Set value of first item in slice in python pandas

I think you can use idxmax for get index of first True value and then set by loc:

np.random.seed(1)df = pd.DataFrame(np.random.randint(4, size=(5,1)))print (df)   00  11  32  03  04  3print ((df[0] == 0).idxmax())2df.loc[(df[0] == 0).idxmax(), 0] = 100print (df)     00    11    32  1003    04    3

df.loc[(df[0] == 3).idxmax(), 0] = 200print (df)     00    11  2002    03    04    3

EDIT:

Solution with not unique index:

np.random.seed(1)df = pd.DataFrame(np.random.randint(4, size=(5,1)), index=[1,2,2,3,4])print (df)   01  12  32  03  04  3df = df.reset_index()df.loc[(df[0] == 3).idxmax(), 0] = 200df = df.set_index('index')df.index.name = Noneprint (df)     01    12  2002    03    04    3

EDIT1:

Solution with MultiIndex:

np.random.seed(1)df = pd.DataFrame(np.random.randint(4, size=(5,1)), index=[1,2,2,3,4])print (df)   01  12  32  03  04  3df.index = [np.arange(len(df.index)), df.index]print (df)     00 1  11 2  32 2  03 3  04 4  3df.loc[(df[0] == 3).idxmax(), 0] = 200df = df.reset_index(level=0, drop=True)print (df)     01    12  2002    03    04    3

EDIT2:

Solution with double cumsum:

np.random.seed(1)df = pd.DataFrame([4,0,4,7,4], index=[1,2,2,3,4])print (df)   01  42  02  43  74  4mask = (df[0] == 0).cumsum().cumsum()print (mask)1    02    12    23    34    4Name: 0, dtype: int32df.loc[mask == 1, 0] = 200print (df)     01    42  2002    43    74    4

Consider the dataframe df

df = pd.DataFrame(dict(A=[1, 2, 3, 4, 5]))print(df)   A0  11  22  33  44  5

Create some arbitrary slice slc

slc = df[df.A > 2]print(slc)   A2  33  44  5

Access the first row of slc within df by using index[0] and loc

df.loc[slc.index[0]] = 0print(df)   A0  11  22  03  44  5

import pandas as pdimport numpy as npdf = pd.DataFrame(np.random.rand(6,1),index=[1,2,2,3,3,3])df[1] = 0df.columns=['a','b']df['b'][df['a']>=0.5]=1df=df.sort(['b','a'],ascending=[0,1])df.loc[df[df['b']==0].index.tolist()[0],'a']=0

In this method extra copy of the dataframe is not created but an extra column is introduced which can be dropped after processing. To choose any index instead o the first one you can change the last line as follows

df.loc[df[df['b']==0].index.tolist()[n],'a']=0

to change any nth item in a slice

df

          a  1  0.111089  2  0.255633  2  0.332682  3  0.434527  3  0.730548  3  0.844724  

df after slicing and labelling them

          a  b1  0.111089  02  0.255633  02  0.332682  03  0.434527  03  0.730548  13  0.844724  1

After changing value of first item in slice (labelled as 0) to 0

          a  b3  0.730548  13  0.844724  11  0.000000  02  0.255633  02  0.332682  03  0.434527  0