I have a somewhat general question about combining multiple excel files together. Normally, I would use pd.read_excel to read the files then concat to join. However, I have some cases where the field names are not exactly the same but similar. For example,
one sheet would have fields like: Apple, Orange, Size, Id
another sheet would be: Apples, orange, Sizes, #
I have use the rename columns function but with this I have to check and compare every names in each files. I wonder if there's any way to combine them without going through all the field names. Any thought? THANKS!
Define what it means for two strings to be the same, then you can do the renaming automatically (you'll also need to determine what the "canonical" form of the string is - the name that you'll actually use in the final dataframe). The problem is pretty general, so you'll have to decide based on the sort of column names that you're willing to consider the same, but one simple thing might be to use a function like this:
def compare_columns(col1: str, col2: str) -> bool:
return col1.lower() == col2.lower()
Here you'd be saying that any two columns with the same name up to differences in case are considered equal. You'd probably want to define the canonical form for a column to be all lowercase letters.
Actually, now that I think about it, since you'll need a canonical form for a column name anyway, the easiest approach would probably be, instead of comparing names, to just convert all names to canonical form and then merge like usual. In the example here, you'd rename all columns of all your dataframes to be their lowercase versions, then they'll merge correctly.
The hard part will be deciding what transforms to apply to each name to get it into canonical form. Any transformation you do has a risk of combining data that wasn't mean to be (even just changing the case), so you'll need to decide for yourself what's reasonable to change based on what you expect from your column names.
As #ako said, you could also do this with something like Levenstein distance, but I think that will be trickier than just determining a set of transforms to use on each column name. With Levenstein or similar, you'll need to decide which name to rename to, but you'll also have to track all names that map to that name and compute the Levenstein distance between the closest member of that group when deciding if a new name maps to that canonical name (e.g. say that you have "Apple" and "Aple" and "Ale" and are merging names with edit distance of 1 or less. "Apple" and "Aple" should be merged, as should "Aple" and "Ale". "Apple" and "Ale" normally shouldn't be (as their distance is 2), but because they both merge with "Aple", they also merge with each other now).
You could also look into autocorrect to try to convert things like "Aple" to "Apple" without needing "Ale" to also merge in; I'm sure there's some library for doing autocorrect in Python. Additionally, there are NLP tools that will help you if you want to do stemming to try to merge things like "Apples" and "Apple".
But it'll all be tricky. Lowercasing things probably works, though =)
Related
I have around 300k unstructured data as below screen.I'm trying to use Google refine or OpenRefine to make this correct. However, I'm unable to find a proper way to do this. I'm new to this tool. Anyone's help would be greatly appreciated.Also, this tool is quite slow to process 300k records. If I am trying out something its taking lots of time to process and give an output.
OR Please suggest any other opensource tools and techniques do this?
As Owen said in comments, your question is probably too broad and cannot receive acceptable answer. We can just provide you with a general procedure to follow.
In Open Refine, you'll need to create a column based on the messy column and apply transformations to delete unwanted characters. You'll have to use regular expressions. But for that, it's necessary to be able to identify patterns. It's not clear to me why the "ST" of "Nat.secu ST." is important, but not the "US" in "Massy Intertech US". Not even the "36" in "Plowk 36" (Google doesn't know this word, so I'm not sure is an organisation name).
On the basis of your fifteen lines, however, we seem to distinguish some clear patterns. For example, it looks like you'll have to remove the tokens (character suites without spaces) at the end of the string that contain a #. For that, the GREL formula in Open Refine could look like this:
value.trim().replace(/\b\w+#\w+\b$/,'')
Here is a screencast if it's not clear to you.
But sometimes a company name may contain a #, in which case you will need to create more complex rules. For example, remove the token only if the string contains more than two words.
if(value.split(' ').length() > 2, value.replace(/\b\w+#\w+\b$/, ''), value)
And so on for the other patterns that you'll find (for example, any number sequence at the end that contains more than 4 numbers and one - between them)
Feel free to check out the Open Refine documentation in case of doubt.
I have two dataset:
one dataset has names of countries, but dirty ones like
Gaule Cisalpine (province romaine)
Gaule belgique
Gaule , Histoire
Gaule
ecc.
the second dataset has two columns with the names of countries (clean) and a code like
Gaule | 1DDF
Is there a way to use cell.cross with value.contains() ? I tried to use reconcile-csv but it didn't work properly (it matches just the exact ones).
