Copy Previous Row Values when Current Value is 0 (Zero)

up vote
2
down vote

favorite

My Dataframe as below:

13664567    74.3    SELL    1581566.4   2378211.6   12:07:59

13665406    74.3    0       0           0           0

13665406    0       0       0           0           0

13665406    74.3    0       0           0           0

13667531    74.3    0       0           0           0

13667531    74.3    0       0           0           0

13672281    74.3    0       0           0           0

13672591    74.3    0       0           0           0

13672591    74.3    BUY     2388742.8   1590276     12:08:01

13681398    74.3    0       0           0           0

13681398    74.3    0       0           0           0

13684573    74.3    0       0           0           0

13685574    74.3    0       0           0           0

13685574    74.3    0       0           0           0

13688544    0       0       0           0           0

13689596    74.3    0       0           0           0

13689596    74.3    0       0           0           0

13705735    74.3    0       0           0           0

13706035    74.3    0       0           0           0

13712130    74.3    SELL    1583219.2   2408620.8   12:08:01

13712130    74.3    0       0           0           0

13715699    74.3    0       0           0           0

13720809    74.3    0       0           0           0

13726310    74.3    0       0           0           0

13726310    74.3    0       0           0           0

13726410    74.3    0       0           0           0

I want to fill the 0 (Zero) values of last 4 Columns with previous row values.

I want dataframe like as below:

13664567    74.3    SELL    1581566.4    2378212    12:07:59

13665406    74.3    SELL    1581566.4    2378212    12:07:59

13665406    0       SELL    1581566.4    2378212    12:07:59

13665406    74.3    SELL    1581566.4    2378212    12:07:59

13667531    74.3    SELL    1581566.4    2378212    12:07:59

13667531    74.3    SELL    1581566.4    2378212    12:07:59

13672281    74.3    SELL    1581566.4    2378212    12:07:59

13672591    74.3    SELL    1581566.4    2378212    12:07:59

13672591    74.3    BUY     2388742.8    1590276    12:08:01

13681398    74.3    BUY     2388742.8    1590276    12:08:01

13681398    74.3    BUY     2388742.8    1590276    12:08:01

13684573    74.3    BUY     2388742.8    1590276    12:08:01

13685574    74.3    BUY     2388742.8    1590276    12:08:01

13685574    74.3    BUY     2388742.8    1590276    12:08:01

13688544    0       BUY     2388742.8    1590276    12:08:01

13689596    74.3    BUY     2388742.8    1590276    12:08:01

13689596    74.3    BUY     2388742.8    1590276    12:08:01

13705735    74.3    BUY     2388742.8    1590276    12:08:01

13706035    74.3    BUY     2388742.8    1590276    12:08:01

13712130    74.3    SELL    1583219.2    2408621    12:08:01

13712130    74.3    SELL    1583219.2    2408621    12:08:01

13715699    74.3    SELL    1583219.2    2408621    12:08:01

13720809    74.3    SELL    1583219.2    2408621    12:08:01

13726310    74.3    SELL    1583219.2    2408621    12:08:01

13726310    74.3    SELL    1583219.2    2408621    12:08:01

13726410    74.3    SELL    1583219.2    2408621    12:08:01

I want to make the changes only to last four columns and copy the previous row values to next row until the New Value is available.

Please help.

edited Nov 4 at 10:21

asked Nov 4 at 10:11

Pravat

698

add a comment |

up vote
2
down vote

favorite

My Dataframe as below:

13664567    74.3    SELL    1581566.4   2378211.6   12:07:59

13665406    74.3    0       0           0           0

13665406    0       0       0           0           0

13665406    74.3    0       0           0           0

13667531    74.3    0       0           0           0

13667531    74.3    0       0           0           0

13672281    74.3    0       0           0           0

13672591    74.3    0       0           0           0

13672591    74.3    BUY     2388742.8   1590276     12:08:01

13681398    74.3    0       0           0           0

13681398    74.3    0       0           0           0

13684573    74.3    0       0           0           0

13685574    74.3    0       0           0           0

13685574    74.3    0       0           0           0

13688544    0       0       0           0           0

13689596    74.3    0       0           0           0

13689596    74.3    0       0           0           0

13705735    74.3    0       0           0           0

13706035    74.3    0       0           0           0

13712130    74.3    SELL    1583219.2   2408620.8   12:08:01

13712130    74.3    0       0           0           0

13715699    74.3    0       0           0           0

13720809    74.3    0       0           0           0

13726310    74.3    0       0           0           0

13726310    74.3    0       0           0           0

13726410    74.3    0       0           0           0

I want to fill the 0 (Zero) values of last 4 Columns with previous row values.

