So I have these strings that I split by spaces (' ') and I just rolled them into a single list I called 'keyLabelRun'
so it looks like this:
keyLabelRun[0-12]:
0 OS=Dengue
1 virus
2 3
3 PE=4
4 SV=1
5 Split=0
6
7 OS=Bacillus
8 subtilis
9 XF-1
10 GN=opuBA
11 PE=4
12 SV=1
I only want the elements that include and are after "OS=", anything else, whether it be "SV=" or "PE=" etc. I want to skip over those elements until I get to the next "OS="
The number of elements to the next "OS=" is arbitrary so that's where I'm having the problem.
This is what I'm currently trying:
OSarr = []
for i in range(len(keyLabelrun)):
if keyLabelrun[i].count('OS='):
OSarr.append(keyLabelrun[i])
if keyLabelrun[i+1].count('=') != 1:
continue
But the elements where "OS=" is not included is what is tripping me up I think.
Also at the end I'm going to join them all back together in their own elements but I feel like I will be able to handle that after this.
In my attempt, I am trying to append all elements I'm looking for in order to an new list 'OSarr'
If anyone can lend a hand, it would be much appreciated.
Thank you.
These list of strings came from a dataset that is a text file in the form:
>tr|W0FSK4|W0FSK4_9FLAV Genome polyprotein (Fragment) OS=Dengue virus 3 PE=4 SV=1 Split=0
MNNQRKKTGKPSINMLKRVRNRVSTGSQLAKRFSKGLLNGQGPMKLVMAFIAFLRFLAIPPTAGVLARWGTFKKSGAIKVLKGFKKEISNMLSIINKRKKTSLCLMMILPAALAFHLTSRDGEPRMIVGKNERGKSLLFKTASGINMCTLIAMDLGEMCDDTVTYKCPHITEVEPEDIDCWCNLTSTWVTYGTCNQAGEHRRDKRSVALAPHVGMGLDTRTQTWMSAEGAWRQVEKVETWALRHPGFTILALFLAHYIGTSLTQKVVIFILLMLVTPSMTMRCVGVGNRDFVEGLSGATWVDVVLEHGGCVTTMAKNKPTLDIELQKTEATQLATLRKLCIEGKITNITTDSRCPTQGEATLPEEQDQNYVCKHTYVDRGWGNGCGLFGKGSLVTCAKFQCLEPIEGKVVQYENLKYTVIITVHTGDQHQVGNETQGVTAEITPQASTTEAILPEYGTLGLECSPRTGLDFNEMILLTMKNKAWMVHRQWFFDLPLPWTSGATTETPTWNRKELLVTFKNAHAKKQEVVVLGSQEGAMHTALTGATEIQNSGGTSIFAGHLKCRLKMDKLELKGMSYAMCTNTFVLKKEVSETQHGTILIKVEYKGEDVPCKIPFSTEDGQGKAHNGRLITANPVVTKKEEPVNIEAEPPFGESNIVIGIGDNALKINWYKKGSSIGKMFEATARGARRMAILGDTAWDFGSVGGVLNSLGKMVHQIFGSAYTALFSGVSWVMKIGIGVLLTWIGLNSKNTSMSFSCIAIGIITLYLGAVVQADMGCVINWKGKELKCGSGIFVTNEVHTWTEQYKFQADSPKRLATAIAGAWENGVCGIRSTTRMENLLWKQIANELNYILWENNIKLTVVVGDIIGVLEQGKRTLTPQPMELKYSWKTWGKAKIVTAETQNSSFIIDGPNTPECPSVSRAWNVWEVEDYGFGVFTTNIWLKLREVYTQLCDHRLMSAAVKDERAVHADMGYWIESQKNGSWKLEKASLIEVKTCTWPKSHTLWSNGVLESDMIIPKSLAGPISQHNHRPGYHTQTAGPWHLGKLELDFNYCEGTTVVITENCGTRGPSLRTTTVSGKLIHEWCCRSCTLPPLRYMGEDGCWYGMEIRPISEKEENMVKSLVSAGSGKVDNFTMGVLCLAILFEEVMRGKFGKKHMIAGVFFTFVLLLSGQITWRDMAHTLIMIGSNASDRMGMGVTYLALIATFKIQPFLALGFFLRKLTSRENLLLGVGLAMATTLQLPEDIEQMANGIALGLMALKLITQFETYQLWTALISLTCSNTIFTLTVAWRTATLILAGVSLLPVCQSSSMRKTDWLPMAVAAMGVPPLPLFIFGLKDTLKRRSWPLNEGVMAVGLVSILASSLLRNDVPMAGPLVAGGLLIACYVITGTSADLTVEKAADITWEEEAEQTGVSHNLMITVDDDGTMRIKDDETENILTVLLKTALLIVSGIFPYSIPATLLVWHTWQKQTQRSGVLWDVPSPPETQKAELEEGVYRIKQQGIFGKTQVGVGVQKEGVFHTMWHVTRGAVLTYNGKRLEPNWASVKKDLISYGGGWRLSAQWQKGEEVQVIAVEPGKNPKNFQTMPGTFQTTTGEIGAIALDFKPGTSGSPIINREGKVVGLYGNGVVTKNGGYVSGIAQTNAEPDGPTPELEEEMFKKRNLTIMDLHPGSGKTRKYLPAIVREAIKRRLRTLILAPTRVVAAEMEEALKGLPIRYQTTATKSEHTGREIVDLMCHATFTMRLLSPVRVPNYNLIIMDEAHFTDPASIAARGYISTRVGMGEAAAIFMTATPPGTADAFPQSNAPIQDEERDIPERSWNSGNEWITDFAGKTVWFVPSIKAGNDIANCLRKNGKKVIQLSRKTFDTEYQKTKLNDWDFVV
>tr|M4KW32|M4KW32_BACIU Choline ABC transporter (ATP-binding protein) OS=Bacillus subtilis XF-1 GN=opuBA PE=4 SV=1 Split=0
MLTLENVSKTYKGGKKAVNNVNLKIAKGEFICFIGPSGCGKTTTMKMINRLIEPSAGKIFIDGENIMDQDPVELRRKIGYVIQQIGLFPHMTIQQNISLVPKLLKWPEQQRKERARELLKLVDMGPEYVDRYPHELSGGQQQRIGVLRALAAEPPLILMDEPFGALDPITRDSLQEEFKKLQKTLHKTIVFVTHDMDEAIKLADRIVILKAGEIVQVGTPDDILRNPADEFVEEFIGKERLIQSSSPDVERVDQIMNTQPVTITADKTLSEAIQLMRQERVDSLLVVDDEHVLQGYVDVEIIDQCRKKANLIGEVLHEDIYTVLGGTLLRDTVRKILKRGVKYVPVVDEDRRLIGIVTRASLVDIVYDSLWGEEKQLAALS
>sp|Q8AWH3|SX17A_XENTR Transcription factor Sox-17-alpha OS=Xenopus tropicalis GN=sox17a PE=2 SV=1 Split=0
MSSPDGGYASDDQNQGKCSVPIMMTGLGQCQWAEPMNSLGEGKLKSDAGSANSRGKAEARIRRPMNAFMVWAKDERKRLAQQNPDLHNAELSKMLGKSWKALTLAEKRPFVEEAERLRVQHMQDHPNYKYRPRRRKQVKRMKRADTGFMHMAEPPESAVLGTDGRMCLESFSLGYHEQTYPHSQLPQGSHYREPQAMAPHYDGYSLPTPESSPLDLAEADPVFFTSPPQDECQMMPYSYNASYTHQQNSGASMLVRQMPQAEQMGQGSPVQGMMGCQSSPQMYYGQMYLPGSARHHQLPQAGQNSPPPEAQQMGRADHIQQVDMLAEVDRTEFEQYLSYVAKSDLGMHYHGQESVVPTADNGPISSVLSDASTAVYYCNYPSA
I got it! :D
OSarr = []
G = 0
for i in range(len(keyLabelrun)):
OSarr.append(keyLabelrun[G])
G += 1
if keyLabelrun[G].count('='):
while keyLabelrun[G].count('OS=') != 1:
G+=1
Maybe next time everyone, thank you!
