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Is there a method in turning user input into csv format?

This is the example data that would be pasted into an input() prompt and ideally I would like it to be processed and made into a csv file through python:
,,,,,,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Expected,Expected,Expected,SCA,SCA,Passes,Passes,Passes,Passes,Carries,Carries,Dribbles,Dribbles,-additional
Player,#,Nation,Pos,Age,Min,Gls,Ast,PK,PKatt,Sh,SoT,CrdY,CrdR,Touches,Press,Tkl,Int,Blocks,xG,npxG,xA,SCA,GCA,Cmp,Att,Cmp%,Prog,Carries,Prog,Succ,Att,-9999
Gabriel Jesus,9,br BRA,FW,25-124,82,0,0,0,0,1,0,0,0,40,13,1,1,0,0.1,0.1,0.0,4,0,20,27,74.1,2,33,1,4,5,b66315ae
Eddie Nketiah,14,eng ENG,FW,23-067,8,0,0,0,0,0,0,0,0,6,2,0,0,0,0.0,0.0,0.1,2,0,4,4,100.0,1,4,1,0,0,a53649b7
Martinelli,11,br BRA,LW,21-048,90,1,0,0,0,2,1,0,0,38,21,0,2,1,0.6,0.6,0.1,1,0,24,28,85.7,1,34,5,3,4,48a5a5d6
Bukayo Saka,7,eng ENG,RW,20-334,90,0,0,0,0,3,0,0,0,52,23,3,0,3,0.2,0.2,0.0,2,1,24,36,66.7,2,37,8,2,2,bc7dc64d
Martin Ødegaard,8,no NOR,AM,23-231,89,0,0,0,0,2,0,0,0,50,22,2,1,2,0.1,0.1,0.0,2,0,30,39,76.9,5,28,3,1,2,79300479
Albert Sambi Lokonga,23,be BEL,CM,22-287,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0.0,0.0,0.0,0,0,1,1,100.0,0,1,1,0,0,1b4f1169
Granit Xhaka,34,ch SUI,DM,29-312,90,0,0,0,0,0,0,1,0,60,5,0,2,3,0.0,0.0,0.0,4,0,42,49,85.7,6,32,2,0,0,e61b8aee
Thomas Partey,5,gh GHA,DM,29-053,90,0,0,0,0,1,0,0,0,62,25,7,1,2,0.1,0.1,0.0,0,0,40,47,85.1,5,26,4,0,1,529f49ab
Oleksandr Zinchenko,35,ua UKR,LB,25-233,82,0,1,0,0,1,1,0,0,64,16,3,3,1,0.0,0.0,0.3,2,1,44,54,81.5,6,36,5,0,0,51cf8561
Kieran Tierney,3,sct SCO,LBWB,25-061,8,0,0,0,0,0,0,0,0,6,1,0,0,0,0.0,0.0,0.0,0,0,2,4,50.0,0,1,0,0,0,fce2302c
Gabriel Dos Santos,6,br BRA,CB,24-229,90,0,0,0,0,0,0,0,0,67,5,1,1,2,0.0,0.0,0.0,0,0,52,58,89.7,1,48,3,0,0,67ac5bb8
William Saliba,12,fr FRA,CB,21-134,90,0,0,0,0,0,0,0,0,58,3,1,2,2,0.0,0.0,0.0,0,0,42,46,91.3,1,35,1,0,0,972aeb2a
Ben White,4,eng ENG,RB,24-301,90,0,0,0,0,0,0,1,0,61,22,7,4,5,0.0,0.0,0.1,1,0,29,40,72.5,5,25,2,1,1,35e413f1
Aaron Ramsdale,1,eng ENG,GK,24-083,90,0,0,0,0,0,0,0,0,33,0,0,0,0,0.0,0.0,0.0,0,0,24,32,75.0,0,21,0,0,0,466fb2c5
14 Players,,,,,990,1,1,0,0,10,2,2,0,599,158,25,17,21,1.1,1.1,0.5,18,2,378,465,81.3,35,361,36,11,15,-9999
The link to the table is: https://fbref.com/en/matches/e62f6e78/Crystal-Palace-Arsenal-August-5-2022-Premier-League#stats_18bb7c10_summary
I have attempted to use pandas dataframe but I am only able to export the first row of headers and nothing else (only the items before player).