I want dataframe like as below:

13664567    74.3    SELL    1581566.4    2378212    12:07:59

13665406    74.3    SELL    1581566.4    2378212    12:07:59

13665406    0       SELL    1581566.4    2378212    12:07:59

13665406    74.3    SELL    1581566.4    2378212    12:07:59

13667531    74.3    SELL    1581566.4    2378212    12:07:59

13667531    74.3    SELL    1581566.4    2378212    12:07:59

13672281    74.3    SELL    1581566.4    2378212    12:07:59

13672591    74.3    SELL    1581566.4    2378212    12:07:59

13672591    74.3    BUY     2388742.8    1590276    12:08:01

13681398    74.3    BUY     2388742.8    1590276    12:08:01

13681398    74.3    BUY     2388742.8    1590276    12:08:01

13684573    74.3    BUY     2388742.8    1590276    12:08:01

13685574    74.3    BUY     2388742.8    1590276    12:08:01

13685574    74.3    BUY     2388742.8    1590276    12:08:01

13688544    0       BUY     2388742.8    1590276    12:08:01

13689596    74.3    BUY     2388742.8    1590276    12:08:01

13689596    74.3    BUY     2388742.8    1590276    12:08:01

13705735    74.3    BUY     2388742.8    1590276    12:08:01

13706035    74.3    BUY     2388742.8    1590276    12:08:01

13712130    74.3    SELL    1583219.2    2408621    12:08:01

13712130    74.3    SELL    1583219.2    2408621    12:08:01

13715699    74.3    SELL    1583219.2    2408621    12:08:01

13720809    74.3    SELL    1583219.2    2408621    12:08:01

13726310    74.3    SELL    1583219.2    2408621    12:08:01

13726310    74.3    SELL    1583219.2    2408621    12:08:01

13726410    74.3    SELL    1583219.2    2408621    12:08:01

I want to make the changes only to last four columns and copy the previous row values to next row until the New Value is available.

Please help.

edited Nov 4 at 10:21

asked Nov 4 at 10:11

Pravat

698

add a comment |

up vote
2
down vote

favorite

My Dataframe as below:

13664567    74.3    SELL    1581566.4   2378211.6   12:07:59

13665406    74.3    0       0           0           0

13665406    0       0       0           0           0

13665406    74.3    0       0           0           0

13667531    74.3    0       0           0           0

13667531    74.3    0       0           0           0

13672281    74.3    0       0           0           0

13672591    74.3    0       0           0           0

13672591    74.3    BUY     2388742.8   1590276     12:08:01

13681398    74.3    0       0           0           0

13681398    74.3    0       0           0           0

13684573    74.3    0       0           0           0

13685574    74.3    0       0           0           0

13685574    74.3    0       0           0           0

13688544    0       0       0           0           0

13689596    74.3    0       0           0           0

13689596    74.3    0       0           0           0

13705735    74.3    0       0           0           0

13706035    74.3    0       0           0           0

13712130    74.3    SELL    1583219.2   2408620.8   12:08:01

13712130    74.3    0       0           0           0

13715699    74.3    0       0           0           0

13720809    74.3    0       0           0           0

13726310    74.3    0       0           0           0

13726310    74.3    0       0           0           0

13726410    74.3    0       0           0           0

I want to fill the 0 (Zero) values of last 4 Columns with previous row values.

