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.
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
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