I have a JSON input file that needs to be split into multiple files based on a keyword and the output should also retain the same JSON format.
Example:
The keyword here is the value of the object EVT.NAME. Depeneding on the value it should route it to the output.
Input has three different values (KEYPRESS,TUNE,TRICK), so 3 different output files should be created.
Input:
{"PV":"1.0","DEV":{"DEV_ID":"P0100011103"},"EVT":{"NAME":"KEYPRESS","ETS":1402672866844,"VALUE":{"KEY":"PLAY"}},"HOST":"XXX"}
{"PV":"1.0","DEV":{"DEV_ID":"P0100011103"},"EVT":{"NAME":"TUNE","ETS":1402672867117,"VALUE":{"KEY":"PLAY"}},"HOST":"XXX"}
{"PV":"1.0","DEV":{"DEV_ID":"P0100011103"},"EVT":{"NAME":"TRICK","ETS":1402672868600,"VALUE":{"KEY":"PLAY"}},"HOST":"XXX"}
{"PV":"1.0","DEV":{"DEV_ID":"P0100011103"},"EVT":{"NAME":"KEYPRESS","ETS":1402672868888,"VALUE":{"KEY":"PLAY"}},"HOST":"XXX"}
{"PV":"1.0","DEV":{"DEV_ID":"P0100011103"},"EVT":{"NAME":"TRICK","ETS":1402673179313,"VALUE":{"KEY":"FAST_FORWARD"}},"HOST":"XXX"}
Output1:
{"PV":"1.0","DEV":{"DEV_ID":"P0100011103"},"EVT":{"NAME":"KEYPRESS","ETS":1402672866844,"VALUE":{"KEY":"PLAY"}},"HOST":"XXX"}
{"PV":"1.0","DEV":{"DEV_ID":"P0100011103"},"EVT":{"NAME":"KEYPRESS","ETS":1402672868888,"VALUE":{"KEY":"PLAY"}},"HOST":"XXX"}
Output 2:
{"PV":"1.0","DEV":{"DEV_ID":"P0100011103"},"EVT":{"NAME":"TUNE","ETS":1402672867117,"VALUE":{"KEY":"PLAY"}},"HOST":"XXX"}
Output 3:
{"PV":"1.0","DEV":{"DEV_ID":"P0100011103"},"EVT":{"NAME":"TRICK","ETS":1402672868600,"VALUE":{"KEY":"PLAY"}},"HOST":"XXX"}
{"PV":"1.0","DEV":{"DEV_ID":"P0100011103"},"EVT":{"NAME":"TRICK","ETS":1402673179313,"VALUE":{"KEY":"FAST_FORWARD"}},"HOST":"XXX"}
You can use JsonLoader and JsonStorage. See this article - http://joshualande.com/read-write-json-apache-pig.
table = LOAD 'file.json'
USING JsonLoader('KEYPRESS:chararray, TUNE:chararray, TRICK:chararray');
Related
I have a snakemake rule aggregating several result files to a single file, per study. So to make it a bit more understandable; I have two roles ['big','small'] that each produce data for 5 studies ['a','b','c','d','e'], and each study produces 3 output files, one per phenotype ['xxx','yyy','zzz']. Now what I want is a rule to aggregate the phenotype results from each study to a single summary file per study (so merging the phenotypes into a single table). In the merge_results rule I give the rule a list of files (per study and role), and aggregate these using a pandas frame, and then spit out the result as a single file.
In the process of merging the results I need the 'pheno' variable from the input file being iterated over. Since pheno is not needed in the aggregated output file, it is not provided in output and as a consequence it is also not available in the wildcards object. Now to get a hold of the pheno I parse the filename to grab it, however this all feels very hacky and I suspect there is something here I have not understood properly. Is there a better way to grab wildcards from input files not used in output files in a better way?
runstudy = ['a','b','c','d','e']
runpheno = ['xxx','yyy','zzz']
runrole = ['big','small']
rule all:
input:
expand(os.path.join(output, '{role}-additive', '{study}', '{study}-summary-merge.txt'), role=runrole, study=runstudy)
rule merge_results:
input:
expand(os.path.join(output, '{{role}}', '{{study}}', '{pheno}', '{pheno}.summary'), pheno=runpheno)
output:
os.path.join(output, '{role}', '{study}', '{study}-summary-merge.txt')
run:
import pandas as pd
import os
# Iterate over input files, read into pandas df
tmplist = []
for f in input:
data = pd.read_csv(f, sep='\t')
# getting the pheno from the input file and adding it to the data frame
pheno = os.path.split(f)[1].split('.')[0]
data['pheno'] = pheno
tmplist.append(data)
resmerged = pd.concat(tmplist)
resmerged.to_csv(output, sep='\t')
You are doing it the right way !
In your line:
expand(os.path.join(output, '{{role}}', '{{study}}', '{pheno}', '{pheno}.summary'), pheno=runpheno)
you have to understand that role and study are wildcards. pheno is not a wildcard and is set by the second argument of the expand function.
In order to get the phenotype if your for loop, you can either parse the file name like you are doing or directly reconstruct the file name since you know the different values that pheno takes and you can access the wildcards:
run:
import pandas as pd
import os
# Iterate over phenotypes, read into pandas df
tmplist = []
for pheno in runpheno:
# conflicting variable name 'output' between a global variable and the rule variable here. Renamed global var outputDir for example
file = os.path.join(outputDir, wildcards.role, wildcards.study, pheno, pheno+'.summary')
data = pd.read_csv(file, sep='\t')
data['pheno'] = pheno
tmplist.append(data)
resmerged = pd.concat(tmplist)
resmerged.to_csv(output, sep='\t')
I don't know if this is better than parsing the file name like you were doing though. I wanted to show that you can access wildcards in the code. Either way, you are defining the input and output correctly.
