In [1]:

## take a look at ages

In [2]:

import pandas as pd

In [3]:

def check_nan(ds):
    for j, index in enumerate(ds.index):
        if ds.transport[j] != ds.transport[j]:
            print (index)

In [4]:

Vic_to_PR_2015 = pd.read_csv('Vic_to_PR_2015_v3.csv', index_col=0, parse_dates=True)
print ('2015', Vic_to_PR_2015.transport.mean()/1000.); check_nan(Vic_to_PR_2015)
Vic_to_PR_2016 = pd.read_csv('Vic_to_PR_2016_v3.csv', index_col=0, parse_dates=True)
print ('2016', Vic_to_PR_2016.transport.mean()/1000.); check_nan(Vic_to_PR_2016)
Vic_to_PR_2017 = pd.read_csv('Vic_to_PR_2017_v3.csv', index_col=0, parse_dates=True)
print ('2017', Vic_to_PR_2017.transport.mean()/1000.); check_nan(Vic_to_PR_2017)
Vic_to_PR_2018 = pd.read_csv('Vic_to_PR_2018_v3.csv', index_col=0, parse_dates=True)
print ('2018', Vic_to_PR_2018.transport.mean()/1000.); check_nan(Vic_to_PR_2018)

2015 23.260960949873397
2016 23.975001880932254
2017 24.207005729799395
2018 23.2257903359523

/tmp/ipykernel_3252/3505476923.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  if ds.transport[j] != ds.transport[j]:
/tmp/ipykernel_3252/3505476923.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  if ds.transport[j] != ds.transport[j]:
/tmp/ipykernel_3252/3505476923.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  if ds.transport[j] != ds.transport[j]:
/tmp/ipykernel_3252/3505476923.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  if ds.transport[j] != ds.transport[j]:

In [5]:

PR_from_Vic_2015 = pd.read_csv('PR_from_Vic_2015_v3.csv', index_col=0, parse_dates=True)
print ('2015', PR_from_Vic_2015.transport.mean()/1000.); check_nan(PR_from_Vic_2015)
PR_from_Vic_2016 = pd.read_csv('PR_from_Vic_2016_v3.csv', index_col=0, parse_dates=True)
print ('2016', PR_from_Vic_2016.transport.mean()/1000.); check_nan(PR_from_Vic_2016)
PR_from_Vic_2017 = pd.read_csv('PR_from_Vic_2017_v3.csv', index_col=0, parse_dates=True)
print ('2017', PR_from_Vic_2017.transport.mean()/1000.); check_nan(PR_from_Vic_2017)
PR_from_Vic_2018 = pd.read_csv('PR_from_Vic_2018_v3.csv', index_col=0, parse_dates=True)
print ('2018', PR_from_Vic_2018.transport.mean()/1000.); check_nan(PR_from_Vic_2018)

2015 23.16950381813652

/tmp/ipykernel_3252/3505476923.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  if ds.transport[j] != ds.transport[j]:

2016 23.663626086798104
2017 24.14207376302193
2018 23.6275020323928

/tmp/ipykernel_3252/3505476923.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  if ds.transport[j] != ds.transport[j]:
/tmp/ipykernel_3252/3505476923.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  if ds.transport[j] != ds.transport[j]:
/tmp/ipykernel_3252/3505476923.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  if ds.transport[j] != ds.transport[j]:

In [6]:

PR_to_Vic_2015 = pd.read_csv('PR_to_Vic_2015_v3.csv', index_col=0, parse_dates=True)
print ('2015', PR_to_Vic_2015.transport.mean()/1000.); check_nan(PR_to_Vic_2015)
PR_to_Vic_2016 = pd.read_csv('PR_to_Vic_2016_v3.csv', index_col=0, parse_dates=True)
print ('2016', PR_to_Vic_2016.transport.mean()/1000.); check_nan(PR_to_Vic_2016)
PR_to_Vic_2017 = pd.read_csv('PR_to_Vic_2017_v3.csv', index_col=0, parse_dates=True)
print ('2017', PR_to_Vic_2017.transport.mean()/1000.); check_nan(PR_to_Vic_2017)
PR_to_Vic_2018 = pd.read_csv('PR_to_Vic_2018_v3.csv', index_col=0, parse_dates=True)
print ('2018', PR_to_Vic_2018.transport.mean()/1000.); check_nan(PR_to_Vic_2018)

2015 23.923387363466393
2016 24.191890364144417
2017 23.97470431375392
2018 23.17832074037491

/tmp/ipykernel_3252/3505476923.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  if ds.transport[j] != ds.transport[j]:
/tmp/ipykernel_3252/3505476923.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  if ds.transport[j] != ds.transport[j]:
/tmp/ipykernel_3252/3505476923.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  if ds.transport[j] != ds.transport[j]:
/tmp/ipykernel_3252/3505476923.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  if ds.transport[j] != ds.transport[j]:

