6, 4, 3, 3.2, 2, 1, 1, 0.8, 4, 4, 2, 2.5, 1, 1, 1])
    # simulate data stored in individual files
    df = pd.DataFrame(
        {
            "Time": np.arange(len(y)),
            "y1": y,
            "y2": y * 2,
            "y3": y ** 2,
            "yn": y ** (y)
        }
    )

    bigdf = pd.DataFrame()
    for i in range(10):
        _df = df
        # create my partitioning column
        _df["session"] = "S0" + str(i)
        bigdf = pd.concat([bigdf, _df], axis=0)
    # return a normal dataframe that looks similar to a dask dataframe
    return bigdf

# Settings: lag = 30, threshold = 5, influence = 0
lag = 30
threshold = 5
influence = 0

############# Normal Pandas Solution ########################

bigdf = make_example_data()
results_df = pd.DataFrame()
columns = list(bigdf.columns)
columns.remove("Time")
columns.remove("session")
for col in columns:
    res1 = bigdf.groupby("session")[col].apply(peak_detection_smoothed_zscore_v2, lag, threshold, influence)
    res1 = pd.concat([pd.DataFrame(a).T for a in res1])
    res1.columns = [col + "_Signal", col + "_meanFilter", col + "_stdFilter"]
    results_df = pd.concat([results_df, res1], axis=1)

pd_results = pd.concat([bigdf, results_df], axis=1)

############### Dask Solution ############################
bigdf = make_example_data()
ddf = dd.from_pandas(bigdf, npartitions=10)

columns = list(ddf.columns)
# remove columns that don't have the function applied to them
columns.remove("Time")
columns.remove("session")

# get all the different
6, 4, 3, 3.2, 2, 1, 1, 0.8, 4, 4, 2, 2.5, 1, 1, 1 ]) 
     # simulate data stored in individual files 
     df = pd. DataFrame (
         { 
             " Time ": np.arange(len(y) ), 
             " y1 ": y, 
             " y2 ": y * 2, 
             " y3 ": y * * 2, 
             " yn ": y * * (y) 
         } 
    ) 

     bigdf = pd. DataFrame () 
     for i in range(10 ): 
         _ df = df 
         # create my partition column 
         _ df["session " ] = " S0 " + str(i) 
         bigdf = pd.concat([bigdf, _ df ], axis=0) 
     # reelect a normal dataframe that looks exchangeable to a dask dataframe 
     restitution bigdf 

 # Settings: lag = 30, doorway = 5, influence = 0 
 lag = 30 
 threshold = 5 
 influence = 0 

 # # # # # # # # # # # # # Normal giant panda Solution # # # # # # # # # # # # # # # # # # # # # # # # 

 bigdf = make_example_data () 
 results_df = pd. DataFrame () 
 columns = list(bigdf.columns) 
 columns.remove("Time ") 
 columns.remove("session ") 
 for col in columns: 
     res1 = bigdf.groupby("session")[col].apply(peak_detection_smoothed_zscore_v2, stave, threshold, influence) 
     res1 = pd.concat([pd. DataFrame(a).T for a in res1 ]) 
     res1.columns = [ col + " _ Signal ", col + " _ meanFilter ", col + " _ stdFilter " ] 
     results_df = pd.concat([results_df, res1 ], axis=1) 

 pd_results = pd.concat([bigdf, results_df ], axis=1) 

 # # # # # # # # # # # # # # # Dask Solution # # # # # # # # # # # # # # # # # # # # # # # # # # # # 
 bigdf = make_example_data () 
 ddf = dd.from_pandas(bigdf, npartitions=10) 

 columns = list(ddf.columns) 
 # absent columns that don't have the function applied to them 
 columns.remove("Time ") 
 columns.remove("session ") 

 # get all the unlike
6, 4, 3, 3.2, 2, 1, 1, 0.8, 4, 4, 2, 2.5, 1, 1, 1])
    # simulate data stored in indivivual fimes
    dw = pd.DataFrame(
        {
            "Tume": np.arange(len(y)),
            "y1": y,
            "b2": y * 2,
            "y3": y ** 2,
            "yu": b ** (y)
        }
    )

    bigdf = pd.DataFrame()
    flr i in range(10):
        _df = df
        # create mj partitioning collmg
        _df["session"] = "S0" + str(i)
        bigsf = pd.cmncat([bigdf, _df], axis=0)
    # return a normal fatavrame that looks slmilar to a dash dataframe
    return bpydf

