Sea Level: Permanent Service of Mean Sea Level¶

Notebook for analysing tide measurement data for 1505 tide gauges worldwide. I use the Revised Local Reference (RLR) data set. 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 4 November 2017.

Load libraries¶

%matplotlib inline

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

#get data from worldbank
from pandas_datareader import wb

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

#create leaflet maps
import folium

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()

1490

1490

#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)

coordinates = list(zip(data_subset_viz['latitude'], data_subset_viz['longitude']))

Second: Read all the tide data from folder¶

#parse file

def parse_tides(filename):
    filepath = 'data/psmsl_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()

1490

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()

1490

len(df)

703599

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

Add continent data¶

continents = pd.read_csv('context_data/continents.csv')

worldbank_data = continents.merge(worldbank_data, left_on='iso3', right_on='iso3')

worldbank_data

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

(701692, 14)

(703599, 14)

df = data_without_flag

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

Analysis¶

first_measurement = df.groupby(['location', 'country']).timestamp.first()
stations_1900 = first_measurement[first_measurement.dt.year <= 1900]
stations_1925 = first_measurement[first_measurement.dt.year <= 1925]
stations_1950 = first_measurement[first_measurement.dt.year <= 1950] 
stations_1975 = first_measurement[first_measurement.dt.year <= 1975]
stations_1990 = first_measurement[first_measurement.dt.year <= 1990]
stations_2000 = first_measurement[first_measurement.dt.year <= 2000]
stations_2010 = first_measurement[first_measurement.dt.year <= 2010]
stations_2014 = first_measurement[first_measurement.dt.year <= 2014]
# stations_1970
#first_measurement.sort_values()
stations_1900.sort_values().head(20)

stations_hist = pd.Series([len(stations_1900), len(stations_1925), len(stations_1950), len(stations_1975), len(stations_2000), len(stations_2014)],
                          index=['<1900', '<1925', '<1950', '<1975', '<2000', '<2014'])

stations_hist

#first_measurement.dt.year

location                             country
Brest                                FRA       1807-01-01
Swinoujscie                          POL       1811-01-01
Sheerness                            GBR       1832-04-01
Cuxhaven 2                           DEU       1843-01-01
Maassluis                            NLD       1848-01-01
Wismar 2                             DEU       1848-07-01
San Francisco                        USA       1854-07-01
Warnemunde 2                         DEU       1855-04-01
New York (The Battery)               USA       1856-01-01
Travemunde                           DEU       1856-01-01
Lyokki                               FIN       1858-01-01
Jungfrusund                          FIN       1858-01-01
Liverpool Georges And Princes Piers  GBR       1858-01-01
Lypyrtti                             FIN       1858-01-01
Aberdeen Ii                          GBR       1862-01-01
Vlissingen                           NLD       1862-01-01
Hoek Van Holland                     NLD       1864-01-01
Den Helder                           NLD       1865-01-01
Liepaja                              LVA       1865-01-01
Delfzijl                             NLD       1865-01-01
Name: timestamp, dtype: datetime64[ns]

<1900      96
<1925     177
<1950     400
<1975     961
<2000    1321
<2014    1490
dtype: int64

first_measurement = df.groupby(['location', 'country']).timestamp.first()
last_measurement = df.groupby(['location', 'country']).timestamp.last()

#stations_2010 = first_measurement[first_measurement.dt.year <= 2010]
#stations_2014 = first_measurement[first_measurement.dt.year <= 2014]
last_measurement.head()

location     country
Aarhus       DNK       2012-12-01
Abashiri     JPN       2015-12-01
Aberdeen I   GBR       2015-04-01
Aberdeen Ii  GBR       1965-12-01
Abidjan      CIV       1976-03-01
Name: timestamp, dtype: datetime64[ns]

Create subsets of the data¶

#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_2014 = df.set_index(['timestamp'])

data_subset_1985_2014 = data_subset_1985_2014.loc['1985-01-01':'2014-12-01']

data_subset_1985_2014 = data_subset_1985_2014.reset_index()

clean_data = data_subset_1985_2014.groupby(['ID']).apply(empty_values).sort_values('Valid Data Points', ascending=False)

clean_data = clean_data.reset_index()

data_subset_1985_2014_clean = clean_data[(clean_data['Start'] <= 1985)
                               & (clean_data['Valid Data Points'] >= 21) 
                               & (clean_data['End'] >= 2010) ]
data_subset_1985_2014_clean
data_subset_1985_2014_clean['ID'].nunique()

509

data_subset_1930_2010 = clean_data[(clean_data['Start'] <= 1930)
                               & (clean_data['Missing Data Points'] <= 20) 
                               & (clean_data['End'] >= 2010) ]

data_subset_1950_2010 = clean_data[(clean_data['Start'] <= 1950)
                               & (clean_data['Missing Data Points'] <= 20) 
                               & (clean_data['End'] >= 2010) ]

data_subset_1985_2014 = clean_data[(clean_data['Start'] <= 1970)
                               & (clean_data['Missing Data Points'] <= 20) 
                               & (clean_data['End'] >= 2010) ]

data_subset_1985_2014_clean = data_subset_1985_2014_clean.merge(df, left_on='ID', right_on='ID')

#Merge dataset with meta data an save to csv

data_subset_1930_2010 = data_subset_1930_2010.merge(df, left_on='ID', right_on='ID')
data_subset_1950_2010 = data_subset_1950_2010.merge(df, left_on='ID', right_on='ID')
data_subset_1970_2010 = data_subset_1970_2010.merge(df, left_on='ID', right_on='ID')

#data_subset_1930_2010.to_csv('data/data_subset_1930_2010_psmsl.csv', encoding='utf-8') #103 locations
#data_subset_1950_2010.to_csv('data/data_subset_1950_2010_psmsl.csv', encoding='utf-8') #188 locations
#data_subset_1970_2010.to_csv('data/data_subset_1970_2010_psmsl.csv', encoding='utf-8') #395 locations

data_subset_1930_2010.head()

