I am trying to create an anime series search using the tool anilistpython, but I am not able to ignore the newline character in the plot tag and need help in align the output in a proper view format.
Tried code :
from AnilistPython import Anilist
import pandas as pd
import re
# db access online
anilist = Anilist()
# User input
ani_search = anilist.get_anime(input('Enter the Anime Name\t:\t'), manual_select=True)
df = ani_search
# for Genres split
cate = []
for gen in df['genres']:
cate.append(gen)
cate1 = (' , '.join(cate))
# for Checking Episode
if df['airing_status'] == 'RELEASING':
print('Ongoing')
x ='Ongoing'
y = df['next_airing_ep']
print(y['episode'])
y1 = y['episode']
elif df['airing_status'] == 'FINISHED':
print('Ended')
x = 'Ended'
y = df['airing_episodes']
print(y)
y1 = y
else:
print('None')
# print other details
print(f"\nTitle_Name\t:\t{df['name_english']}\nRomji_Title\t:\t{df['name_romaji']}\nPlot\t:\t{re.split('<br>', df['desc'])}\nAiring_Format\t:\t{df['airing_format']}\nStatus\t:\t{x}\nEpisodes_Count\t:\t{y1}\nGenres\t:\t{cate1}\nRating\t:\t{df['average_score']}/100\n")
The output it generated :
Enter the Anime Name : Bleach
1. BLEACH
2. BEACH
3. Akkanbee da
Please select the anime that you are searching for in number: 1
Title_Name : Bleach
Romji_Title : BLEACH
Plot : ["Ichigo Kurosaki is a rather normal high school student apart from the fact he has the ability to see ghosts. This ability never impacted his life in a major way until the day he encounters the Shinigami Kuchiki Rukia, who saves him and his family's lives from a Hollow, a corrupt spirit that devours human souls. \n", '', '\nWounded during the fight against the Hollow, Rukia chooses the only option available to defeat the monster and passes her Shinigami powers to Ichigo. Now forced to act as a substitute until Rukia recovers, Ichigo hunts down the Hollows that plague his town. \n\n\n']
Airing_Format : TV
Status : Ended
Episodes_Count : 366
Genres : Action , Adventure , Supernatural
Rating : 76/100
I am looking for the format to look like this:
Title_Name : Bleach
Romji_Title : BLEACH
Plot : Ichigo Kurosaki is a rather normal high school student apart from the fact he has the ability to see ghosts. This ability never impacted his life in a major way until the day he encounters the Shinigami Kuchiki Rukia, who saves him and his family's lives from a Hollow, a corrupt spirit that devours human souls. Wounded during the fight against the Hollow, Rukia chooses the only option available to defeat the monster and passes her Shinigami powers to Ichigo. Now forced to act as a substitute until Rukia recovers, Ichigo hunts down the Hollows that plague his town.
Airing_Format : TV
Status : Ended
Episodes_Count : 366
Genres : Action , Adventure , Supernatural
Rating : 76/100
[{'id': 2, 'Registered Address': 'Line 1: 1 Any Street Line 2: Any locale City: Any City Region / State: Any Region Postcode / Zip code: BA2 2SA Country: GB Jurisdiction: Any Jurisdiction'}]
I have the above read into a dataframe and that is the output so far. The issue is I need to break out the individual elements - due to names of places etc the values may or may not have spaces in them - looking at the above my keys are Line 1, Line 2, City, Region / State, Postcode / Zip, Country, Jurisdiction.
Output required for the "Registered Address"-'key'is the keys and values
"Line 1": "1 Any Street"
"Line 2": "Any locale"
"City": "Any City"
"Region / State": "Any Region"
"Postcode / Zip code": "BA2 2SA"
"Country": "GB"
"Jurisdiction": "Any Jurisdiction"
Just struggling to find a way to get to the end result.I have tried to pop out and use urllib.prse but fell short - is anypone able to point me in the best direction please?