I've not been able to think of a great way of doing this, but given the substring you want to match between the two files is always the first thing in the 'messy' string, and if you want to do this in OpenRefine, I can see a way that might work by creating a 'match' column in each project for the cross matching.
In the 'clean' project use 'Add column based on this column' on the 'Country name' column, and in the GREL transform use:
value.fingerprint()
The 'fingerprint' transformation is the same as the one used when doing clustering with key collision/fingerprint and basically I'm just using it here to get rid of any minor differences between country names (like upper/lower case or special characters)
Then in the 'messy' project create a new column based on the dirty 'name of country' column again using the 'Add column based on this column' but in this case use the GREL transform something like:
value.split(/[\s,-\.\(\)]/)[0].fingerprint()
The first part of this "value.split(/[\s,-.()]/)" splits the string into individual words (using space, comma, fullstop, open or closed bracket as a separator). Then the '[0]' takes the first string (so the first word in the cell), then again uses the fingerprint algorithm.
Now you have columns in each of the projects which should match on the exact cell content. You can use this to do the look up between the two projects.
This isn't going to be completely ideal - for example if you have some country names which consist of multiple words it isn't going to work. However you could add some additional key columns to the 'messy' project which use the first 2,3,4 strings etc. rather than just the first one as given here.
e.g.
filter(value.split(/[\s,-\.\(\)]/),v,isNonBlank(v)).get(0,2).join(" ").fingerprint()
filter(value.split(/[\s,-\.\(\)]/),v,isNonBlank(v)).get(0,3).join(" ").fingerprint()
etc. (I've done a bit more work here to make sure blank entries are ignored - it's the get() command that's the key bit for getting the different numbers of words).
I'm guessing that most country names are going to be only a few words long, so it would only be a few columns needed.
I've not been able to come up with a better approach so far. I'll post some more here if I come up with anything else. You could also try asking on the OpenRefine forum https://groups.google.com/forum/#!forum/openrefine
Or maybe the question should be: What's the best way to represent a string as a number, such that sorting their numeric representations would give the same result as if sorted as strings? I devised a way that could sort up to 9 characters per string, but it seems like there should be a much better way.
In advance, I don't think using Redis's lexicographical commands will work. (See the following example.)
Example: Suppose I want to presort all of the names linked to some ID so that I can use ZINTERSTORE to quickly get an ordered list of IDs based on their names (without using redis' SORT command). Ideally I would have the IDs as the zset's members, and the numeric representation of each name would be the zset's scores.
Does that make sense? Or am I going about it wrong?
You're trying to use an order preserving hash function to generate a score for each id. While it appears you've written one, you've already found out that the score's range allows you to use only the first 9 characters (it would be interesting to see your function btw).
Instead of this approach, here's a simpler one that would be easier IMO - use set members of the form <name>:<id> and set the score to 0. You'll be able to use lexicographical ordering this way and use something like split(':') to get the id from the set's members.
I have a large number of phrases (~ several million), each less than six or seven words and the large majority less than five, and I would like to see if they "phrase match" each other. This is a search engine marketing term - essentially, A phrase matches B if A is contained in B. Right now, they are stored in a db (postgres), and I am performing a join on regexes (see this question). It is running impossibly slowly even after trying all basic optimization tricks (indexing, etc) and trying the suggestions provided.
Is there an easier way to do this? I am not averse to a non-DB solution. Is there any reason to think that regexes are overkill and are taking way longer than a different solution?
An ideal algorithm for doing sub-string matching is AhoCorsick.
Although you will have to read the data out of the database to use it, it is tremendously fast, when compared to more naive methods.
See here for a related question on substring matching:
And here for an AhoCorsick implementation in Java:
It would be great to get a little more context as to why you need to see which phrases are subsets of others: for example, it seems strange that the DB would be built in such a way anyway: you're having to do the work now because the DB is not in an appropriate format, so it makes sense that you should 'fix' the DB or the way in which it is built, instead.
It depends massively on what you are doing with the data and why, but I have found it useful in the past to break things down into single words and pairs of words, then link resources or phrases to those singles/pairs.
