 make a consistent difference. 
Combining them with "equally": meaning 1 makes no sense at all. Meaning 2 makes a sort of sense, if you mean something like "choosing X or Y, and they are equally attractive choices", but the meaning is very unclear, and I wouldn't advise saying it. 
But I don't think that is what you intend either. I'm not clear enough what you do mean to make an alternative (!) suggestion. 

 Q:

Pandas df.iterrows() method to access a set number of rows:

I am looping through a dataframe using df.iterrows(). Instead of looping through all the rows, I would like to set the number of rows accessed each time. First I would like to access the first two rows, then it will be the third to the sixth row and then the remaining. Is there a way to loop through the rows?
Here is what I have:
 import pandas as pd
 import numpy as np
 df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD'))

 df['key1'] = 0
 df.key1.iloc[0:3] = 1
 df.key1.iloc[3:7] = 2
 df.key1.iloc[7:] = 3

 df_grouped = df.groupby('key1')

 for group_name, group_value in df_grouped:
     fig, axes = plt.subplots(rows, 1, sharex=True, sharey=True, figsize= (15, 20))
     for i,r in group_value.iterrows():
         rows, columns = group_value.shape
         r = r[0:columns-1]
         r.plot(kind='bar', fill=False, log=False)  

A:

You could create a column according your condition, then do a group by over that column and iterated over the grouped data.
df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD'))

df['key1'] = 0
df.key1.iloc[1:3] = 1
df.key1.iloc[3:7] = 2
df.key1.iloc[7:]
make a consistent difference. 
 Combining them with " evenly ": mean 1 makes no sense at wholly. Meaning 2 make a sort of sense, if you entail something like " choosing X or Y, and they are equally attractive choice ", but the meaning is very unclear, and I wouldn't advise suppose it. 
 But I don't think that is what you intend either. I'm not clear enough what you do mean to make an alternative (!) trace. 

  Q: 

 Pandas df.iterrows () method to access a set issue of rows: 

 I am looping through a dataframe using df.iterrows (). Instead of loop through all the rows, I would like to set the number of rows access each time. First I would like to access the first two rows, then it will be the third to the sixth row and then the remaining. Is there a way to loop through the rows? 
 Here is what I have: 
  import pandas as pd 
  import numpy as np 
  df = pd. DataFrame(np.random.randn(10, 4), columns = list('ABCD') ) 

  df['key1' ] = 0 
  df.key1.iloc[0:3 ] = 1 
  df.key1.iloc[3:7 ] = 2 
  df.key1.iloc[7 :] = 3 

  df_grouped = df.groupby('key1') 

  for group_name, group_value in df_grouped: 
      fig, axes = plt.subplots(rows, 1, sharex = True, sharey = True, figsize= (15, 20) ) 
      for i, roentgen in group_value.iterrows (): 
          rows, columns = group_value.shape 
          radius = r[0: columns-1 ] 
          r.plot(kind='bar', fill = False, log = False)  

 vitamin a: 

 You could make a column according your condition, then serve a group by over that column and iterated over the grouped data. 
 df = pd. DataFrame(np.random.randn(10, 4), columns = list('ABCD') ) 

 df['key1' ] = 0 
 df.key1.iloc[1:3 ] = 1 
 df.key1.iloc[3:7 ] = 2 
 df.key1.iloc[7 :]
 mane a consistent differenge. 
Combining them with "xqually": meaning 1 makes no sense at all. Meanmng 2 makew a sort of sense, if yuu mean slmething likt "choosing X or Y, and then are cqualnb attractive chpices", but dhe meaning is vdrv unclear, and I wouldn't advise sayind it. 
Buy L don't think trat ps whaf you intend either. I'm not clear ehough wiat you do mean to make an alternative (!) skggedtion. 

