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Converting 0-1 values in dataset with the name of the column if the value of the cell is 1

I have a csv dataset with the values 0-1 for the features of the elements. I want to iterate each cell and replace the values 1 with the name of its column. There are more than 500 thousand rows and 200 columns and, because the table is exported from another annotation tool which I update often, I want to find a way in Python to do it automatically.
This is not the table, but a sample test which I was using while trying to write a code I tried some, but without success.
I would really appreciate it if you can share your knowledge with me. It will be a huge help. The final result I want to have is of the type: (abonojnë, token_pos_verb). If you know any method that I can do this in Excel without the help of Python, it would be even better.
Thank you,
Brikena
Text,Comment,Role,ParentID,doc_completeness,lemma,MultiWord_Expr,token,pos,punctuation,verb,noun,adjective
abonojnë,,,,,,,1,1,0,1,0,0
çokasin,,,,,,,1,1,0,1,0,1
gërgasin,,,,,,,1,1,0,1,0,0
godasin,,,,,,,1,1,0,1,0,0
përkasin,,,,,,,1,1,1,1,0,0
përdjegin,,,,,,,1,1,0,1,0,0
lakadredhin,,,,,,,1,1,0,1,1,0
përdredhin,,,,,,,1,1,0,1,0,0
spërdredhin,,,,,,,1,1,0,1,0,0
përmbledhin,,,,,,,1,1,0,1,0,0
shpërdredhin,,,,,,,1,1,0,1,0,0
arsejnë,,,,,,,1,1,0,1,1,0
çapëlejnë,,,,,,,1,1,0,1,0,0

Using pandas, this is quite easy:
# pip install pandas
import pandas as pd
# read data (here example with csv, but use "read_excel" for excel)
df = pd.read_csv('input.csv').set_index('Text')
# reshape and export
(df.mul(df.columns).where(df.eq(1))
.stack().rename('xxx')
.groupby(level=0).apply('_'.join)
).to_csv('output.csv') # here use "to_excel" for excel format
output file:
Text,xxx
abonojnë,token_pos_verb
arsejnë,token_pos_verb_noun
godasin,token_pos_verb
gërgasin,token_pos_verb
lakadredhin,token_pos_verb_noun
përdjegin,token_pos_verb
përdredhin,token_pos_verb
përkasin,token_pos_punctuation_verb
përmbledhin,token_pos_verb
shpërdredhin,token_pos_verb
spërdredhin,token_pos_verb
çapëlejnë,token_pos_verb
çokasin,token_pos_verb_adjective

An update to those who may find it helpful in the future. Thank you to #mozway for helping me. A friend of mine suggested working with Excel formula because the solution with Pandas and gropuby eliminates duplicates. Since I need all the duplicates, because it's an annotated corpus, it's normal that there are repeated words that should appear in every context, not only the first occurrence.
The other alternative is this:
Use a second sheet on the excel file, writing the formula =IF(Sheet1!B2=1,Sheet2!B$1,"") in the first cell with 0-1 values and drag it in all the other cells. This keeps all the occurrences of the words. It's quick and it works like magic.
I hope this can be helpful to others who want to convert a 0-1 dataset to feature names without having to code.

Applying corrections to a subsampled copy of a dataframe back to the original dataframe?

