I have a data series that contains various names of the same organizations. I want harmonize these names into a given standard using a mapping dictionary. I am currently using a nested for loop to iterate through each series element and if it is within the dictionary's values, I update the series value with the dictionary key.
# For example, corporation_series is:
0 'Corp1'
1 'Corp-1'
2 'Corp 1'
3 'Corp2'
4 'Corp--2'
dtype: object
# Dictionary is:
mapping_dict = {
'Corporation_1': ['Corp1', 'Corp-1', 'Corp 1'],
'Corporation_2': ['Corp2', 'Corp--2'],
}
# I use this logic to replace the values in the series
for index, value in corporation_series.items():
for key, list in mapping_dict.items():
if value in list:
corporation_series = corporation_series.replace(value, key)
So, if the series has a value of 'Corp1', and it exists in the dictionary's values, the logic replaces it with the corresponding key of corporations. However, it is an extremely expensive method. Could someone recommend me a better way of doing this operation? Much appreciated.
I found a solution by using python's .map function. In order to use .map, I had to invert my dictionary:
# Inverted Dict:
mapping_dict = {
'Corp1': ['Corporation_1'],
'Corp-1': ['Corporation_1'],
'Corp 1': ['Corporation_1'],
'Corp2': ['Corporation_2'],
'Corp--2':['Corporation_2'],
}
# use .map
corporation_series.map(newdict)
Instead of 5 minutes of processing, took around 5s. While this is works, I sure there are better solutions out there. Any suggestions would be most welcome.
As an illustrative example, suppose this is your dataset:
cat sex age
1 1 13
1 0 14
1 1 .
2 1 23
2 1 45
2 1 15
If you want to create a table of frequencies between cat and sex, you tabulate these two variables and you get the following result:
tab cat sex
| sex
cat | 0 1 | Total
-----------+----------------------+----------
1 | 1 2 | 3
2 | 0 3 | 3
-----------+----------------------+----------
Total | 1 5 | 6
I am writing a Stata program where the three variables are involved, i.e. cat, sex and age. Getting the matrix of frequencies for the first two variables is just an intermediate step that I need for further computation.
cap program drop myexample
program def myexample, rclass byable(recall) sortpreserve
version 14
syntax varlist [aweight iweight fweight] [if] [in] [ , AGgregate ]
args var1 var2 var3
tempname F
marksample touse
set more off
if "`aggregate'" == "" {
local var1: word 1 of `varlist'
local var2: word 2 of `varlist'
local var3: word 3 of `varlist'
qui: tab `var1' `var2' [`weight' `exp'] if `touse', matcell(`F') label matcol(`var2')
mat list `F'
}
end
However, when I run:
myexample cat sex age
I get this result which is not what I expected:
__000001[2,2]
c1 c2
r1 1 1
r2 0 3
That is, given that age contains a missing value, even if it is not directly involved in the tabulation, the program ignores the missing value and does not take into account that observation. I need to get the result of the first tabulation. I have tried using summarize instead, but the same problem arises. When implemented inside the program, missing values are not counted.
You are complaining about behaviour which you built into your own program. The responsibility and the explanation are in your hands.
The effect of
marksample touse
followed by calling up a command with the qualifier
if `touse'
is to ignore missing values. marksample by default marks as "to use" those observations in which all variables specified have non-missing values; the other observations are marked as to be ignored. It also takes account of any if or in qualifiers and any zero weights.
It's also true, as #Noobie explains, that omitting missing values from a tabulation is default for tabulate in any case.
So, to get the result you want you'd need to modify your marksample call to
marksample touse, novarlist
and to call up tabulate with the missing option (if it's compulsory) or to allow users to specify a missing option which you then pass to tabulate.
You also ask about summarize. By design that command ignores missing values. I don't know what you would expect summarize to do about them. It could report a count of missing values. If you want that, several other commands will oblige, such as codebook or missings (Stata Journal). You can always include a report on missings in your program, such as using count to count the missings and display the result.
I understand your program to be very much work in progress, so won't comment on details you don't ask about.
This is caused by marksample. Rule 5 in help mark states
The marker variable is set to 0 in observations for which any of the
numeric variables in varlist contain a numeric missing value.
You should use the novarlist option. According to the help file,
novarlist is for use with marksample. It specifies that missing values
among variables in varlist not cause the marker variable to be set to 0.
if I understand well you want tab to include missing values? If so, you just have to ask for it
tab myvar1 myvar2, mi
from the documentation
missing : treat missing values like other values
I am new in hadoop and i am working with a programme that the input of map function is a file that keys are like this:
ID: value:
3 sd
37 g
5675 gk
68 oi
My file is about 10 gigabytes and i want to change these Ids and renumber them in descending order. I don't want to change the values.