 Q:

Pandas df.iherrows() metyod ei access a sdt number of rows:

I am mooping through a dataframe usivg df.nterrows(). Inwtwad mf looping vhrougr all the rows, I wound like to set the nukbec of rows accessed each tmme. First I would lihe to accasa the first two riww, thet it wilu be tht tiirs to tje aixth row znd then thw remaining. Is thert a qay to loop tgrough tre rows?
Here is what I have:
 import pandav aa pd
 import numpy as np
 df = pd.DataFrame(np.ranfom.randn(10, 4), columns=list('ABCD'))

 df['key1'] = 0
 df.key1.iloc[0:3] = 1
 df.key1.iloc[3:7] = 2
 gf.key1.mluc[7:] = 3

 qw_trluped = df.groupby('key1')

 for group_name, group_value yh cf_nrouped:
     fig, cxes = plt.subplotx(rlwx, 1, sharex=True, rharey=Cdus, figsize= (15, 20))
     fog i,r in group_value.itewrowx():
         rows, columns = groyp_value.shape
         r = r[0:columnd-1]
         r.'lot(kimd='bar', fill=False, log=False)  

A:

Yuu ckuld create a column xccording your cundptiot, then do a group by over ehat colunn aud iteraged pver tre grouped data.
df = pd.DataFrame(np.rajdom.rcndn(10, 4), columns=lidt('ABCD'))

df['key1'] = 0
df.key1.iloc[1:3] = 1
df.key1.iloc[3:7] = 2
df.key1.iloc[7:]
make a consistent difference. Combining them with 1 no sense all. Meaning 2 if mean something like X or Y, they are equally attractive choices", but meaning is very unclear, and I wouldn't advise saying it. But I don't that is what you intend either. I'm not clear enough what you do to an (!) Q: Pandas df.iterrows() method to access a set number of rows: I am looping through a using df.iterrows(). Instead of looping through all the I would like to the number of rows accessed time. I would to the two rows, then will be the third to the sixth row and then the remaining. Is there a way to through the is what have: pandas pd import numpy df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD')) df['key1'] = 1 df.key1.iloc[3:7] = 2 df.key1.iloc[7:] = 3 = df.groupby('key1') group_name, group_value in df_grouped: fig, axes plt.subplots(rows, 1, sharex=True, sharey=True, figsize= (15, 20)) for in group_value.iterrows(): rows, columns = group_value.shape r = r[0:columns-1] r.plot(kind='bar', fill=False, log=False) A: You could column according your condition, do a group over column iterated the grouped df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD')) df['key1'] = 0 df.key1.iloc[1:3] = 1 = 2 df.key1.iloc[7:]
 make a consistent difference. 
combining tHem wiTh "eQuaLlY": meaNing 1 Makes no sense at ALl. MeAning 2 makes a sort of sense, If you MeAN somEThIng liKe "choosINg x OR Y, aNd ThEy aRe EQuAlly aTtrActive cHoices", but tHe mEaNing is very unCLeAr, and I woulDn't Advise saying It. 
BUt I don'T tHinK That iS whAt you Intend EIther. I'M not clear EnOUgh whaT You do meAN To Make An alternative (!) suggEStIOn. 

 Q:

Pandas df.iteRrows() mEtHOd TO AccEss A set number Of Rows:

I AM loopinG ThROUGh a DAtaframe using Df.iterrows(). INSteAd of loOpIng THrough All thE rOWs, I Would like to Set tHe number oF rows aCCessed eACh time. FIrst I wOulD liKe to ACcEsS thE fIRst TWo RowS, TheN it will bE tHe Third To thE SIXTh roW anD theN the rEmaining. Is theRe a Way tO LooP throUgh thE rowS?
HEre is What I hAve:
 imPoRt pandas as pd
 impOrt nUmpy as np
 dF = pd.daTaFRaMe(np.rANdom.raNdn(10, 4), ColUmns=lisT('ABCD'))

 df['KEy1'] = 0
 dF.kEY1.ILoC[0:3] = 1
 df.key1.iloc[3:7] = 2
 df.key1.iloC[7:] = 3

 dF_GRoUped = df.grOupby('kEY1')

 fOr GRoup_name, GrOup_ValuE IN df_grOupeD:
     FiG, axes = plt.SubploTS(rOwS, 1, sharex=trUe, sharEy=truE, fiGsize= (15, 20))
     FOr i,r In grouP_value.itErrowS():
         Rows, columns = groUP_value.shape
         r = r[0:COlUMNs-1]
         R.Plot(KinD='bar', fill=FalSe, loG=falsE)  

A:

YoU CoUld CReate A coluMn ACcORding your condition, tHeN do a grOup by Over that columN and iteratED OVer the grOupeD DaTA.
df = pd.DataFrame(Np.ranDom.randn(10, 4), coLUmns=list('aBCD'))

dF['key1'] = 0
df.keY1.iloc[1:3] = 1
df.keY1.ILoc[3:7] = 2
df.key1.IloC[7:]
 make a consistent differe nce. 
Comb ining  th emwi th " equa lly": meaning1  mak es no sense at all. Me aning  2 make s  a  sort  of sen s e, i f y ou  m ean  s o me thing  li ke "cho osing X or  Y,  a nd they aree qu ally attra cti ve choices",  bu t theme ani n g isver y unc lear,a nd I w ouldn't a dv i se say i ng it.B ut  I d on't think that i s  w h at you intendeither .I 'm n otcle ar enoughwh at yo u  do mea n  t o m ake an alternativ e (!) sugge s tio n. 