Would have been nice for you to include your attempt.
Pandas works just fine:
import pandas as pd
url = 'https://fbref.com/en/matches/e62f6e78/Crystal-Palace-Arsenal-August-5-2022-Premier-League#stats_18bb7c10_summary'
df = pd.read_html(url)[10]
cols = [f'{each[0]}_{each[1]}' if 'Unnamed' not in each[0] else f'{each[1]}' for each in df.columns]
df.columns = cols
df.to_csv('output.csv', index=False)
Output:
print(df.to_markdown())
| | Player | # | Nation | Pos | Age | Min | Gls | Ast | PK | PKatt | Sh | SoT | CrdY | CrdR | Touches | Press | Tkl | Int | Blocks | xG | npxG | xA | SCA | GCA | Cmp | Att | Cmp% | Prog | Carries | Prog.1 | Succ | Att.1 |
|---:|:---------------------|----:|:---------|:------|:-------|------:|------:|------:|-----:|--------:|-----:|------:|-------:|-------:|----------:|--------:|------:|------:|---------:|-----:|-------:|-----:|------:|------:|------:|------:|-------:|-------:|----------:|---------:|-------:|--------:|
| 0 | Gabriel Jesus | 9 | br BRA | FW | 25-124 | 82 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 40 | 13 | 1 | 1 | 0 | 0.1 | 0.1 | 0 | 4 | 0 | 20 | 27 | 74.1 | 2 | 33 | 1 | 4 | 5 |
| 1 | Eddie Nketiah | 14 | eng ENG | FW | 23-067 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 2 | 0 | 0 | 0 | 0 | 0 | 0.1 | 2 | 0 | 4 | 4 | 100 | 1 | 4 | 1 | 0 | 0 |
| 2 | Martinelli | 11 | br BRA | LW | 21-048 | 90 | 1 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 38 | 21 | 0 | 2 | 1 | 0.6 | 0.6 | 0.1 | 1 | 0 | 24 | 28 | 85.7 | 1 | 34 | 5 | 3 | 4 |
| 3 | Bukayo Saka | 7 | eng ENG | RW | 20-334 | 90 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 52 | 23 | 3 | 0 | 3 | 0.2 | 0.2 | 0 | 2 | 1 | 24 | 36 | 66.7 | 2 | 37 | 8 | 2 | 2 |
| 4 | Martin Ødegaard | 8 | no NOR | AM | 23-231 | 89 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 50 | 22 | 2 | 1 | 2 | 0.1 | 0.1 | 0 | 2 | 0 | 30 | 39 | 76.9 | 5 | 28 | 3 | 1 | 2 |
| 5 | Albert Sambi Lokonga | 23 | be BEL | CM | 22-287 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 100 | 0 | 1 | 1 | 0 | 0 |
| 6 | Granit Xhaka | 34 | ch SUI | DM | 29-312 | 90 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 60 | 5 | 0 | 2 | 3 | 0 | 0 | 0 | 4 | 0 | 42 | 49 | 85.7 | 6 | 32 | 2 | 0 | 0 |
| 7 | Thomas Partey | 5 | gh GHA | DM | 29-053 | 90 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 62 | 25 | 7 | 1 | 2 | 0.1 | 0.1 | 0 | 0 | 0 | 40 | 47 | 85.1 | 5 | 26 | 4 | 0 | 1 |
| 8 | Oleksandr Zinchenko | 35 | ua UKR | LB | 25-233 | 82 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 64 | 16 | 3 | 3 | 1 | 0 | 0 | 0.3 | 2 | 1 | 44 | 54 | 81.5 | 6 | 36 | 5 | 0 | 0 |
| 9 | Kieran Tierney | 3 | sct SCO | LB,WB | 25-061 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 50 | 0 | 1 | 0 | 0 | 0 |
| 10 | Gabriel Dos Santos | 6 | br BRA | CB | 24-229 | 90 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 67 | 5 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 52 | 58 | 89.7 | 1 | 48 | 3 | 0 | 0 |
| 11 | William Saliba | 12 | fr FRA | CB | 21-134 | 90 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 58 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 42 | 46 | 91.3 | 1 | 35 | 1 | 0 | 0 |
| 12 | Ben White | 4 | eng ENG | RB | 24-301 | 90 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 61 | 22 | 7 | 4 | 5 | 0 | 0 | 0.1 | 1 | 0 | 29 | 40 | 72.5 | 5 | 25 | 2 | 1 | 1 |
| 13 | Aaron Ramsdale | 1 | eng ENG | GK | 24-083 | 90 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 33 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 32 | 75 | 0 | 21 | 0 | 0 | 0 |
| 14 | 14 Players | nan | nan | nan | nan | 990 | 1 | 1 | 0 | 0 | 10 | 2 | 2 | 0 | 599 | 158 | 25 | 17 | 21 | 1.1 | 1.1 | 0.5 | 18 | 2 | 378 | 465 | 81.3 | 35 | 361 | 36 | 11 | 15 |

could you elaborate more?
maybe you could split the raw text by comma and then convert it to a dataframe
like:
list_of_string = input.split(',')
df = pd.DataFrame(list_of_string)
df.to_csv('yourfile.csv')

The correct approach is as proposed by chitown88, however if you want to copy paste the data by hand into the terminal and get a csv you can do something like this:
import pandas as pd
from datetime import datetime
while True:
print("Enter/Paste your content. Ctrl-D or Ctrl-Z ( windows ) to save it.")
contents = []
while True:
try:
line = input()
except EOFError:
break
contents.append(line)
df = pd.DataFrame(contents)
df.to_csv(f"df_{int(datetime.now().timestamp())}.csv", index=None)
Start the Python script, paste the data into the terminal, press CTRL+D and press enter to export the data you pasted into the terminal into a csv file.