Tried to write a code that generalizes your question, but there were some limitations, regarding your data format. Anyway I would do this:
def address_spliter(my_data, my_keys):
address_data = my_data[0]['Registered Address']
key_address = {}
for i,k in enumerate(keys):
print(k)
if k == 'Jurisdiction:':
key_address[k] = address_data.split('Jurisdiction:')[1].removeprefix(' ').removesuffix(' ')
else:
key_address[k] = address_data.split(k)[1].split(keys[i+1])[0].removeprefix(' ').removesuffix(' ')
return key_address
were you can call this function like this:
my_data = [{'id': 2, 'Registered Address': 'Line 1: 1 Any Street Line 2: Any locale City: Any City Region / State: Any Region Postcode / Zip code: BA2 2SA Country: GB Jurisdiction: Any Jurisdiction'}]
and
my_keys = ['Line 1:','Line 2:','City:', 'Region / State:', 'Postcode / Zip code:', 'Country:', 'Jurisdiction']
As you can see It'll work if only the sequence of keys is not changed. But anyway, you can work around this idea and change it base on your problem accordingly if it doesn't go as expected.
I am trying to normalize weight units in a string.
Eg:
1.SUCO MARACUJA COM GENGIBRE PCS 300 Millilitre - SUCO MARACUJA COM GENGIBRE PCS 300 ML
2. OVOS CAIPIRAS ANA MARIA BRAGA 10UN - OVOS CAIPIRAS ANA MARIA BRAGA 10U
3. SUCO MARACUJA MAMAO PCS 300 Gram - SUCO MARACUJA MAMAO PCS 300 G
4. SUCO ABACAXI COM MACA PCS 300Milli litre - SUCO ABACAXI COM MACA PCS 300ML
The keyword table is :
unit = ['Kilo','Kilogram','Gram','Milligram','Millilitre','Milli
litre','Dozen','Litre','Un','Und','Unid','Unidad','Unidade','Unidades']
norm_unit = ['KG','KG','G','MG','ML','ML','DZ','L','U','U','U','U','U','U']
I tried to take up these lists as a table but am having difficulty in comparing two dataframes or tables in python.
I tried the below code.
unit = ['Kilo','Kilogram','Gram','Milligram','Millilitre','Milli
litre','Dozen','Litre','Un','Und','Unid','Unidad','Unidade','Unidades']
norm_unit = ['KG','KG','G','MG','ML','ML','DZ','L','U','U','U','U','U','U']
z='SUCO MARACUJA COM GENGIBRE PCS 300 Millilitre'
#for row in mongo_docs:
#z = row['clean_hntproductname']
for x in unit:
for y in norm_unit:
if (re.search(r'\s'+x+r'$',z,re.I)):
# clean_hntproductname = t.lower().replace(x.lower(),y.lower())
# myquery3 = { "_id" : row['_id']}
# newvalues3 = { "$set": {"clean_hntproductname" : 'clean_hntproductname'} }
# ds_hnt_prod_data.update_one(myquery3, newvalues3)
I'm using Python(Jupyter) with MongoDb(Compass). Fetching data from Mongo and writing back to it.
From my understanding you want to:
Update all the rows in a table which contain the words in the unit array, to the ones in norm_unit.
(Disclaimer: I'm not familiar with MongoDB or Python.)
What you want is to create a mapping (using a hash) of the words you want to change.
Here's a trivial solution (i.e. not best solution but would probably point you in the right direction.)
unit_conversions = {
'Kilo': 'KG'
'Kilogram': 'KG',
'Gram': 'G'
}
# pseudo-code
for each row that you want to update
item_description = get the value of the string in the column
for each key in unit_conversion (e.g. 'Kilo')
see if the item_description contains the key
if it does, replace it with unit_convertion[key] (e.g. 'KG')
update the row
I am trying to parse raw data results from a text file into an organised tuple but having trouble getting it right.