I want dataframe like as below:

13664567    74.3    SELL    1581566.4    2378212    12:07:59

13665406    74.3    SELL    1581566.4    2378212    12:07:59

13665406    0       SELL    1581566.4    2378212    12:07:59

13665406    74.3    SELL    1581566.4    2378212    12:07:59

13667531    74.3    SELL    1581566.4    2378212    12:07:59

13667531    74.3    SELL    1581566.4    2378212    12:07:59

13672281    74.3    SELL    1581566.4    2378212    12:07:59

13672591    74.3    SELL    1581566.4    2378212    12:07:59

13672591    74.3    BUY     2388742.8    1590276    12:08:01

13681398    74.3    BUY     2388742.8    1590276    12:08:01

13681398    74.3    BUY     2388742.8    1590276    12:08:01

13684573    74.3    BUY     2388742.8    1590276    12:08:01

13685574    74.3    BUY     2388742.8    1590276    12:08:01

13685574    74.3    BUY     2388742.8    1590276    12:08:01

13688544    0       BUY     2388742.8    1590276    12:08:01

13689596    74.3    BUY     2388742.8    1590276    12:08:01

13689596    74.3    BUY     2388742.8    1590276    12:08:01

13705735    74.3    BUY     2388742.8    1590276    12:08:01

13706035    74.3    BUY     2388742.8    1590276    12:08:01

13712130    74.3    SELL    1583219.2    2408621    12:08:01

13712130    74.3    SELL    1583219.2    2408621    12:08:01

13715699    74.3    SELL    1583219.2    2408621    12:08:01

13720809    74.3    SELL    1583219.2    2408621    12:08:01

13726310    74.3    SELL    1583219.2    2408621    12:08:01

13726310    74.3    SELL    1583219.2    2408621    12:08:01

13726410    74.3    SELL    1583219.2    2408621    12:08:01

I want to make the changes only to last four columns and copy the previous row values to next row until the New Value is available.

Please help.

edited Nov 4 at 10:21

asked Nov 4 at 10:11

Pravat

698

My Dataframe as below:

13664567    74.3    SELL    1581566.4   2378211.6   12:07:59

13665406    74.3    0       0           0           0

13665406    0       0       0           0           0

13665406    74.3    0       0           0           0

13667531    74.3    0       0           0           0

13667531    74.3    0       0           0           0

13672281    74.3    0       0           0           0

13672591    74.3    0       0           0           0

13672591    74.3    BUY     2388742.8   1590276     12:08:01

13681398    74.3    0       0           0           0

13681398    74.3    0       0           0           0

13684573    74.3    0       0           0           0

13685574    74.3    0       0           0           0

13685574    74.3    0       0           0           0

13688544    0       0       0           0           0

13689596    74.3    0       0           0           0

13689596    74.3    0       0           0           0

13705735    74.3    0       0           0           0

13706035    74.3    0       0           0           0

13712130    74.3    SELL    1583219.2   2408620.8   12:08:01

13712130    74.3    0       0           0           0

13715699    74.3    0       0           0           0

13720809    74.3    0       0           0           0

13726310    74.3    0       0           0           0

13726310    74.3    0       0           0           0

13726410    74.3    0       0           0           0

I want to fill the 0 (Zero) values of last 4 Columns with previous row values.

I want dataframe like as below:

13664567    74.3    SELL    1581566.4    2378212    12:07:59

13665406    74.3    SELL    1581566.4    2378212    12:07:59

13665406    0       SELL    1581566.4    2378212    12:07:59

13665406    74.3    SELL    1581566.4    2378212    12:07:59

13667531    74.3    SELL    1581566.4    2378212    12:07:59

13667531    74.3    SELL    1581566.4    2378212    12:07:59

13672281    74.3    SELL    1581566.4    2378212    12:07:59

13672591    74.3    SELL    1581566.4    2378212    12:07:59

13672591    74.3    BUY     2388742.8    1590276    12:08:01

13681398    74.3    BUY     2388742.8    1590276    12:08:01

13681398    74.3    BUY     2388742.8    1590276    12:08:01

13684573    74.3    BUY     2388742.8    1590276    12:08:01

13685574    74.3    BUY     2388742.8    1590276    12:08:01

13685574    74.3    BUY     2388742.8    1590276    12:08:01

13688544    0       BUY     2388742.8    1590276    12:08:01

13689596    74.3    BUY     2388742.8    1590276    12:08:01

13689596    74.3    BUY     2388742.8    1590276    12:08:01

13705735    74.3    BUY     2388742.8    1590276    12:08:01

13706035    74.3    BUY     2388742.8    1590276    12:08:01

13712130    74.3    SELL    1583219.2    2408621    12:08:01

13712130    74.3    SELL    1583219.2    2408621    12:08:01

13715699    74.3    SELL    1583219.2    2408621    12:08:01

13720809    74.3    SELL    1583219.2    2408621    12:08:01

13726310    74.3    SELL    1583219.2    2408621    12:08:01

13726310    74.3    SELL    1583219.2    2408621    12:08:01

13726410    74.3    SELL    1583219.2    2408621    12:08:01

I want to make the changes only to last four columns and copy the previous row values to next row until the New Value is available.