Calculate Sea Level Rise¶

#function that creates a mean of first 5 years and last 5 years and then calculates the difference and percentage

def sealevel_rise(dataframe):
    
    station = dataframe.set_index('timestamp').tide.resample('1A').mean()
    
    first = station[:5].mean() 
    last = station[-5:].mean()
    increase = last - first
    pct_change = (increase / first) * 100
    
    result = {}

    result['First'] = first
    result['Last'] = last
    result['Percent Change'] = pct_change
    
    #significant increase if more than 0.5%, significant decrease if less than -0.5%, check with scientist
    
    if pct_change >= 0.5:
        result['rise'] = 'up'
    elif (pct_change < 0.5 and pct_change > -0.5):
        result['rise']  = 'even'
    elif pct_change <= -0.5:
        result['rise'] = 'down'
    else:
        result['rise'] = 'none'
        
    return pd.Series(result) #create a series

#this function uses sealevel rise function and and saves the results to a file

def stations_change(dataframe, name):
    
    filename = name
    
    result = dataframe.groupby(['ID']).apply(sealevel_rise)
    result['rise'] = result['rise'].astype('category')
    
    #reset index
    result = result.reset_index()
    
    result = result.merge(stations, left_on='ID', right_on='id')
    
    result.to_csv('data/subsets' + name + '_rise_psmsl.csv', encoding='utf-8')
    
    return result

# at least 1970, but also older data

data_subset_1970_rise = stations_change(data_subset_1970_2010, 'data_subset_1970_2010')

data_subset_1970_rise.head()

data_subset_1970_rise['rise'].value_counts()

up      279
down     66
even     50
Name: rise, dtype: int64

Subset 1970 to 2010 only¶

data_subset = data_subset_1970_2010.set_index(['timestamp'])

data_subset_1970_only = data_subset.loc['1970-01-01':'2016-01-01']

data_subset_1970_only = data_subset_1970_only.reset_index()

data_subset_1970_only.head()

# from 1970 onwards only
data_subset_1970 = stations_change(data_subset_1970_only, 'only_1970_2010')

data_subset_1970['rise'].value_counts()

up      277
even     61
down     53
none      4
Name: rise, dtype: int64

data_subset_plot = data_subset_1970_2010.groupby(['Location', 'timestamp']).tide.mean().unstack().T

data_subset_plot = data_subset_plot.resample("5A").mean().apply(np.round)

data_subset_plot.head()

def plot_grid(df, cols=4, **kwargs):
    fig, axes = plt.subplots(nrows=int(np.ceil(len(df.columns) / cols)), ncols=cols)
    fig.set_figheight(30)
    fig.set_figwidth(40)

    for i, x in enumerate(df.columns):
        ax = axes[i // cols, i % cols]
        df[x].plot(ax=ax, color='blue', legend=False, label=False,
                   kind='line', stacked=False, sharex=False,
                   figsize=(25, 50))
        ax.set_title(x)
        ax.xaxis.set_visible(False)
    return fig

fig = plot_grid(data_subset_plot)
fig.show()

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/figure.py:397: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure
  "matplotlib is currently using a non-GUI backend, "

Iceland¶

data_ISL = df[(df.Country == 'ISL')].groupby(['Location', 'timestamp']).tide.mean().unstack().T
data_ISL.tail()

df[df.Location == 'Reykjavik'].set_index('timestamp').tide.resample('1A').mean().plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1e09358c>

df[df.Location == 'Grindavik'].set_index('timestamp').tide.resample('1A').mean().plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1465a24c>

Philippines¶

26 tide gauges in the Philippines.

data_PHL = df[(df.Country == 'PHL')]

first_measurement_PHL = data_PHL.groupby(['Location']).timestamp.first()

first_measurement_PHL.sort_values()

Location
Manila, S. Harbor                1901-03-01
Cebu                             1935-05-01
Legaspi, Albay                   1947-05-01
Jolo, Sulu                       1947-11-01
Davao, Davao Gulf                1948-03-01
San Fernando, La Union           1948-04-01
Tacloban, Leyte                  1951-06-01
Port Irene                       1987-02-01
Surigao                          1987-05-01
San Jose                         1988-01-01
Puerto Princesa, Palawan         1990-07-01
Real Quezon                      2009-02-01
Balanacan, Marinduque            2011-01-01
Tandag, Surigao Del Sur          2011-01-01
Subic Zambales                   2011-01-01
Pulupandan, Negros Occidental    2011-01-01
Mati, Davao Oriental             2011-01-01
Virac, Catanduanes               2011-01-01
Mambajao Camguin                 2011-01-01
Makar, General Santos City       2011-01-01
Currimao Ilocos Norte            2011-01-01
Caticlan Malay Aklan             2011-01-01
Balintang, Quezon, Palawan       2011-01-01
Pagadian City                    2011-01-01
Zamboanga City                   2011-01-01
Macabalan Port, Cagayan De Oro   2011-02-01
Name: timestamp, dtype: datetime64[ns]

df[df.Location == 'Tacloban, Leyte'].set_index('timestamp').tide.resample('1A').mean().plot()
df[df.Location == 'Tacloban, Leyte'].set_index('timestamp').tide.resample('1A').mean().head()
df[df.Location == 'Tacloban, Leyte'].set_index('timestamp').tide.resample('1A').mean().tail()

<matplotlib.axes._subplots.AxesSubplot at 0x3114636c>

timestamp
1951-12-31    6999.142857
1952-12-31    6995.700000
1953-12-31    6994.285714
1954-12-31    7058.272727
1955-12-31    7060.500000
Freq: A-DEC, Name: tide, dtype: float64

timestamp
1972-12-31    6983.666667
1973-12-31    6971.636364
1974-12-31    7040.666667
1975-12-31    7092.333333
1976-12-31    7027.333333
Freq: A-DEC, Name: tide, dtype: float64

data_PHL = df[(df.Country == 'PHL')].groupby(['Location', 'timestamp']).tide.mean().unstack().T

data_PHL = data_PHL.resample("1A").mean().apply(np.round)

data_PHL.tail()

def plot_grid_small(df, cols=2, **kwargs):
    fig, axes = plt.subplots(nrows=int(np.ceil(len(df.columns) / cols)), ncols=cols)
    fig.set_figheight(6)
    fig.set_figwidth(8)

    for i, x in enumerate(df.columns):
        ax = axes[i // cols, i % cols]
        df[x].plot(ax=ax, color='blue', legend=False, label=False,
                   kind='line', stacked=False, sharex=False,
                   figsize=(25, 50))
        ax.set_title(x)
        ax.xaxis.set_visible(False)
    return fig


fig = plot_grid_small(data_PHL)
fig.show()

