Sea Level: Tidal Data

Notebook for analysing tide measurement data for 1512 tide gauges worldwide. I use the Revised Local Reference (RLR) data set from Permanent Service of Mean Sea Level . The RLR datum is set to 7000 mm below mean sea level therefore the values are all in the range of 6000 to 8000 mm.

All UK tide gauge data is included in the PSMSL data set, most of the data from the University of Hawaii is included as well.

Data source is Permanent Service of Mean Sea Level (http://www.psmsl.org/data/obtaining/complete.php, RLR monthly)

Data set with monthly mean data.

Data has been obtained on 30 January 2019.

Load libraries

%matplotlib inline

from IPython.core.interactiveshell import InteractiveShell
from datetime import datetime

import glob
import os
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib
import json
import matplotlib.pyplot as plt

InteractiveShell.ast_node_interactivity = "all"
pd.options.display.max_rows = 200
#matplotlib.rcParams['svg.fonttype'] = 'none'

Setup the data

First: Read meta data for all stations of PSMSL

#parse errorflags
stations_errorflag = 'data/psmsl_data/filelist.txt'

errors = pd.read_csv(stations_errorflag, sep=';', header=None, usecols=[0, 6], names=['id', 'latitude', 'longitude', 'locataion', 'coastline', 'station', 'flag' ])

#parse meta data
meta_info = 'data/metadata_psmsl.csv'

stations = pd.read_csv(meta_info, header=0, names=['location', 'ID', 'latitude', 'longitude', 'gloss id', 'country', 'date', 'coastline', 'station'])

#comibe meta data and error flags
stations = stations.merge(errors, left_on='ID', right_on='id')

stations = stations.drop('ID', 1)

len(errors)
len(stations)

stations.head()

1512

1512

	location	latitude	longitude	gloss id	country	date	coastline	station	id	flag
0	REYKJAVIK	64.151	-21.940	229.0	ISL	27/06/2018	10	1	638	N
1	GRINDAVIK	63.833	-22.433	NaN	ISL	01/01/1980	10	11	877	N
2	TORSHAVN	62.017	-6.767	237.0	FRO	30/10/2007	15	11	839	N
3	BARENTSBURG	78.067	14.250	231.0	SJM	09/01/2018	25	1	541	N
4	BARENTSBURG II (SPITSBERGEN)	78.067	14.250	231.0	SJM	17/01/2003	25	2	547	N

#convert capitals to lowercase with a capitalized first letter
def convert_location_names(str):
    conversion = str.title()
    return conversion

stations['location'] = stations['location'].apply(convert_location_names)

Second: Read all the tide data from folder

#parse file

def parse_tides(filename):
    filepath = 'data/psmsl_data/data/' + str(filename) + '.rlrdata'
    df = pd.read_csv(filepath, sep=';', header=None, names=['year-month', 'tide', 'missing day', 'flag for attention'])

    df['flag for attention'] = df['flag for attention'].apply(lambda x: '{0:0>3}'.format(x))
    df['year'] = df['year-month'].apply(np.floor).astype('int')
    df['month'] = ((((df['year-month'] - df['year'])*24)+1)/2).apply(np.around).astype('int')
    df['day'] = 1 #due the monthly averages, no days are present, set days to 1
    df['timestamp'] = pd.to_datetime(df[['year', 'month', 'day']], errors='coerce')
    df['ID'] = filename
    df['ID'] = df['ID'].astype('int')

    df = df.drop('year-month', 1)
    df = df.drop('year', 1)
    df = df.drop('month', 1)
    df = df.drop('day', 1)
    
    #print (filepath)
    
    return df

#filepath for complete tide data set
filepath = 'data/psmsl_data/tideData_psmsl_complete.csv'

if os.path.exists(filepath):
    df = pd.read_csv(filepath, usecols=[1,2,3,4,5])
    df['flag for attention'] = df['flag for attention'].apply(lambda x: '{0:0>3}'.format(x))
    df['timestamp'] = pd.to_datetime(df['timestamp'], errors='coerce')
else:
    df = pd.concat([parse_tides(f) for f in stations['id']])
    #replace null values
    df['tide'] = df['tide'].replace(-99999,np.nan)
    df.to_csv(filepath)

df['ID'].nunique()
df.head()

1512

	tide	missing day	flag for attention	timestamp	ID
0	6980.0	0	000	1956-05-01	638
1	6840.0	0	000	1956-06-01	638
2	6890.0	0	000	1956-07-01	638
3	6830.0	0	000	1956-08-01	638
4	6830.0	0	000	1956-09-01	638