Please help.

python python-3.x dataframe

edited Nov 4 at 10:21

asked Nov 4 at 10:11

Pravat

698

edited Nov 4 at 10:21

asked Nov 4 at 10:11

Pravat

698

edited Nov 4 at 10:21

asked Nov 4 at 10:11

Pravat

698

asked Nov 4 at 10:11

Pravat

698

asked Nov 4 at 10:11

Pravat

698

add a comment |

2 Answers
2

active

oldest

votes

up vote
3
down vote

accepted

Use update with select last 4 columns by iloc, replace by missing values and forward fill last previous values:

df.update(df.iloc[:, -4:].replace([0,'0'], np.nan).ffill())

Or use mask for replace 0:

df.update(df.iloc[:, -4:].mask(lambda x: x.isin([0, '0'])).ffill())

#if no string 0

#df.update(df.iloc[:, -4:].mask(lambda x: x == 0)).ffill())



print (df)

           a     b     c          d          e         f

0   13664567  74.3  SELL  1581566.4  2378211.6  12:07:59

1   13665406  74.3  SELL  1581566.4  2378211.6  12:07:59

2   13665406  74.3  SELL  1581566.4  2378211.6  12:07:59

3   13665406  74.3  SELL  1581566.4  2378211.6  12:07:59

4   13667531  74.3  SELL  1581566.4  2378211.6  12:07:59

5   13667531  74.3  SELL  1581566.4  2378211.6  12:07:59

6   13672281  74.3  SELL  1581566.4  2378211.6  12:07:59

7   13672591  74.3  SELL  1581566.4  2378211.6  12:07:59

8   13672591  74.3   BUY  2388742.8  1590276.0  12:08:01

9   13681398  74.3   BUY  2388742.8  1590276.0  12:08:01

10  13681398  74.3   BUY  2388742.8  1590276.0  12:08:01

11  13684573  74.3   BUY  2388742.8  1590276.0  12:08:01

12  13685574  74.3   BUY  2388742.8  1590276.0  12:08:01

13  13685574  74.3   BUY  2388742.8  1590276.0  12:08:01

14  13688544  74.3   BUY  2388742.8  1590276.0  12:08:01

15  13689596  74.3   BUY  2388742.8  1590276.0  12:08:01

16  13689596  74.3   BUY  2388742.8  1590276.0  12:08:01

17  13705735  74.3   BUY  2388742.8  1590276.0  12:08:01

18  13706035  74.3   BUY  2388742.8  1590276.0  12:08:01

19  13712130  74.3  SELL  1583219.2  2408620.8  12:08:01

20  13712130  74.3  SELL  1583219.2  2408620.8  12:08:01

21  13715699  74.3  SELL  1583219.2  2408620.8  12:08:01

22  13720809  74.3  SELL  1583219.2  2408620.8  12:08:01

23  13726310  74.3  SELL  1583219.2  2408620.8  12:08:01

24  13726310  74.3  SELL  1583219.2  2408620.8  12:08:01

25  13726410  74.3  SELL  1583219.2  2408620.8  12:08:01

edited Nov 4 at 10:26

answered Nov 4 at 10:12

jezrael

303k20233309

1

Sir, Is it possible to select particular columns as I want to apply to last 4 columns only.
– Pravat
Nov 4 at 10:16

1

@Pravat - Super, glad can help! :)
– jezrael
Nov 4 at 10:32

add a comment |

up vote
1
down vote

You can use mask + ffill:

values = df.iloc[:, -4:]

df.iloc[:, -4:] = values.mask(values.isin([0, '0'])).ffill()

answered Nov 4 at 10:28

jpp

78.8k184592

add a comment |

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2 Answers
2

active

oldest

votes

2 Answers
2

active

oldest

votes

up vote
3
down vote

accepted

Use update with select last 4 columns by iloc, replace by missing values and forward fill last previous values:

df.update(df.iloc[:, -4:].replace([0,'0'], np.nan).ffill())

Or use mask for replace 0:

df.update(df.iloc[:, -4:].mask(lambda x: x.isin([0, '0'])).ffill())

