I am looking at an extractive text summarization problem. Eventually, I want to generate a list of words (not sentences) that seem to be the most important. One of the ideas that I had was to the words that appear early in the document more heavily.
I have two dataframes. the first is a set of words with their occurrence counts:
words.head()
words occurrences
0 '' 2
1 11-1 1
2 2nd 1
3 april 1
4 b.
And the second is a set of sentences. 0 is the first sentence in the document, 1 is the secont.. etc.
sentences.head()
sentences
0 Site Menu expandHave a correction?...
1 This will be a chance for ...
2 The event will include...
3 Further, this...
4 Contact:Share:
I managed to accomplish my goal like this:
weights = []
for value in words.index.values:
weights.append(((len(sentences) - sentences.index.values) *
sentences['sentences'].str.contains(words['words'][value])).sum())
weights
[0,
5,
5,
0,
12,...]
words['occurrences'] *= weights
words.head()
words occurrences
0 '' 0
1 11-1 5
2 2nd 5
3 april 0
4 b. 12
However, this seems sort of sloppy. I know that I can use list comprehension (I thought it would be easier to read on here without it) - but, other than that, does anyone have thoughts on a more elegant solution to this problem?
Related
I have a list and a data frame. I want to find the number of each word in the list (some words in the list are pair) for each "emotions" in the data frame.
Here is my list:
[(frozenset({'know'}), 16528),
(frozenset({'im'}), 39047),
(frozenset({'feeling'}), 99455),
(frozenset({'like'}), 49332),
(frozenset({'feel', 'im'}), 16602),
(frozenset({'feeling', 'im'}), 23488),
(frozenset({'feel'}), 202985),
(frozenset({'feel', 'like'}), 42162),
(frozenset({'time'}), 17203),
(frozenset({'really'}), 17247)]
and this is my data frame:
Unnamed: 0 id text emotions
0 0 27383 [feel, awful, job, get, position, succeed, hap... sadness
1 1 110083 [im, alone, feel, awful] sadness
2 2 140764 [ive, probably, mentioned, really, feel, proud... joy
3 3 100071 [feeling, little, low, day, back] sadness
4 4 2837 [beleive, much, sensitive, people, feeling, te... love
Here is the expected output:
6 columns for six existed emotions and the last column is for totall count.
I have a pandas dataframe which includes columns (amongst others) like this, with RATING being integers 0 to 5 and COMMENT is string:
RATING COMMENT
1 some text
2 more text
3 other text
... ...
I would now like to mine (for lack of better word ) the key words for a list of strings:
list = ['like', trust', 'etc etc etc']
and would like to iterate through the COMMENT and count the number of key words by rating to get a df out like so
KEYWORD RATING COUNT
like 1 202
like 2 325
like 3 0
like 4 967
like 5 534
...
trust 1 126
....
how can I achieve this?
I am beginner so would really appreciate your help (and the simpler and more understandable the better)
thank you
hi at the moment I have been iterating through manually,
ie
#DATA_df is the original data
word_list = ['word', 'words', 'words', 'more']
values = [0] * len(word_list)
tot_val=[values]*5
rating_table = pd.DataFrame(tot_val, columns=word_list)
for i in len(word_list):
for g in len (DATA_df[COMMENT]):
if i in DATA_df[COMMENT][g]:
rating_table[i][DATA_df[RATING]-1] +=1
this give a DF like so
word words words more
0 0 0 0 0
1 0 0 0 0
2 0 0 0 0
3 0 0 0 0
4 0 0 0 0
that I am then trying to add to.... it appears really clunky
I managed to solve it, key points learnt are use group by to pre-select data based on the rating, this slices the data and it is possible to alternate through the groups. also use of str.lower() in combination with str.count() worked well.