Third: Merge meta data and tide data

df = df.merge(stations, left_on='ID', right_on='id')

df = df.drop('id', 1)

df['ID'].nunique()
df.head()

1512

	tide	missing day	flag for attention	timestamp	ID	location	latitude	longitude	gloss id	country	date	coastline	station	flag
0	6980.0	0	000	1956-05-01	638	Reykjavik	64.151	-21.94	229.0	ISL	27/06/2018	10	1	N
1	6840.0	0	000	1956-06-01	638	Reykjavik	64.151	-21.94	229.0	ISL	27/06/2018	10	1	N
2	6890.0	0	000	1956-07-01	638	Reykjavik	64.151	-21.94	229.0	ISL	27/06/2018	10	1	N
3	6830.0	0	000	1956-08-01	638	Reykjavik	64.151	-21.94	229.0	ISL	27/06/2018	10	1	N
4	6830.0	0	000	1956-09-01	638	Reykjavik	64.151	-21.94	229.0	ISL	27/06/2018	10	1	N

len(df)

725650

df.to_csv('data/psmsl_data_complete_including_metadata.csv')

Filter data by quality flags

Data flagged for attention: 001 means data should be treated with caution, 010 indicates a mean tidal level (MTL) value in a mean sea level (MSL) time series

df.flag = df.flag.str.strip()

df['missing day'] = df['missing day'].replace("99",np.nan)

data_flagged = df[(df['flag for attention'] == '011')]

#data_flagged.sort_values('missing day', ascending=False)

data_without_flag = df[df['flag for attention'].isin(['001', '011']) == False]
#data_without_flag = df[df['flag for attention'].isin(['011']) == False]

#data_without_flag.sort_values('flag for attention')

data_without_flag.shape
df.shape

#89 Location sind geflaggt mit 001 und eine Location mit 011, gesamte Anzahl Locations 1478

(723605, 14)

(725650, 14)

df = data_without_flag

data_flagged = df[(df['flag for attention'] == '011')]
data_flagged

	tide	missing day	flag for attention	timestamp	ID	location	latitude	longitude	gloss id	country	date	coastline	station	flag

Data selction

#function that evaluates how many empty values a station has and start and end of each station

def empty_values(dataframe):
    
    station = dataframe.set_index('timestamp').tide.resample('1A').mean()
    
    first_year = station.first_valid_index().year
    last_year = station.last_valid_index().year
    count_missing = station.isnull().sum()
    count_values = station.count()
    
    
    #tide_first = station.iloc[0]
    #station = station.sort_values(ascending=False)
    #tide_last = station.iloc[0]
    #tide_change = tide_last - tide_first
    
    result = {}

    result['Start'] = first_year
    result['End'] = last_year
    result['Valid Data Points'] = count_values
    result['Missing Data Points'] = count_missing
    
    return pd.Series(result) #create a series


#call function and collect all data
#clean_data = df.groupby(['Location', 'Country']).apply(empty_values).sort_values('Valid Data Points', ascending=False)

#clean_data = df.groupby(['ID']).apply(empty_values).sort_values('Valid Data Points', ascending=False)


#reset index

#clean_data = clean_data.reset_index()

data_subset_1985_2016 = df.set_index(['timestamp'])

data_subset_1985_2016 = data_subset_1985_2016.loc['1985-01-01':'2016-12-01']

data_subset_1985_2016 = data_subset_1985_2016.reset_index()

clean_data = data_subset_1985_2016.groupby(['ID']).apply(empty_values).sort_values('Valid Data Points', ascending=False)

clean_data = clean_data.reset_index()

data_subset_1985_2016_clean = clean_data[(clean_data['Start'] <= 1985)
                               & (clean_data['Valid Data Points'] >= 21) 
                               & (clean_data['End'] >= 2010) ]
data_subset_1985_2016_clean
data_subset_1985_2016_clean['ID'].nunique()

