Develop Reference

How to extract strings from a list in a column in a python pandas dataframe?

Let's say I have a list
lst = ["fi", "ap", "ko", "co", "ex"]
and we have this series
Explanation
a "fi doesn't work correctly"
b "apples are cool"
c "this works but translation is ko"
and I'm looking to get something like this:
Explanation Explanation Extracted
a "fi doesn't work correctly" "fi"
b "apples are cool" "N/A"
c "this works but translation is ko" "ko"

With a dataframe like
df = pd.DataFrame(
{"Explanation": ["fi doesn't co work correctly",
"apples are cool",
"this works but translation is ko"]},
index=["a", "b", "c"]
)
you can use .str.extract() to do
lst = ["fi", "ap", "ko", "co", "ex"]
pattern = r"(?:^|\s+)(" + "|".join(lst) + r")(?:\s+|$)"
df["Explanation Extracted"] = df.Explanation.str.extract(pattern, expand=False)
to get
Explanation Explanation Extracted
a fi doesn't co work correctly fi
b apples are cool NaN
c this works but translation is ko ko
The regex pattern r"(?:^|\s+)(" + "|".join(lst) + r")(?:\s+|$)" looks for an occurrence of one of the lst items either at the beginning with withespace afterwards, in the middle with whitespace before and after, or at the end with withespace before. str.extract() extracts the capture group (the part in the middle in ()). Without a match the return is NaN.
If you want to extract multiple matches, you could use .str.findall() and then ", ".join the results:
pattern = r"(?:^|\s+)(" + "|".join(lst) + r")(?:\s+|$)"
df["Explanation Extracted"] = (
df.Explanation.str.findall(pattern).str.join(", ").replace({"": None})
)
Alternative without regex:
df.index = df.index.astype("category")
matches = df.Explanation.str.split().explode().loc[lambda s: s.isin(lst)]
df["Explanation Extracted"] = (
matches.groupby(level=0).agg(set).str.join(", ").replace({"": None})
)
If you only want to match at the beginning or end of the sentences, then replace the first part with:
df.index = df.index.astype("category")
splitted = df.Explanation.str.split()
matches = (
(splitted.str[:1] + splitted.str[-1:]).explode().loc[lambda s: s.isin(lst)]
)
...

I think this solves your problem.
import pandas as pd
lst = ["fi", "ap", "ko", "co", "ex"]
df = pd.DataFrame([["fi doesn't work correctly"],["apples are cool"],["this works but translation is ko"]],columns=["Explanation"])
extracted =[]
for index, row in df.iterrows():
tempList =[]
rowSplit = row['Explanation'].split(" ")
for val in rowSplit:
if val in lst:
tempList.append(val)
if len(tempList)>0:
extracted.append(','.join(tempList))
else:
extracted.append('N/A')
df['Explanation Extracted'] = extracted

apply function of Pandas might be helpful
def extract_explanation(dataframe):
custom_substring = ["fi", "ap", "ko", "co", "ex"]
substrings = dataframe['explanation'].split(" ")
explanation = "N/A"
for string in substrings:
if string in custom_substring:
explanation = string
return explanation
df['Explanation Extracted'] = df.apply(extract_explanation, axis=1)
The catch here is assumption of only one explanation, but it can be converted into a list, if multiple explanations are expected.

