Develop Reference

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.

Selecting rows from DF1 where column values match values from a column from DF2

This problem has been solved (I think). Excel was the problem and not python after all. The below code should work for my needs and doesn't seem to be dropping rows after all.
Rows Highlighted in yellow are the rows I want to select in DF1. The selection should be made based on the values in column_2 of DF1 that match the values of column_1 of DF2
Here was my preferred solution using Pandas package in python after a lot of trail and error/searching:
NEW_MATCHED_DF1 = DF1.loc[DF1['column 2'].isin(DF2['column_1'])]
The problem I am seeing is that when I compare my results to what happens in excel when I do the same thing, I am getting almost double the results and I think that my python technique is dropping duplicates. Of course, it is possible that I am doing something wrong in excel, or excel is incorrect for some other reason, but it is something I have verified in the past and much more familiar with excel so I am suspecting that it is more likely that I am doing something wrong in python. EXCEL IS THE PROBLEM AFTER ALL!! :/
Ultimately, I would like to use python to select any and all rows in DF1 where column_2 of DF1 matches column_1 of DF2. Excel is absurdly slow and I would like to move away from using excel for manipulating large dataframes.
I appreciate any help or directions to help. I really haven't been able to figure out if my code is in fact dropping duplicates and/or if there is another solution that I can be confident that wont do this.

Try this using np.where:
import numpy as np
list_df2 = df2['column1'].unique().tolist()
df1['matching_rows'] = np.where(df1['column2'].isin(list_df2),'Match','No Match')
And then create a new dataframe with the matches:
matched_df = df1[df1['matching_rows']=='Match']

How to get rid of rows with pandas in a CSV where the value of cells in a specific column is under 100 Billion?

I'm trying to filter through a CSV and make a new CSV which is the exact same except for it gets rid of any rows that have a value of greater than 100 billion in the 'marketcap' column.
The code I've written so just spits out the same CSV as the original out over again and doesn't cut out any lines from the old CSV to the new CSV.
Code:
db = pd.read_csv('SF1_original.csv')
db = db[db['marketcap']<= 100000000000]
db.to_csv('new_SF1_original.csv')
Example of old CSV (It's long don't look through whole thing, just to give you an idea):
ticker,dimension,calendardate,datekey,reportperiod,lastupdated,accoci,assets,assetsavg,assetsc,assetsnc,assetturnover,bvps,capex,cashneq,cashnequsd,cor,consolinc,currentratio,de,debt,debtc,debtnc,debtusd,deferredrev,depamor,deposits,divyield,dps,ebit,ebitda,ebitdamargin,ebitdausd,ebitusd,ebt,eps,epsdil,epsusd,equity,equityavg,equityusd,ev,evebit,evebitda,fcf,fcfps,fxusd,gp,grossmargin,intangibles,intexp,invcap,invcapavg,inventory,investments,investmentsc,investmentsnc,liabilities,liabilitiesc,liabilitiesnc,marketcap,ncf,ncfbus,ncfcommon,ncfdebt,ncfdiv,ncff,ncfi,ncfinv,ncfo,ncfx,netinc,netinccmn,netinccmnusd,netincdis,netincnci,netmargin,opex,opinc,payables,payoutratio,pb,pe,pe1,ppnenet,prefdivis,price,ps,ps1,receivables,retearn,revenue,revenueusd,rnd,roa,roe,roic,ros,sbcomp,sgna,sharefactor,sharesbas,shareswa,shareswadil,sps,tangibles,taxassets,taxexp,taxliabilities,tbvps,workingcapital
A,ARQ,1999-12-31,2000-03-15,2000-01-31,2020-09-01,53000000,7107000000,,4982000000,2125000000,,10.219,-30000000,1368000000,1368000000,1160000000,131000000,2.41,0.584,665000000,111000000,554000000,665000000,281000000,96000000,0,0.0,0.0,202000000,298000000,0.133,298000000,202000000,202000000,0.3,0.3,0.3,4486000000,,4486000000,50960600000,,,354000000,0.806,1.0,1086000000,0.484,0,0,4337000000,,1567000000,42000000,42000000,0,2621000000,2067000000,554000000,51663600000,1368000000,-160000000,2068000000,111000000,0,1192000000,-208000000,-42000000,384000000,0,131000000,131000000,131000000,0,0,0.058,915000000,171000000,635000000,0.0,11.517,,,1408000000,0,114.3,,,1445000000,131000000,2246000000,2246000000,290000000,,,,,0,625000000,1.0,452000000,439000000,440000000,5.116,7107000000,0,71000000,113000000,16.189,2915000000
Example New CSV (Exact same when this line should have been cut):
,ticker,dimension,calendardate,datekey,reportperiod,lastupdated,accoci,assets,assetsavg,assetsc,assetsnc,assetturnover,bvps,capex,cashneq,cashnequsd,cor,consolinc,currentratio,de,debt,debtc,debtnc,debtusd,deferredrev,depamor,deposits,divyield,dps,ebit,ebitda,ebitdamargin,ebitdausd,ebitusd,ebt,eps,epsdil,epsusd,equity,equityavg,equityusd,ev,evebit,evebitda,fcf,fcfps,fxusd,gp,grossmargin,intangibles,intexp,invcap,invcapavg,inventory,investments,investmentsc,investmentsnc,liabilities,liabilitiesc,liabilitiesnc,marketcap,ncf,ncfbus,ncfcommon,ncfdebt,ncfdiv,ncff,ncfi,ncfinv,ncfo,ncfx,netinc,netinccmn,netinccmnusd,netincdis,netincnci,netmargin,opex,opinc,payables,payoutratio,pb,pe,pe1,ppnenet,prefdivis,price,ps,ps1,receivables,retearn,revenue,revenueusd,rnd,roa,roe,roic,ros,sbcomp,sgna,sharefactor,sharesbas,shareswa,shareswadil,sps,tangibles,taxassets,taxexp,taxliabilities,tbvps,workingcapital
0,A,ARQ,1999-12-31,2000-03-15,2000-01-31,2020-09-01,53000000.0,7107000000.0,,4982000000.0,2125000000.0,,10.219,-30000000.0,1368000000.0,1368000000.0,1160000000.0,131000000.0,2.41,0.584,665000000.0,111000000.0,554000000.0,665000000.0,281000000.0,96000000.0,0.0,0.0,0.0,202000000.0,298000000.0,0.133,298000000.0,202000000.0,202000000.0,0.3,0.3,0.3,4486000000.0,,4486000000.0,50960600000.0,,,354000000.0,0.8059999999999999,1.0,1086000000.0,0.484,0.0,0.0,4337000000.0,,1567000000.0,42000000.0,42000000.0,0.0,2621000000.0,2067000000.0,554000000.0,51663600000.0,1368000000.0,-160000000.0,2068000000.0,111000000.0,0.0,1192000000.0,-208000000.0,-42000000.0,384000000.0,0.0,131000000.0,131000000.0,131000000.0,0.0,0.0,0.057999999999999996,915000000.0,171000000.0,635000000.0,0.0,11.517000000000001,,,1408000000.0,0.0,114.3,,,1445000000.0,131000000.0,2246000000.0,2246000000.0,290000000.0,,,,,0.0,625000000.0,1.0,452000000.0,439000000.0,440000000.0,5.1160000000000005,7107000000.0,0.0,71000000.0,113000000.0,16.189,2915000000.0
I've seen two questions somewhat related to this on StackOverflow, but they haven't helped me much. This one uses CSV library instead of pandas (which is an option for me). This one is more helpful since it uses pandas but still hasn't been interacted with and isn't exactly the same as my use case.