Due to the syntax, you have to keep track of which part (OS, PE, etc) you're currently parsing. Here's a function to extract the species name from the FASTA header:
def extract_species(description):
species_parts = []
is_os = False
for word in description.split():
if word[:3] == 'OS=':
is_os = True
species_parts.append(word[3:])
elif '=' in word:
is_os = False
elif is_os:
species_parts.append(word)
return ' '.join(species_parts)
You can call it when processing your input file, e.g.:
from Bio import SeqIO
for record in SeqIO.parse('input.fa', 'fasta'):
species = extract_species(record.description)
I'm quite new to Python. I'm trying to load a .csv file with Panda but it returns a 50x1 matrix instead of expected 50x7. I'm a bit uncertain whether it is becaue my data contains numbers with "," (although I thought the quotechar attribute would solve that problem).
EDIT: Should perhaps mention that including the attribute sep=',' doesn't solve the issue)
My code looks like this
df = pd.read_csv('data.csv', header=None, quotechar='"')
print(df.head)
print(len(df.columns))
print(len(df.index))
Any ideas? Thanks in advance
Here is a subset of the data as text
10-01-2021,813,116927,"2,01",-,-,-
11-01-2021,657,117584,"2,02",-,-,-
12-01-2021,462,118046,"2,03",-,-,-
13-01-2021,12728,130774,"2,24",-,-,-
14-01-2021,17895,148669,"2,55",-,-,-
15-01-2021,15206,163875,"2,81",5,5,"0,0001"
16-01-2021,4612,168487,"2,89",7,12,"0,0002"
17-01-2021,2536,171023,"2,93",717,729,"0,01"
18-01-2021,3883,174906,"3,00",2147,2876,"0,05"
Here is the output of the head-function
0
0 27-12-2020,6492,6492,"0,11",-,-,-
1 28-12-2020,1987,8479,"0,15",-,-,-
2 29-12-2020,8961,17440,"0,30",-,-,-
3 30-12-2020,11477,28917,"0,50",-,-,-
4 31-12-2020,6197,35114,"0,60",-,-,-
5 01-01-2021,2344,37458,"0,64",-,-,-
6 02-01-2021,8895,46353,"0,80",-,-,-
7 03-01-2021,6024,52377,"0,90",-,-,-
8 04-01-2021,2403,54780,"0,94",-,-,-
Using your data I got the expected result. (even without quotechar='"')
Could you maybe show us your output?
import pandas as pd
df = pd.read_csv('data.csv', header=None)
print(df)
> 0 1 2 3 4 5 6
> 0 10-01-2021 813 116927 2,01 - - -
> 1 11-01-2021 657 117584 2,02 - - -
> 2 12-01-2021 462 118046 2,03 - - -
> 3 13-01-2021 12728 130774 2,24 - - -
> 4 14-01-2021 17895 148669 2,55 - - -
> 5 15-01-2021 15206 163875 2,81 5 5 0,0001
> 6 16-01-2021 4612 168487 2,89 7 12 0,0002
> 7 17-01-2021 2536 171023 2,93 717 729 0,01
> 8 18-01-2021 3883 174906 3,00 2147 2876 0,05
You need to define the seperator and delimiter, like this:
df = pd.read_csv('data.csv', header=None, sep = ',', delimiter=',' , quotechar='"')
My data is like below - stored in a .OUT file:
{ID=ISIN Name=yes PROGRAM=abc START_of_FIELDS CODE END-OF-FIELDS TIMESTARTED=Mon Nov 30 20:45:56
START-OF-DATA
CODE|ERR CODE|NUM|EXCH_CODE|
912828U rp|0|1|BERLIN|
1392917 rp|0|1|IND|
3CB0248 rp|0|1|BRAZIL|
END-OF-DATA***}
I need to extract the lines between START-OF-DATA and END-OF-DATA from above .OUT file using Python and load it in CSV file.