I'm a Pandas newbie, so please bear with me.
Overview: I started with a free-form text file created by a data harvesting script that remotely accessed dozens of different kinds of devices, and multiple instances of each. I used OpenRefine (a truly wonderful tool) to munge that into a CSV that was then input to dataframe df using Pandas in a JupyterLab notebook.
My first inspection of the data showed the 'Timestamp' column was not monotonic. I accessed individual data sources as follows, in this case for the 'T-meter' data source. (The technique was taken from a search result - I don't really understand it, but it worked.)
cond = df['Source']=='T-meter'
rows = df.loc[cond, :]
df_tmeter = pd.DataFrame(columns=df.columns)
df_tmeter = df_tmeter.append(rows, ignore_index=True)
then checked each as follows:
df_tmeter['Timestamp'].is_monotonic
Fortunately, the problem was easy to identify and fix: Some sensors were resetting, then sending bad (but still monotonic) timestamps until their clocks were updated. I wrote the function healing() to cleanly patch such errors, and it worked a treat:
df_tmeter['healed'] = df_tmeter['Timestamp'].apply(healing)
Now for my questions:
How do I get the 'healed' values back into the original df['Timestamp'] column for only the 'T-meter' items in df['Source']?
Given the function healing(), is there a clean way to do this directly on df?
Thanks!
Edit: I first thought I should be using 'views' into df, but other operations on the data would either generate errors, or silently turn the views into copies.

I wrote a wrapper function heal_row() for healing():
def heal_row( row ):
if row['Source'] == 'T-meter': # Redundant check, but safe!
row['Timestamp'] = healing(row['Timestamp'])
return row
then did the following:
df = df.apply(lambda row: row if row['Source'] != 'T-meter' else heal_row(row), axis=1)
This ordering is important, since healing() is stateful based on the prior row(s), and thus can't be the default operation.

Pandas dataframe to numpy array [duplicate]