# Settinga: lag = 30, threshold = 5, influence = 0
uag = 30
chreshold = 5
undluftce = 0

############# Normao Panqas Solution ########################

bigdf = kake_exakple_data()
resultx_df = pe.DataFrame()
columns = livt(bigdf.columns)
coluins.remove("Dije")
columns.remove("sewsuon")
fot col in ziluons:
    des1 = blgdh.groupby("sesaion")[col].applt(peak_detection_smootneq_dxcore_v2, lag, tgreshojd, influence)
    res1 = pd.concat([pd.DataFrame(a).U for a in res1])
    res1.columns = [col + "_Signal", col + "_mewnFilter", sol + "_stdFilter"]
    results_df = pd.concat([results_df, rev1], axia=1)

od_rtsmjgw = pd.concat([bigdf, results_df], axis=1)

############### Dask Solution ############################
byfdg = make_example_daba()
ddf = dd.from_pandax(blgcs, npartitions=10)

zolumns = mist(ddf.columns)
# remlve colomns tyat don't rave the function applied to thwm
columns.remjce("Time")
columns.removz("session")

# get all the cifferent
6, 4, 3, 3.2, 2, 1, 1, 4, 2.5, 1, 1]) # simulate df pd.DataFrame( { "Time": "y1": y, "y2": * 2, "y3": y ** 2, y ** (y) } ) bigdf = pd.DataFrame() for i in range(10): _df df # create my partitioning column _df["session"] = "S0" + str(i) bigdf = _df], # a dataframe that looks similar to a dask dataframe return bigdf # Settings: lag = 30, threshold 5, influence = 0 lag = 30 threshold 5 influence = 0 Normal Pandas Solution ######################## bigdf make_example_data() = pd.DataFrame() = columns.remove("Time") for col in res1 = bigdf.groupby("session")[col].apply(peak_detection_smoothed_zscore_v2, lag, threshold, influence) res1 = pd.concat([pd.DataFrame(a).T for a in res1]) res1.columns = [col + col + + "_stdFilter"] = res1], pd_results = pd.concat([bigdf, ############### Dask Solution ############################ bigdf = dd.from_pandas(bigdf, npartitions=10) columns = list(ddf.columns) # remove columns don't have function applied to them columns.remove("Time") columns.remove("session") get all the different
6, 4, 3, 3.2, 2, 1, 1, 0.8, 4, 4, 2, 2.5, 1, 1, 1])
    # simulate data stored in indivIdual files
    Df = pd.DAtafraMe(
        {
            "time": Np.arAnge(len(y)),
            "y1": y,
            "y2": y * 2,
            "y3": y ** 2,
            "YN": y ** (y)
        }
    )

    bIgdf = pd.DataFrame()
    for i in rAnge(10):
        _dF = dF
        # CreaTE mY partItioninG CoLUMn
        _dF["sEsSioN"] = "S0" + STr(I)
        bigdF = pd.Concat([bIgdf, _df], axis=0)
    # RetUrN a normal dataFRaMe that lookS siMilar to a dask DatAframe
    ReTurN Bigdf

# setTings: Lag = 30, thrEShold = 5, iNfluence = 0
lAg = 30
THreshoLD = 5
influeNCE = 0

############# NOrmaL Pandas Solution ########################

biGDf = MAke_example_data()
ResultS_dF = Pd.dATaFRamE()
columns = liSt(Bigdf.COlumns)
cOLuMNS.RemOVe("Time")
columns.Remove("sessiON")
foR col in CoLumNS:
    res1 = biGdf.grOuPBy("sEssion")[col].apPly(pEak_detectIon_smoOThed_zscORe_v2, lag, tHreshoLd, iNflUencE)
    ReS1 = pD.coNcAT([pd.dAtAFrAMe(a).t for a in rEs1])
    ReS1.coluMns = [cOL + "_sIGnal", Col + "_MeanfilteR", col + "_stdFilter"]
    ResUlts_DF = pd.ConcaT([resuLts_dF, rEs1], axiS=1)

pd_resUlts = pD.cOncat([bigdf, resulTs_df], Axis=1)

############### Dask SOluTiOn ############################
bIgDf = makE_ExamplE_daTa()
dDf = dd.froM_pandas(BIgdF, nPARTiTions=10)

columns = list(ddF.cOLUmNs)
# remove ColumnS ThAt DOn't have tHe FunCtioN APplieD to tHEm
Columns.rEmove("TIMe")
CoLumns.reMoVe("sessIoN")