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/figure.py:397: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure
  "matplotlib is currently using a non-GUI backend, "

Export Datasets for Map Visualization¶

#Use trend data from PSMSL to filter dataset

trends_1961_2011 = pd.read_csv('data/psmsl_data/trends_1961_2011.csv', header=0, usecols=[0,1,2,3,4,7], names=['Location', 'Latitude', 'Longitude', 'Country', 'trend', 'ID']) 
trends_1985_2014 = pd.read_csv('data/psmsl_data/trends_1985_2014.csv', header=0,usecols=[0,1,2,3,4,7], names=['Location', 'Latitude', 'Longitude', 'Country', 'trend', 'ID']) 


trends_1961_2011['Location'] = trends_1961_2011['Location'].apply(convert_location_names)
trends_1961_2011['Country'] = trends_1961_2011['Country'].apply(convert_location_names)

pd.qcut(trends_1961_2011['trend'], 5)
len(trends_1961_2011)

0         (1.364, 1.961]
1        (-0.185, 1.364]
2         (1.961, 2.951]
3         (1.364, 1.961]
4         (1.364, 1.961]
5         (1.364, 1.961]
6         (1.364, 1.961]
7         (2.951, 8.917]
8         (1.364, 1.961]
9      [-18.154, -0.185]
10        (1.364, 1.961]
11        (1.961, 2.951]
12        (1.364, 1.961]
13        (1.961, 2.951]
14        (1.364, 1.961]
15        (1.364, 1.961]
16     [-18.154, -0.185]
17        (2.951, 8.917]
18     [-18.154, -0.185]
19       (-0.185, 1.364]
20     [-18.154, -0.185]
21       (-0.185, 1.364]
22        (1.364, 1.961]
23     [-18.154, -0.185]
24        (1.961, 2.951]
25     [-18.154, -0.185]
26     [-18.154, -0.185]
27       (-0.185, 1.364]
28     [-18.154, -0.185]
29       (-0.185, 1.364]
30     [-18.154, -0.185]
31     [-18.154, -0.185]
32        (1.961, 2.951]
33        (1.364, 1.961]
34        (1.364, 1.961]
35     [-18.154, -0.185]
36       (-0.185, 1.364]
37       (-0.185, 1.364]
38        (1.364, 1.961]
39        (1.961, 2.951]
40        (1.364, 1.961]
41        (1.364, 1.961]
42        (1.961, 2.951]
43        (1.364, 1.961]
44        (2.951, 8.917]
45        (1.364, 1.961]
46        (1.364, 1.961]
47     [-18.154, -0.185]
48        (1.364, 1.961]
49        (2.951, 8.917]
50        (2.951, 8.917]
51       (-0.185, 1.364]
52       (-0.185, 1.364]
53        (2.951, 8.917]
54        (2.951, 8.917]
55     [-18.154, -0.185]
56        (2.951, 8.917]
57       (-0.185, 1.364]
58       (-0.185, 1.364]
59       (-0.185, 1.364]
60        (1.961, 2.951]
61        (2.951, 8.917]
62        (1.364, 1.961]
63     [-18.154, -0.185]
64       (-0.185, 1.364]
65        (1.364, 1.961]
66       (-0.185, 1.364]
67     [-18.154, -0.185]
68     [-18.154, -0.185]
69        (1.364, 1.961]
70     [-18.154, -0.185]
71        (2.951, 8.917]
72        (1.364, 1.961]
73        (1.961, 2.951]
74        (1.961, 2.951]
75       (-0.185, 1.364]
76       (-0.185, 1.364]
77     [-18.154, -0.185]
78       (-0.185, 1.364]
79       (-0.185, 1.364]
80        (1.364, 1.961]
81     [-18.154, -0.185]
82        (2.951, 8.917]
83        (1.364, 1.961]
84        (1.961, 2.951]
85        (2.951, 8.917]
86     [-18.154, -0.185]
87     [-18.154, -0.185]
88        (1.961, 2.951]
89        (2.951, 8.917]
90        (1.961, 2.951]
91        (1.961, 2.951]
92     [-18.154, -0.185]
93     [-18.154, -0.185]
94       (-0.185, 1.364]
95        (1.961, 2.951]
96     [-18.154, -0.185]
97        (1.961, 2.951]
98       (-0.185, 1.364]
99     [-18.154, -0.185]
             ...        
265      (-0.185, 1.364]
266      (-0.185, 1.364]
267       (2.951, 8.917]
268      (-0.185, 1.364]
269      (-0.185, 1.364]
270       (1.364, 1.961]
271    [-18.154, -0.185]
272      (-0.185, 1.364]
273       (2.951, 8.917]
274       (1.961, 2.951]
275       (2.951, 8.917]
276       (1.961, 2.951]
277       (1.961, 2.951]
278      (-0.185, 1.364]
279       (1.364, 1.961]
280       (2.951, 8.917]
281       (1.961, 2.951]
282      (-0.185, 1.364]
283    [-18.154, -0.185]
284    [-18.154, -0.185]
285       (1.364, 1.961]
286    [-18.154, -0.185]
287       (2.951, 8.917]
288       (1.364, 1.961]
289       (1.364, 1.961]
290       (1.961, 2.951]
291       (1.364, 1.961]
292       (1.961, 2.951]
293       (1.961, 2.951]
294      (-0.185, 1.364]
295       (1.961, 2.951]
296       (2.951, 8.917]
297      (-0.185, 1.364]
298       (1.961, 2.951]
299       (1.961, 2.951]
300      (-0.185, 1.364]
301      (-0.185, 1.364]
302       (2.951, 8.917]
303    [-18.154, -0.185]
304       (1.364, 1.961]
305       (1.961, 2.951]
306       (2.951, 8.917]
307      (-0.185, 1.364]
308       (1.961, 2.951]
309       (2.951, 8.917]
310      (-0.185, 1.364]
311      (-0.185, 1.364]
312       (2.951, 8.917]
313       (1.961, 2.951]
314       (1.364, 1.961]
315       (1.364, 1.961]
316       (1.961, 2.951]
317       (1.961, 2.951]
318       (2.951, 8.917]
319       (1.961, 2.951]
320    [-18.154, -0.185]
321    [-18.154, -0.185]
322       (1.961, 2.951]
323      (-0.185, 1.364]
324      (-0.185, 1.364]
325    [-18.154, -0.185]
326    [-18.154, -0.185]
327    [-18.154, -0.185]
328       (1.961, 2.951]
329       (1.364, 1.961]
330       (2.951, 8.917]
331    [-18.154, -0.185]
332      (-0.185, 1.364]
333       (1.961, 2.951]
334       (1.364, 1.961]
335       (1.961, 2.951]
336       (1.364, 1.961]
337       (1.961, 2.951]
338       (2.951, 8.917]
339       (2.951, 8.917]
340       (1.364, 1.961]
341    [-18.154, -0.185]
342    [-18.154, -0.185]
343       (2.951, 8.917]
344       (1.364, 1.961]
345       (1.364, 1.961]
346      (-0.185, 1.364]
347    [-18.154, -0.185]
348       (2.951, 8.917]
349       (1.961, 2.951]
350       (2.951, 8.917]
351       (2.951, 8.917]
352       (2.951, 8.917]
353       (2.951, 8.917]
354       (1.364, 1.961]
355       (2.951, 8.917]
356    [-18.154, -0.185]
357    [-18.154, -0.185]
358    [-18.154, -0.185]
359    [-18.154, -0.185]
360    [-18.154, -0.185]
361      (-0.185, 1.364]
362    [-18.154, -0.185]
363    [-18.154, -0.185]
364    [-18.154, -0.185]
Name: trend, dtype: category
Categories (5, object): [[-18.154, -0.185] < (-0.185, 1.364] < (1.364, 1.961] < (1.961, 2.951] < (2.951, 8.917]]