#if no string 0

#df.update(df.iloc[:, -4:].mask(lambda x: x == 0)).ffill())



print (df)

           a     b     c          d          e         f

0   13664567  74.3  SELL  1581566.4  2378211.6  12:07:59

1   13665406  74.3  SELL  1581566.4  2378211.6  12:07:59

2   13665406  74.3  SELL  1581566.4  2378211.6  12:07:59

3   13665406  74.3  SELL  1581566.4  2378211.6  12:07:59

4   13667531  74.3  SELL  1581566.4  2378211.6  12:07:59

5   13667531  74.3  SELL  1581566.4  2378211.6  12:07:59

6   13672281  74.3  SELL  1581566.4  2378211.6  12:07:59

7   13672591  74.3  SELL  1581566.4  2378211.6  12:07:59

8   13672591  74.3   BUY  2388742.8  1590276.0  12:08:01

9   13681398  74.3   BUY  2388742.8  1590276.0  12:08:01

10  13681398  74.3   BUY  2388742.8  1590276.0  12:08:01

11  13684573  74.3   BUY  2388742.8  1590276.0  12:08:01

12  13685574  74.3   BUY  2388742.8  1590276.0  12:08:01

13  13685574  74.3   BUY  2388742.8  1590276.0  12:08:01

14  13688544  74.3   BUY  2388742.8  1590276.0  12:08:01

15  13689596  74.3   BUY  2388742.8  1590276.0  12:08:01

16  13689596  74.3   BUY  2388742.8  1590276.0  12:08:01

17  13705735  74.3   BUY  2388742.8  1590276.0  12:08:01

18  13706035  74.3   BUY  2388742.8  1590276.0  12:08:01

19  13712130  74.3  SELL  1583219.2  2408620.8  12:08:01

20  13712130  74.3  SELL  1583219.2  2408620.8  12:08:01

21  13715699  74.3  SELL  1583219.2  2408620.8  12:08:01

22  13720809  74.3  SELL  1583219.2  2408620.8  12:08:01

23  13726310  74.3  SELL  1583219.2  2408620.8  12:08:01

24  13726310  74.3  SELL  1583219.2  2408620.8  12:08:01

25  13726410  74.3  SELL  1583219.2  2408620.8  12:08:01

edited Nov 4 at 10:26

answered Nov 4 at 10:12

jezrael

303k20233309

1

Sir, Is it possible to select particular columns as I want to apply to last 4 columns only.
– Pravat
Nov 4 at 10:16

1

@Pravat - Super, glad can help! :)
– jezrael
Nov 4 at 10:32

add a comment |

up vote
3
down vote

accepted

Use update with select last 4 columns by iloc, replace by missing values and forward fill last previous values:

df.update(df.iloc[:, -4:].replace([0,'0'], np.nan).ffill())

Or use mask for replace 0:

df.update(df.iloc[:, -4:].mask(lambda x: x.isin([0, '0'])).ffill())

#if no string 0

#df.update(df.iloc[:, -4:].mask(lambda x: x == 0)).ffill())



print (df)