You can use user input controlled while loop to get user input. Finally, you may exit depending on the user’s choice. Look at the code below:
user_input = 'Y'
while user_input.lower() == 'y':
# Run your code here.
user_input = input('Do you want to add one more entry: Y or N?')

This is most intuitive and understandable solution I could come up with uses of basic linear algebra to solve the problem which I find pretty neat. I recommend you to find an another way to parse the data. Check out beautifulsoup and requests.
import pandas as pd#for dataframe
data = '''
,,,,,,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Expected,Expected,Expected,SCA,SCA,Passes,Passes,Passes,Passes,Carries,Carries,Dribbles,Dribbles,-additional
Player,#,Nation,Pos,Age,Min,Gls,Ast,PK,PKatt,Sh,SoT,CrdY,CrdR,Touches,Press,Tkl,Int,Blocks,xG,npxG,xA,SCA,GCA,Cmp,Att,Cmp%,Prog,Carries,Prog,Succ,Att,-9999
Gabriel Jesus,9,br BRA,FW,25-124,82,0,0,0,0,1,0,0,0,40,13,1,1,0,0.1,0.1,0.0,4,0,20,27,74.1,2,33,1,4,5,b66315ae
Eddie Nketiah,14,eng ENG,FW,23-067,8,0,0,0,0,0,0,0,0,6,2,0,0,0,0.0,0.0,0.1,2,0,4,4,100.0,1,4,1,0,0,a53649b7
Martinelli,11,br BRA,LW,21-048,90,1,0,0,0,2,1,0,0,38,21,0,2,1,0.6,0.6,0.1,1,0,24,28,85.7,1,34,5,3,4,48a5a5d6
Bukayo Saka,7,eng ENG,RW,20-334,90,0,0,0,0,3,0,0,0,52,23,3,0,3,0.2,0.2,0.0,2,1,24,36,66.7,2,37,8,2,2,bc7dc64d
Martin Ødegaard,8,no NOR,AM,23-231,89,0,0,0,0,2,0,0,0,50,22,2,1,2,0.1,0.1,0.0,2,0,30,39,76.9,5,28,3,1,2,79300479
Albert Sambi Lokonga,23,be BEL,CM,22-287,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0.0,0.0,0.0,0,0,1,1,100.0,0,1,1,0,0,1b4f1169
Granit Xhaka,34,ch SUI,DM,29-312,90,0,0,0,0,0,0,1,0,60,5,0,2,3,0.0,0.0,0.0,4,0,42,49,85.7,6,32,2,0,0,e61b8aee
Thomas Partey,5,gh GHA,DM,29-053,90,0,0,0,0,1,0,0,0,62,25,7,1,2,0.1,0.1,0.0,0,0,40,47,85.1,5,26,4,0,1,529f49ab
Oleksandr Zinchenko,35,ua UKR,LB,25-233,82,0,1,0,0,1,1,0,0,64,16,3,3,1,0.0,0.0,0.3,2,1,44,54,81.5,6,36,5,0,0,51cf8561
Kieran Tierney,3,sct SCO,LBWB,25-061,8,0,0,0,0,0,0,0,0,6,1,0,0,0,0.0,0.0,0.0,0,0,2,4,50.0,0,1,0,0,0,fce2302c
Gabriel Dos Santos,6,br BRA,CB,24-229,90,0,0,0,0,0,0,0,0,67,5,1,1,2,0.0,0.0,0.0,0,0,52,58,89.7,1,48,3,0,0,67ac5bb8
William Saliba,12,fr FRA,CB,21-134,90,0,0,0,0,0,0,0,0,58,3,1,2,2,0.0,0.0,0.0,0,0,42,46,91.3,1,35,1,0,0,972aeb2a
Ben White,4,eng ENG,RB,24-301,90,0,0,0,0,0,0,1,0,61,22,7,4,5,0.0,0.0,0.1,1,0,29,40,72.5,5,25,2,1,1,35e413f1
Aaron Ramsdale,1,eng ENG,GK,24-083,90,0,0,0,0,0,0,0,0,33,0,0,0,0,0.0,0.0,0.0,0,0,24,32,75.0,0,21,0,0,0,466fb2c5
14 Players,,,,,990,1,1,0,0,10,2,2,0,599,158,25,17,21,1.1,1.1,0.5,18,2,378,465,81.3,35,361,36,11,15,-9999
'''
#you can just replace data with user input
def tryNum(x):#input a value and if its a number then it returns a number, if not it returns itself back
try:
x = float(x)
return x
except:
return x
rows = [i.split(',')[:-1] for i in data.split('\n')[2:-2]]#removing useless lines
col_names = [i for i in rows[0]]#fetching all column names
cols = [[tryNum(rows[j][i]) for j in range(1,len(rows))] for i in range(len(rows[0]))]#get all column info by transposing the "matrix" if you will
full = {}#setting up the dictionary
for i,y in zip(col_names,cols):#putting the data in the dict
full[i]=y
df = pd.DataFrame(data = full)#uploading it all to the df
print(df.head())

