I have a pandas dataframe in Python that contains a list of different stocks by ticker symbol, and for each one, it also records current price and a low and high price alert threshold value.
Below shows a sample of the dataframe:
| TICKER | CURRENT PRICE($) | ALERT PRICE HIGH ($) | ALERT PRICE LOW ($) |
|---|---|---|---|
| AMZN | 114 | 180 | 105 |
| APPL | 140 | 110 | 190 |
| MSFT | 235 | 340 | 210 |
NOTE: I’ve excluded pandas Index column above as this integer can change for the same TICKER stock, depending on the order they randomly get returned in the API request.
For each row in the dataframe, I want to test if the [‘CURRENT PRICE ($)’] is above the [‘ALERT PRICE HIGH ($)’] or below the [‘ALERT PRICE LOW (£)’].
Where the condition if true, I want to pass the ‘TICKER’ ID to a print statement that notifies of the price alert being reached.
In pseudo-code it would be along the lines of the below:
for each row in df:
if CURRENT PRICE($) > ALERT PRICE HIGH ($)
print('High Price Alert for' + TICKER)
if CURRENT PRICE($) < ALERT PRICE LOW ($)
print('Low Price Alert for:' + TICKER)
Being fairly new to dataframes, I’m not sure how to translate this into code that will achieve my goal, or if looping over a dataframe in this way is even the best method for this. Hoping someone can help please.
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Answer
You can loop over a dataframe but you should use vectorized code whenever possible. In your case, I doubt if either method makes a difference.
Here’s how I would do it:
# high_ and low_alert are series to True/False values
high_alert = df["CURRENT PRICE($)"] > df["ALERT PRICE HIGH ($)"]
low_alert = df["CURRENT PRICE($)"] < df["ALERT PRICE LOW ($)"]
# df.loc[high_alert, "TICKER"] get rows where `high_alert == True`
# and extract the TICKER column.
# Likewise for df.loc[low_alert, "TICKER"]
print("High Prices Alert for: " + " ".join(df.loc[high_alert, "TICKER"]))
print("Low Prices Alert for: " + " ".join(df.loc[low_alert, "TICKER"]))