           a     b     c          d          e         f

0   13664567  74.3  SELL  1581566.4  2378211.6  12:07:59

1   13665406  74.3  SELL  1581566.4  2378211.6  12:07:59

2   13665406  74.3  SELL  1581566.4  2378211.6  12:07:59

3   13665406  74.3  SELL  1581566.4  2378211.6  12:07:59

4   13667531  74.3  SELL  1581566.4  2378211.6  12:07:59

5   13667531  74.3  SELL  1581566.4  2378211.6  12:07:59

6   13672281  74.3  SELL  1581566.4  2378211.6  12:07:59

7   13672591  74.3  SELL  1581566.4  2378211.6  12:07:59

8   13672591  74.3   BUY  2388742.8  1590276.0  12:08:01

9   13681398  74.3   BUY  2388742.8  1590276.0  12:08:01

10  13681398  74.3   BUY  2388742.8  1590276.0  12:08:01

11  13684573  74.3   BUY  2388742.8  1590276.0  12:08:01

12  13685574  74.3   BUY  2388742.8  1590276.0  12:08:01

13  13685574  74.3   BUY  2388742.8  1590276.0  12:08:01

14  13688544  74.3   BUY  2388742.8  1590276.0  12:08:01

15  13689596  74.3   BUY  2388742.8  1590276.0  12:08:01

16  13689596  74.3   BUY  2388742.8  1590276.0  12:08:01

17  13705735  74.3   BUY  2388742.8  1590276.0  12:08:01

18  13706035  74.3   BUY  2388742.8  1590276.0  12:08:01

19  13712130  74.3  SELL  1583219.2  2408620.8  12:08:01

20  13712130  74.3  SELL  1583219.2  2408620.8  12:08:01

21  13715699  74.3  SELL  1583219.2  2408620.8  12:08:01

22  13720809  74.3  SELL  1583219.2  2408620.8  12:08:01

23  13726310  74.3  SELL  1583219.2  2408620.8  12:08:01

24  13726310  74.3  SELL  1583219.2  2408620.8  12:08:01

25  13726410  74.3  SELL  1583219.2  2408620.8  12:08:01

edited Nov 4 at 10:26

answered Nov 4 at 10:12

jezrael

303k20233309

1

Sir, Is it possible to select particular columns as I want to apply to last 4 columns only.
– Pravat
Nov 4 at 10:16

1

@Pravat - Super, glad can help! :)
– jezrael
Nov 4 at 10:32

add a comment |

up vote
3
down vote

accepted

Use update with select last 4 columns by iloc, replace by missing values and forward fill last previous values:

df.update(df.iloc[:, -4:].replace([0,'0'], np.nan).ffill())

Or use mask for replace 0:

df.update(df.iloc[:, -4:].mask(lambda x: x.isin([0, '0'])).ffill())

#if no string 0

#df.update(df.iloc[:, -4:].mask(lambda x: x == 0)).ffill())



print (df)

           a     b     c          d          e         f

0   13664567  74.3  SELL  1581566.4  2378211.6  12:07:59

1   13665406  74.3  SELL  1581566.4  2378211.6  12:07:59

2   13665406  74.3  SELL  1581566.4  2378211.6  12:07:59

3   13665406  74.3  SELL  1581566.4  2378211.6  12:07:59

4   13667531  74.3  SELL  1581566.4  2378211.6  12:07:59

5   13667531  74.3  SELL  1581566.4  2378211.6  12:07:59

6   13672281  74.3  SELL  1581566.4  2378211.6  12:07:59

7   13672591  74.3  SELL  1581566.4  2378211.6  12:07:59

8   13672591  74.3   BUY  2388742.8  1590276.0  12:08:01

9   13681398  74.3   BUY  2388742.8  1590276.0  12:08:01

10  13681398  74.3   BUY  2388742.8  1590276.0  12:08:01

11  13684573  74.3   BUY  2388742.8  1590276.0  12:08:01

12  13685574  74.3   BUY  2388742.8  1590276.0  12:08:01

13  13685574  74.3   BUY  2388742.8  1590276.0  12:08:01

14  13688544  74.3   BUY  2388742.8  1590276.0  12:08:01

15  13689596  74.3   BUY  2388742.8  1590276.0  12:08:01

16  13689596  74.3   BUY  2388742.8  1590276.0  12:08:01

17  13705735  74.3   BUY  2388742.8  1590276.0  12:08:01

18  13706035  74.3   BUY  2388742.8  1590276.0  12:08:01

19  13712130  74.3  SELL  1583219.2  2408620.8  12:08:01

20  13712130  74.3  SELL  1583219.2  2408620.8  12:08:01

21  13715699  74.3  SELL  1583219.2  2408620.8  12:08:01

22  13720809  74.3  SELL  1583219.2  2408620.8  12:08:01

23  13726310  74.3  SELL  1583219.2  2408620.8  12:08:01

24  13726310  74.3  SELL  1583219.2  2408620.8  12:08:01

25  13726410  74.3  SELL  1583219.2  2408620.8  12:08:01

edited Nov 4 at 10:26

answered Nov 4 at 10:12

jezrael

303k20233309

Use update with select last 4 columns by iloc, replace by missing values and forward fill last previous values:

df.update(df.iloc[:, -4:].replace([0,'0'], np.nan).ffill())

Or use mask for replace 0:

df.update(df.iloc[:, -4:].mask(lambda x: x.isin([0, '0'])).ffill())