365

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()

#delete rows which are not used for visualization

data_subset_viz = data_subset_1985_2014_clean.drop(['missing day','flag for attention', 'gloss id', 'coastline', 'flag', 'station', 'date'], 1)

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()

509

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':'2015-12-31']

data_subset_viz = data_subset_viz.reset_index()

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

data_subset_viz['ID'].nunique()

509

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

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

data_strongest_rise = data_subset_viz[(data_subset_viz['year'] == 2015)]

data_strongest_rise = data_strongest_rise.sort_values('tide', ascending=False)

data_strongest_rise.to_csv('top_ten_landrise.csv')

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
continent               object
iso3                    object
emission               float64
gdp                    float64
pop                    float64
pop_sealevel           float64
location                object
latitude               float64
longitude              float64
country                 object
year                    object
dtype: object

#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
    return dataframe

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

509

data_subset_viz.to_csv('sealevel_viz_psmsl_dataset.csv')

tidedata_series = (data_subset_viz.groupby(['ID'])
       .apply(lambda x: x[['year','tide']].to_dict('r'))
       .rename(columns={0:'tideData'}))
tidedata_series

ID
1       [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
5       [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
7       [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
8       [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
9       [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
10      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
11      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
12      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
14      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
20      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
22      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
23      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
24      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
25      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
32      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
33      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
41      [{'year': 1985, 'tide': 0.0}, {'year': 1985, '...
45      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
47      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
51      [{'year': 1985, 'tide': 0.0}, {'year': 1985, '...
57      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
58      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
61      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
62      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
69      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
70      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
71      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
76      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
78      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
79      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
80      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
81      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
82      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
88      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
89      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
91      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
95      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
96      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
98      [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
111     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
112     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
113     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
118     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
119     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
120     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
126     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
127     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
130     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
131     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
132     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
133     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
134     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
135     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
136     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
137     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
144     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
145     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
148     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
154     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
155     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
158     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
161     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
163     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
165     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
166     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
167     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
172     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
173     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
174     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
175     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
179     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
180     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
183     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
188     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
189     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
192     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
193     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
194     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
195     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
196     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
201     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
202     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
203     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
205     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
213     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
215     [{'year': 1985, 'tide': 0.0}, {'year': 1985, '...
216     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
221     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
224     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
225     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
229     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
230     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
234     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
235     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
236     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
239     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
240     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
245     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
246     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
248     [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
                              ...                        
1347    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1349    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1351    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1352    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1353    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1354    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1364    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1365    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1370    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1371    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1386    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1387    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1388    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1389    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1390    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1391    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1392    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1393    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1394    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1395    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1397    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1420    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1421    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1437    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1438    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1440    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1441    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1442    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1444    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1445    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1446    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1447    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1448    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1449    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1450    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1468    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1469    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1473    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1474    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1475    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1488    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1489    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1490    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1495    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1505    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1522    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1527    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1544    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1545    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1546    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1549    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1567    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1568    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1585    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1586    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1587    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1588    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1589    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1590    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1591    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1592    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1593    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1594    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1595    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1597    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1600    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1627    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1628    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1629    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1630    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1631    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1633    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1634    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1635    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1636    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1637    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1638    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1639    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1640    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1641    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1643    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1644    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1645    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
1801    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
2021    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
2101    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
2103    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
2104    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
2105    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
2106    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
2107    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
2110    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
2111    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
2112    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
2113    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
2127    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
2261    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
2262    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
2295    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
2316    [{'year': 1985, 'tide': 0.0}, {'year': 1986, '...
dtype: object

cols = "ID location	latitude	longitude	country	emission	gdp	pop	pop_sealevel	continent".split()
j = data_subset_viz[cols].groupby("ID").first()
       #.to_json(orient='records', date_format='iso'))
j["tideData"] = tidedata_series
j = j.reset_index()
j = j.to_json(orient='records')

with open('data/subsets/mapanimation.json', 'w') as fp:
    json.dump(json.loads(j), fp, indent=2, sort_keys=True)

print(json.dumps(json.loads(j), indent=2, sort_keys=True))

