buffer.slice(mouse,highlight).distinct
Now when I perform this it seems to apply .distinct to the whole string rather than the selection I use with slice. (mouse and highlight are just index positions and buffer is a StringBuilder). I'm just wondering what is the reason for this.
Your approach is correct. Please see below code for more clarification.
slice() function gives you the sub-string so in your approach It will first find the sub-string and then distinct.
Please follow below step by step approach for more understanding.
val buffer=new StringBuilder
buffer.append("bbbaabbbcccbdbcdbd")
val sl=buffer.slice(2,10)
The variable sl contains
sl= baabbbcc
Now you can apply distinct on sl variable
val result=sl.distinct
Finally your output
result= bac
This is the how your single line of code is working.
Related
For checking if a single string is contained in rows of one column. (for example, "abc" is contained in "abcdef"), the following code is useful:
df_filtered = df.filter(df.columnName.contains('abc'))
The result would be for example "_wordabc","thisabce","2abc1".
How can I check for multiple strings (for example ['ab1','cd2','ef3']) at the same time?
I'm ideally searching for something like this:
df_filtered = df.filter(df.columnName.contains(['word1','word2','word3']))
The result would be for example "x_ab1","_cd2_","abef3".
Please, post scalable solutions (no for loops, for example) because the aim is to check a big list around 1000 elements.
All you need is isin
df_filtered = df.filter(df['columnName'].isin('word1','word2','word3')
Edit
You need rlike function to achieve your result
words="(aaa|bbb|ccc)"
df.filter(df['columnName'].rlike(words))
Let's say I have a column of a table whose data type is a character array. I want to pass in a functional select where clause, where the column is in a list of given strings. However, I cannot simply use (in; `col; myList) for reasons. Instead, I need to do the equivalent of:
max col like/: myList
which effectively gives the same result. However, I have tried to put this in functional form
(max; (like/:; `col; myList))
And I am getting a type error. Any ideas on how I could make this work?
A nice trick when dealing with this problem is using parse on a string of the select statement you want to functionalize. For example:
q)parse"select from t where max col like/: myList"
?
`t
,,(max;((/:;like);`col;`myList))
0b
()
Or specifically in your case you want the 3rd element of the result list (the where clause):
q)(parse"select from t where max col like/: myList")2
max ((/:;like);`col;`myList)
I even think using this pattern in your actual code can be a good idea, as functionalized statements like max ((/:;like);`col;`myList) can get pretty unreadable pretty quickly!
Hope that helps!
(any; ((/:;like); `col; enlist,myList))
it should be: (max;((/:;like);`col;`mylist))
So, I'm reading data from a JSON file and creating a DataFrame. Usually, I would use
sqlContext.read.json("//line//to//some-file.json")
Problem is that my JSON file isn't consistent. So, for each line in the file, there are 2 JSONs. Each line looks like this
{...data I don't need....}, {...data I need....}
I only need my DataFrame to be formed from the data I need, i.e. the second JSON of each line. So I read each line as a string and substring the part that I need, like so
val lines = sc.textFile(link, 2)
val part = lines.map( x => x.substring(x.lastIndexOf('{')).trim)
I want to get all the elements in 'part' as an Array[String] then turn the Array[String] into one string and make the DataFrame. Like so
val strings = part .collect() //doesn't work
val strings = part.take(1000) //works
val jsonStr = "[".concat(strings.mkString(", ")).concat("]")
The problem is, if I call part.collect(), it doesn't work but if I call part.take(N) it works. However, I'd like to get all my data and not just the first N.
Also, if I try part.take(part.count().toInt) it still doesn't work.
Any Ideas??
EDIT
I realized my problem after a good sleep. It was a silly mistake on my part. The very last line of my input file had a different format from the rest of the file.
So part.take(N) would work for all N less than part.count(). That's why part.collect() wasn't working.
Thanks for the help though!
This is my first time using Scala and ApacheSpark for a project. I'm trying to print the contents of an matrix when I run my code in the terminal, but nothing I try is working so far.
Instead I only get this printed:
org.apache.spark.mllib.linalg.distributed.MatrixEntry;#71870da7
org.apache.spark.mllib.linalg.distributed.CoordinateMatrix#1dcca8d3
I just using println() but when I use collect(), that doesn't give a good result either.
