Pandas: assigning columns with multiple conditions and date thresholds

Question

Edited: I have a financial portfolio in a pandas dataframe df, where the index is the date and I have multiple financial stocks per date. Eg dataframe: ``` Date Stock Weight Percentile Final weight 1/1/2000 Apple 0.010 0.75 0.010 1/1/2000 IBM 0.011 0.4 0 1/1/2000 Google 0.012 0.45 0 1/1/2000 Nokia 0.022 0.81 0.022 2/1/2000 Apple 0.014 0.56 0 2/1/2000 Google 0.015 0.45 0 2/1/2000 Nokia 0.016 0.55 0 3/1/2000 Apple 0.020 0.52 0 3/1/2000 Google 0.030 0.51 0 3/1/2000 Nokia 0.040 0.47 0 ``` I created `Final_weight` by doing assigning values of `Weight` whenever `Percentile` is greater than `0.7` Now I want this to be a bit more sophisticated, I still want `Weight` to be assigned to `Final_weight` when `Percentile is > 0.7`, however after this date (at any point in the future), rather than become 0 when a stocks `Percentile` is not `>0.7`, we would still get a weight as long as the Stocks `Percentile` is above `0.5` (ie holding the position for longer than just one day). Then if the stock go…

Accepted Answer

This solution is more explicit and less pandas-esque, but it involves only a single pass through all rows without creating tons of temporary columns, and is therefore possibly faster. It needs an additional state variable, which I wrapped it into a closure for not having to make a class.

```
def closure():
    cur_weight = {}
    def func(x):
        if x["Percentile"] > 0.7:
            next_weight = x["Weight"]
        elif x["Percentile"] < 0.5 :
            next_weight = 0
        else:
            next_weight = x["Weight"] if cur_weight.get(x["Stock"], 0) > 0 else 0
        cur_weight[x["Stock"]] = next_weight
        return next_weight
    return func

df["FinalWeight"] = df.apply(closure(), axis=1)
```

Pandas: assigning columns with multiple conditions and date thresholds

Solution

Problem