My raw data from the textfile looks something like this:
Episode Cumulative Results
EpisodeXD0281119
Date collected21/10/2019
Time collected10:00
Real time PCR for M. tuberculosis (Xpert MTB/Rif Ultra):
PCR result Mycobacterium tuberculosis complex NOT detected
Bacterial Culture:
Bottle: Type FAN Aerobic Plus
Result No growth after 5 days
EpisodeST32423457
Date collected23/02/2019
Time collected09:00
Gram Stain:
Neutrophils Occasional
Gram positive bacilli Moderate (2+)
Gram negative bacilli Numerous (3+)
Gram negative cocci Moderate (2+)
EpisodeST23423457
Date collected23/02/2019
Time collected09:00
Bacterial Culture:
A heavy growth of
1) Klebsiella pneumoniae subsp pneumoniae (KLEPP)
ensure that this organism does not spread in the ward/unit.
A heavy growth of
2) Enterococcus species (ENCSP)
Antibiotic/Culture KLEPP ENCSP
Trimethoprim-sulfam R
Ampicillin / Amoxic R S
Amoxicillin-clavula R
Ciprofloxacin R
Cefuroxime (Parente R
Cefuroxime (Oral) R
Cefotaxime / Ceftri R
Ceftazidime R
Cefepime R
Gentamicin S
Piperacillin/tazoba R
Ertapenem R
Imipenem S
Meropenem R
S - Sensitive ; I - Intermediate ; R - Resistant ; SDD - Sensitive Dose Dependant
Comment for organism KLEPP:
** Please note: this is a carbapenem-RESISTANT organism. Although some
carbapenems may appear susceptible in vitro, these agents should NOT be used as
MONOTHERAPY in the treatment of this patient. **
Please isolate this patient and practice strict contact precautions. Please
inform Infection Prevention and Control as contact screening might be
indicated.
For further advice on the treatment of this isolate, please contact.
The currently available laboratory methods for performing colistin
susceptibility results are unreliable and may not predict clinical outcome.
Based on published data and clinical experience, colistin is a suitable
therapeutic alternative for carbapenem resistant Acinetobacter spp, as well as
carbapenem resistant Enterobacteriaceae. If colistin is clinically indicated,
please carefully assess clinical response.
EpisodeST234234057
Date collected23/02/2019
Time collected09:00
Authorised by xxxx on 27/02/2019 at 10:35
MIC by E-test:
Organism Klebsiella pneumoniae (KLEPN)
Antibiotic Meropenem
MIC corrected 4 ug/mL
MIC interpretation Resistant
Antibiotic Imipenem
MIC corrected 1 ug/mL
MIC interpretation Sensitive
Antibiotic Ertapenem
MIC corrected 2 ug/mL
MIC interpretation Resistant
EpisodeST23423493
Date collected18/02/2019
Time collected03:15
Potassium 4.4 mmol/L 3.5 - 5.1
EpisodeST45445293
Date collected18/02/2019
Time collected03:15
Creatinine 32 L umol/L 49 - 90
eGFR (MDRD formula) >60 mL/min/1.73 m2
Creatinine 28 L umol/L 49 - 90
eGFR (MDRD formula) >60 mL/min/1.73 m2
Essentially the pattern is that ALL information starts with a unique EPISODE NUMBER and follows with a DATE and TIME and then the result of whatever test. This is the pattern throughout.
What I am trying to parse into my tuple is the date, time, name of the test and the result - whatever it might be. I have the following code:
with open(filename) as f:
data = f.read()
data = data.splitlines()
DS = namedtuple('DS', 'date time name value')
parsed = list()
idx_date = [i for i, r in enumerate(data) if r.strip().startswith('Date')]
for start, stop in zip(idx_date[:-1], idx_date[1:]):
chunk = data[start:stop]
date = time = name = value = None
for row in chunk:
if not row: continue
row = row.strip()
if row.startswith('Episode'): continue
if row.startswith('Date'):
_, date = row.split()
date = date.replace('collected', '')
elif row.startswith('Time'):
_, time = row.split()
time = time.replace('collected', '')
else:
name, value, *_ = row.split()
print (name)
parsed.append(DS(date, time, name, value))
print(parsed)
My error is that I am unable to find a way to parse the heterogeneity of the test RESULT in a way that I can use later, for example for the tuple DS ('DS', 'date time name value'):
DATE = 21/10/2019
TIME = 10:00
NAME = Real time PCR for M tuberculosis or Potassium
RESULT = Negative or 4.7
Any advice appreciated. I have hit a brick wall.