Filter all rows from groupby object

I have a dataframe like below
+-----------+------------+---------------+------+-----+-------+
| InvoiceNo | CategoryNo | Invoice Value | Item | Qty | Price |
+-----------+------------+---------------+------+-----+-------+
| 1 | 1 | 77 | 128 | 1 | 10 |
| 1 | 1 | 77 | 101 | 1 | 11 |
| 1 | 2 | 77 | 105 | 3 | 12 |
| 1 | 3 | 77 | 129 | 2 | 10 |
| 2 | 1 | 21 | 145 | 1 | 9 |
| 2 | 2 | 21 | 130 | 1 | 12 |
+-----------+------------+---------------+------+-----+-------+
I want to filter the entire group, if any of the items in the list item_list = [128,129,130] is present in that group, after grouping by 'InvoiceNo' &'CategoryNo'.
My desired out put is as below
+-----------+------------+---------------+------+-----+-------+
| InvoiceNo | CategoryNo | Invoice Value | Item | Qty | Price |
+-----------+------------+---------------+------+-----+-------+
| 1 | 1 | 77 | 128 | 1 | 10 |
| 1 | 1 | 77 | 101 | 1 | 11 |
| 1 | 3 | 77 | 129 | 2 | 10 |
| 2 | 2 | 21 | 130 | 1 | 12 |
+-----------+------------+---------------+------+-----+-------+
I know how to filter a dataframe using isin(). But, not sure how to do it with groupby()
so far i have tried below
import pandas as pd
df = pd.read_csv('data.csv')
item_list = [128,129,130]
df.groupby(['InvoiceNo','CategoryNo'])['Item'].isin(item_list)
but nothing happens. please guide me how to solve this issue.

You can do something like this:
s = (df['Item'].isin(item_list)
.groupby([df['InvoiceNo'], df['CategoryNo']])
.transform('any')
)
df[s]

Logical indexing in pandas dataframes [duplicate]

This question already has answers here:
How do I Pandas group-by to get sum?
(11 answers)
Closed 3 years ago.
I have some data like this:
+-----------+---------+-------+
| Duration | Outcome | Event |
+-----------+---------+-------+
| 421 | 0 | 1 |
| 421 | 0 | 1 |
| 261 | 0 | 1 |
| 24 | 0 | 1 |
| 27 | 0 | 1 |
| 613 | 0 | 1 |
| 2454 | 0 | 1 |
| 227 | 0 | 1 |
| 2560 | 0 | 1 |
| 229 | 0 | 1 |
| 2242 | 0 | 1 |
| 6680 | 0 | 1 |
| 1172 | 0 | 1 |
| 5656 | 0 | 1 |
| 5082 | 0 | 1 |
| 7239 | 0 | 1 |
| 127 | 0 | 1 |
| 128 | 0 | 1 |
| 128 | 0 | 1 |
| 7569 | 1 | 1 |
| 324 | 0 | 2 |
| 6395 | 0 | 2 |
| 6196 | 0 | 2 |
| 31 | 0 | 2 |
| 228 | 0 | 2 |
| 274 | 0 | 2 |
| 270 | 0 | 2 |
| 275 | 0 | 2 |
| 232 | 0 | 2 |
| 7310 | 0 | 2 |
| 7644 | 1 | 2 |
| 6949 | 0 | 3 |
| 6903 | 1 | 3 |
| 6942 | 0 | 4 |
| 7031 | 1 | 4 |
+-----------+---------+-------+
Now, for each Event, with the Outcome 0/1 considered as Fail/Pass, I want to sum the total Duration of Fail/Pass events separately in 2 new columns (or 1, whatever ensures readability).
I'm new to dataframes and I feel significant logical indexing is involved here. What is the best way to approach this problem?

df.groupby(['Event', 'Outcome'])['Duration'].sum()
So you group by both the event then the outcome, look at the duration column then take the sum of each group.

You can also try:
pd.pivot_table(index='Event',
columns='Outcome',
values='Duration',
data=df,
aggfunc='sum')
which gives you a table with two columns:
+---------+-------+------+
| Outcome | 0 | 1 |
+---------+-------+------+
| Event | | |
+---------+-------+------+
| 1 | 35691 | 7569 |
| 2 | 21535 | 7644 |
| 3 | 6949 | 6903 |
| 4 | 6942 | 7031 |
+---------+-------+------+

Assigning states of Hidden Markov Models by idealized values intensity values.