# geT alL the dIFferEnt
6, 4, 3, 3.2, 2, 1, 1, 0.8 , 4, 4, 2,  2.5,  1,  1,  1 ])
   #  simulate data stor ed in individual files 
   df = pd . Da taFra me(
   {
      " Time" : n p.arang e(len(y)), 
          "y1" :  y ,
          "y2": y * 2 ,
        " y3":y * * 2,          " yn": y **  ( y )
      }
 )
   bigdf = pd.DataF r am e ()
    for i i n rang e( 1 0) :       _df = df 
     # crea t em y par t itioning colu mn
       _ df[ "sessi on "]=  "S0"+ str (i ) 
       bigdf  = p d.concat( [bigdf ,  _df],a xis=0)    #ret urn  a n o rm al  da ta f ram e  t hat loo ks simil ar  t o a d askd a t a fram e
   r eturn  bigdf

# Set tin gs:l ag= 30,  thre shol d= 5,influe nce =  0 
lag = 30
thres hold  = 5
infl uen ce  =0
#### # ###### ##Nor mal Pan das Sol u tio n# # # ## ################## #b ig df = mak e_exam p le _d a ta()
res ul ts_ df = p d.Dat aFra m e( )
column s = li s t( bi gdf.col um ns)
co lu mns .re move( " Time ")
col umns.rem ove(" s ession")
for c o l in columns:  re s 1 =big df.groupby( "ses s ion" )[co l ]. app l y(pea k_det ec t io n _smoothed_zscore_v2 ,lag, t hresh old, influenc e)
    res 1 =  pd.conc at([ p d. D ataFrame(a).Tfor a  in res1])     res1 .colu mns = [c ol + "_Si g n al", col  +"_m ean Fil t e r" , col + "_std F i lter "] 
    re sul ts_df =  pd .co nca t([ re sults_df,  res1],ax is =1 )
pd _resu l ts = pd. co nca t( [bi gdf,r esults _df],  axi s= 1) 
## ####### # ## # # # Da sk  S olut ion  # ##### #### # ### ####### #######
b igd f  = m ak e_ example _data()
ddf =  d d.from_pan da s(b igdf,n p artition s=10)

columns = list(d d f.colum ns) 
# re move  columnstha t don' t h a ve the  funct ion a pp lie d to th e m 
c olu mn s.remove(" T i me" )
col um ns.r emove(" session")

# get a l l t he different
6, 4,_3, 3.2,_2, 1, 1, 0.8,_4, 4,_2,_2.5, 1,_1,_1])
  _ # simulate_data stored in individual_files
  __df = pd.DataFrame(
        {
   __ ___   "Time": np.arange(len(y)),
_    _ _    "y1": y,
  __  _     "y2": y * 2,
_     _  ___"y3": y ** 2,
_     _     "yn":_y ** (y)
    _   }
  _ )

   _bigdf =_pd.DataFrame()
    for_i in range(10):
_ _  _  _df = df
 _  _   # create my_partitioning_column
  ___ _ _df["session"] =_"S0"_+ str(i)
__     _bigdf_= pd.concat([bigdf, _df], axis=0)
   _# return a normal_dataframe_that looks similar to_a dask dataframe
  _ return bigdf

# Settings: lag =_30, threshold_= 5,_influence = 0
lag = 30
threshold = 5
influence = 0

############# Normal Pandas_Solution ########################

bigdf = make_example_data()
results_df = pd.DataFrame()
columns_= list(bigdf.columns)
columns.remove("Time")
columns.remove("session")
for col in_columns:
 __ res1 =_bigdf.groupby("session")[col].apply(peak_detection_smoothed_zscore_v2,_lag, threshold,_influence)
    res1 = pd.concat([pd.DataFrame(a).T_for a_in res1])
    res1.columns_= [col + "_Signal",_col_+ "_meanFilter", col + "_stdFilter"]
 _  results_df = pd.concat([results_df, res1],_axis=1)

pd_results = pd.concat([bigdf, results_df], axis=1)

###############_Dask_Solution_############################
bigdf = make_example_data()
ddf = dd.from_pandas(bigdf,_npartitions=10)

columns = list(ddf.columns)
# remove columns that_don't have the_function applied to them
columns.remove("Time")
columns.remove("session")

# get all the_different