I have the following dataframe:
TIME PREC ET PET YIELD date year intensity
0 1 1.21 0.02 0.02 0.00 1991-01-01 1991 1
1 2 0.00 0.03 0.04 0.00 1991-01-02 1991 1
2 3 0.00 0.03 0.05 0.00 1991-01-03 1991 1
3 4 0.00 0.04 0.05 0.00 1991-01-04 1991 1
4 5 0.00 0.05 0.07 0.00 1991-01-05 1991 1
5 6 0.00 0.03 0.05 0.00 1991-01-06 1991 1
6 7 0.00 0.02 0.04 0.00 1991-01-07 1991 1
7 8 1.14 0.03 0.04 0.00 1991-01-08 1991 1
8 9 0.10 0.02 0.03 0.00 1991-01-09 1991 1
9 10 0.00 0.03 0.04 0.00 1991-01-10 1991 1
10 11 0.10 0.05 0.11 0.00 1991-01-11 1991 1
11 12 0.00 0.06 0.15 0.00 1991-01-12 1991 1
12 13 2.30 0.14 0.44 0.00 1991-01-13 1991 1
13 14 0.17 0.09 0.29 0.00 1991-01-14 1991 1
14 15 0.00 0.13 0.35 0.00 1991-01-15 1991 1
15 16 0.00 0.14 0.39 0.00 1991-01-16 1991 1
16 17 0.00 0.10 0.31 0.00 1991-01-17 1991 1
17 18 0.00 0.15 0.51 0.00 1991-01-18 1991 1
18 19 0.00 0.22 0.58 0.00 1991-01-19 1991 1
19 20 0.10 0.04 0.09 0.00 1991-01-20 1991 1
20 21 0.00 0.04 0.06 0.00 1991-01-21 1991 1
21 22 0.27 0.13 0.43 0.00 1991-01-22 1991 1
22 23 0.00 0.10 0.25 0.00 1991-01-23 1991 1
23 24 0.00 0.03 0.04 0.00 1991-01-24 1991 1
24 25 0.00 0.04 0.05 0.00 1991-01-25 1991 1
25 26 0.43 0.04 0.15 0.00 1991-01-26 1991 1
26 27 0.17 0.06 0.23 0.00 1991-01-27 1991 1
27 28 0.50 0.02 0.04 0.00 1991-01-28 1991 1
28 29 0.00 0.03 0.04 0.00 1991-01-29 1991 1
29 30 0.00 0.04 0.08 0.00 1991-01-30 1991 1
... ... ... ... ... ... ... ...
7275 336 0.00 0.04 0.06 0.03 2010-12-02 2010 1
7276 337 0.30 0.05 0.08 0.02 2010-12-03 2010 1
7277 338 1.62 0.08 0.12 0.02 2010-12-04 2010 1
7278 339 0.00 0.10 0.15 0.02 2010-12-05 2010 1
7279 340 0.00 0.09 0.15 0.02 2010-12-06 2010 1
7280 341 0.00 0.04 0.06 0.02 2010-12-07 2010 1
7281 342 0.00 0.04 0.06 0.02 2010-12-08 2010 1
7282 343 0.00 0.16 0.25 0.02 2010-12-09 2010 1
7283 344 0.00 0.22 0.35 0.02 2010-12-10 2010 1
7284 345 0.04 0.04 0.06 0.02 2010-12-11 2010 1
7285 346 0.01 0.02 0.03 0.02 2010-12-12 2010 1
7286 347 0.04 0.02 0.03 0.02 2010-12-13 2010 1
7287 348 0.00 0.05 0.08 0.02 2010-12-14 2010 1
7288 349 0.37 0.08 0.13 0.02 2010-12-15 2010 1
7289 350 13.19 0.08 0.13 0.02 2010-12-16 2010 1
7290 351 1.33 0.06 0.10 0.02 2010-12-17 2010 1
7291 352 0.00 0.08 0.12 0.02 2010-12-18 2010 1
7292 353 0.01 0.05 0.08 0.02 2010-12-19 2010 1
7293 354 0.00 0.03 0.04 0.02 2010-12-20 2010 1
7294 355 9.70 0.07 0.12 0.02 2010-12-21 2010 1
7295 356 0.00 0.06 0.11 0.02 2010-12-22 2010 1
7296 357 0.00 0.07 0.12 0.02 2010-12-23 2010 1
7297 358 0.13 0.08 0.13 0.02 2010-12-24 2010 1
7298 359 0.10 0.09 0.15 0.02 2010-12-25 2010 1
7299 360 0.00 0.07 0.12 0.02 2010-12-26 2010 1
7300 361 0.00 0.08 0.14 0.02 2010-12-27 2010 1
7301 362 0.61 0.18 0.32 0.02 2010-12-28 2010 1
7302 363 0.00 0.15 0.26 0.02 2010-12-29 2010 1
7303 364 3.95 0.10 0.18 0.02 2010-12-30 2010 1
7304 365 0.10 0.10 0.18 0.01 2010-12-31 2010 1
I would like to categorize the data based on the values in PREC and year columns. Here's the template of the code that I have:
for y in range(1991,2011):
for i in df.loc[df.year == y, 'PREC']:
if i <= np.percentile(df.loc[df.year == y, 'PREC'], 10):
df.loc[df.PREC.isin([i]), 'intensity'] = 'light'
However, this code categorize intensity in the whole dataframe, but I need to categorize PREC for each year. So, how to do this?
So, basically, I should have used & to combine two conditions:
for y in range(1991,2011):
for i in df.loc[df.year == y, 'PREC']:
if i <= np.percentile(df.loc[df.year == y, 'PREC'], 10):
df.loc[(df['year'] == y) & (df['PREC'] == i), 'intensity'] = 'light'