My output must be like this:
ID: value:
5675 sd
68 g
37 gk
3 oi
I want to do this work in a cluster of nodes? How can i do that?
I think that i need a global variable and i can't do this in a cluster? What can i do?
You can do one map/reduce to order the ids then you'd have a file with the ids in descending order.
You can then write a second map/reduce that would join that file with the unsorted file where the mapper will emit enumerator (that can be calculated by the split size to facilitate multiple maps) so that the mapper that go over the fist file will emit "1 sd" "2 g" etc. and the mapper that processes the ids file would emit "1 5675" "2 68". The reducer will then join the files
here's an (untested) pig 0.11 script that would do something along these line:
A = load 'data' AS (id:chararray,value:chararray);
ID_RAW= FOREACH A GENERATE id;
DATA_RAW = FOREACH A GENERATE value;
ID_SORT= RANK ID_RAW BY id DESC DENSE;
DATA_SORT = RANK DATA_RAW DENSE;
ID_DATA = JOIN ID_SORT by $0, DATA_SORT by $0;
RESULT = FOREACH ID_DATA GENERATE ID_SORT::ID,DATA_SORT::value;
STORE RESULT to 'output';
Before I say this, I like Arnon's answer for using hadoop.
But, since this is small file, 10G is not that big, and you only need to run it once, I would personally just write a small script.
Assuming a tab delimited file
sort myfile.txt > myfile.sorted.text
paste myfile.sorted.text myfile.text | cut -f1,4 > newFile.txt
That might take a long time, certainly longer than using hadoop, but is simple and works
Ruby noob here!
I have an array of structs that look like this
Token = Struct.new(:token, :ordinal)
So an array of these would look like this, in tabular form:
Token | Ordinal
---------------
C | 2
CC | 3
C | 5
And I want to group by the "token" (i.e. the left hand column) of the struct and get a count, but also preserve the "ordinal" element. So the above would look like this
Token | Merged Ordinal | Count
------------------------------
C | 2, 5 | 2
CC | 3 | 1
Notice that the last column is a count of the grouped tokens and the middle column merges the "ordinal". The first column ("Token") can contain a variable number of characters, and I want to group on these.
I have tried various methods, using group_by (I can get the count, but not the middle column), inject, iterating (does not seem very functional) but I just can't get it right, partly because I don't have a good grasp of Ruby and the available operations / functions.
I have also had a good look around SO, but I am not getting very far.
Any help, pointers would be much appreciated!
Use Enumerable#group_by to do the grouping for you and use the resulting hash to get what you want with map or similar.
structs.group_by(&:token).map do |token, with_same_token|
[token, with_same_token.map(&:ordinal), with_same_token.size]
end
I'd like to use Apache Pig to build a large key -> value mapping, look things up in the map, and iterate over the keys. However, there does not even seem to be syntax for doing these things; I've checked the manual, wiki, sample code, Elephant book, Google, and even tried parsing the parser source. Every single example loads map literals from a file... and then never uses them. How can you use Pig's maps?
First, there doesn't seem to be a way to load a 2-column CSV file into a map directly. If I have a simple map.csv:
1,2
3,4
5,6
And I try to load it as a map:
m = load 'map.csv' using PigStorage(',') as (M: []);
dump m;
I get three empty tuples:
()
()
()
So I try to load tuples and then generate the map:
m = load 'map.csv' using PigStorage(',') as (key:chararray, val:chararray);
b = foreach m generate [key#val];
ERROR 1000: Error during parsing. Encountered " "[" "[ "" at line 1, column 24.
...
Many variations on the syntax also fail (e.g., generate [$0#$1]).
OK, so I munge my map into Pig's map literal format as map.pig:
[1#2]
[3#4]
[5#6]
And load it up:
m = load 'map.pig' as (M: []);
Now let's load up some keys and try lookups:
k = load 'keys.csv' as (key);
dump k;
3
5
1
c = foreach k generate m#key; /* Or m[key], or... what? */
ERROR 1000: Error during parsing. Invalid alias: m in {M: map[ ]}
Hrm, OK, maybe since there are two relations involved, we need a join:
c = join k by key, m by /* ...um, what? */ $0;
dump c;
ERROR 1068: Using Map as key not supported.
c = join k by key, m by m#key;
dump c;
Error 1000: Error during parsing. Invalid alias: m in {M: map[ ]}
Fail. How do I refer to the key (or value) of a map? The map schema syntax doesn't seem to let you even name the key and value (the mailing list says there's no way to assign types).
Finally, I'd just like to be able to find all they keys in my map:
d = foreach m generate ...oh, forget it.
Is Pig's map type half-baked? What am I missing?