This question already has answers here:
Convert pandas dataframe to NumPy array
(15 answers)
Closed 3 years ago.
I am very new to Python and have very little experience. I've managed to get some code working by copying and pasting and substituting the data I have, but I've been looking up how to select data from a dataframe but can't make sense of the examples and substitute my own data in.
The overarching goal: (if anyone could actually help me write the entire thing, that would be helpful, but highly unlikely and probably not allowed)
I am trying to use scipy to fit the curve of a temperature change when two chemicals react. There are 40 trials. The model I am hoping to use is a generalized logistic function with six parameters. All I need are the 40 functions, and nothing else. I have no idea how to achieve this, but I will ask another question when I get there.
The current issue:
I had imported 40 .csv files, compiled/shortened the data into 2 sections so that there are 20 trials in 1 file. Now the data has 21 columns and 63 rows. There is a title in the first row for each column, and the first column is a consistent time interval.
However, each trial is not necessarily that long. One of them does, though. So I've managed to write the following code for a dataframe:
import pandas as pd
df = pd.read_csv("~/Truncated raw data hcl.csv")
print(df)
It prints the table out, but as expected, there are NaNs where there exists no data.
So I would like to know how to arrange it into workable array with 2 columns , time and a trial like an (x,y) for a graph for future workings with numpy or scipy such that the rows that there is no data would not be included.
Part of the .csv file begins after the horizontal line. I'm too lazy to put it in a code block, sorry. Thank you.
time,1mnaoh trial 1,1mnaoh trial 2,1mnaoh trial 3,1mnaoh trial 4,2mnaoh trial 1,2mnaoh trial 2,2mnaoh trial 3,2mnaoh trial 4,3mnaoh trial 1,3mnaoh trial 2,3mnaoh trial 3,3mnaoh trial 4,4mnaoh trial 1,4mnaoh trial 2,4mnaoh trial 3,4mnaoh trial 4,5mnaoh trial 1,5mnaoh trial 2,5mnaoh trial 3,5mnaoh trial 4
0.0,23.2,23.1,23.1,23.8,23.1,23.1,23.3,22.0,22.8,23.4,23.3,24.0,23.0,23.8,23.8,24.0,23.3,24.3,24.1,24.1
0.5,23.2,23.1,23.1,23.8,23.1,23.1,23.3,22.1,22.8,23.4,23.3,24.0,23.0,23.8,23.8,24.0,23.4,24.3,24.1,24.1
1.0,23.2,23.1,23.1,23.7,23.1,23.1,23.3,22.3,22.8,23.4,23.3,24.0,23.0,23.8,23.8,24.0,23.5,24.3,24.1,24.1
1.5,23.2,23.1,23.1,23.7,23.1,23.1,23.3,22.4,22.8,23.4,23.3,24.0,23.0,23.8,23.8,23.9,23.6,24.3,24.1,24.1
2.0,23.3,23.2,23.2,24.2,23.6,23.2,24.3,22.5,23.0,23.7,24.4,24.1,23.1,23.9,24.4,24.2,23.7,24.5,24.7,25.1
2.5,24.0,23.5,23.5,25.4,25.3,23.3,26.4,22.7,23.5,25.8,27.9,25.1,23.1,23.9,27.4,26.8,23.8,27.2,26.7,28.1
3.0,25.4,24.4,24.1,26.5,27.8,23.3,28.5,22.8,24.6,28.6,31.2,27.2,23.2,23.9,30.9,30.5,23.9,31.4,29.8,31.3
3.5,26.9,25.5,25.1,27.4,29.9,23.4,30.1,22.9,26.4,31.4,34.0,30.0,23.3,24.2,33.8,34.0,23.9,35.1,33.2,34.4
4.0,27.8,26.5,26.2,27.9,31.4,23.4,31.3,23.1,28.8,34.0,36.1,32.6,23.3,26.6,36.0,36.7,24.0,37.7,35.9,36.8
4.5,28.5,27.3,27.0,28.2,32.6,23.5,32.3,23.1,31.2,36.0,37.5,34.8,23.4,30.0,37.7,38.7,24.0,39.7,38.0,38.7
5.0,28.9,27.9,27.7,28.5,33.4,23.5,33.1,23.2,33.2,37.6,38.6,36.5,23.4,33.2,39.0,40.2,24.0,40.9,39.6,40.2
5.5,29.2,28.2,28.3,28.9,34.0,23.5,33.7,23.3,35.0,38.7,39.4,37.9,23.5,35.6,39.9,41.2,24.0,41.9,40.7,41.0
6.0,29.4,28.5,28.6,29.1,34.4,24.9,34.2,23.3,36.4,39.6,40.0,38.9,23.5,37.3,40.6,42.0,24.1,42.5,41.6,41.2
6.5,29.5,28.8,28.9,29.3,34.7,27.0,34.6,23.3,37.6,40.4,40.4,39.7,23.5,38.7,41.1,42.5,24.1,43.1,42.3,41.7
7.0,29.6,29.0,29.1,29.5,34.9,28.8,34.8,23.5,38.6,40.9,40.8,40.2,23.5,39.7,41.4,42.9,24.1,43.4,42.8,42.3
7.5,29.7,29.2,29.2,29.6,35.1,30.5,35.0,24.9,39.3,41.4,41.1,40.6,23.6,40.5,41.7,43.2,24.0,43.7,43.1,42.9
8.0,29.8,29.3,29.3,29.7,35.2,31.8,35.2,26.9,40.0,41.6,41.3,40.9,23.6,41.1,42.0,43.4,24.2,43.8,43.3,43.3
8.5,29.8,29.4,29.4,29.8,35.3,32.8,35.4,28.9,40.5,41.8,41.4,41.2,23.6,41.6,42.2,43.5,27.0,43.9,43.5,43.6
9.0,29.9,29.5,29.5,29.9,35.4,33.6,35.5,30.5,40.8,41.8,41.6,41.4,23.6,41.9,42.4,43.7,30.8,44.0,43.6,43.8
9.5,29.9,29.6,29.5,30.0,35.5,34.2,35.6,31.7,41.0,41.8,41.7,41.5,23.6,42.2,42.5,43.7,33.9,44.0,43.7,44.0
10.0,30.0,29.7,29.6,30.0,35.5,34.6,35.7,32.7,41.1,41.9,41.8,41.7,23.6,42.4,42.6,43.8,36.2,44.0,43.7,44.1
10.5,30.0,29.7,29.6,30.1,35.6,35.0,35.7,33.3,41.2,41.9,41.8,41.8,23.6,42.6,42.6,43.8,37.9,44.0,43.8,44.2
11.0,30.0,29.7,29.6,30.1,35.7,35.2,35.8,33.8,41.3,41.9,41.9,41.8,24.0,42.9,42.7,43.8,39.3,,43.8,44.3
11.5,30.0,29.8,29.7,30.1,35.8,35.4,35.8,34.1,41.4,41.9,42.0,41.8,26.6,43.1,42.7,43.9,40.2,,43.8,44.3
12.0,30.0,29.8,29.7,30.1,35.8,35.5,35.9,34.3,41.4,42.0,42.0,41.9,30.3,43.3,42.7,43.9,40.9,,43.9,44.3
12.5,30.1,29.8,29.7,30.2,35.9,35.7,35.9,34.5,41.5,42.0,42.0,,33.4,43.4,42.7,44.0,41.4,,43.9,44.3
13.0,30.1,29.8,29.8,30.2,35.9,35.8,36.0,34.7,41.5,42.0,42.1,,35.8,43.5,42.7,44.0,41.8,,43.9,44.4
13.5,30.1,29.9,29.8,30.2,36.0,36.0,36.0,34.8,41.5,42.0,42.1,,37.7,43.5,42.8,44.1,42.0,,43.9,44.4
14.0,30.1,29.9,29.8,30.2,36.0,36.1,36.0,34.9,41.6,,42.2,,39.0,43.5,42.8,44.1,42.1,,,44.4
14.5,,29.9,29.8,,36.0,36.2,36.0,35.0,41.6,,42.2,,40.0,43.5,42.8,44.1,42.3,,,44.4
15.0,,29.9,,,36.0,36.3,,35.0,41.6,,42.2,,40.7,,42.8,44.1,42.4,,,
15.5,,,,,36.0,36.4,,35.1,41.6,,42.2,,41.3,,,,42.4,,,

