes without any errors, but the words aren't printed, even though I know there is an entry in the json file with that exact date, and that exact type, any thoughts?
SOLUTION:
Thanks to everyone for helping me out, I had made a couple of very basic errors, the code that works as expected is below::
for each in data["items"]:
date = each['date']
typevariable = each['type']
if date == '2016-09-15' and typevariable == "RM01":
print("wwwwww")
This prints the word "wwwwww" 3 times, which is correct seeing as there are 3 entries in the JSON that fulfil those criteria.
You need to first convert your date variable to a datetime type using datetime.strptime()
You are comparing a list type variable date with datetime type variable date_threshold.
Been learning Python the last couple of days for the function of completing a data extraction. I'm not getting anywhere & hope one of you lovely people can advise.
I need to extract data that follows: RESP, CRESP, RTTime and RT.
Here's a snippit for an example of the mess I have to deal with.
Thoughts?
Level: 4
*** LogFrame Start ***
Procedure: ActProcScenarios
No: 1
Line1: It is almost time for your town's spring festival. A friend of yours is
Line2: on the committee and asks if you would be prepared to help out with the
Line3: barbecue in the park. There is a large barn for use if it rains.
Line4: You hope that on that day it will be
pfrag: s-n-y
pword: sunny
pletter: u
Quest: Does the town have an autumn festival?
Correct: {LEFTARROW}
ScenarioListPract: 1
Topic: practice
Subtheme: practice
ActPracScenarios: 1
Running: ActPracScenarios
ActPracScenarios.Cycle: 1
ActPracScenarios.Sample: 1
DisplayFragInstr.OnsetDelay: 17
DisplayFragInstr.OnsetTime: 98031
DisplayFragInstr.DurationError: -999999
DisplayFragInstr.RTTime: 103886
DisplayFragInstr.ACC: 0
DisplayFragInstr.RT: 5855
DisplayFragInstr.RESP: {DOWNARROW}
DisplayFragInstr.CRESP:
FragInput.OnsetDelay: 13
FragInput.OnsetTime: 103899
FragInput.DurationError: -999999
FragInput.RTTime: 104998
I think regular expressions would be the right tool here because the \b word boundary anchors allow you to make sure that RESP only matches a whole word RESP and not just part of a longer word (like CRESP).
Something like this should get you started:
>>> import re
>>> for line in myfile:
... match = re.search(r"\b(RT|RTTime|RESP|CRESP): (.*)", line)
... if match:
... print("Matched {0} with value {1}".format(match.group(1),
... match.group(2)))
Output:
Matched RTTime with value 103886
Matched RT with value 5855
Matched RESP with value {DOWNARROW}
Matched CRESP with value
Matched RTTime with value 104998
transform it to a dict first, then just get items from the dict as you wish
d = {k.strip(): v.strip() for (k, v) in
[line.split(':') for line in s.split('\n') if line.find(':') != -1]}
print (d['DisplayFragInstr.RESP'], d['DisplayFragInstr.CRESP'],
d['DisplayFragInstr.RTTime'], d['DisplayFragInstr.RT'])
>>> ('{DOWNARROW}', '', '103886', '5855')
I think you may be making things harder for yourself than needed. E-prime has a file format called .edat that is designed for the purpose you are describing. An edat file is another format that contains the same information as the .txt file but it a way that makes extracting variables easier. I personally only use the type of text file you have posted here as a form of data storage redundancy.
If you are doing things this way because you do not have a software key, it might help to know that the E-Merge and E-DataAid programs for eprime don't require a key. You only need the key for editing build files. Whoever provided you with the .txt files should probably have an install disk for these programs. If not, it is available on the PST website (I believe you need a serial code to create an account, but not certain)
Eprime generally creates a .edat file that matches the content of the text file you have posted an example of. Sometimes though if eprime crashes you don't get the edat file and only have the .txt. Luckily you can generate the edat file from the .txt file.
Here's how I would approach this issue: If you do not have the edat files available first use E-DataAid to recover the files.
Then presuming you have multiple participants you can use e-merge to merge all of the edat files together for all participants in who completed this task.
Open the merged file. It might look a little chaotic depending on how much you have in the file. You can got to Go to tools->Arrange columns This will show a list of all your variables. Adjust so that only the desired variables are in the right hand box. Hit ok.
Looking at the file you posted it says level 4 at the top so I'm guessing there are a lot of procedures in this experiment. If you have many procedures in the program you might at this point have lines that just have startup info and NULL in the locations where your variables or interest are. You and fix this by going to tools->filter and creating a filter to eliminate those lines. Sometimes also depending on file structure you might also end up with duplicate lines of the same data. You can also fix this with filtering.
You can then export this file as a csv
import re
import pprint
def parse_logs(file_name):
with open(file_name, "r") as f:
lines = [line.strip() for line in f.readlines()]
base_regex = r'^.*{0}: (.*)$'
match_terms = ["RESP", "CRESP", "RTTime", "RT"]
regexes = {term: base_regex.format(term) for term in match_terms}
output_list = []
for line in lines:
for key, regex in regexes.items():
match = re.match(regex, line)
if match:
match_tuple = (key, match.groups()[0])
output_list.append(match_tuple)
return output_list
pprint.pprint(parse_logs("respregex"))
Edit: Tim and Guy's answers are both better. I was in a hurry to write something and missed two much more elegant solutions.