You can get the indexes of the rows with "marketcap" over 100 billion rows like so:
df.loc[df["marketcap"] > 100000000000]["marketcap"].index
All that's left to do is drop them from the DataFrame:
df.drop(df.loc[df["marketcap"] > 100000000000]["marketcap"].index, inplace=True)
Reading from CSV and writing to the CSV is already correctly taken care of in your code.

Splitting column of dataframe based on text characters in cells

I imported a .csv file with a single column of data into a dataframe that I am trying to clean up by splitting the column based on various string occurrences within the cells. I've tried numerous means to split the column, but can't seem to get it to work. My latest attempt was using the following:
df.loc[:,'DataCol'] = df.DataCol.str.split(pat=':\n',expand=True)
df
The result is a dataframe that is still one column and completely unchanged. What am I doing wrong? This is my first time doing anything like this so please forgive the simple question.

Df.loc creates a copy of the column you've selected - try replacing the code below with df['DataCol'], which references the actual column in the original dataframe.
df.loc[:,'DataCol']

How to append dataframes in Pandas without staggered format

I was able to append dataframes but as they are added, they appear at the end of the one previously appended an so on.
Each dataframe has a different header name.
Here’s what I’ve tried so far:
df1 = df1.append(dforiginal,sort=False, ignore_index=False)
What’s more, every time they are appended, their index is set back to 0. Is it possible to append each dataframe all starting at Index=0?
The screenshots below show what I'm getting(top image) and what I'm trying to accomplish (bottom image).
Thanks.
[1

If I got your point correctly you want to add rows instead of columns to your Dataframe, dont you?
Nevertheless, you could use for example this website to get a general overview on how to use the append function: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html
Moreover, you can reset the index if you set the keyword ignore_index as True.