	ID	End	Missing Data Points	Start	Valid Data Points
0	1	2016	0	1985	32
1	1067	2016	0	1985	32
2	1065	2016	0	1985	32
3	1062	2016	0	1985	32
4	1060	2016	0	1985	32
5	1037	2016	0	1985	32
6	1036	2016	0	1985	32
7	1028	2016	0	1985	32
8	1027	2016	0	1985	32
9	1026	2016	0	1985	32
10	1001	2016	0	1985	32
11	999	2016	0	1985	32
12	997	2016	0	1985	32
13	996	2016	0	1985	32
14	985	2016	0	1985	32
15	984	2016	0	1985	32
16	982	2016	0	1985	32
17	955	2016	0	1985	32
18	954	2016	0	1985	32
19	953	2016	0	1985	32
20	934	2016	0	1985	32
21	933	2016	0	1985	32
22	837	2016	0	1985	32
23	835	2016	0	1985	32
24	832	2016	0	1985	32
25	829	2016	0	1985	32
26	825	2016	0	1985	32
27	1066	2016	0	1985	32
28	1068	2016	0	1985	32
29	823	2016	0	1985	32
30	1069	2016	0	1985	32
31	1116	2016	0	1985	32
32	1113	2016	0	1985	32
33	1112	2016	0	1985	32
34	1111	2016	0	1985	32
35	1109	2016	0	1985	32
36	1108	2016	0	1985	32
37	1107	2016	0	1985	32
38	1105	2016	0	1985	32
39	1104	2016	0	1985	32
40	1103	2016	0	1985	32
41	1102	2016	0	1985	32
42	1101	2016	0	1985	32
43	1100	2016	0	1985	32
44	1099	2016	0	1985	32
45	1098	2016	0	1985	32
46	1097	2016	0	1985	32
47	1096	2016	0	1985	32
48	1095	2016	0	1985	32
49	1094	2016	0	1985	32
50	1093	2016	0	1985	32
51	1091	2016	0	1985	32
52	1090	2016	0	1985	32
53	1089	2016	0	1985	32
54	1071	2016	0	1985	32
55	1070	2016	0	1985	32
56	824	2016	0	1985	32
57	822	2016	0	1985	32
58	483	2016	0	1985	32
59	637	2016	0	1985	32
60	595	2016	0	1985	32
61	571	2016	0	1985	32
62	567	2016	0	1985	32
63	566	2016	0	1985	32
64	564	2016	0	1985	32
65	563	2016	0	1985	32
66	541	2016	0	1985	32
67	539	2016	0	1985	32
68	538	2016	0	1985	32
69	526	2016	0	1985	32
70	523	2016	0	1985	32
71	522	2016	0	1985	32
72	521	2016	0	1985	32
73	520	2016	0	1985	32
74	518	2016	0	1985	32
75	510	2016	0	1985	32
76	509	2016	0	1985	32
77	508	2016	0	1985	32
78	499	2016	0	1985	32
79	498	2016	0	1985	32
80	497	2016	0	1985	32
81	489	2016	0	1985	32
82	487	2016	0	1985	32
83	486	2016	0	1985	32
84	485	2016	0	1985	32
85	636	2016	0	1985	32
86	638	2016	0	1985	32
87	819	2016	0	1985	32
88	680	2016	0	1985	32
89	817	2016	0	1985	32
90	816	2016	0	1985	32
91	815	2016	0	1985	32
92	814	2016	0	1985	32
93	813	2016	0	1985	32
94	812	2016	0	1985	32
95	809	2016	0	1985	32
96	808	2016	0	1985	32
97	786	2016	0	1985	32
98	759	2016	0	1985	32
99	758	2016	0	1985	32
...	...	...	...	...	...
444	408	2015	1	1985	30
448	761	2014	0	1985	30
449	2316	2014	0	1985	30
450	468	2016	2	1985	30
451	470	2016	2	1985	30
453	1549	2015	2	1985	29
454	1149	2016	3	1985	29
455	728	2013	0	1985	29
456	764	2016	3	1985	29
457	1495	2013	0	1985	29
458	61	2016	3	1985	29
459	704	2013	0	1985	29
460	1475	2013	0	1985	29
461	642	2013	0	1985	29
462	1468	2016	3	1985	29
463	1449	2013	0	1985	29
464	1448	2016	3	1985	29
466	641	2013	0	1985	29
467	606	2013	0	1985	29
468	604	2013	0	1985	29
469	602	2013	0	1985	29
470	599	2013	0	1985	29
471	598	2016	3	1985	29
474	446	2016	3	1985	29
475	496	2013	0	1985	29
476	1003	2013	0	1985	29
478	910	2016	3	1985	29
479	820	2016	3	1985	29
480	841	2013	0	1985	29
483	917	2013	0	1985	29
484	353	2013	0	1985	29
486	593	2016	4	1985	28
487	5	2016	4	1985	28
488	173	2012	0	1985	28
492	729	2012	0	1985	28
493	205	2014	2	1985	28
495	258	2016	4	1985	28
499	425	2016	4	1985	28
500	1473	2016	4	1985	28
502	374	2016	4	1985	28
503	1329	2016	4	1985	28
505	1156	2016	4	1985	28
507	1637	2016	4	1985	28
510	1421	2016	4	1985	28
511	1392	2013	4	1985	28
514	543	2014	2	1985	28
515	1192	2016	4	1985	28
516	810	2012	0	1985	28
518	826	2016	5	1985	27
519	1270	2014	3	1985	27
521	131	2011	1	1985	27
523	685	2011	0	1985	27
525	1297	2016	5	1985	27
527	1266	2011	0	1985	27
529	1215	2011	0	1985	27
530	1232	2011	0	1985	27
532	1345	2011	0	1985	27
533	1346	2011	2	1985	27
535	635	2011	1	1985	27
536	137	2016	5	1985	27
537	397	2016	5	1985	27
539	118	2011	0	1985	27
540	640	2011	0	1985	27
541	1638	2016	5	1985	27
542	828	2011	0	1985	27
543	471	2016	5	1985	27
546	1364	2011	1	1985	27
547	213	2016	6	1985	26
549	1395	2013	3	1985	26
550	569	2010	0	1985	26
551	1469	2016	6	1985	26
554	71	2016	6	1985	26
557	601	2012	2	1985	26
560	409	2016	6	1985	26
561	438	2013	3	1985	26
562	411	2016	6	1985	26
568	1195	2016	7	1985	25
570	634	2011	2	1985	25
572	1161	2013	4	1985	25
574	1628	2016	7	1985	25
585	51	2015	6	1985	25
600	836	2016	8	1985	24
602	469	2016	8	1985	24
603	605	2013	5	1985	24
605	3	2015	8	1985	24
616	414	2013	5	1985	24
621	1643	2016	8	1985	24
638	369	2014	6	1985	24
646	2021	2016	9	1985	23
659	2215	2016	9	1985	23
660	474	2016	9	1985	23
663	375	2010	3	1985	23
664	600	2013	6	1985	23
676	936	2015	10	1985	22
685	787	2016	10	1985	22
686	537	2016	10	1985	22
690	1501	2016	10	1985	22
691	950	2016	11	1985	21
692	612	2012	7	1985	21
696	1423	2013	8	1985	21