CODE|ERR CODE|NUM|EXCH_CODE|
912828U rp|0|1|BERLIN|
1392917 rp|0|1|IND|
3CB0248 rp|0|1|FRANKFURT|
You can use non greedy quantifier regex to get the entries between two strings.
with open('file.txt', 'r') as file:
data = file.read()
pattern = pattern = re.compile(r'(?:START-OF-DATA(.*?)END-OF-DATA)', re.MULTILINE|re.IGNORECASE | re.DOTALL)
g = re.findall(pattern,data)
O/P
[' \nCODE|ERR CODE|NUM|EXCH_CODE|\n912828U rp|0|1|BERLIN|\n1392917 rp|0|1|IND| \n3CB0248 rp|0|1|BRAZIL| \n']
#remove whitespaces and split by new line and remove empty entries of list
t = g[0].replace(" ","").split("\n")
new = list(filter(None, t))
O/P
['CODE|ERRCODE|NUM|EXCH_CODE|', '912828Urp|0|1|BERLIN|', '1392917rp|0|1|IND|', '3CB0248rp|0|1|BRAZIL|']
#create dataframe with pipe demoted
df = pd.DataFrame([i.split('|') for i in new])
O/P
0 1 2 3
0 CODE ERRCODE NUM EXCH_CODE
1 912828Urp 0 1 BERLIN
2 1392917rp 0 1 IND
3 3CB0248rp 0 1 BRAZIL
#create csv from df
df.to_csv('file.csv')
The regex pattern defined here will capture everything whenever a match is found for a string that begins with "START-OF-DATA" and ends with "END-OF-DATA" and leave you its output
I would like to get the S&P 500 ['Adj Close'] column and replace the column with the corresponding stock symbol, however, I am not able to replace the dataframe columns because it gives me an error: KeyError: '5'
What I would like to achieve is to loop through all the available stocks from the list and replace the Adj Close with the stock symbol.
This is what I did:
First I have scraped the stock symbols from Wikipedia and added them to a list.
data = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')
symbols = data[0] # get first column
symbols.head()
stock = symbols['Symbol'].to_list()
print(stock[0:5])
this gives me a list of stock symbols as below:
['MMM', 'ABT', 'ABBV', 'ABMD', 'ACN']
then I scraped Yahoo finance to get the daily financial data as below
stock_url = 'https://query1.finance.yahoo.com/v7/finance/download/{}?'
params = {
'range' : '1y',
'interval' : '1d',
'events' : 'history'
}
response = requests.get(stock_url.format(stock[0]), params=params)
file = StringIO(response.text)
reader = csv.reader(file)
data = list(reader)
df = pd.DataFrame(data)
stock_data = df['5']
Fix for key error
You are calling the the url using the list 'stock' and it gives a 404 response when I tried.
Call the URL with individual stock like below,
requests.get(stock_url.format(stock[0]), params=params)
Also do below, The column 5 is stored as integer instead of character. That is the reason you got 'key error'
stock_data = df[5]
I tried for stock 'MMM' - stock[0] and it prints below:
0 1 2 3 4 5 \
0 Date Open High Low Close Adj Close
1 2019-12-11 168.380005 168.839996 167.330002 168.740005 162.682480
2 2019-12-12 166.729996 170.850006 166.330002 168.559998 162.508926
3 2019-12-13 169.619995 171.119995 168.080002 168.789993 162.730667
4 2019-12-16 168.940002 170.830002 168.190002 170.750000 164.620316
.. ... ... ... ... ... ...