I'm running the pomegranate HMM (http://pomegranate.readthedocs.io/en/latest/HiddenMarkovModel.html) on my data, and I load the results into a Pandas DF, and define the idealized intensity as the median of all the points in that state: df["hmm_idealized"] = df.groupby(["hmm_state"],as_index = False)["Raw"].transform("median"). Sample data:
+-----+-----------------+-------------+------------+
| | hmm_idealized | hmm_state | hmm_diff |
|-----+-----------------+-------------+------------|
| 0 | 99862 | 3 | nan |
| 1 | 99862 | 3 | 0 |
| 2 | 99862 | 3 | 0 |
| 3 | 99862 | 3 | 0 |
| 4 | 99862 | 3 | 0 |
| 5 | 99862 | 3 | 0 |
| 6 | 117759 | 4 | 1 |
| 7 | 117759 | 4 | 0 |
| 8 | 117759 | 4 | 0 |
| 9 | 117759 | 4 | 0 |
| 10 | 117759 | 4 | 0 |
| 11 | 117759 | 4 | 0 |
| 12 | 117759 | 4 | 0 |
| 13 | 117759 | 4 | 0 |
| 14 | 124934 | 2 | -2 |
| 15 | 124934 | 2 | 0 |
| 16 | 124934 | 2 | 0 |
| 17 | 124934 | 2 | 0 |
| 18 | 124934 | 2 | 0 |
| 19 | 117759 | 4 | 2 |
| 20 | 117759 | 4 | 0 |
| 21 | 117759 | 4 | 0 |
| 22 | 117759 | 4 | 0 |
| 23 | 117759 | 4 | 0 |
| 24 | 117759 | 4 | 0 |
| 25 | 117759 | 4 | 0 |
| 26 | 117759 | 4 | 0 |
| 27 | 117759 | 4 | 0 |
| 28 | 117759 | 4 | 0 |
| 29 | 117759 | 4 | 0 |
| 30 | 117759 | 4 | 0 |
| 31 | 117759 | 4 | 0 |
| 32 | 117759 | 4 | 0 |
| 33 | 117759 | 4 | 0 |
| 34 | 117759 | 4 | 0 |
| 35 | 117759 | 4 | 0 |
| 36 | 117759 | 4 | 0 |
| 37 | 117759 | 4 | 0 |
| 38 | 117759 | 4 | 0 |
| 39 | 117759 | 4 | 0 |
| 40 | 106169 | 1 | -3 |
| 41 | 106169 | 1 | 0 |
| 42 | 106169 | 1 | 0 |
| 43 | 106169 | 1 | 0 |
| 44 | 106169 | 1 | 0 |
| 45 | 106169 | 1 | 0 |
| 46 | 106169 | 1 | 0 |
| 47 | 106169 | 1 | 0 |
| 48 | 106169 | 1 | 0 |
| 49 | 106169 | 1 | 0 |
| 50 | 106169 | 1 | 0 |
| 51 | 106169 | 1 | 0 |
| 52 | 106169 | 1 | 0 |
| 53 | 106169 | 1 | 0 |
| 54 | 106169 | 1 | 0 |
| 55 | 106169 | 1 | 0 |
| 56 | 106169 | 1 | 0 |
| 57 | 106169 | 1 | 0 |
| 58 | 106169 | 1 | 0 |
| 59 | 106169 | 1 | 0 |
| 60 | 106169 | 1 | 0 |
| 61 | 106169 | 1 | 0 |
| 62 | 106169 | 1 | 0 |
| 63 | 106169 | 1 | 0 |
| 64 | 106169 | 1 | 0 |
| 65 | 106169 | 1 | 0 |
| 66 | 106169 | 1 | 0 |
| 67 | 106169 | 1 | 0 |
| 68 | 106169 | 1 | 0 |
| 69 | 106169 | 1 | 0 |
| 70 | 106169 | 1 | 0 |
| 71 | 106169 | 1 | 0 |
| 72 | 106169 | 1 | 0 |
| 73 | 106169 | 1 | 0 |
| 74 | 106169 | 1 | 0 |
| 75 | 99862 | 3 | 2 |
| 76 | 99862 | 3 | 0 |
| 77 | 99862 | 3 | 0 |
| 78 | 99862 | 3 | 0 |
| 79 | 99862 | 3 | 0 |
| 80 | 99862 | 3 | 0 |
| 81 | 99862 | 3 | 0 |
| 82 | 99862 | 3 | 0 |
| 83 | 99862 | 3 | 0 |
| 84 | 99862 | 3 | 0 |
| 85 | 99862 | 3 | 0 |
| 86 | 99862 | 3 | 0 |
| 87 | 99862 | 3 | 0 |
| 88 | 99862 | 3 | 0 |
| 89 | 99862 | 3 | 0 |
| 90 | 99862 | 3 | 0 |
| 91 | 99862 | 3 | 0 |
| 92 | 99862 | 3 | 0 |
| 93 | 99862 | 3 | 0 |
| 94 | 99862 | 3 | 0 |
| 95 | 99862 | 3 | 0 |
| 96 | 99862 | 3 | 0 |
| 97 | 99862 | 3 | 0 |
| 98 | 99862 | 3 | 0 |
| 99 | 99862 | 3 | 0 |
| 100 | 99862 | 3 | 0 |
| 101 | 99862 | 3 | 0 |
| 102 | 99862 | 3 | 0 |
| 103 | 99862 | 3 | 0 |
| 104 | 99862 | 3 | 0 |
