How to do operations with different columns based on "first" condition using pandas Dataframe?

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I have this dataframe:

df

I need to perform some addictions and substractions based on conditions.

If we first have a non-NaN value in the tp, then do this: tp-entry

if, instead, the first non-NaN value is contained inside "sl" column, then do this sl-entry.

We will store these values inside a new column called "pl", so the final datafram will look like this:

df wanted

I tried (with no success) this (reproducible code):

tbl = {"date" :["2022-02-27", "2022-02-27", "2022-02-27", "2022-02-27", "2022-02-28", 
                  "2022-02-28","2022-02-28", "2022-02-28"],
      "entry" : ["NaN", "NaN", 1.2, "NaN", "NaN", 1.3, "NaN", "NaN"],
      "tp" : ["NaN", "NaN", "NaN", 1.4, "NaN", "NaN", "NaN", "NaN"],
      "sl" : ["NaN", "NaN", "NaN", "NaN", "NaN", "NaN", "NaN", 1.15]}

df = pd.DataFrame(tbl)


df.sort_values(by = "date", inplace=True)



df['pl'] = np.where(df["entry"])  #i don't know how to continue...

Any ideas? Do you know better way?

Edit

In the photo entry-sl is wrong, I need sl-entry

pandas dataframe python
1 Answers

IIUC, you can combine "tp" and "pl", then bfill per group. Also bfill "entry" per group. Then assign the difference only on the first (i.e. non duplicate) row per date:

group = df['date']
s1 = df['tp'].fillna(df['sl']).groupby(group).bfill()
s2 = df['entry'].groupby(group).bfill()

df.loc[~group.duplicated(), 'pl'] = s1-s2

NB. if you really have dates with times, use instead as group:

group = pd.to_datetime(df['date']).dt.date

output:

         date  entry   tp    sl    pl
0  2022-02-27    NaN  NaN   NaN  0.20
1  2022-02-27    NaN  NaN   NaN   NaN
2  2022-02-27    1.2  NaN   NaN   NaN
3  2022-02-27    NaN  1.4   NaN   NaN
4  2022-02-28    NaN  NaN   NaN -0.15
5  2022-02-28    1.3  NaN   NaN   NaN
6  2022-02-28    NaN  NaN   NaN   NaN
7  2022-02-28    NaN  NaN  1.15   NaN
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