519 rows × 5 columns

519

data_subset_1985_2016_clean = data_subset_1985_2016_clean.merge(df, left_on='ID', right_on='ID')

Export Datasets for Map Visualization

def yearly_mean(dataframe):
    
    mean_dataframe = dataframe.set_index('timestamp').tide.resample('1A').mean()
    mean_dataframe = mean_dataframe.reset_index()
    
    return pd.DataFrame(mean_dataframe)

def rolling_mean(dataframe):
    
    mean_dataframe = dataframe.set_index('timestamp').tide.resample('1A').mean()

    mean_dataframe = mean_dataframe.rolling(center=False, window=5, min_periods=1).mean()
    mean_dataframe = mean_dataframe.reset_index()
    
    
    return pd.DataFrame(mean_dataframe)


normal = df.groupby(['ID']).apply(yearly_mean)

rolling = df.groupby(['ID']).apply(rolling_mean)

normal.head()

rolling.head()

		timestamp	tide
ID
1	0	1807-12-31	6970.333333
	1	1808-12-31	6867.333333
	2	1809-12-31	6954.916667
	3	1810-12-31	6946.416667
	4	1811-12-31	6977.166667

		timestamp	tide
ID
1	0	1807-12-31	6970.333333
	1	1808-12-31	6918.833333
	2	1809-12-31	6930.861111
	3	1810-12-31	6934.750000
	4	1811-12-31	6943.233333

#delete rows which are not used for visualization

data_subset_viz = data_subset_1985_2016_clean.drop(['missing day','flag for attention', 'gloss id', 'coastline', 'flag', 'station', 'date'], 1)

#rolling if data for animation, yearly if data for explorer
data_subset_viz = data_subset_viz.groupby(['ID']).apply(rolling_mean)

data_subset_viz = data_subset_viz.reset_index()

#delete rows which are not used for visualization

data_subset_viz = data_subset_viz.drop(['level_1'], 1)

#filter by trend data
#data_subset_viz = data_subset_viz.merge(trends_1985_2014, left_on='ID', right_on='ID')