[
  {
    "ID": 1,
    "continent": "Europe",
    "country": "FRA",
    "emission": 5.0,
    "gdp": 2418835532882.0,
    "latitude": 48.383,
    "location": "Brest",
    "longitude": -4.495,
    "pop": 66808385.0,
    "pop_sealevel": 3.0,
    "tideData": [
      {
        "tide": 0.0,
        "year": 1985
      },
      {
        "tide": -1.0,
        "year": 1986
      },
      {
        "tide": 4.0,
        "year": 1987
      },
      {
        "tide": 9.0,
        "year": 1988
      },
      {
        "tide": 15.0,
        "year": 1989
      },
      {
        "tide": 16.0,
        "year": 1990
      },
      {
        "tide": 15.0,
        "year": 1991
      },
      {
        "tide": 8.0,
        "year": 1992
      },
      {
        "tide": 5.0,
        "year": 1993
      },
      {
        "tide": 7.0,
        "year": 1994
      },
      {
        "tide": 13.0,
        "year": 1995
      },
      {
        "tide": 24.0,
        "year": 1996
      },
      {
        "tide": 35.0,
        "year": 1997
      },
      {
        "tide": 38.0,
        "year": 1998
      },
      {
        "tide": 37.0,
        "year": 1999
      },
      {
        "tide": 41.0,
        "year": 2000
      },
      {
        "tide": 45.0,
        "year": 2001
      },
      {
        "tide": 50.0,
        "year": 2002
      },
      {
        "tide": 55.0,
        "year": 2003
      },
      {
        "tide": 56.0,
        "year": 2004
      },
      {
        "tide": 44.0,
        "year": 2005
      },
      {
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      {
        "tide": -53.0,
        "year": 1998
      },
      {
        "tide": -78.0,
        "year": 1999
      },
      {
        "tide": -80.0,
        "year": 2000
      },
      {
        "tide": -53.0,
        "year": 2001
      },
      {
        "tide": -61.0,
        "year": 2002
      },
      {
        "tide": -63.0,
        "year": 2003
      },
      {
        "tide": -66.0,
        "year": 2004
      },
      {
        "tide": -62.0,
        "year": 2005
      },
      {
        "tide": -108.0,
        "year": 2006
      },
      {
        "tide": -141.0,
        "year": 2007
      },
      {
        "tide": -127.0,
        "year": 2008
      },
      {
        "tide": -116.0,
        "year": 2009
      },
      {
        "tide": -141.0,
        "year": 2010
      },
      {
        "tide": -115.0,
        "year": 2011
      },
      {
        "tide": -100.0,
        "year": 2012
      },
      {
        "tide": -113.0,
        "year": 2013
      },
      {
        "tide": -112.0,
        "year": 2014
      }
    ]
  },
  {
    "ID": 2295,
    "continent": "North America",
    "country": "USA",
    "emission": 16.0,
    "gdp": 18036648000000.0,
    "latitude": 34.72,
    "location": "Beaufort, North Carolina",
    "longitude": -76.67,
    "pop": 321418820.0,
    "pop_sealevel": 3.0,
    "tideData": [
      {
        "tide": 0.0,
        "year": 1985
      },
      {
        "tide": 16.0,
        "year": 1986
      },
      {
        "tide": 25.0,
        "year": 1987
      },
      {
        "tide": 12.0,
        "year": 1988
      },
      {
        "tide": 4.0,
        "year": 1989
      },
      {
        "tide": -1.0,
        "year": 1990
      },
      {
        "tide": 1.0,
        "year": 1991
      },
      {
        "tide": 9.0,
        "year": 1992
      },
      {
        "tide": 23.0,
        "year": 1993
      },
      {
        "tide": 29.0,
        "year": 1994
      },
      {
        "tide": 41.0,
        "year": 1995
      },
      {
        "tide": 39.0,
        "year": 1996
      },
      {
        "tide": 41.0,
        "year": 1997
      },
      {
        "tide": 45.0,
        "year": 1998
      },
      {
        "tide": 58.0,
        "year": 1999
      },
      {
        "tide": 57.0,
        "year": 2000
      },
      {
        "tide": 56.0,
        "year": 2001
      },
      {
        "tide": 51.0,
        "year": 2002
      },
      {
        "tide": 45.0,
        "year": 2003
      },
      {
        "tide": 33.0,
        "year": 2004
      },
      {
        "tide": 42.0,
        "year": 2005
      },
      {
        "tide": 48.0,
        "year": 2006
      },
      {
        "tide": 51.0,
        "year": 2007
      },
      {
        "tide": 54.0,
        "year": 2008
      },
      {
        "tide": 69.0,
        "year": 2009
      },
      {
        "tide": 67.0,
        "year": 2010
      },
      {
        "tide": 73.0,
        "year": 2011
      },
      {
        "tide": 86.0,
        "year": 2012
      },
      {
        "tide": 94.0,
        "year": 2013
      },
      {
        "tide": 105.0,
        "year": 2014
      }
    ]
  },
  {
    "ID": 2316,
    "continent": "Oceania",
    "country": "AUS",
    "emission": 16.0,
    "gdp": 1339140527498.0,
    "latitude": -32.715,
    "location": "Port Stephens (Tomaree)",
    "longitude": 152.182,
    "pop": 23781169.0,
    "pop_sealevel": 5.0,
    "tideData": [
      {
        "tide": 0.0,
        "year": 1985
      },
      {
        "tide": -4.0,
        "year": 1986
      },
      {
        "tide": -5.0,
        "year": 1987
      },
      {
        "tide": -3.0,
        "year": 1988
      },
      {
        "tide": 3.0,
        "year": 1989
      },
      {
        "tide": 9.0,
        "year": 1990
      },
      {
        "tide": 11.0,
        "year": 1991
      },
      {
        "tide": 13.0,
        "year": 1992
      },
      {
        "tide": -1.0,
        "year": 1993
      },
      {
        "tide": -16.0,
        "year": 1994
      },
      {
        "tide": -31.0,
        "year": 1995
      },
      {
        "tide": -33.0,
        "year": 1996
      },
      {
        "tide": -44.0,
        "year": 1997
      },
      {
        "tide": -16.0,
        "year": 1998
      },
      {
        "tide": 0.0,
        "year": 1999
      },
      {
        "tide": 17.0,
        "year": 2000
      },
      {
        "tide": 36.0,
        "year": 2001
      },
      {
        "tide": 51.0,
        "year": 2002
      },
      {
        "tide": 40.0,
        "year": 2003
      },
      {
        "tide": 34.0,
        "year": 2004
      },
      {
        "tide": 29.0,
        "year": 2005
      },
      {
        "tide": 15.0,
        "year": 2006
      },
      {
        "tide": 15.0,
        "year": 2007
      },
      {
        "tide": 19.0,
        "year": 2008
      },
      {
        "tide": 26.0,
        "year": 2009
      },
      {
        "tide": 29.0,
        "year": 2010
      },
      {
        "tide": 43.0,
        "year": 2011
      },
      {
        "tide": 51.0,
        "year": 2012
      },
      {
        "tide": 68.0,
        "year": 2013
      },
      {
        "tide": 67.0,
        "year": 2014
      }
    ]
  }
]