| 105 | 99862 | 3 | 0 |
| 106 | 99862 | 3 | 0 |
| 107 | 99862 | 3 | 0 |
| 108 | 94127 | 0 | -3 |
| 109 | 94127 | 0 | 0 |
| 110 | 94127 | 0 | 0 |
| 111 | 94127 | 0 | 0 |
| 112 | 94127 | 0 | 0 |
| 113 | 94127 | 0 | 0 |
| 114 | 94127 | 0 | 0 |
| 115 | 94127 | 0 | 0 |
| 116 | 94127 | 0 | 0 |
| 117 | 94127 | 0 | 0 |
| 118 | 94127 | 0 | 0 |
| 119 | 94127 | 0 | 0 |
| 120 | 94127 | 0 | 0 |
| 121 | 94127 | 0 | 0 |
| 122 | 94127 | 0 | 0 |
| 123 | 94127 | 0 | 0 |
| 124 | 94127 | 0 | 0 |
| 125 | 94127 | 0 | 0 |
| 126 | 94127 | 0 | 0 |
| 127 | 94127 | 0 | 0 |
| 128 | 94127 | 0 | 0 |
| 129 | 94127 | 0 | 0 |
| 130 | 94127 | 0 | 0 |
| 131 | 94127 | 0 | 0 |
| 132 | 94127 | 0 | 0 |
| 133 | 94127 | 0 | 0 |
| 134 | 94127 | 0 | 0 |
| 135 | 94127 | 0 | 0 |
| 136 | 94127 | 0 | 0 |
| 137 | 94127 | 0 | 0 |
| 138 | 94127 | 0 | 0 |
| 139 | 94127 | 0 | 0 |
| 140 | 94127 | 0 | 0 |
| 141 | 94127 | 0 | 0 |
| 142 | 94127 | 0 | 0 |
| 143 | 94127 | 0 | 0 |
| 144 | 94127 | 0 | 0 |
| 145 | 94127 | 0 | 0 |
| 146 | 94127 | 0 | 0 |
| 147 | 94127 | 0 | 0 |
| 148 | 94127 | 0 | 0 |
| 149 | 94127 | 0 | 0 |
| 150 | 94127 | 0 | 0 |
| 151 | 94127 | 0 | 0 |
| 152 | 94127 | 0 | 0 |
| 153 | 94127 | 0 | 0 |
| 154 | 94127 | 0 | 0 |
| 155 | 94127 | 0 | 0 |
| 156 | 94127 | 0 | 0 |
| 157 | 94127 | 0 | 0 |
| 158 | 94127 | 0 | 0 |
| 159 | 94127 | 0 | 0 |
| 160 | 94127 | 0 | 0 |
| 161 | 94127 | 0 | 0 |
| 162 | 94127 | 0 | 0 |
| 163 | 94127 | 0 | 0 |
| 164 | 94127 | 0 | 0 |
| 165 | 94127 | 0 | 0 |
| 166 | 94127 | 0 | 0 |
| 167 | 94127 | 0 | 0 |
| 168 | 94127 | 0 | 0 |
| 169 | 94127 | 0 | 0 |
| 170 | 94127 | 0 | 0 |
| 171 | 94127 | 0 | 0 |
| 172 | 94127 | 0 | 0 |
| 173 | 94127 | 0 | 0 |
| 174 | 94127 | 0 | 0 |
| 175 | 94127 | 0 | 0 |
| 176 | 94127 | 0 | 0 |
| 177 | 94127 | 0 | 0 |
| 178 | 94127 | 0 | 0 |
| 179 | 94127 | 0 | 0 |
| 180 | 94127 | 0 | 0 |
| 181 | 94127 | 0 | 0 |
| 182 | 94127 | 0 | 0 |
| 183 | 94127 | 0 | 0 |
| 184 | 94127 | 0 | 0 |
| 185 | 94127 | 0 | 0 |
| 186 | 94127 | 0 | 0 |
| 187 | 94127 | 0 | 0 |
| 188 | 94127 | 0 | 0 |
| 189 | 94127 | 0 | 0 |
| 190 | 94127 | 0 | 0 |
| 191 | 94127 | 0 | 0 |
| 192 | 94127 | 0 | 0 |
| 193 | 94127 | 0 | 0 |
| 194 | 94127 | 0 | 0 |
| 195 | 94127 | 0 | 0 |
| 196 | 94127 | 0 | 0 |
| 197 | 94127 | 0 | 0 |
| 198 | 94127 | 0 | 0 |
| 199 | 94127 | 0 | 0 |
| 200 | 94127 | 0 | 0 |
+-----+-----------------+-------------+------------+
When analyzing the results, I want to count the number of increases in the model. I would like to use the df.where(data.hmm_diff > 0).count() function to read off how many times I increment by one state. However, the increase sometimes spans two states (i.e. it skips the middle state), so I need to reassign the HMM-state label by sorting the idealized values, so that the lowest state would be 0, the highest 4, etc. Is there a way to reassign the hmm_state label from arbitrary to dependent on idealized intensity?
For example,the hmm_state labeled as "1" lies in between of hmm_state 3 and 4