#add worldbank data
#data_subset_viz = data_subset_viz.merge(worldbank_data, left_on='ID', right_on='ID')

data_subset_viz = data_subset_viz.round()

data_subset_viz.head()
data_subset_viz['ID'].nunique()

	ID	timestamp	tide
0	1	1807-12-31	6970.0
1	1	1808-12-31	6919.0
2	1	1809-12-31	6931.0
3	1	1810-12-31	6935.0
4	1	1811-12-31	6943.0

519

Use only for map animation in order to reduce data to 30 years

data_subset_viz = data_subset_viz.set_index(['timestamp'])

data_subset_viz = data_subset_viz.loc['1985-12-31':'2016-12-31']

data_subset_viz = data_subset_viz.reset_index()

#interpolate missing data in order to make animation smoother

data_subset_viz['tide'] = data_subset_viz['tide'].interpolate()

#data_subset_viz.sort_values('timestamp', ascending=False)

data_subset_viz['ID'].nunique()

519

Add geodata and meta data again

data_subset_viz = data_subset_viz.merge(stations, left_on='ID', right_on='id')

data_subset_viz = data_subset_viz.drop(['gloss id', 'coastline', 'flag', 'station', 'date', 'id'], 1)

data_subset_viz['year'] = data_subset_viz.timestamp.dt.year

data_subset_viz['ID'].nunique()

519

#function to use first tide measurement as zero reference point

def to_zero(dataframe):
    first_value = dataframe['tide'].iloc[0]
    dataframe['tide'] = dataframe['tide'] - first_value
    dataframe['tide'] = dataframe['tide'].round(decimals=2)
    return dataframe

data_subset_viz = data_subset_viz.groupby(['ID']).apply(to_zero)

data_subset_viz['ID'].nunique()

519

data_subset_viz.head()

	timestamp	ID	tide	location	latitude	longitude	country	year
0	1985-12-31	1	0.0	Brest	48.383	-4.495	FRA	1985
1	1986-12-31	1	-1.0	Brest	48.383	-4.495	FRA	1986
2	1987-12-31	1	4.0	Brest	48.383	-4.495	FRA	1987
3	1988-12-31	1	9.0	Brest	48.383	-4.495	FRA	1988
4	1989-12-31	1	15.0	Brest	48.383	-4.495	FRA	1989

Convert year to object to get rid of 1985.0

data_subset_viz['year'] = data_subset_viz['year'].astype(object)

data_subset_viz.dtypes

timestamp    datetime64[ns]
ID                    int64
tide                float64
location             object
latitude            float64
longitude           float64
country              object
year                 object
dtype: object

data_subset_viz = data_subset_viz.rename(columns={'ID': 'id'})

data_subset_viz.to_csv('context_data/sealevel_viz_psmsl_1985_2015.csv', index=False)

# shorten data set

data_subset_viz_short = data_subset_viz[['id', 'tide', 'year']]

data_subset_viz_resampled = data_subset_viz_short.pivot(index='id', columns='year', values='tide')

data_subset_viz_resampled = data_subset_viz_resampled.reset_index()