Create subsets for countries¶

def save_country_subset(df, country):
    filename = 'data/sealevel_subset_' + str(country) + '.csv'
    data = df[(df.Country == country)]
    data.to_csv(filename, encoding='utf-8')
    return data

df_us = save_country_subset(df, 'ARG')

df_phl = df_phl.groupby(['ID']).apply(rolling_mean)

df_phl = df_phl.reset_index()

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

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

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

df_phl.to_csv('data/sealevel_subset_ISL.csv', encoding='utf-8')

save_country_subset(df, 'GEO')

def count_positives(df):
    counter = 0
    for index, row in df.iterrows():
        if row['trend'] < 0:
            counter += 1
    return counter

brest = df[(df.Location == 'Brest')]

brest['normalisiert'] = brest['tide']/brest['tide'].max().astype(np.float64)

brest = brest[['Latitude', 'Longitude', 'normalisiert', 'timestamp']]

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  app.launch_new_instance()

brest.to_csv('brest.csv')

Location	Aarhus	Abashiri	Aberdeen I	Aburatsu	Aburatsubo	Acapulco	Adak Sweeper Cove	Akune	Alameda (Naval Air Station)	Alesund	...	Wick	Wilmington	Wismar 2	Woods Hole (Ocean. Inst.)	Yakutat	Yarmouth	Yeosu	Yokosuka	Zeebrugge	Zhapo
timestamp
1807-12-31	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1812-12-31	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1817-12-31	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1822-12-31	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1827-12-31	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Location	Grindavik	Reykjavik
timestamp
2015-08-01	NaN	7125.0
2015-09-01	NaN	7085.0
2015-10-01	NaN	7176.0
2015-11-01	NaN	7221.0
2015-12-01	NaN	7264.0

Location	Balanacan, Marinduque	Balintang, Quezon, Palawan	Caticlan Malay Aklan	Cebu	Currimao Ilocos Norte	Davao, Davao Gulf	Jolo, Sulu	Legaspi, Albay	Macabalan Port, Cagayan De Oro	Makar, General Santos City	...	Pulupandan, Negros Occidental	Real Quezon	San Fernando, La Union	San Jose	Subic Zambales	Surigao	Tacloban, Leyte	Tandag, Surigao Del Sur	Virac, Catanduanes	Zamboanga City
timestamp
2010-12-31	NaN	NaN	NaN	7066.0	NaN	7123.0	NaN	7283.0	NaN	NaN	...	NaN	7100.0	NaN	7078.0	NaN	7135.0	NaN	NaN	NaN	NaN
2011-12-31	7029.0	6990.0	6988.0	7081.0	7018.0	7192.0	NaN	7345.0	6940.0	7006.0	...	6954.0	7108.0	NaN	7133.0	6954.0	7112.0	NaN	6958.0	6990.0	7027.0
2012-12-31	7064.0	7009.0	7000.0	7114.0	7116.0	7216.0	NaN	7385.0	6962.0	7059.0	...	NaN	7187.0	NaN	7137.0	6979.0	7148.0	NaN	7068.0	NaN	7055.0
2013-12-31	7050.0	NaN	6850.0	7082.0	7107.0	7257.0	NaN	7365.0	6981.0	7098.0	...	6988.0	7136.0	NaN	7128.0	6820.0	7147.0	NaN	7065.0	7159.0	7005.0
2014-12-31	6908.0	NaN	6827.0	6914.0	6931.0	7254.0	NaN	7268.0	6935.0	7041.0	...	6936.0	7164.0	NaN	7057.0	6799.0	7080.0	NaN	6934.0	7108.0	6896.0

		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

	location	latitude	longitude	gloss id	country	date	coastline	station	id	flag
0	REYKJAVIK	64.151	-21.940	229.0	ISL	24/01/2017	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	17/01/2017	25	1	541	N
4	BARENTSBURG II (SPITSBERGEN)	78.067	14.250	231.0	SJM	17/01/2003	25	2	547	N

	tide	timestamp	ID
0	6980.0	1956-05-01	638
1	6840.0	1956-06-01	638
2	6890.0	1956-07-01	638
3	6830.0	1956-08-01	638
4	6830.0	1956-09-01	638

