I'm following the documentation about serializers in this link, I'm not sure if there's lack of documentation regarding on decimal serializers ?. I defined an Item with a scrapy field like this:
prize = scrapy.Field(serializer=Decimal, output_processor=TakeFirst())
I'm getting several errors when scrapinhub stores this value, especially with numbers containing commas.
Is there any standard method for serializing decimals?
This is not a scrapinghub, or scrapy error, you cannot convert a strings with comma into a number, you'll first have to remove that comma and then convert:
def decimal_serializer(value):
return float(value.replace(',', ''))
...
prize = scrapy.Field(serializer=decimal_serializer, output_processor=TakeFirst())
Related
I am trying to build a TF/IDF transformer (maps sets of words into count vectors) based on a Pandas series, in the following code:
tf_idf_transformer = TfidfTransformer()
return tf_idf_transformer.fit_transform( excerpts )
This fails with the following message:
ValueError: could not convert string to float: "I'm trying to work out, in general terms..."
Now, "excerpts" is a Pandas Series consisting of a bunch of text strings excerpted from StackOverflow posts, but when I look at the dtype of excerpts,
it says object. So, I reason that the problem might be that something is inferring the type of that Series to be float. So, I tried several ways to make the Series have dtype str:
I tried forcing the column types for the dataframe that includes "excerpts" to be str, but when I look at the dtype of the resulting Series, it's still object
I tried casting the entire dataframe that includes "excerpts" to dtypes str using Pandas.DataFrame.astype(), but the "excerpts" stubbornly have dtype object.
These may be red herrings; the real problem is with fit_transform. Can anyone suggest some way whereby I can see which entries in "excerpts" are causing problems or, alternatively, simply ignore them (leaving out their contribution to the TF/IDF).
I see the problem. I thought that tf_idf_transformer.fit_transform takes as the source argument an array-like of text strings. Instead, I now understand that it takes an (n,2)-array of text strings / token counts. The correct usage is more like:
count_vect = CountVectorizer()
excerpts_token_counts = count_vect.fit_transform( excerpts)
tf_idf_transformer = TfidfTransformer()
return tf_idf_transformer.fit_transform( excerpts_token_counts )
Sorry for my confusion (I should have looked at "Sample pipeline for text feature extraction and evaluation" in the TfidfTransformer documentation for sklearn).
I need to execute a SQL query, which converts a String column to a Array and then validate the size of that array
I was able to do it easily with postgresql:
e.g.
select
cardinality(string_to_array('a$b','$')),
cardinality(string_to_array('a$b$','$')),
cardinality(string_to_array('a$b$$$$$','$')),
But for some reason trying to convert String on vertica to array is not that simple, Saw this links:
https://www.vertica.com/blog/vertica-quick-tip-dynamically-split-string/
https://forum.vertica.com/discussion/239031/how-to-create-an-array-in-vertica
And much more that non of them helped.
I also tried using:
select REGEXP_COUNT('a$b$$$$$','$')
But i get an incorrect value - 1.
How can i Convert String to array on Vertica and gets his Length ?
$ has a special meaning in a regular expression. It represents the end of the string.
Try escaping it:
select REGEXP_COUNT('a$b$$$$$', '[$]')
You could create a UDx scalar function (UDSF) in Java, C++, R or Python. The input would be a string and the output would be an integer. https://www.vertica.com/docs/9.2.x/HTML/Content/Authoring/ExtendingVertica/UDx/ScalarFunctions/ScalarFunctions.htm
This will allow you to use language specific array logic on the strings passed in. For example in python, you could include this logic:
input_list = input.split("$")
filtered_input_list = list(filter(None, input_list))
list_count = len(filtered_input_list)
These examples are a good starting point for writing UDx's for Vertica. https://github.com/vertica/UDx-Examples
I wasn't able to convert to an array - but Im able to get the length of the values
What i do is convert to Rows an use count - its not best performance wise
But with this way Im able to do also manipulation like filtering of each value between delimiter - and i dont need to use [] for characters like $
select (select count(1)
from (select StringTokenizerDelim('a$b$c','$') over ()) t)
Return 3
In my Room model, I have an attribute named available_days, which is being stored as an array.
For example:
Room.first.available_days
=> ["wed", "thurs", "fri"]
What is the best way to find all Rooms where the size of the array is equal to 3?
I've tried something like
Room.where('LENGTH(available_days) = ?', 3)
with no success.