Option 1
Assuming that one wants to extract the exact string in the list lst one can start by creating a regex
regex = f'\\b({"|".join(lst)})\\b'
where \b is the word boundary (beginning or end of a word) that indicates the word is not followed by additional characters, or with characters before. So, considering that one has the string ap in the list lst, if one has the word apple in the dataframe, that won't be considered.
And then, using pandas.Series.str.extract, and, to make it case insensitive, use re.IGNORECASE
import re
df['Explanation Extracted'] = df['Explanation'].str.extract(regex, flags=re.IGNORECASE, expand=False)
[Out]:
ID Explanation Explanation Extracted
0 1 fi doesn't work correctly fi
1 2 cap ples are cool NaN
2 3 this works but translation is ko ko
Option 2
One can also use pandas.Series.apply with a custom lambda function.
df['Explanation Extracted'] = df['Explanation'].apply(lambda x: next((i for i in lst if i.lower() in x.lower().split()), 'N/A'))
[Out]:
ID Explanation Explanation Extracted
0 1 fi doesn't work correctly fi
1 2 cap ples are cool N/A
2 3 this works but translation is ko ko
Notes:
.lower() is to make it case insensitive.
.split() is one way to prevent that even though ap is in the list, the string apple doesn't appear in the Explanation Extracted column.

Removing words and symbols from columns which do not match specific criteria

I would need to remove from rows words which are not in English and specific symbols, like | or -, and three dots (...) if they are at the end of each row.
In order to do this, I was considering to use googletranslate or langdetect packages in Python for detecting and removing from text words not in English, and create a list for symbols.
To apply them, I was doing as follows:
df['Text'] == df['Text'].apply(lambda x: detect(x) == 'en') # but this just detect the rows. I would like to remove only not English words within rows, not the whole rows.
df['Text'] = df['Text'].map(lambda x: str(x)[:-4]) # I would need to consider however a logical condition: if the last three characters are ..., then remove these three dots from the string.
to_remove=['|','-', '(',')']
df['Text'] = df['Text'].str.contains(|, to_remove)
english_data = [word for word in df['Text'].tolist() if detect_language(word) == 'English']
The column I should apply these changes is
Text
The is in with a... - KIDS ...
BoneMA – Synthesis and Characterization of a Methacrylated ...
新型冠状病毒肺炎诊疗方案 (试行第七版) - Law Translate
Expected output:
Text
The is in with a... KIDS
BoneMA Synthesis and Characterization of a Methacrylated
Law Translate
Any help and suggestions would be appreciated.

like regex
df['Text'].str.replace('[^0-9a-zA-Z.]|[.]+$',' ').str.replace('\s{2,}',' ')
Output
0 The is in with a... KIDS
1 BoneMA Synthesis and Characteriof a M
2 Law Translate

How to get all sentences that contain multiple words in Python

I am trying to make a regular expressions to get all sentences containing two words (order doesn't matter), but I can't find the solution for this.
"Supermarket. This apple costs 0.99."
I want to get back the following sentence:
This apple costs 0.99.
I tried:
([^.]*?(apple)*?(costs)[^.]*\.)
I have problems because the price contains a dot. Also this expressions gives back results with only one of the words.

Approach: For each Phrase, we have to find the sentences which contain all the words of the phrase. So, for each word in the given phrase, we check if a sentence contains it. We do this for each sentence. This process of searching may become faster if the words in the sentence are stored in a set instead of a list.
Below is the implementation of above approach in python:
def getRes(sent, ph):
sentHash = dict()
# Loop for adding hased sentences to sentHash
for s in range(1, len(sent)+1):
sentHash[s] = set(sent[s-1].split())
# For Each Phrase
for p in range(0, len(ph)):
print("Phrase"+str(p + 1)+":")
# Get the list of Words
wordList = ph[p].split()
res = []
# Then Check in every Sentence
for s in range(1, len(sentHash)+1):
wCount = len(wordList)
# Every word in the Phrase
for w in wordList:
if w in sentHash[s]:
wCount -= 1
# If every word in phrase matches
if wCount == 0:
# add Sentence Index to result Array
res.append(s)
if(len(res) == 0):
print("NONE")
else:
print('% s' % ' '.join(map(str, res)))
# Driver Function
def main():
sent = ["Strings are an array of characters",
"Sentences are an array of words"]
ph = ["an array of", "sentences are strings"]
getRes(sent, ph)
main()