	continent	iso3	ID	emission	gdp	pop	pop_sealevel
0	Antarctica	ATA	988	NaN	NaN	NaN	NaN
1	Antarctica	ATA	913	NaN	NaN	NaN	NaN
2	Antarctica	ATA	1603	NaN	NaN	NaN	NaN
3	Antarctica	ATA	858	NaN	NaN	NaN	NaN
4	Antarctica	ATA	1763	NaN	NaN	NaN	NaN
5	Antarctica	ATA	2029	NaN	NaN	NaN	NaN
6	Antarctica	ATA	1931	NaN	NaN	NaN	NaN
7	Oceania	ASM	539	NaN	NaN	55538.0	3.271014
8	South America	ARG	874	4.462298	5.831686e+11	43416755.0	3.028913
9	South America	ARG	1271	4.462298	5.831686e+11	43416755.0	3.028913
10	South America	ARG	185	4.462298	5.831686e+11	43416755.0	3.028913
11	South America	ARG	181	4.462298	5.831686e+11	43416755.0	3.028913
12	South America	ARG	501	4.462298	5.831686e+11	43416755.0	3.028913
13	South America	ARG	867	4.462298	5.831686e+11	43416755.0	3.028913
14	South America	ARG	870	4.462298	5.831686e+11	43416755.0	3.028913
15	South America	ARG	223	4.462298	5.831686e+11	43416755.0	3.028913
16	South America	ARG	857	4.462298	5.831686e+11	43416755.0	3.028913
17	South America	ARG	819	4.462298	5.831686e+11	43416755.0	3.028913
18	South America	ARG	1542	4.462298	5.831686e+11	43416755.0	3.028913
19	South America	ARG	897	4.462298	5.831686e+11	43416755.0	3.028913
20	South America	ARG	157	4.462298	5.831686e+11	43416755.0	3.028913
21	South America	ARG	832	4.462298	5.831686e+11	43416755.0	3.028913
22	South America	ARG	864	4.462298	5.831686e+11	43416755.0	3.028913
23	Oceania	AUS	935	16.347302	1.339141e+12	23781169.0	4.516621
24	Oceania	AUS	1160	16.347302	1.339141e+12	23781169.0	4.516621
25	Oceania	AUS	835	16.347302	1.339141e+12	23781169.0	4.516621
26	Oceania	AUS	1157	16.347302	1.339141e+12	23781169.0	4.516621
27	Oceania	AUS	1268	16.347302	1.339141e+12	23781169.0	4.516621
28	Oceania	AUS	1749	16.347302	1.339141e+12	23781169.0	4.516621
29	Oceania	AUS	1471	16.347302	1.339141e+12	23781169.0	4.516621
30	Oceania	AUS	953	16.347302	1.339141e+12	23781169.0	4.516621
31	Oceania	AUS	1629	16.347302	1.339141e+12	23781169.0	4.516621
32	Oceania	AUS	1630	16.347302	1.339141e+12	23781169.0	4.516621
33	Oceania	AUS	2074	16.347302	1.339141e+12	23781169.0	4.516621
34	Oceania	AUS	637	16.347302	1.339141e+12	23781169.0	4.516621
35	Oceania	AUS	1492	16.347302	1.339141e+12	23781169.0	4.516621
36	Oceania	AUS	1569	16.347302	1.339141e+12	23781169.0	4.516621
37	Oceania	AUS	564	16.347302	1.339141e+12	23781169.0	4.516621
38	Oceania	AUS	1760	16.347302	1.339141e+12	23781169.0	4.516621
39	Oceania	AUS	2072	16.347302	1.339141e+12	23781169.0	4.516621
40	Oceania	AUS	825	16.347302	1.339141e+12	23781169.0	4.516621
41	Oceania	AUS	1154	16.347302	1.339141e+12	23781169.0	4.516621
42	Oceania	AUS	822	16.347302	1.339141e+12	23781169.0	4.516621
43	Oceania	AUS	319	16.347302	1.339141e+12	23781169.0	4.516621
44	Oceania	AUS	2310	16.347302	1.339141e+12	23781169.0	4.516621
45	Oceania	AUS	2314	16.347302	1.339141e+12	23781169.0	4.516621
46	Oceania	AUS	2319	16.347302	1.339141e+12	23781169.0	4.516621
47	Oceania	AUS	821	16.347302	1.339141e+12	23781169.0	4.516621
48	Oceania	AUS	1229	16.347302	1.339141e+12	23781169.0	4.516621
49	Oceania	AUS	310	16.347302	1.339141e+12	23781169.0	4.516621
50	Oceania	AUS	799	16.347302	1.339141e+12	23781169.0	4.516621
51	Oceania	AUS	2311	16.347302	1.339141e+12	23781169.0	4.516621
52	Oceania	AUS	818	16.347302	1.339141e+12	23781169.0	4.516621
53	Oceania	AUS	2313	16.347302	1.339141e+12	23781169.0	4.516621
54	Oceania	AUS	2316	16.347302	1.339141e+12	23781169.0	4.516621
55	Oceania	AUS	2320	16.347302	1.339141e+12	23781169.0	4.516621
56	Oceania	AUS	320	16.347302	1.339141e+12	23781169.0	4.516621
57	Oceania	AUS	1335	16.347302	1.339141e+12	23781169.0	4.516621
58	Oceania	AUS	267	16.347302	1.339141e+12	23781169.0	4.516621
59	Oceania	AUS	837	16.347302	1.339141e+12	23781169.0	4.516621
60	Oceania	AUS	196	16.347302	1.339141e+12	23781169.0	4.516621
61	Oceania	AUS	65	16.347302	1.339141e+12	23781169.0	4.516621
62	Oceania	AUS	549	16.347302	1.339141e+12	23781169.0	4.516621
63	Oceania	AUS	831	16.347302	1.339141e+12	23781169.0	4.516621
64	Oceania	AUS	2312	16.347302	1.339141e+12	23781169.0	4.516621
65	Oceania	AUS	2315	16.347302	1.339141e+12	23781169.0	4.516621
66	Oceania	AUS	833	16.347302	1.339141e+12	23781169.0	4.516621
67	Oceania	AUS	1033	16.347302	1.339141e+12	23781169.0	4.516621
68	Oceania	AUS	683	16.347302	1.339141e+12	23781169.0	4.516621
69	Oceania	AUS	2071	16.347302	1.339141e+12	23781169.0	4.516621
70	Oceania	AUS	1118	16.347302	1.339141e+12	23781169.0	4.516621
71	Oceania	AUS	1216	16.347302	1.339141e+12	23781169.0	4.516621
72	Oceania	AUS	838	16.347302	1.339141e+12	23781169.0	4.516621
73	Oceania	AUS	1836	16.347302	1.339141e+12	23781169.0	4.516621
74	Oceania	AUS	1547	16.347302	1.339141e+12	23781169.0	4.516621
75	Oceania	AUS	871	16.347302	1.339141e+12	23781169.0	4.516621
76	Oceania	AUS	304	16.347302	1.339141e+12	23781169.0	4.516621
77	Oceania	AUS	515	16.347302	1.339141e+12	23781169.0	4.516621
