I want to perform an regexp_replace operation on a pyspark dataframe column using dictionary.
Dictionary : {'RD':'ROAD','DR':'DRIVE','AVE':'AVENUE',....}
The dictionary will have around 270 key value pair.
Input Dataframe:
ID | Address
1 | 22, COLLINS RD
2 | 11, HEMINGWAY DR
3 | AVIATOR BUILDING
4 | 33, PARK AVE MULLOHAND DR
Desired Output Dataframe:
ID | Address | Address_Clean
1 | 22, COLLINS RD | 22, COLLINS ROAD
2 | 11, HEMINGWAY DR | 11, HEMINGWAY DRIVE
3 | AVIATOR BUILDING | AVIATOR BUILDING
4 | 33, PARK AVE MULLOHAND DR | 33, PARK AVENUE MULLOHAND DRIVE
I cannot find any documentation on internet. And if trying to pass dictionary as below codes-
data=data.withColumn('Address_Clean',regexp_replace('Address',dict))
Throws an error "regexp_replace takes 3 arguments, 2 given".
Dataset will be around 20 million in size. Hence, UDF solution will be slow (due to row wise operation) and we don't have access to spark 2.3.0 which supports pandas_udf.
Is there any efficient method of doing it other than may be using a loop?
It is trowing you this error because regexp_replace() needs three arguments:
regexp_replace('column_to_change','pattern_to_be_changed','new_pattern')
But you are right, you don't need a UDF or a loop here. You just need some more regexp and a directory table that looks exactly like your original directory :)
Here is my solution for this:
# You need to get rid of all the things you want to replace.
# You can use the OR (|) operator for that.
# You could probably automate that and pass it a string that looks like that instead but I will leave that for you to decide.
input_df = input_df.withColumn('start_address', sf.regexp_replace("original_address","RD|DR|etc...",""))
# You will still need the old ends in a separate column
# This way you have something to join on your directory table.
input_df = input_df.withColumn('end_of_address',sf.regexp_extract('original_address',"(.*) (.*)", 2))
# Now we join the directory table that has two columns - ends you want to replace and ends you want to have instead.
input_df = directory_df.join(input_df,'end_of_address')
# And now you just need to concatenate the address with the correct ending.
input_df = input_df.withColumn('address_clean',sf.concat('start_address','correct_end'))
Related
I have a jsonb structure on postgres named data where each row (there are around 3 million of them) looks like this:
[
{
"number": 100,
"key": "this-is-your-key",
"listr": "20 Purple block, THE-CITY, Columbia",
"realcode": "LA40",
"ainfo": {
"city": "THE-CITY",
"county": "Columbia",
"street": "20 Purple block",
"var_1": ""
},
"booleanval": true,
"min_address": "20 Purple block, THE-CITY, Columbia LA40"
},
.....
]
I would like to query the min_address field in the fastest possible way. In Django I tried to use:
APModel.objects.filter(data__0__min_address__icontains=search_term)
but this takes ages to complete (also, "THE-CITY" is in uppercase, so, I have to use icontains here. I tried dropping to rawsql like so:
cursor.execute("""\
SELECT * FROM "apmodel_ap_model"
WHERE ("apmodel_ap_model"."data"
#>> array['0', 'min_address'])
#> %s \
""",\
[json.dumps([{'min_address': search_term}])]
)
but this throws me strange errors like:
LINE 4: #> '[{"min_address": "some lane"}]'
^
HINT: No operator matches the given name and argument type(s). You might need to add explicit type casts.
I am wondering what is the fastest way I can query the field min_address by using rawsql cursors.
Late answer, probably it won't help OP anymore. Also I'm not at all an expert in Postgres/JSONB, so this might be a terrible idea.
Given this setup;
so49263641=# \d apmodel_ap_model;
Table "public.apmodel_ap_model"
Column | Type | Collation | Nullable | Default
--------+-------+-----------+----------+---------
data | jsonb | | |
so49263641=# select * from apmodel_ap_model ;
data
-------------------------------------------------------------------------------------------
[{"number": 1, "min_address": "Columbia"}, {"number": 2, "min_address": "colorado"}]
[{"number": 3, "min_address": " columbia "}, {"number": 4, "min_address": "California"}]
(2 rows)
The following query "expands" objects from data arrays to individual rows. Then it applies pattern matching to the min_address field.
so49263641=# SELECT element->'number' as number, element->'min_address' as min_address
FROM apmodel_ap_model ap, JSONB_ARRAY_ELEMENTS(ap.data) element
WHERE element->>'min_address' ILIKE '%col%';
number | min_address
--------+---------------
1 | "Columbia"
2 | "colorado"
3 | " columbia "
(3 rows)
However, I doubt it will perform well on large datasets as the min_address values are casted to text before pattern matching.