In [7]:

Vic_from_PR_2015 = pd.read_csv('Vic_from_PR_2015_v3.csv', index_col=0, parse_dates=True)
print ('2015', Vic_from_PR_2015.transport.mean()/1000.); check_nan(Vic_from_PR_2015)
Vic_from_PR_2016 = pd.read_csv('Vic_from_PR_2016_v3.csv', index_col=0, parse_dates=True)
print ('2016', Vic_from_PR_2016.transport.mean()/1000.); check_nan(Vic_from_PR_2016)
Vic_from_PR_2017 = pd.read_csv('Vic_from_PR_2017_v3.csv', index_col=0, parse_dates=True)
print ('2017', Vic_from_PR_2017.transport.mean()/1000.); check_nan(Vic_from_PR_2017)
Vic_from_PR_2018 = pd.read_csv('Vic_from_PR_2018_v3.csv', index_col=0, parse_dates=True)
print ('2018', Vic_from_PR_2018.transport.mean()/1000.); check_nan(Vic_from_PR_2018)

2015 24.05030665710093
2016 24.045999770340103
2017 23.990312937788694
2018 23.092685865657987

/tmp/ipykernel_3252/3505476923.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  if ds.transport[j] != ds.transport[j]:
/tmp/ipykernel_3252/3505476923.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  if ds.transport[j] != ds.transport[j]:
/tmp/ipykernel_3252/3505476923.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  if ds.transport[j] != ds.transport[j]:
/tmp/ipykernel_3252/3505476923.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  if ds.transport[j] != ds.transport[j]:

In [16]:

aa = 0.25*(Vic_from_PR_2015.final_age.mean() + Vic_from_PR_2016.final_age.mean() + Vic_from_PR_2017.final_age.mean() + Vic_from_PR_2018.final_age.mean())
aa

Out[16]:

13.566945961865464

In [19]:

dd = 0.25*(Vic_to_PR_2015.final_age.mean() + Vic_to_PR_2016.final_age.mean() + Vic_to_PR_2017.final_age.mean() + Vic_to_PR_2018.final_age.mean())
dd

Out[19]:

16.867819232282763

In [17]:

ab = 0.25*(PR_to_Vic_2015.final_age.mean() + PR_to_Vic_2016.final_age.mean() + PR_to_Vic_2017.final_age.mean() + PR_to_Vic_2018.final_age.mean())
ab

Out[17]:

13.71986337750608

In [20]:

db = 0.25*(PR_from_Vic_2015.final_age.mean() + PR_from_Vic_2016.final_age.mean() + PR_from_Vic_2017.final_age.mean() + PR_from_Vic_2018.final_age.mean())
db

Out[20]:

16.829606137174334

In [18]:

(aa + ab)/2 

Out[18]:

13.643404669685772

In [21]:

Out[21]:

	transport	final_age	final_depth	final_salt	final_temp	final_lon	init_depth	init_salt	init_temp	init_lat
2016-01-01 00:00:00+00:00	5291.494251	21.208800	-38.681566	31.492639	8.969030	-123.347175	-84.327773	30.115465	8.910218	48.924583
2016-01-02 00:00:00+00:00	5550.015639	20.577325	-39.410060	31.400583	8.985300	-123.355776	-88.367499	30.130296	8.886164	48.921065
2016-01-03 00:00:00+00:00	5660.882716	21.633671	-38.561435	31.366363	8.996675	-123.353684	-87.997609	30.116576	8.843807	48.921006
2016-01-04 00:00:00+00:00	6210.446689	20.362758	-37.484604	31.308099	9.008349	-123.356458	-94.903324	30.181858	8.897932	48.919276
2016-01-05 00:00:00+00:00	9561.456084	18.178807	-42.191863	31.301096	9.008894	-123.360976	-94.847994	30.230211	8.885795	48.917318
...	...	...	...	...	...	...	...	...	...	...
2016-12-27 00:00:00+00:00	9239.320327	17.864120	-58.749614	31.771817	9.712236	-123.360505	-68.746821	29.976041	8.786927	48.939701
2016-12-28 00:00:00+00:00	13199.439713	16.501642	-57.670818	31.860888	9.585216	-123.356184	-73.063848	30.166577	8.816433	48.945536
2016-12-29 00:00:00+00:00	15173.529359	16.303234	-57.925839	31.914344	9.532530	-123.354214	-73.855145	30.100272	8.785727	48.935865
2016-12-30 00:00:00+00:00	16451.476423	16.888965	-59.062236	31.941152	9.512147	-123.354540	-74.871472	30.090665	8.789643	48.929824
2016-12-31 00:00:00+00:00	19745.119058	17.259144	-59.706419	31.925776	9.475800	-123.354114	-83.349381	30.150163	8.804370	48.924302

366 rows × 10 columns

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