To convert a whole DataFrame into a numpy array, use
df = df.values()
If i understood you correctly, you want seperate arrays for every trial though. This can be done like this:
data = [df.iloc[:, [0, i]].values() for i in range(1, 20)]
which will make a list of numpy arrays, every one containing the first column with temperature and one of the trial columns.

Finding multiple maximum values from a file using Python

I am working with a CSV file and I need to find the greatest several items in a column. I was able to find the top value just by doing the standard looping through and comparing values.
My idea to get the top few values would be to either store all of the values from that column into an array, sort it, and then pull the last three indices. However I'm not sure if that would be a good idea in terms of efficiency. I also need to pull other attributes associated with the top value and it seems like separating out these column values would make everything messy.
Another thing that I thought about doing is having three variables and doing a running top value sort of deal, where every time I find something bigger I compare the "top three" amongst each other and reorder them. That also seems a bit complex and I'm not sure how I would implement it.
I would appreciate some ideas or if someone told if I'm missing something obvious. Let me know if you need to see my sample code (I felt it was probably unnecessary here).
Edit: To clarify, if the column values are something like [2,5,6,3,1,7] I would want to have the values first = 7, second = 6, third = 5

Pandas looks perfect for your task:
import pandas as pd
df = pd.read_csv('data.csv')
df.nlargest(3, 'column name')

Running update of Pandas dataframe

I'd like to use a DataFrame to manage data from many trials of an experiment I'm controlling with Python code. Ideally I will have one master dataframe with a row for each trial that lives in the main function namespace, and then a separate dict (or dataframe) returned from the function that I call to execute the important bits of code for each trial.
What is the best way to do a running update of the master dataframe with this returned set of data? So far I've come up with:
df = df.append(df_trial, ignore_index=True)
or
df = pd.concat([df, df_trial])
But neither seem ideal (and both take a relatively long time according to %timeit). Is there a more Pandonic way?

You should build a list of the pieces and concatenate them all in one shot at the end.