It looks like you just need to define a sorted HMM state like this:
state_orders = {v: i for i, v in enumerate(sorted(df.hmm_idealized.unique()))}
df['sorted_state'] = df.hmm_idealized.map(state_orders)
Then you can continue as you did in the question, but taking a diff on this column, and counting the jumps on it.

Numpy version of finding the highest and lowest value locations within an interval of another column?

Given the following numpy array. How can I find the highest and lowest value locations of column 0 within the interval on column 1 using numpy?
import numpy as np
data = np.array([
[1879.289,np.nan],[1879.281,np.nan],[1879.292,1],[1879.295,1],[1879.481,1],[1879.294,1],[1879.268,1],
[1879.293,1],[1879.277,1],[1879.285,1],[1879.464,1],[1879.475,1],[1879.971,1],[1879.779,1],
[1879.986,1],[1880.791,1],[1880.29,1],[1879.253,np.nan],[1878.268,np.nan],[1875.73,1],[1876.792,1],
[1875.977,1],[1876.408,1],[1877.159,1],[1877.187,1],[1883.164,1],[1883.171,1],[1883.495,1],
[1883.962,1],[1885.158,1],[1885.974,1],[1886.479,np.nan],[1885.969,np.nan],[1884.693,1],[1884.977,1],
[1884.967,1],[1884.691,1],[1886.171,1],[1886.166,np.nan],[1884.476,np.nan],[1884.66,1],[1882.962,1],
[1881.496,1],[1871.163,1],[1874.985,1],[1874.979,1],[1871.173,np.nan],[1871.973,np.nan],[1871.682,np.nan],
[1872.476,np.nan],[1882.361,1],[1880.869,1],[1882.165,1],[1881.857,1],[1880.375,1],[1880.66,1],
[1880.891,1],[1880.377,1],[1881.663,1],[1881.66,1],[1877.888,1],[1875.69,1],[1875.161,1],
[1876.697,np.nan],[1876.671,np.nan],[1879.666,np.nan],[1877.182,np.nan],[1878.898,1],[1878.668,1],[1878.871,1],
[1878.882,1],[1879.173,1],[1878.887,1],[1878.68,1],[1878.872,1],[1878.677,1],[1877.877,1],
[1877.669,1],[1877.69,1],[1877.684,1],[1877.68,1],[1877.885,1],[1877.863,1],[1877.674,1],
[1877.676,1],[1877.687,1],[1878.367,1],[1878.179,1],[1877.696,1],[1877.665,1],[1877.667,np.nan],
[1878.678,np.nan],[1878.661,1],[1878.171,1],[1877.371,1],[1877.359,1],[1878.381,1],[1875.185,1],
[1875.367,np.nan],[1865.492,np.nan],[1865.495,1],[1866.995,1],[1866.672,1],[1867.465,1],[1867.663,1],
[1867.186,1],[1867.687,1],[1867.459,1],[1867.168,1],[1869.689,1],[1869.693,1],[1871.676,1],
[1873.174,1],[1873.691,np.nan],[1873.685,np.nan]
])
In the third column below you can see where the max and min is for each interval.
+-------+----------+-----------+---------+
| index | Value | Intervals | Min/Max |
+-------+----------+-----------+---------+
| 0 | 1879.289 | np.nan | |
| 1 | 1879.281 | np.nan | |
| 2 | 1879.292 | 1 | |
| 3 | 1879.295 | 1 | |
| 4 | 1879.481 | 1 | |
| 5 | 1879.294 | 1 | |
| 6 | 1879.268 | 1 | -1 | min
| 7 | 1879.293 | 1 | |
| 8 | 1879.277 | 1 | |
| 9 | 1879.285 | 1 | |
| 10 | 1879.464 | 1 | |
| 11 | 1879.475 | 1 | |
| 12 | 1879.971 | 1 | |
| 13 | 1879.779 | 1 | |
| 17 | 1879.986 | 1 | |
| 18 | 1880.791 | 1 | 1 | max
| 19 | 1880.29 | 1 | |
| 55 | 1879.253 | np.nan | |
| 56 | 1878.268 | np.nan | |
| 57 | 1875.73 | 1 | -1 |min
| 58 | 1876.792 | 1 | |
| 59 | 1875.977 | 1 | |
| 60 | 1876.408 | 1 | |
| 61 | 1877.159 | 1 | |
| 62 | 1877.187 | 1 | |
| 63 | 1883.164 | 1 | |
| 64 | 1883.171 | 1 | |
| 65 | 1883.495 | 1 | |
| 66 | 1883.962 | 1 | |
| 67 | 1885.158 | 1 | |
| 68 | 1885.974 | 1 | 1 | max
| 69 | 1886.479 | np.nan | |
| 70 | 1885.969 | np.nan | |
| 71 | 1884.693 | 1 | |
| 72 | 1884.977 | 1 | |
| 73 | 1884.967 | 1 | |
| 74 | 1884.691 | 1 | -1 | min