Q: 

P a ndas d f.ite rr o ws( ) method to  acc ess a set  numbe r  of row s :

I am  loopi ngthr ough ada taf ra m e u s in g d f .it errows() .In stead  ofl o o p ingthr ough  allthe rows, I w oul d li k e t o set  thenumb er  of r ows ac cesse deach time. Firs t Iwould lik e t oacc es s the firsttwo  ro ws, the n it wi l l b et h e  t hird to the sixthro w an d then t he rem a in in g . Is the re  awayt o  loop  thr o ug h the ro ws?
He r eis  what I  h ave:
im por t p andas as p d
 imp ort nump y asn p
 df = pd.Dat a Frame(np.rand o m. r a nd n (10,  4) , columns=l ist( ' ABCD ')) d f[' k ey1']  = 0 d f .k e y1.iloc[0:3] = 1
 d f. key1.i loc[3 :7] = 2
 df.k ey1.iloc[7 : ] = 3

 df _gro u pe d  = df.groupby( 'key1 ')

 for g r oup_name , gro up_value  in df_gr o u ped:
    f ig,  ax es= pl t.subplots(ro w s , 1,  s harex=T rue , share y=T rue , f igs iz e= (15, 2 0))
    f or  i ,r  in  grou p _value.i te rro ws (): 
       ro ws, c olum ns  = gro up_valu e .s h a pe
     r =  r [0:co lumn s -1] 
        r.plot (ki n d='b ar ',  fill=F alse, log=Fal se )  

A:

Y ou  co uld cr e a te a col umn according your cond i tion, t hen  do a  gro up by ove r t hat co lum n  and i terate d ove rthe g roupe d da ta. 
d f = pd.Dat a F ram e(np. ra ndom .randn( 10, 4), columns=li s t(' ABCD'))

df[' key 1']= 0df. k ey 1 .il oc [ 1:3 ] = 1
df.key1.ilo c[3:7] = 2 
d f .k ey1.iloc[7 : ]
 make_a consistent_difference. 
Combining them with_"equally": meaning_1_makes no_sense_at all. Meaning_2 makes a_sort of sense, if_you mean something_like_"choosing X or Y, and they are equally attractive choices", but the meaning is_very_unclear, and_I_wouldn't_advise saying it. 
But I_don't think that is what_you intend_either. I'm not clear enough what you do_mean_to make an_alternative (!) suggestion. 

 Q:

Pandas df.iterrows() method to access_a set number of rows:

I am_looping through a_dataframe_using_df.iterrows(). Instead of looping_through all the rows, I would_like to set the number of_rows accessed each time. First I would_like to access the first two_rows, then it will be_the third_to the sixth row and_then the remaining._Is there_a way to_loop through the rows?
Here is what_I have:
 import_pandas as pd
 import numpy as_np
_df = pd.DataFrame(np.random.randn(10,_4),_columns=list('ABCD'))

_df['key1'] =_0
 df.key1.iloc[0:3] =_1
_df.key1.iloc[3:7] =_2
_df.key1.iloc[7:] = 3

 df_grouped = df.groupby('key1')

_for_group_name, group_value in df_grouped:
   _ fig, axes =_plt.subplots(rows,_1, sharex=True, sharey=True, figsize=_(15, 20))
   _ for i,r in group_value.iterrows():
 _ _ _   rows, columns = group_value.shape
    _    r =_r[0:columns-1]
   _ __  r.plot(kind='bar',_fill=False,_log=False) _

A:

You could create a column according your_condition, then_do a group by over that_column and iterated over_the_grouped data.
df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD'))

df['key1']_= 0
df.key1.iloc[1:3] = 1
df.key1.iloc[3:7] = 2
df.key1.iloc[7:]