Edit: Some great advice here on indexing JSONB data for search https://stackoverflow.com/a/33028467/1284043
I'm not sure how to do this in a dataframe context
I have the table below here with text information
TEXT |
-------------------------------------------|
"Get some new #turbo #stacks today!" |
"Is it one or three? #phone" |
"Mayhaps it be three afterall..." |
"So many new issues with phone... #iphone" |
And I want to edit it down to where only the words with a '#' symbol are kept, like in the result below.
TEXT |
-----------------|
"#turbo #stacks" |
"#phone" |
"" |
"#iphone" |
In some cases, I'd also like to know if it's possible to eliminate the rows that are empty by checking for NaN as true or if you run a different kind of condition to get this result:
TEXT |
-----------------|
"#turbo #stacks" |
"#phone" |
"#iphone" |
Python 2.7 and pandas for this.
You could try using regex and extractall:
df.TEXT.str.extractall('(#\w+)').groupby(level=0)[0].apply(' '.join)
Output:
0 #turbo #stacks
1 #phone
3 #iphone
Name: 0, dtype: object
I have a dataset with the addresses of authors' affiliations. Addresses have differing length. But the information before the first comma is the name of he institution and that after the last comma the country. What I want to do is to extract the country and create a new variable for it.
I tried this code in Stata. It works to extract the name of institutions.
generate splitat = strpos(institutions ,",")
generate str80 univ = substr(institutions, 1, splitat - 1)
I am wondering whether this code also can be applied to extract the country.
I thought it could check from the end instead from the start?
My dataset looks like the following example:
Natl Taiwan Univ, Inst Epidemiol, Taipei 106, Taiwan
Radboud Univ Nijmegen, Inst Water & Wetland Res, Dept Anim Ecol & Ecophysiol, NL-6525 AJ Nijmegen, Netherlands
There is a specific function in Stata 14+ to look for the last occurrence of a substring (e.g. a specific character) in a string. See help string functions in Stata 14 for documentation of strrpos().
If that is not in your version of Stata, you merely reverse the string, find the substring using the method you already know, and then reverse what you found.
If you are not using the latest version of Stata, it is always a good idea to specify that in questions in any forum that supports Stata questions,
clear
input str244 institutions
"Natl Taiwan Univ, Inst Epidemiol, Taipei 106, Taiwan"
"Radboud Univ Nijmegen, Inst Water & Wetland Res, Dept Anim Ecol & Ecophysiol, NL-6525 AJ Nijmegen, Netherlands"
end
compress
gen country = substr(institutions, strrpos(institutions, ",") + 1, .)
local rev strreverse(institutions)
gen country2 = strreverse(substr(`rev', 1, strpos(`rev', ",") - 1))
assert country == country2
l country
+--------------+
| country |
|--------------|
1. | Taiwan |
2. | Netherlands |
+--------------+
I have param_Value column that have different values. I need to extract these values and create columns for all of them.
|PARAM_NAME |param_Value |
__________|____________
|Step 4 | SP:0.09 |
|Procedure | MAX:125 |
|Step 4 | SP:Ambient|
|(null) | +/-:N/A |
|Steam | SP:2 |
|Step 3 | MIN:0 |
|Step 4 | RDPHN427B |
|Testing De | N/A |
I only want columns with: And give them names:
SP: SET_POINT_VALUE,
MAX: MAX_LIMIT,
MIN: MIN_LIMIT,
+/-: UPPER_LOWER_LIMIT
So what I have so far is:
CREATE OR REPLACE FORCE VIEW PROCESS_STEPS
("PARAM_NAME", "SET_POINT_VALUE", "UPPER_LOWER_LIMIT", "MAX_VALUE", "MIN_VALUE")
AS
SELECT PARAM_NAME,
REGEXP_LIKE("param_Value", 'SP:') SET_POINT_VALUE,
REGEXP_LIKE("param_Value", '+/-:') UPPER_LOWER_LIMIT,
REGEXP_LIKE("param_Value", 'MAX:') MAX_VALUE,
REGEXP_LIKE("param_Value", 'MIN:') MIN_VALUE
FROM PROCESS_STEPS
;
I'm more familiar with TSQL and MySQL, but this ought to do what I think you're looking for. If it doesn't exactly, it should at least point you in the right direction.