249 2020-12-04 172.130005 173.160004 171.539993 172.460007 172.460007
250 2020-12-07 171.720001 172.500000 169.179993 170.149994 170.149994
251 2020-12-08 169.740005 172.830002 169.699997 172.460007 172.460007
252 2020-12-09 172.669998 175.639999 171.929993 175.289993 175.289993
253 2020-12-10 174.869995 175.399994 172.690002 173.490005 173.490005
[254 rows x 7 columns]
Loop through stocks and replace Adj Close (Edited as per requirements from comments)
Code for looping through stocks and replacing Adj close with Stock symbol.
stock_url = 'https://query1.finance.yahoo.com/v7/finance/download/{}?'
params = {
'range' : '1y',
'interval' : '1d',
'events' : 'history'
}
df = pd.DataFrame()
for i in stock:
response = requests.get(stock_url.format(i), params=params)
file = io.StringIO(response.text)
reader = csv.reader(file)
data = list(reader)
df1 = pd.DataFrame(data)
df1.loc[df1[5] == 'Adj Close',5] = i
df = df.append(df1)
Tried the code for first 3 stocks and here it is:
I'm struggling with getting a simple correlation done. I've tried all that was suggested under similar questions.
Here are the relevant parts of the code, the various attempts I've made and their results.
import numpy as np
import pandas as pd
try01 = data[['ESA Index_close_px', 'CCMP Index_close_px' ]].corr(method='pearson')
print (try01)
Out:
Empty DataFrame
Columns: []
Index: []
try04 = data['ESA Index_close_px'][5:50].corr(data['CCMP Index_close_px'][5:50])
print (try04)
Out:
**AttributeError: 'float' object has no attribute 'sqrt'**
using numpy
try05 = np.corrcoef(data['ESA Index_close_px'],data['CCMP Index_close_px'])
print (try05)
Out:
AttributeError: 'float' object has no attribute 'sqrt'
converting the columns to lists
ESA_Index_close_px_list = list()
start_value = 1
end_value = len (data['ESA Index_close_px']) +1
for items in data['ESA Index_close_px']:
ESA_Index_close_px_list.append(items)
start_value = start_value+1
if start_value == end_value:
break
else:
continue
CCMP_Index_close_px_list = list()
start_value = 1
end_value = len (data['CCMP Index_close_px']) +1
for items in data['CCMP Index_close_px']:
CCMP_Index_close_px_list.append(items)
start_value = start_value+1
if start_value == end_value:
break
else:
continue
try06 = np.corrcoef(['ESA_Index_close_px_list','CCMP_Index_close_px_list'])
print (try06)
Out:
****TypeError: cannot perform reduce with flexible type****
Also tried .astype but not made any difference.
data['ESA Index_close_px'].astype(float)
data['CCMP Index_close_px'].astype(float)
Using Python 3.5, pandas 0.18.1 and numpy 1.11.1
Would really appreciate any suggestion.
**edit1:*
Data is coming from an excel spreadsheet
data = pd.read_excel('C:\\Users\\Ako\\Desktop\\ako_files\\for_corr_tool.xlsx') prior to the correlation attempts, there are only column renames and
data = data.drop(data.index[0])
to get rid of a line
regarding the types:
print (type (data['ESA Index_close_px']))
print (type (data['ESA Index_close_px'][1]))
Out:
**edit2*
parts of the data:
print (data['ESA Index_close_px'][1:10])
print (data['CCMP Index_close_px'][1:10])
Out:
2 2137
3 2138
4 2132
5 2123
6 2127
7 2126.25
8 2131.5
9 2134.5
10 2159
Name: ESA Index_close_px, dtype: object
2 5241.83
3 5246.41
4 5243.84
5 5199.82
6 5214.16
7 5213.33
8 5239.02
9 5246.79
10 5328.67
Name: CCMP Index_close_px, dtype: object
Well, I've encountered the same problem today.
try use .astype('float64') to help make the type correct.
data['ESA Index_close_px'][5:50].astype('float64').corr(data['CCMP Index_close_px'][5:50].astype('float64'))
This works well for me. Hope it can help you as well.
You can try as following:
Top15['Citable docs per capita']=(Top15['Citable docs per capita']*100000)
Top15['Citable docs per capita'].astype('int').corr(Top15['Energy Supply per Capita'].astype('int'))
It worked for me.