Update: the data type for available_days is a string, but in order to store an array, I am serializing the attribute from my model:
app/models/room.rb
serialize :available_days
Can't think of a purely sql way of doing it for sqlite since available_days is a string.
But here's one way of doing it without loading all records at once.
rooms = []
Room.in_batches(of: 10).each_record do |r|
rooms << r if r.available_days.length == 3
end
p rooms
If you're using postgres you can parse the serialized string to an array type, then query on the length of the array. I expect other databases may have similar approaches. How to do this depends on how the text is being serialized, but by default for Rails 4 should be YAML, so I expect you data is encoded like this:
---
- first
- second
The following SQL will remove the leading ---\n- as well as the final newline, then split the remaining string on - into an array. It's not strictly necessary to cleanup the extra characters to find the length, but if you want to do other operations you may find it useful to have a cleaned up array (no leading characters or trailing newline). This will only work for simple YAML arrays and simple strings.
Room.where("ARRAY_LENGTH(STRING_TO_ARRAY(RTRIM(REPLACE(available_days,'---\n- ',''),'\n'), '\n- '), 1) = ?", 3)
As you can see, this approach is rather complex. If possible you may want to add a new structured column (array or jsonb) and migrate the serialized string into the a typed column to make this easier and more performant. Rails supports jsonb serialization for postgres.
I am taking a mooc.
It has one assignment where a column needs to be converted to the lower case. sentence=lower(column) does the trick. But initially I thought that the syntax should be sentence=column.lower(). I looked at the documentation and I couldnt figure out the problem with my syntax. Would it be possible to explain how I could have figured out that I have a wrong syntax by searching online documentation and function definition?
I am specially confused as This link shows that string.lower() does the trick in case of the regular string python objects
from pyspark.sql.functions import regexp_replace, trim, col, lower
def removePunctuation(column):
"""Removes punctuation, changes to lower case, and strips leading and trailing spaces.
Note:
Only spaces, letters, and numbers should be retained. Other characters should should be
eliminated (e.g. it's becomes its). Leading and trailing spaces should be removed after
punctuation is removed.
Args:
column (Column): A Column containing a sentence.
Returns:
Column: A Column named 'sentence' with clean-up operations applied.
"""
sentence=lower(column)
return sentence
sentenceDF = sqlContext.createDataFrame([('Hi, you!',),
(' No under_score!',),
(' * Remove punctuation then spaces * ',)], ['sentence'])
sentenceDF.show(truncate=False)
(sentenceDF
.select(removePunctuation(col('sentence')))
.show(truncate=False))
You are correct. When you are working with a string, if you want to convert it to lowercase, you should use str.lower().
And if you check the String page in the Python Documentation, you will see it has a lower method that should work as you expect:
a_string = "StringToConvert"
a_string.lower() # "stringtoconvert"
However. in the Spark example you provided, in your function removePunctuation you are NOT working with a singlestring, you are working with a Column. And a Column is a different object than a string, that is way you should use a method that works with a Column.
Specifically, you are working with this pyspark sql method. The next time you are in doubt on which method you need to implement, double check the datatype of your objects. Also, if you check the list of imports, you will see it is calling the lower method from pyspark.sql.functions
This is how i managed to do it:
lowered = lower(column)
np_lowered = regexp_replace(lowered, '[^\w\s]', '')
trimmed_np_lowered = trim(np_lowered)
return trimmed_np_lowered
return trim(lower(regexp_replace(column, "\p{Punct}", ""))).alias('sentence')
I have a string data which I need to parse into a dictionary object. Here is my code:
NSString *barcode = [NSString stringWithString:#"{\"OTP\": 24923313, \"Person\": 100000000000112, \"Coupons\": [ 54900012445, 499030000003, 00000005662 ] }"];
NSLog(#"%#",[barcode objectFromJSONString]);
In this log, I get NULL result. But if I pass only one value in Coupons, I get the results. How to get all three values ?
00000005662 might not be a proper integer number as it's prefixed by zeroes (which means it's octal, IIRC). Try removing them.
Cyrille is right, here is the autoritative answer:
The application/json Media Type for JavaScript Object Notation (JSON): 2.4 Numbers
The representation of numbers is similar to that used in most programming languages. A number contains an integer component that may be prefixed with an optional minus sign, which may be followed by a fraction part and/or an exponent part.
Octal and hex forms are not allowed. Leading zeros are not allowed.