For example to implement a search I have done:
Source text: Testing phrases to see
Entries:
testing
testing phrases
phrases
phrases to
to
to see
see
To see if another phrase was similar (granted, not contained within) you would break down the other phrase in the same way and count the number of phrases common between them.
It has the nice side effect of still matching if you were to use (for example) "see phases to testing": because the individual words would match.. but because the order is different the pairs wouldn't, so it's taking phrases (consecutive words) into account at the same time, the number of matches wouldn't be as high, good for use as a 'score' in matching.
As I say that -kind- of thing has worked for me, but it would be great to hear some more background/context, so we can see if we can find a better solution.
When you have the 'cleaned column' from MaasSQL's previous answer, you could, depending on the way "phrase match" works exactly (I don't know), sort this column based on the length of the containing string.
Then make sure you run the comparison query in a converging manner in a procedure instead of a flat query, by stepping through your table (with a cursor) and eliminating candidates for comparison through WHERE statements and through deleting candidates that have already been tested (completely). You may need a temporary table to do this.
What do I mean with 'WHERE' statement previously? Well, if the comparison value is in a column sorted on length, you'll never have to test whether a longer string matches inside a shorter string.
And with deleting candidates: starting with the shortest strings, once you've tested all strings of a certain length, you'll can remove them from the comparison table, as any next test you'll do will never get a match.
Of course, this requires a bit more programming than just one SQL statement. And is dependent on the way "phrase match" works exactly.
DTS or SSIS may be your friend here as well.
I am trying to figure out the best way to model a spreadsheet (from the database point of view), taking into account :
The spreadsheet can contain a variable number of rows.
The spreadsheet can contain a variable number of columns.
Each column can contain one single value, but its type is unknown (integer, date, string).
It has to be easy (and performant) to generate a CSV file containing the data.
I am thinking about something like :
class Cell(models.Model):
column = models.ForeignKey(Column)
row_number = models.IntegerField()
value = models.CharField(max_length=100)
class Column(models.Model):
spreadsheet = models.ForeignKey(Spreadsheet)
name = models.CharField(max_length=100)
type = models.CharField(max_length=100)
class Spreadsheet(models.Model):
name = models.CharField(max_length=100)
creation_date = models.DateField()
Can you think about a better way to model a spreadsheet ? My approach allows to store the data as a String. I am worried about it being too slow to generate the CSV file.
from a relational viewpoint:
Spreadsheet <-->> Cell : RowId, ColumnId, ValueType, Contents
there is no requirement for row and column to be entities, but you can if you like
Databases aren't designed for this. But you can try a couple of different ways.
The naiive way to do it is to do a version of One Table To Rule Them All. That is, create a giant generic table, all types being (n)varchars, that has enough columns to cover any forseeable spreadsheet. Then, you'll need a second table to store metadata about the first, such as what Column1's spreadsheet column name is, what type it stores (so you can cast in and out), etc. Then you'll need triggers to run against inserts that check the data coming in and the metadata to make sure the data isn't corrupt, etc etc etc. As you can see, this way is a complete and utter cluster. I'd run screaming from it.
The second option is to store your data as XML. Most modern databases have XML data types and some support for xpath within queries. You can also use XSDs to provide some kind of data validation, and xslts to transform that data into CSVs. I'm currently doing something similar with configuration files, and its working out okay so far. No word on performance issues yet, but I'm trusting Knuth on that one.
The first option is probably much easier to search and faster to retrieve data from, but the second is probably more stable and definitely easier to program against.
It's times like this I wish Celko had a SO account.
You may want to study EAV (Entity-attribute-value) data models, as they are trying to solve a similar problem.
Entity-Attribute-Value - Wikipedia
The best solution greatly depends of the way the database will be used. Try to find a couple of top use cases you expect and then decide the design. For example if there is no use case to get the value of a certain cell from database (the data is always loaded at row level, or even in group of rows) then is no need to have a 'cell' stored as such.
That is a good question that calls for many answers, depending how you approach it, I'd love to share an opinion with you.
This topic is one the various we searched about at Zenkit, we even wrote an article about, we'd love your opinion on it: https://zenkit.com/en/blog/spreadsheets-vs-databases/