I am thankful if more experienced programmers could show me a better solution, but at least this works.
rating = [1,2,3,4,5]
rategroup = tp_clean.groupby('Rating')
#print (rategroup.groups)
results_list =[]
for w in word_list:
current = [w]
for r in rating:
stargroup = rategroup.get_group(str(r))
found = stargroup['Content'].str.lower().str.count(w)
c = found.sum()
current.append(c)
results_list.append(current)
results_df = pd.DataFrame (results_list, columns=['Keyword','1 Star','2 Star','3 Star','4 Star','5 Star'])
The one thing I am still struggling with is how to use regex to make it look for full words. I believe \b is the right one but how do I put it into str.count function?
Full disclosure, this is for an assignment I don't think I'm looking for spoon feeding, more so just a general question. Am a I allowed to break that into a group of 8 and 2 groups of 4, or do all group sizes have to be equal, ie 4 groups of 4
1 0 1 1
0 0 0 0
1 1 1 1
1 1 1 1
Sorry if this is obvious, but my searches haven't been explicit and my teacher was quite vague. Thanks!
TL;DR: Groups don't have to be equal in size.
Let see what happens if, in your case, you take 11 groups of one. Then you will have an equation of eleven terms. (ie. case_1 or case_2 or... case_11).
By making big group, in your case 1 group of 8 and 2 groups of 4, you will have a very short and simplified equation like: case_group_8 or case_group_4_1 or case_group_4_2.
Both grouping are correct (we took all the one in the map) but the second is the most optimized. (i.e. you cannot simplified more)
Making 4 groups of 4 will bring you an equation that can be simplified more.
The best way now is for you to try both grouping (all 4 vs 8/4/4) and see the output result.
Could you please help me to understand this problem:
Convert the input sequence of N (1 ≤ N ≤ 20) input numbers so that
the subsequences of the same numbers are replaced with the first
numbers of the subsequences. Each input number is in the range [1, 2
000 000 000].
For example, the input sequence 1 2 2 3 1 1 1 4 4 is converted into
1 2 3 1 4.
Input: First, the number T of test cases is given. Each test case is
specified using two lines. The first one contains the number N and the
second one contains the numbers of the sequence.
Output: The converted sequence. The result for each test case should
be printed in a separate line.
For example, the input sequence 1 2 2 3 1 1 1 4 4 is converted into 1 2 3 1 4.
It looks like the idea is to remove duplicate numbers that occur adjacent to each other when creating the output.
You can do that by just keeping a state variable recording what the previous value was. When you get a new value, compare it to the state value. If it's the same, skip. If different, output it and update the state variable. Remember to initialize the state variable to a value not found in the input stream (e.g. -1 should work in this case).
I'm trying to make groups on a database with 10.000+ rows.
I need to be fast and efficient, so I'm doing binary variables for each cluster.
One, Two, Four, Five and Six is in Group1.
But 'Two' might also be in Group nr. 2, because of errors I cannot overcome because my dataset is from a webscrape. I try to sort everything in a unique way, but it's basically impossible not to do errors, if I wish to be efficient and fast.
ID Title Group1 Group2 Group3 Ungrouped
1 One 1 0 0 0
2 Two 1 1 0 0
3 Three 0 1 1 0
4 Four 1 0 1 0
5 Five 1 0 0 0
6 Six 1 1 1 0
7 Seven 0 0 0 1
My idea for a sollution:
Assign groups (one's) until everything is grouped one or more times.
Make a query for everything that has more than one group assigned (2, 3, 4, 6)
Manually decide which 1's to remove, until they only have one group assigned each.
It's actually a good idea to do the 3rd part manually, because it requires content analysis of the documents)
My question:
How do I specify, that I need to see everything with more than one group? Does it have something to do with constraints and unique values, or is there a more simple and obvious way that I'm not seeing?
If your clusters are stored as integers, you can just do:
select c.*
from clusters c
where (cluster1 + cluster2 + cluster3) > 1;
I don't know what a "binary variable" is in SQLite. Some databases do support binary flags, and you would need to convert the values to integers for the where clause.