| 75 | 1886.171 | 1 | 1 | max
| 76 | 1886.166 | np.nan | |
| 77 | 1884.476 | np.nan | |
| 78 | 1884.66 | 1 | 1 | max
| 79 | 1882.962 | 1 | |
| 80 | 1881.496 | 1 | |
| 81 | 1871.163 | 1 | -1 | min
| 82 | 1874.985 | 1 | |
| 83 | 1874.979 | 1 | |
| 84 | 1871.173 | np.nan | |
| 85 | 1871.973 | np.nan | |
| 86 | 1871.682 | np.nan | |
| 87 | 1872.476 | np.nan | |
| 88 | 1882.361 | 1 | 1 | max
| 89 | 1880.869 | 1 | |
| 90 | 1882.165 | 1 | |
| 91 | 1881.857 | 1 | |
| 92 | 1880.375 | 1 | |
| 93 | 1880.66 | 1 | |
| 94 | 1880.891 | 1 | |
| 95 | 1880.377 | 1 | |
| 96 | 1881.663 | 1 | |
| 97 | 1881.66 | 1 | |
| 98 | 1877.888 | 1 | |
| 99 | 1875.69 | 1 | |
| 100 | 1875.161 | 1 | -1 | min
| 101 | 1876.697 | np.nan | |
| 102 | 1876.671 | np.nan | |
| 103 | 1879.666 | np.nan | |
| 111 | 1877.182 | np.nan | |
| 112 | 1878.898 | 1 | |
| 113 | 1878.668 | 1 | |
| 114 | 1878.871 | 1 | |
| 115 | 1878.882 | 1 | |
| 116 | 1879.173 | 1 | 1 | max
| 117 | 1878.887 | 1 | |
| 118 | 1878.68 | 1 | |
| 119 | 1878.872 | 1 | |
| 120 | 1878.677 | 1 | |
| 121 | 1877.877 | 1 | |
| 122 | 1877.669 | 1 | |
| 123 | 1877.69 | 1 | |
| 124 | 1877.684 | 1 | |
| 125 | 1877.68 | 1 | |
| 126 | 1877.885 | 1 | |
| 127 | 1877.863 | 1 | |
| 128 | 1877.674 | 1 | |
| 129 | 1877.676 | 1 | |
| 130 | 1877.687 | 1 | |
| 131 | 1878.367 | 1 | |
| 132 | 1878.179 | 1 | |
| 133 | 1877.696 | 1 | |
| 134 | 1877.665 | 1 | -1 | min
| 135 | 1877.667 | np.nan | |
| 136 | 1878.678 | np.nan | |
| 137 | 1878.661 | 1 | 1 | max
| 138 | 1878.171 | 1 | |
| 139 | 1877.371 | 1 | |
| 140 | 1877.359 | 1 | |
| 141 | 1878.381 | 1 | |
| 142 | 1875.185 | 1 | -1 | min
| 143 | 1875.367 | np.nan | |
| 144 | 1865.492 | np.nan | |
| 145 | 1865.495 | 1 | -1 | min
| 146 | 1866.995 | 1 | |
| 147 | 1866.672 | 1 | |
| 148 | 1867.465 | 1 | |
| 149 | 1867.663 | 1 | |
| 150 | 1867.186 | 1 | |
| 151 | 1867.687 | 1 | |
| 152 | 1867.459 | 1 | |
| 153 | 1867.168 | 1 | |
| 154 | 1869.689 | 1 | |
| 155 | 1869.693 | 1 | |
| 156 | 1871.676 | 1 | |
| 157 | 1873.174 | 1 | 1 | max
| 158 | 1873.691 | np.nan | |
| 159 | 1873.685 | np.nan | |
+-------+----------+-----------+---------+
I must specify upfront that this question has already been answered here with a pandas solution. The solution performs reasonable at about 300 seconds for a table of around 1 million rows. But after some more testing, I see that if the table is over 3 million rows, the execution time increases dramatically to over 2500 seconds and even more. This is obviously too long for such a simple task. How would the same problem be solved with numpy?

Here's one NumPy approach -
mask = ~np.isnan(data[:,1])
s0 = np.flatnonzero(mask[1:] > mask[:-1])+1
s1 = np.flatnonzero(mask[1:] < mask[:-1])+1
lens = s1 - s0
tags = np.repeat(np.arange(len(lens)), lens)
idx = np.lexsort((data[mask,0], tags))
starts = np.r_[0,lens.cumsum()]
offsets = np.r_[s0[0], s0[1:] - s1[:-1]]
offsets_cumsum = offsets.cumsum()
min_ids = idx[starts[:-1]] + offsets_cumsum
max_ids = idx[starts[1:]-1] + offsets_cumsum
out = np.full(data.shape[0], np.nan)
out[min_ids] = -1
out[max_ids] = 1

So this is a bit of a cheat since it uses scipy:
import numpy as np
from scipy import ndimage
markers = np.isnan(data[:, 1])
groups = np.cumsum(markers)
mins, max, min_idx, max_idx = ndimage.measurements.extrema(
data[:, 0], labels=groups, index=range(2, groups.max(), 2))