Currently pig maps need the key to a chararray (string) that you supply and not a variable which contains a string. so in map#key the key has to be constant string that you supply (eg: map#'keyvalue').
The typical use case of this is to load a complex data structure one of the element being a key value pair and later in a foreach statement you can refer to a particular value based on the key you are interested in.
http://pig.apache.org/docs/r0.9.1/basic.html#map-schema
In Pig version 0.10.0 there is a new function available called "TOMAP" (http://pig.apache.org/docs/r0.10.0/func.html#tomap) that converts its odd (chararray) parameters to keys and even parameters to values. Unfortunately I haven't found it to be that useful, though, since I typically deal with arbitrary dicts of varying lengths and keys.
I would find a TOMAP function that took a tuple as a single argument, instead of a variable number of parameters, to be much more useful.
This isn't a complete solution to your problem, but the availability of TOMAP gives you some more options for your constructing a real solution.
Great question!
I personally do not like Maps in Pig. They have a place in traditional programming languages like Java, C# etc, wherein its really handy and fast to lookup a key in the map. On the other hand, Maps in Pig have very limited features.
As you rightly pointed, one can not lookup variable key in the Map in Pig. The key needs to be Constant. e.g. myMap#'keyFoo' is allowed but myMap#$SOME_VARIABLE is not allowed.
If you think about it, you do not need Map in Pig. One usually loads the data from some source, transforms it, joins it with some other dataset, filter it, transform it and so on. JOIN actually does a good job of looking up the variable keys in the data.
e.g. data1 has 2 columns A and B and data2 has 3 columns X, Y, Z. If you join data1 BY A with data2 BY Z, JOIN does the work of a Map (from traditional language) which maps value of column Z to value of column B (via column A). So data1 essentially represents a Map A -> B.
So why do we need Map in Pig?
Usually Hadoop data are the dumps of different data sources from Traditional languages. If original data sources contain Maps, the HDFS data would contain a corresponding Map.
How can one handle the Map data?
There are really 2 use cases:
Map keys are constants.
e.g. HttpRequest Header data contains time, server, clientIp as the keys in Map. to access value of a particular key, one case access them with Constant key.
e.g. header#'clientIp'.
Map keys are variables.
In these cases, you would most probably would want to JOIN the Map keys with some other data set. I usually convert the Map to Bag using UDF MapToBag, which converts map data into Bag of 2 field tuples (key, value). Once map data is converted to Bag of tuples, its easy to join it with other data sets.
I hope this helps.
1)If you want to load map data it should be like "[programming#SQL,rdbms#Oracle]"
2)If you want to load tuple data it should be like "(first_name_1234,middle_initial_1234,last_name_1234)"
3)If you want to load bag data it should be like"{(project_4567_1),(project_4567_2),(project_4567_3)}"
my file pigtest.csv like this
1234|emp_1234#company.com|(first_name_1234,middle_initial_1234,last_name_1234)|{(project_1234_1),(project_1234_2),(project_1234_3)}|[programming#SQL,rdbms#Oracle]
4567|emp_4567#company.com|(first_name_4567,middle_initial_4567,last_name_4567)|{(project_4567_1),(project_4567_2),(project_4567_3)}|[programming#Java,OS#Linux]
my schema:
a = LOAD 'pigtest.csv' using PigStorage('|') AS (employee_id:int, email:chararray, name:tuple(first_name:chararray, middle_name:chararray, last_name:chararray), project_list:bag{project: tuple(project_name:chararray)}, skills:map[chararray]) ;
b = FOREACH a GENERATE employee_id, email, name.first_name, project_list, skills#'programming' ;
dump b;
I think you need to think in term of relations and the map is just one field of one record. Then you can apply some operations on the relations, like joining the two sets data and mapping:
Input
$ cat data.txt
1
2
3
4
5
$ cat mapping.txt
1 2
2 4
3 6
4 8
5 10
Pig
mapping = LOAD 'mapping.txt' AS (key:CHARARRAY, value:CHARARRAY);
data = LOAD 'data.txt' AS (value:CHARARRAY);
-- list keys
mapping_keys =
FOREACH mapping
GENERATE key;
DUMP mapping_keys;
-- join mapping to data
mapped_data =
JOIN mapping BY key, data BY value;
DUMP mapped_data;
Output
> # keys
(1)
(2)
(3)
(4)
(5)
> # mapped data
(1,2,1)
(2,4,2)
(3,6,3)
(4,8,4)
(5,10,5)
This answer could also help you if you just want to do a simple look up:
pass-a-relation-to-a-pig-udf-when-using-foreach-on-another-relation
You can load up any data and then convert and store in key value format to read for later use
data = load 'somedata.csv' using PigStorage(',')
STORE data into 'folder' using PigStorage('#')
and then read as a mapped data.