data_subset_viz_resampled

year	id	1985	1986	1987	1988	1989	1990	1991	1992	1993	...	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
0	1	0.0	-1.0	4.0	9.0	15.0	16.0	15.0	8.0	5.0	...	34.0	36.0	41.0	58.0	59.0	63.0	65.0	77.0	70.0	78.0
1	3	0.0	0.0	-4.0	-8.0	-4.0	-10.0	-24.0	-29.0	-41.0	...	32.0	30.0	38.0	42.0	47.0	53.0	52.0	27.5	3.0	3.0
2	5	0.0	-5.0	-8.0	-7.0	4.0	9.0	3.0	4.0	1.0	...	34.0	33.0	36.0	48.0	50.0	67.0	71.0	81.0	77.0	73.0
3	7	0.0	-16.0	-24.0	-29.0	-13.0	16.0	23.0	28.0	9.0	...	68.0	79.0	67.0	62.0	66.0	50.0	33.0	35.0	59.0	60.0
4	8	0.0	-11.0	-1.0	0.0	25.0	34.0	29.0	28.0	18.0	...	31.0	42.0	42.0	49.0	59.0	53.0	50.0	45.0	45.0	47.0
5	9	0.0	-5.0	-3.0	5.0	12.0	23.0	12.0	9.0	-5.0	...	22.0	31.0	28.0	32.0	34.0	26.0	21.0	27.0	33.0	40.0
6	10	0.0	13.0	1.0	-40.0	-50.0	-46.0	-55.0	-43.0	-23.0	...	-12.0	-16.0	-17.0	-20.0	-29.0	-18.0	-17.0	-4.0	12.0	20.0
7	11	0.0	-13.0	-5.0	-9.0	18.0	28.0	20.0	19.0	9.0	...	51.0	59.0	55.0	58.0	63.0	51.0	43.0	34.0	35.0	33.0
8	12	0.0	10.0	23.0	7.0	-6.0	-6.0	-3.0	-3.0	12.0	...	70.0	78.0	92.0	101.0	113.0	132.0	132.0	131.0	116.0	106.0
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504	2105	0.0	-15.0	-21.0	-38.0	-7.0	18.0	15.0	27.0	11.0	...	-24.0	-11.0	-26.0	-43.0	-37.0	-48.0	-67.0	-73.0	-43.0	-49.0
505	2106	0.0	-16.0	-18.0	-32.0	-5.0	19.0	14.0	23.0	9.0	...	-10.0	8.0	-6.0	-15.0	-6.0	-19.0	-41.0	-51.0	-29.0	-36.0
506	2107	0.0	1.0	8.0	5.0	31.0	51.0	45.0	50.0	35.0	...	41.0	56.0	46.0	44.0	50.0	40.0	25.0	19.0	29.0	27.0
507	2110	0.0	-13.0	-18.0	-25.0	2.0	21.0	21.0	30.0	21.0	...	25.0	35.0	25.0	18.0	28.0	18.0	6.0	2.0	16.0	7.0
508	2111	0.0	-10.0	-23.0	-30.0	-7.0	15.0	14.0	22.0	10.0	...	-8.0	5.0	-3.0	-15.0	-6.0	-16.0	-33.0	-35.0	-15.0	-24.0
509	2112	0.0	-8.0	-23.0	-31.0	-10.0	10.0	3.0	5.0	-19.0	...	-43.0	-37.0	-44.0	-59.0	-49.0	-57.0	-72.0	-70.0	-44.0	-55.0
510	2113	0.0	-8.0	-25.0	-34.0	-13.0	5.0	-1.0	3.0	-14.0	...	-39.0	-29.0	-37.0	-57.0	-49.0	-60.0	-78.0	-80.0	-56.0	-72.0
511	2127	0.0	-1.0	-9.0	-41.0	-53.0	-42.0	-47.0	-38.0	-22.0	...	-22.0	-33.0	-38.0	-27.0	-34.0	-20.0	-26.0	-11.0	-13.0	2.0
512	2215	0.0	9.0	8.0	-1.0	-9.0	-4.0	10.0	21.0	18.0	...	28.0	43.0	57.0	75.0	84.0	102.0	111.0	106.0	118.0	147.0
513	2261	0.0	-3.0	5.0	3.0	8.0	24.0	39.0	42.0	59.0	...	-7.0	-4.0	-3.0	0.0	14.0	21.0	6.0	0.0	16.0	17.0
514	2262	0.0	-20.0	-20.0	-55.0	-57.0	-67.0	-65.0	-70.0	-46.0	...	-141.0	-127.0	-116.0	-141.0	-115.0	-100.0	-113.0	-112.0	-88.0	-72.0
515	2295	0.0	16.0	25.0	12.0	4.0	-1.0	1.0	9.0	23.0	...	51.0	54.0	69.0	67.0	73.0	86.0	94.0	105.0	114.0	133.0
516	2316	0.0	-4.0	-5.0	-3.0	3.0	9.0	11.0	13.0	-1.0	...	15.0	19.0	26.0	29.0	43.0	51.0	68.0	67.0	NaN	NaN
517	2324	0.0	11.0	20.0	2.0	-8.0	-10.0	-3.0	1.0	22.0	...	116.0	118.0	131.0	135.0	148.0	166.0	173.0	174.0	165.0	161.0
518	2330	0.0	15.0	-7.0	-68.0	-84.0	-85.0	-103.0	-94.0	-69.0	...	-10.0	-12.0	-18.0	-23.0	-34.0	-23.0	-25.0	-17.0	-6.0	-6.0

519 rows × 33 columns

data_subset_viz_resampled['id'].nunique()

519

#data_subset_viz_resampled.to_csv('data/dataviz/sealevel_viz_whole_timeseries.csv', index=False)

data_subset_viz_resampled.to_csv('data/dataviz/sealevel_viz_psmsl_1985_2015.csv', index=False)