I have this csv file with only two entries. Here it is:
Meat One,['Abattoirs', 'Exporters', 'Food Delivery', 'Butchers Retail', 'Meat Dealers-Retail', 'Meat Freezer', 'Meat Packers']
First one is title and second is a business headings.
Problem lies with entry two.
Here is my code:
import csv
with open('phonebookCOMPK-Directory.csv', "rt") as textfile:
reader = csv.reader(textfile)
for row in reader:
row5 = row[5].replace("[", "").replace("]", "")
listt = [(''.join(row5))]
print (listt[0])
it prints:
'Abattoirs', 'Exporters', 'Food Delivery', 'Butchers Retail', 'Meat Dealers-Retail', 'Meat Freezer', 'Meat Packers'
What i need to do is that i want to create a list containing these words and then print them like this using for loop to print every item separately:
Abattoirs
Exporters
Food Delivery
Butchers Retail
Meat Dealers-Retail
Meat Freezer
Meat Packers
Actually I am trying to reformat my current csv file and clean it so it can be more precise and understandable.
Complete 1st line of csv is this:
Meat One,+92-21-111163281,Al Shaheer Corporation,Retailers,2008,"['Abattoirs', 'Exporters', 'Food Delivery', 'Butchers Retail', 'Meat Dealers-Retail', 'Meat Freezer', 'Meat Packers']","[[' Outlets Address : Shop No. Z-10, Station Shopping Complex, MES Market, Malir-Cantt, Karachi. Landmarks : MES Market, Station Shopping Complex City : Karachi UAN : +92-21-111163281 '], [' Outlets Address : Shop 13, Ground Floor, Plot 14-D, Sky Garden, Main Tipu Sultan Road, KDA Scheme No.1, Karachi. Landmarks : Nadra Chowrangi, Sky Garden, Tipu Sultan Road City : Karachi UAN : +92-21-111163281 '], ["" Outlets Address : Near Jan's Broast, Boat Basin, Khayaban-e-Roomi, Block 5, Clifton, Karachi. Landmarks : Boat Basin, Jans Broast, Khayaban-e-Roomi City : Karachi UAN : +92-21-111163281 View Map ""], [' Outlets Address : Gulistan-e-Johar, Karachi. Landmarks : Perfume Chowk City : Karachi UAN : +92-21-111163281 '], [' Outlets Address : Tee Emm Mart, Creek Vista Appartments, Khayaban-e-Shaheen, Phase VIII, DHA, Karachi. Landmarks : Creek Vista Appartments, Nueplex Cinema, Tee Emm Mart, The Place City : Karachi Mobile : 0302-8333666 '], [' Outlets Address : Y-Block, DHA, Lahore. Landmarks : Y-Block City : Lahore UAN : +92-42-111163281 '], [' Outlets Address : Adj. PSO, Main Bhittai Road, Jinnah Supermarket, F-7 Markaz, Islamabad. Landmarks : Bhittai Road, Jinnah Super Market, PSO Petrol Pump City : Islamabad UAN : +92-51-111163281 ']]","Agriculture, fishing & Forestry > Farming equipment & services > Abattoirs in Pakistan"
First column is Name
Second column is Number
Third column is Owner
Forth column is Business type
Fifth column is Y.O.E
Sixth column is Business Headings
Seventh column is Outlets (List of lists containing every branch address)
Eighth column is classification
There is no restriction of using csv.reader, I am open to any technique available to clean my file.