The default toString prints the name of a class followed by an address in memory.
org.apache.spark.mllib.linalg.distributed.MatrixEntry;#71870da7
You're going to want to find a way to iterate through your matrix and print each element.
Building on #zero323 's comment ( aside would you like to put an answer out there?): given an RDD[SomeType] you can call
rdd.collect()
or
rdd.take(k)
Then you can print out the results using normal toString() methods that depend on the type of the rdd contents. So if SomeType were a List[Double] then the
println(s"${rdd.collect().mkString(",")}")
would give you a single-line comma separated output of the results.
As #zero323 another consideration is: "do you really want to print out the contents of your rdd?" More likely you might only want a summary - such as
println(s"Number of entries in RDD is ${rdd.count()}")
Iterate over the rdd like this,
rdd.foreach(println)
scala>val rdd1 = sc.parallelize(List(1,2,3,4)).map(_*2)
To print the data within RDD
scala> rdd1.collect().foreach(println)
Output:
2
4
6
8
I new to pandas and trying to learn how to work with it. Im having a problem when trying to use an example I saw in one of wes videos and notebooks on my data. I have a csv file that looks like this:
filePath,vp,score
E:\Audio\7168965711_5601_4.wav,Cust_9709495726,-2
E:\Audio\7168965711_5601_4.wav,Cust_9708568031,-80
E:\Audio\7168965711_5601_4.wav,Cust_9702445777,-2
E:\Audio\7168965711_5601_4.wav,Cust_7023544759,-35
E:\Audio\7168965711_5601_4.wav,Cust_9702229339,-77
E:\Audio\7168965711_5601_4.wav,Cust_9513243289,25
E:\Audio\7168965711_5601_4.wav,Cust_2102513187,18
E:\Audio\7168965711_5601_4.wav,Cust_6625625104,-56
E:\Audio\7168965711_5601_4.wav,Cust_6073165338,-40
E:\Audio\7168965711_5601_4.wav,Cust_5105831247,-30
E:\Audio\7168965711_5601_4.wav,Cust_9513082770,-55
E:\Audio\7168965711_5601_4.wav,Cust_5753907026,-79
E:\Audio\7168965711_5601_4.wav,Cust_7403410322,11
E:\Audio\7168965711_5601_4.wav,Cust_4062144116,-70
I loading it to a data frame and the group it by "filePath" and "vp", the code is:
res = df.groupby(['filePath','vp']).size()
res.index
and the output is:
[E:\Audio\7168965711_5601_4.wav Cust_2102513187,
Cust_4062144116, Cust_5105831247,
Cust_5753907026, Cust_6073165338,
Cust_6625625104, Cust_7023544759,
Cust_7403410322, Cust_9513082770,
Cust_9513243289, Cust_9702229339,
Cust_9702445777, Cust_9708568031,
Cust_9709495726]
Now Im trying to approach the index like a dict, as i saw in examples, but when im doing
res['Cust_4062144116']
I get an error:
KeyError: 'Cust_4062144116'
I do succeed to get a result when im putting the filepath, but as i understand and saw in previouse examples i should be able to use the vp keys as well, isnt is so?
Sorry if its a trivial one, i just cant understand why it is working in one example but not in the other.
Rutger you are not correct. It is possible to "partial" index a multiIndex series. I simply did it the wrong way.
The index first level is the file name (e.g. E:\Audio\7168965711_5601_4.wav above) and the second level is vp. Meaning, for each file name i have multiple vps.
Now, this is correct:
res['E:\Audio\7168965711_5601_4.wav]
and will return:
Cust_2102513187 2
Cust_4062144116 8
....
but trying to index by the inner index (the Cust_ indexes) will fail.
You groupby two columns and therefore get a MultiIndex in return. This means you also have to slice using those to columns, not with a single index value.
Your .size() on the groupby object converts it into a Series. If you force it in a DataFrame you can use the .xs method to slice a single level:
res = pd.DataFrame(df.groupby(['filePath','vp']).size())
res.xs('Cust_4062144116', level=1)
That works. If you want to keep it as a series, boolean indexing can help, something like:
res[res.index.get_level_values(1) == 'Cust_4062144116']
The last option is a bit less readable, but sometimes also more flexibile, you could test for multiple values at once for example:
res[res.index.get_level_values(1).isin(['Cust_4062144116', 'Cust_6073165338'])]