#if no string 0

#df.update(df.iloc[:, -4:].mask(lambda x: x == 0)).ffill())



print (df)

           a     b     c          d          e         f

0   13664567  74.3  SELL  1581566.4  2378211.6  12:07:59

1   13665406  74.3  SELL  1581566.4  2378211.6  12:07:59

2   13665406  74.3  SELL  1581566.4  2378211.6  12:07:59

3   13665406  74.3  SELL  1581566.4  2378211.6  12:07:59

4   13667531  74.3  SELL  1581566.4  2378211.6  12:07:59

5   13667531  74.3  SELL  1581566.4  2378211.6  12:07:59

6   13672281  74.3  SELL  1581566.4  2378211.6  12:07:59

7   13672591  74.3  SELL  1581566.4  2378211.6  12:07:59

8   13672591  74.3   BUY  2388742.8  1590276.0  12:08:01

9   13681398  74.3   BUY  2388742.8  1590276.0  12:08:01

10  13681398  74.3   BUY  2388742.8  1590276.0  12:08:01

11  13684573  74.3   BUY  2388742.8  1590276.0  12:08:01

12  13685574  74.3   BUY  2388742.8  1590276.0  12:08:01

13  13685574  74.3   BUY  2388742.8  1590276.0  12:08:01

14  13688544  74.3   BUY  2388742.8  1590276.0  12:08:01

15  13689596  74.3   BUY  2388742.8  1590276.0  12:08:01

16  13689596  74.3   BUY  2388742.8  1590276.0  12:08:01

17  13705735  74.3   BUY  2388742.8  1590276.0  12:08:01

18  13706035  74.3   BUY  2388742.8  1590276.0  12:08:01

19  13712130  74.3  SELL  1583219.2  2408620.8  12:08:01

20  13712130  74.3  SELL  1583219.2  2408620.8  12:08:01

21  13715699  74.3  SELL  1583219.2  2408620.8  12:08:01

22  13720809  74.3  SELL  1583219.2  2408620.8  12:08:01

23  13726310  74.3  SELL  1583219.2  2408620.8  12:08:01

24  13726310  74.3  SELL  1583219.2  2408620.8  12:08:01

25  13726410  74.3  SELL  1583219.2  2408620.8  12:08:01

edited Nov 4 at 10:26

answered Nov 4 at 10:12

jezrael

303k20233309

edited Nov 4 at 10:26

answered Nov 4 at 10:12

jezrael

303k20233309

answered Nov 4 at 10:12

jezrael

303k20233309

answered Nov 4 at 10:12

jezrael

303k20233309

1

Sir, Is it possible to select particular columns as I want to apply to last 4 columns only.
– Pravat
Nov 4 at 10:16

1

@Pravat - Super, glad can help! :)
– jezrael
Nov 4 at 10:32

add a comment |

1

Sir, Is it possible to select particular columns as I want to apply to last 4 columns only.
– Pravat
Nov 4 at 10:16

1

@Pravat - Super, glad can help! :)
– jezrael
Nov 4 at 10:32

Sir, Is it possible to select particular columns as I want to apply to last 4 columns only.
– Pravat
Nov 4 at 10:16

@Pravat - Super, glad can help! :)
– jezrael
Nov 4 at 10:32

add a comment |

up vote
1
down vote

You can use mask + ffill:

values = df.iloc[:, -4:]

df.iloc[:, -4:] = values.mask(values.isin([0, '0'])).ffill()

answered Nov 4 at 10:28

jpp

78.8k184592

add a comment |

up vote
1
down vote

You can use mask + ffill:

values = df.iloc[:, -4:]

df.iloc[:, -4:] = values.mask(values.isin([0, '0'])).ffill()

answered Nov 4 at 10:28

jpp

78.8k184592

add a comment |

up vote
1
down vote

You can use mask + ffill:

values = df.iloc[:, -4:]

df.iloc[:, -4:] = values.mask(values.isin([0, '0'])).ffill()

answered Nov 4 at 10:28

jpp

78.8k184592

You can use mask + ffill:

values = df.iloc[:, -4:]

df.iloc[:, -4:] = values.mask(values.isin([0, '0'])).ffill()

answered Nov 4 at 10:28

jpp

78.8k184592

answered Nov 4 at 10:28

jpp

78.8k184592

answered Nov 4 at 10:28

jpp

78.8k184592

answered Nov 4 at 10:28

jpp

78.8k184592

add a comment |

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