You use a negated character class [^.] which matches any character except a dot.
But in your example data Supermarket. This apple costs 0.99. there are 2 dots before the dot at the end, so you can not cross the dot after Supermarket. to match apple
You could for example match until the first dot, then assert costs and use a capture group to match the part with apple and make sure the line ends with a dot.
The assertion for word 1 with a match for word 2 will match the words in both combinations.
^[^.]*\.\s*(?=.*\bcosts\b)(.*\bapple\b.*\.)$
Explanation
^[^.]*\. From the start of the string, match until and including the first dot
\s* Match 0+ whitespace character
(?=.*\bcosts\b) Positive lookahead, assert costs at the right
( Capture group 1 (this has the desired value)
.*\bapple\b.*\. Match the rest of the line that includes apple and ends with a dot
) Close group 1
$ Assert end of string
Regex demo | Python demo
import re
regex = r"^[^.]*\.\s*(?=.*\bcosts\b)(.*\bapple\b.*\.)$"
test_str = ("Supermarket. This apple costs 0.99.\n"
"Supermarket. This costs apple 0.99.\n"
"Supermarket. This apple is 0.99.\n"
"Supermarket. This orange costs 0.99.")
print(re.findall(regex, test_str, re.MULTILINE))
Output
['This apple costs 0.99.', 'This costs apple 0.99.']

I also suggest to first extract sentences and then find sentences that have both words.
However, the problem of splitting text into sentences is pretty hard because of existence of abbreviations, unusual names, etc. One way to do it is by using nltk.tokenize.punkt module.
You'll need to install NLTK and then run this in Python:
import nltk
nltk.download('punkt')
After that you can use English language sentence tokenizer with two regexes:
TEXT = 'Mr. Bean is in supermarket. iPhone 12 by Apple Inc. costs $999.99.'
WORD1 = 'apple'
WORD2 = 'costs'
import nltk.data, re
# Regex helper
find_word = lambda w, s: re.search(r'(^|\W)' + w + r'(\W|$)', s, re.I)
eng_sent_detector = nltk.data.load('tokenizers/punkt/english.pickle')
for sent in eng_sent_detector.tokenize(TEXT):
if find_word(WORD1, sent) and find_word(WORD2, sent):
print (sent,"\n----")
Output:
iPhone 12 by Apple Inc. costs $999.99.
----
Notice that it handles numbers and abbreviations for you.

How to determine the number of negation words per sentence

I would like to know how to count how many negative words (no, not) and abbreviation (n't) there are in a sentence and in the whole text.
For number of sentences I am applying the following one:
df["sent"]=df['text'].str.count('[\w][\.!\?]')
However this gives me the count of sentences in a text. I would need to look per each sentence at the number of negation words and within the whole text.
Can you please give me some tips?
The expected output for text column is shown below
text sent count_n_s count_tot
I haven't tried it yet 1 1 1
I do not like it. What do you think? 2 0.5 1
It's marvellous!!! 1 0 0
No, I prefer the other one. 2 1 1
count_n_s is given by counting the total number of negotiation words per sentence, then dividing by the number of sentences.
I tried
split_w = re.split("\w+",df['text'])
neg_words=['no','not','n\'t']
words = [w for i,w in enumerate(split_w) if i and (split_w[i-1] in neg_words)]

This would get a count of total negations in the text (not for individual sentences):
import re
NEG = r"""(?:^(?:no|not)$)|n't"""
NEG_RE = re.compile(NEG, re.VERBOSE)
def get_count(text):
count = 0
for word in text:
if NEG_RE .search(word):
count+=1
continue
else:
pass
return count
df['text_list'] = df['text'].apply(lambda x: x.split())
df['count'] = df['text_list'].apply(lambda x: get_count(x))