Think of it in terms of two separate tasks:
Collect some data items from a ‘dirty’ source (this CSV file)
Store that data somewhere so that it’s easy to access and manipulate programmatically (according to what you want to do with it)
Processing dirty CSV
One way to do this is to have a function deserialize_business() to distill structured business information from each incoming line in your CSV. This function can be complex because that’s the nature of the task, but still it’s advisable to split it into self-containing smaller functions (such as get_outlets(), get_headings(), and so on). This function can return a dictionary but depending on what you want it can be a [named] tuple, a custom object, etc.
This function would be an ‘adapter’ for this particular CSV data source.
Example of deserialization function:
def deserialize_business(csv_line):
"""
Distills structured business information from given raw CSV line.
Returns a dictionary like {name, phone, owner,
btype, yoe, headings[], outlets[], category}.
"""
pieces = [piece.strip("[[\"\']] ") for piece in line.strip().split(',')]
name = pieces[0]
phone = pieces[1]
owner = pieces[2]
btype = pieces[3]
yoe = pieces[4]
# after yoe headings begin, until substring Outlets Address
headings = pieces[4:pieces.index("Outlets Address")]
# outlets go from substring Outlets Address until category
outlet_pieces = pieces[pieces.index("Outlets Address"):-1]
# combine each individual outlet information into a string
# and let ``deserialize_outlet()`` deal with that
raw_outlets = ', '.join(outlet_pieces).split("Outlets Address")
outlets = [deserialize_outlet(outlet) for outlet in raw_outlets]
# category is the last piece
category = pieces[-1]
return {
'name': name,
'phone': phone,
'owner': owner,
'btype': btype,
'yoe': yoe,
'headings': headings,
'outlets': outlets,
'category': category,
}
Example of calling it:
with open("phonebookCOMPK-Directory.csv") as f:
lineno = 0
for line in f:
lineno += 1
try:
business = deserialize_business(line)
except:
# Bad line formatting?
log.exception(u"Failed to deserialize line #%s!", lineno)
else:
# All is well
store_business(business)
Storing the data
You’ll have the store_business() function take your data structure and write it somewhere. Maybe it’ll be another CSV that’s better structured, maybe multiple CSVs, a JSON file, or you can make use of SQLite relational database facilities since Python has it built-in.
It all depends on what you want to do later.
Relational example
In this case your data would be split across multiple tables. (I’m using the word “table” but it can be a CSV file, although you can as well make use of an SQLite DB since Python has that built-in.)
Table identifying all possible business headings:
business heading ID, name
1, Abattoirs
2, Exporters
3, Food Delivery
4, Butchers Retail
5, Meat Dealers-Retail
6, Meat Freezer
7, Meat Packers
Table identifying all possible categories:
category ID, parent category, name
1, NULL, "Agriculture, fishing & Forestry"
2, 1, "Farming equipment & services"
3, 2, "Abattoirs in Pakistan"
Table identifying businesses:
business ID, name, phone, owner, type, yoe, category
1, Meat One, +92-21-111163281, Al Shaheer Corporation, Retailers, 2008, 3
Table describing their outlets:
business ID, city, address, landmarks, phone
1, Karachi UAN, "Shop 13, Ground Floor, Plot 14-D, Sky Garden, Main Tipu Sultan Road, KDA Scheme No.1, Karachi", "Nadra Chowrangi, Sky Garden, Tipu Sultan Road", +92-21-111163281
1, Karachi UAN, "Near Jan's Broast, Boat Basin, Khayaban-e-Roomi, Block 5, Clifton, Karachi", "Boat Basin, Jans Broast, Khayaban-e-Roomi", +92-21-111163281
Table describing their headings:
business ID, business heading ID
1, 1
1, 2
1, 3
…
Handling all this would require a complex store_business() function. It may be worth looking into SQLite and some ORM framework, if going with relational way of keeping the data.
You can just replace the line :
print(listt[0])
with :
print(*listt[0], sep='\n')