CREATE OR REPLACE FORCE VIEW PROCESS_STEPS
("PARAM_NAME", "SET_POINT_VALUE", "UPPER_LOWER_LIMIT", "MAX_VALUE", "MIN_VALUE")
AS
SELECT PARAM_NAME
, CASE WHEN "param_Value" LIKE 'SP:%'
THEN SUBSTR("param_Value", INSTR("param_Value", ':')+1)
ELSE Null
END SET_POINT_VALUE
, CASE WHEN "param_Value" LIKE '+/-:%'
THEN SUBSTR("param_Value", INSTR("param_Value", ':')+1)
ELSE Null
END UPPER_LOWER_LIMIT
, CASE WHEN "param_Value" LIKE 'MAX:%'
THEN SUBSTR("param_Value", INSTR("param_Value", ':')+1)
ELSE Null
END MAX_VALUE
, CASE WHEN "param_Value" LIKE 'MIN:%'
THEN SUBSTR("param_Value", INSTR("param_Value", ':')+1)
ELSE Null
END MIN_VALUE
FROM PROCESS_STEPS
;
The basic concept here is identifying the information you want via LIKE, then using SUBSTR and INSTR to extract it. While LIKE is normally something to stay away from, since there's no leading % in your case, it's Sargable, and thus probably not a total efficiency sink.
Really, though, I have to ask you to question why you're laying out your data like this - substring operations are slow in any language, and a DB is no exception. Why not use another column for your limit type? Why not lay it out in the view you're currently looking at?
I have 53 Stata .dta files each of them is 150 - 200 Mb and contain identical set of variables, but for different years. It is not useful to combine or merge them due to their size .
I need to retrieve some averaged values (percentages etc.) Therefore, I want to create a new Stata file New.dta and write a .do file that would run on that new Stata file in the following way: it should open each of those 53 Stata files, make certain calulations, and store the results in the new Stata file, New.dta.
I am not sure how i can keep two Stata file open simultaneuosly, and how can i store the calculated values?
When I open a second .dta file, how can i make the first one still be open? How can i store the calculated values in the global variable?
What springs to mind here is the use of postfile.
Here is a simple example. First, I set up an example of several datasets. You already have this.
clear
forval i = 1/10 {
set obs 100
gen foo = `i' * runiform()
save test`i'
clear
}
Now I set up postfile. I need to set up a handle, what variables will be used, and what file will be used. Although I am using a numeric variable to hold file identifiers, it will perhaps be more typical to use a string variable. Also, looping over filenames may be a bit more challenging than this. fs from SSC is a convenience command that helps put a set of filenames into a local macro; its use is not illustrated here.
postfile mypost what mean using alltest.dta
forval i = 1/10 {
use test`i', clear
su foo, meanonly
post mypost (`i') (`r(mean)')
}
Now flush results
postclose mypost
and see what we have.
u alltest
list
+-----------------+
| what mean |
|-----------------|
1. | 1 .5110765 |
2. | 2 1.016858 |
3. | 3 1.425967 |
4. | 4 2.144528 |
5. | 5 2.438035 |
|-----------------|
6. | 6 3.030457 |
7. | 7 3.356905 |
8. | 8 4.449655 |
9. | 9 4.381101 |
10. | 10 5.017308 |
+-----------------+
I didn't use any global macros (not global variables) here; you should not need to.
An alternative approach is to loop over files and use collapse to "condense" these files to the relevant means, and than append these condensed files. Here is an adaptation of Nick's example:
// create the example datasets
clear
forval i = 1/10 {
set obs 100
gen foo = `i' * runiform()
gen year = `i'
save test`i', replace
clear
}
// use collapse and append
// to create the dataset you want
use test1, clear
collapse (mean) year foo
save means, replace
forvalues i = 2/10 {
use test`i', clear
collapse (mean) year foo
append using means
save means, replace
}
// admire the result
list
Note that if your data sets are not named sequentially like test1.dta, test2.dta, ..., test53.dta, but rather like results-alaska.dta, result_in_alabama.dta, ..., "wyoming data.dta" (note the space and hence the quotes), you would have to organize the cycle over these files somewhat differently:
local allfiles : dir . files "*.dta"
foreach f of local allfiles {
use `"`f'"', clear
* all other code from Maarten's or Nick's approach
}
This is a more advanced of local macros, see help extended macro functions. Note also that Stata will produce a list that will look like "results-alaska.dta" "result_in_alabama.dta" "wyoming data.dta" with quotes around file names, so when you invoke use, you will have to enclose the file name into compound quotes.