Print the first value of a dataframe based on condition, then iterate to the next sequence - python-3.x

Question

I'm looking to perform data analysis on 100-years of climatological data for select U.S. locations (8 in particular), for each day spanning the 100-years. I have a pandas dataFrame set up with columns for Max temperature, Min temperature, Avg temperature, Snowfall, Precip Total, and then Day, Year, and Month values (then, I have an index also based on a date-time value). Right now, I want to set up a for loop to print the first Maximum temperature of 90 degrees F or greater from each year, but ONLY the first. Eventually, I want to narrow this down to each of my 8 locations, but first I just want to get the for loop to work.
Experimented with various iterations of a for loop.
for year in range(len(climate['Year'])):
if (climate['Max'][year] >=90).all():
print (climate.index[year])
break
Unsurprisingly, the output of the loop I provided prints the first 90 degree day period (from the year 1919, the beginning of my data frame) and breaks.
for year in range(len(climate['Year'])):
if (climate['Max'][year] >=90).all():
print (climate.index[year])
break
1919-06-12 00:00:00
That's fine. If I take out the break statement, all of the 90 degree days print, including multiple in the same year. I just want the first value from each year to print. Do I need to set up a second for loop to increment through the year? If I explicitly state the year, ala below, while trying to loop through a counter, the loop still begins in 1919 and eventually reaches an out of bounds index. I know this logic is incorrect.
count = 1919
while count < 2019:
for year in range(len(climate['Year'])):
if (climate[climate['Year']==count]['Max'][year] >=90).all():
print (climate.index[year])
count = count+1
Any input is sincerely appreciated.

Answer 1

You can achieve this without having a second for loop. Assuming the climate dataframe is ordered chronologically, this should do what you want:
current_year = None
for i in range(climate.shape[0]):
if climate['Max'][i] >= 90 and climate['Year'][i] != current_year:
print(climate.index[i])
current_year = climate['Year'][i]
Notice that we're using the current_year variable to keep track of the latest year that we've already printed the result for. Then, in the if check, we're checking if we've already printed a result for the year of the current row in the loop.
That's one way to do it, but I would suggest taking a look at pandas.DataFrame.groupby because I think it fits your use case well. You could get a dataframe that contains the first >=90 max days per year with the following (again assuming climate is ordered chronologically):
climate[climate.Max >= 90].groupby('Year').first()
This just filters the dataframe to only contain the >=90 max days, groups rows from the same year together, and retains only the first row from each group. If you had an additional column Location, you could extend this to get the same except per location per year:
climate[climate.Max >= 90].groupby(['Location', 'Year']).first()