I am trying to compare two CSV files, most of the time it will have same data but order of data will not be the same. Eg
csv file1
AAA,111,A1A1
BBB,222,B2B2
CCC,333,C3C3
CSV File2
CCC,333,C3C3
BBB,212,B2B2
AAA,111,A1A1
so I want to use third column as Primary key to compare other values. Report the difference. Is this possible to do it in Robotframework or Panda?
If you are making use of robotframework you need to do the following,
install robotframework-csvlib
Use Built-in Collections
Input from your question
csv file1
AAA,111,A1A1
BBB,222,B2B2
CCC,333,C3C3
csv file2
CCC,333,C3C3
BBB,212,B2B2
AAA,111,A1A1
My Solution
In the below approach, we are first reading csv into list of lists for both csv files and then comparing all the list of list items by making use of Collections KW List Should Contain Sub List, here, notice that we are passing an argument "values=True" which compares the value as well.
Code that compares 2 csv files
*** Settings ***
Library CSVLib
Library Collections
*** Test Cases ***
Test CSV
${list1}= read csv as list csv1.csv
log to console ${list1}
${list2}= read csv as list csv2.csv
log to console ${list2}
List Should Contain Sub List ${list1} ${list2} values=True
OUTPUT
(rf1) C:\Users\kgurupra>robot s1.robot
==============================================================================
S1
==============================================================================
Test CSV .[['C1,C2,C3'], ['AAA,111,A1A1'], ['BBB,222,B2B2'], ['CCC,333,C3C3']]
..[['C1,C2,C3'], ['CCC,333,C3C3'], ['BBB,212,B2B2'], ['AAA,111,A1A1']]
Test CSV | FAIL |
Following values were not found from first list: ['BBB,212,B2B2']
------------------------------------------------------------------------------
S1 | FAIL |
1 critical test, 0 passed, 1 failed
1 test total, 0 passed, 1 failed
==============================================================================
Output: C:\Users\kgurupra\output.xml
Log: C:\Users\kgurupra\log.html
Report: C:\Users\kgurupra\report.html
Assuming you've imported your CSV files as pandas DataFrames you can do the following to merge the two while retaining fundamental differences:
df = csv1.merge(csv2, on='<insert name primary key column here>',how='outer')
Adding the suffixes option allows you to more clearly differentiate between identically named columns from each file:
df = csv1.merge(csv2, on='<insert name>',how='outer',suffixes=['_csv1','_csv2'])
After that it depends on what kind of differences you are looking to spot but perhaps a starting point is:
df['difference_1'] = df['column1_csv1'] == df['column1_csv2']
this will create a boolean column which indicates True if observations are the same and False otherwise.
But there are nearly endless options for comparison.
There are different lists available in pelicanconf.py such as
SOCIAL = (('Facebook','www.facebook.com'),)
LINKS =
etc.
I want to manage these content and create my own lists by loading these values from an external file which can be edited independently. I tried importing data as a text file using python but it doesn't work. Is there any other way?
What exactly did not work? Can you provide code?
You can execute arbitrary python code in your pelicanconf.py.
Example for a very simple CSV reader:
# in pelicanconf.py
def fn_to_list(fn):
with open(fn, 'r') as res:
return tuple(map(lambda line: tuple(line[:-1].split(';')), res.readlines()))
print(fn_to_list("data"))
CSV file data:
A;1
B;2
C;3
D;4
E;5
F;6
Together, this yields the following when running pelican:
# ...
((u'A', u'1'), (u'B', u'2'), (u'C', u'3'), (u'D', u'4'), (u'E', u'5'), (u'F', u'6'))
# ...
Instead of printing you can also assign this list to a variable, say LINKS.
I wish to use a database column value in the output file name.
example:
select max(id) from process;
suppose the result of above query is 111
-- wish to use this value in the output file name as shown below.
output file name: file_111
how can i achieve this in pentaho kettle?
Please advice.
Depending on the type of file you want to create, you can simply create a column in your stream that contains the file name and then use the Accept file name from field-function that some output steps provide. The text file output for example does have this function, the XML output unfortunately doesn't.
to create the file name itself you can e.g. use the javascript step, or use the concat fields step together with the Add constants step.
Please follow the below steps:
Step 1: Table input :- select max(id) as max_id from process;
step 2: Modified Java Script Value:- put bellow code in this step.
eg:- var dummy= 'C:/Users/Venkatesh/Desktop/file_'+ max_id ;
in same step in the bottom ADD Field Name is dummy, Type is string and
Replace value 'Fieldname' or 'Rename to' is N
step 3: Text file output:-
select the **Add filenames to result**
**file name field** => dummy
Finally execute and see the result..
I have input.csv file with 3 columns viz, name,age,address. And, I have an output.csv file with 5 columns viz, Person name,Person age,Person address,Person salary,Person pass criteria.
I need to map my input.csv to output.csv. Please help me out with this. I tried Select values step, but it does not work.
You can do this in 4 steps.
1) Using CSV file input step you can get the name,age,address fields
2) Using Select values step you can rename name,age,address fields to Person name,Person age,Person address.
3) Using Add constants step you can add the additional fields, Person salary,Person pass criteria.
4) Using Text file output step you can output to a csv file. Here as the extension, type csv and separator as ,.