To get count of negations for individual lines use the code below. For words like haven't you can add it to neg_words since it is not a negation if you strip the word of everything else if it has n't
import re
str1 = '''I haven't tried it yet
I do not like it. What do you think?
It's marvellous!!!
No, I prefer the other one.'''
neg_words=['no','not','n\'t']
for text in str1.split('\n'):
split_w = re.split("\s", text.lower())
# to get rid of special characters such as comma in 'No,' use the below search
split_w = [re.search('^\w+', w).group(0) for w in split_w]
words = [w for w in split_w if w in neg_words]
print(len(words))

Python Text processing (str.contains)

I am using str.contains for text analytics in Pandas. If for the sentence "My latest Data job was an Analyst" , I want a combination of the words "Data" & "Analyst" but at the same time I want to specify the number of words between the two words used for the combination( here it is 2 words between "Data" and "Analyst".Currently I am using (DataFile.XXX.str.contains('job') & DataFile.XXX.str.contains('Analyst') to get the counts for "job Analyst".
How can I Specify the number of words in between the 2 words in the str.contains syntax.
Thanks in advance

You can't. At least, not in a simple or standardized way.
Even the basics, like how you define a "word," are a lot more complex than you probably imagine. Both word parsing and lexical proximity (e.g. "are two words within distance D of one another in sentence s?") is the realm of natural language processing (NLP). NLP and proximity searches are not part of basic Pandas, nor of Python's standard string processing. You could import something like NLTK, the Natural Language Toolkit to solve this problem in a general way, but that's a whole 'nother story.
Let's look at a simple approach. First you need a way to parse a string into words. The following is rough by NLP standards, but will work for simpler cases:
def parse_words(s):
"""
Simple parser to grab English words from string.
CAUTION: A simplistic solution to a hard problem.
Many possibly-important edge- and corner-cases
not handled. Just one example: Hyphenated words.
"""
return re.findall(r"\w+(?:'[st])?", s, re.I)
E.g.:
>>> parse_words("and don't think this day's last moment won't come ")
['and', "don't", 'think', 'this', "day's", 'last', 'moment', "won't", 'come']
Then you need a way to find all the indices in a list where a target word is found:
def list_indices(target, seq):
"""
Return all indices in seq at which the target is found.
"""
indices = []
cursor = 0
while True:
try:
index = seq.index(target, cursor)
except ValueError:
return indices
else:
indices.append(index)
cursor = index + 1
And finally a decision making wrapper:
def words_within(target_words, s, max_distance, case_insensitive=True):
"""
Determine if the two target words are within max_distance positiones of one
another in the string s.
"""
if len(target_words) != 2:
raise ValueError('must provide 2 target words')
# fold case for case insensitivity
if case_insensitive:
s = s.casefold()
target_words = [tw.casefold() for tw in target_words]
# for Python 2, replace `casefold` with `lower`
# parse words and establish their logical positions in the string
words = parse_words(s)
target_indices = [list_indices(t, words) for t in target_words]
# words not present
if not target_indices[0] or not target_indices[1]:
return False
# compute all combinations of distance for the two words
# (there may be more than one occurance of a word in s)
actual_distances = [i2 - i1 for i2 in target_indices[1] for i1 in target_indices[0]]
# answer whether the minimum observed distance is <= our specified threshold
return min(actual_distances) <= max_distance
So then:
>>> s = "and don't think this day's last moment won't come at last"
>>> words_within(["THIS", 'last'], s, 2)
True
>>> words_within(["think", 'moment'], s, 2)
False
The only thing left to do is map that back to Pandas:
df = pd.DataFrame({'desc': [
'My latest Data job was an Analyst',
'some day my prince will come',
'Oh, somewhere over the rainbow bluebirds fly',
"Won't you share a common disaster?",
'job! rainbow! analyst.'
]})
df['ja2'] = df.desc.apply(lambda x: words_within(["job", 'analyst'], x, 2))
df['ja3'] = df.desc.apply(lambda x: words_within(["job", 'analyst'], x, 3))
This is basically how you'd solve the problem. Keep in mind, it's a rough and simplistic solution. Some simply-posed questions are not simply-answered. NLP questions are often among them.