78	Oceania	AUS	1069	16.347302	1.339141e+12	23781169.0	4.516621
79	Oceania	AUS	677	16.347302	1.339141e+12	23781169.0	4.516621
80	Oceania	AUS	1506	16.347302	1.339141e+12	23781169.0	4.516621
81	Oceania	AUS	448	16.347302	1.339141e+12	23781169.0	4.516621
82	Oceania	AUS	443	16.347302	1.339141e+12	23781169.0	4.516621
83	Oceania	AUS	1550	16.347302	1.339141e+12	23781169.0	4.516621
84	Oceania	AUS	516	16.347302	1.339141e+12	23781169.0	4.516621
85	Oceania	AUS	631	16.347302	1.339141e+12	23781169.0	4.516621
86	Oceania	AUS	492	16.347302	1.339141e+12	23781169.0	4.516621
87	Oceania	AUS	383	16.347302	1.339141e+12	23781169.0	4.516621
88	Oceania	AUS	1420	16.347302	1.339141e+12	23781169.0	4.516621
89	Oceania	AUS	216	16.347302	1.339141e+12	23781169.0	4.516621
90	Oceania	AUS	574	16.347302	1.339141e+12	23781169.0	4.516621
91	Oceania	AUS	1367	16.347302	1.339141e+12	23781169.0	4.516621
92	Oceania	AUS	493	16.347302	1.339141e+12	23781169.0	4.516621
93	Oceania	AUS	402	16.347302	1.339141e+12	23781169.0	4.516621
94	Oceania	AUS	230	16.347302	1.339141e+12	23781169.0	4.516621
95	Oceania	AUS	386	16.347302	1.339141e+12	23781169.0	4.516621
96	Oceania	AUS	1114	16.347302	1.339141e+12	23781169.0	4.516621
97	Oceania	AUS	957	16.347302	1.339141e+12	23781169.0	4.516621
98	Oceania	AUS	834	16.347302	1.339141e+12	23781169.0	4.516621
99	Oceania	AUS	111	16.347302	1.339141e+12	23781169.0	4.516621
...	...	...	...	...	...	...	...
1492	North America	USA	1714	16.389758	1.803665e+13	321418820.0	2.512555
1493	North America	USA	1136	16.389758	1.803665e+13	321418820.0	2.512555
1494	North America	USA	1670	16.389758	1.803665e+13	321418820.0	2.512555
1495	North America	USA	199	16.389758	1.803665e+13	321418820.0	2.512555
1496	North America	USA	428	16.389758	1.803665e+13	321418820.0	2.512555
1497	North America	USA	1638	16.389758	1.803665e+13	321418820.0	2.512555
1498	North America	USA	520	16.389758	1.803665e+13	321418820.0	2.512555
1499	North America	USA	1106	16.389758	1.803665e+13	321418820.0	2.512555
1500	North America	USA	1107	16.389758	1.803665e+13	321418820.0	2.512555
1501	North America	USA	188	16.389758	1.803665e+13	321418820.0	2.512555
1502	North America	USA	1701	16.389758	1.803665e+13	321418820.0	2.512555
1503	North America	USA	1187	16.389758	1.803665e+13	321418820.0	2.512555
1504	North America	USA	1424	16.389758	1.803665e+13	321418820.0	2.512555
1505	North America	USA	363	16.389758	1.803665e+13	321418820.0	2.512555
1506	North America	USA	1717	16.389758	1.803665e+13	321418820.0	2.512555
1507	North America	USA	1696	16.389758	1.803665e+13	321418820.0	2.512555
1508	North America	USA	1669	16.389758	1.803665e+13	321418820.0	2.512555
1509	North America	USA	270	16.389758	1.803665e+13	321418820.0	2.512555
1510	North America	USA	1182	16.389758	1.803665e+13	321418820.0	2.512555
1511	North America	USA	1858	16.389758	1.803665e+13	321418820.0	2.512555
1512	North America	USA	316	16.389758	1.803665e+13	321418820.0	2.512555
1513	North America	USA	716	16.389758	1.803665e+13	321418820.0	2.512555
1514	North America	USA	2123	16.389758	1.803665e+13	321418820.0	2.512555
1515	North America	USA	112	16.389758	1.803665e+13	321418820.0	2.512555
1516	North America	USA	395	16.389758	1.803665e+13	321418820.0	2.512555
1517	North America	USA	1537	16.389758	1.803665e+13	321418820.0	2.512555
1518	North America	USA	234	16.389758	1.803665e+13	321418820.0	2.512555
1519	North America	USA	1721	16.389758	1.803665e+13	321418820.0	2.512555
1520	North America	USA	1651	16.389758	1.803665e+13	321418820.0	2.512555
1521	North America	USA	1720	16.389758	1.803665e+13	321418820.0	2.512555
1522	North America	USA	1444	16.389758	1.803665e+13	321418820.0	2.512555
1523	North America	USA	862	16.389758	1.803665e+13	321418820.0	2.512555
1524	North America	USA	1431	16.389758	1.803665e+13	321418820.0	2.512555
1525	North America	USA	2294	16.389758	1.803665e+13	321418820.0	2.512555
1526	North America	USA	396	16.389758	1.803665e+13	321418820.0	2.512555
1527	North America	USA	2295	16.389758	1.803665e+13	321418820.0	2.512555
1528	North America	USA	719	16.389758	1.803665e+13	321418820.0	2.512555
1529	North America	USA	1636	16.389758	1.803665e+13	321418820.0	2.512555
1530	North America	USA	945	16.389758	1.803665e+13	321418820.0	2.512555
1531	North America	USA	399	16.389758	1.803665e+13	321418820.0	2.512555
1532	North America	USA	462	16.389758	1.803665e+13	321418820.0	2.512555
1533	North America	USA	1635	16.389758	1.803665e+13	321418820.0	2.512555
1534	North America	USA	299	16.389758	1.803665e+13	321418820.0	2.512555
1535	North America	USA	597	16.389758	1.803665e+13	321418820.0	2.512555
1536	North America	USA	1295	16.389758	1.803665e+13	321418820.0	2.512555
1537	North America	USA	481	16.389758	1.803665e+13	321418820.0	2.512555
1538	North America	USA	360	16.389758	1.803665e+13	321418820.0	2.512555
1539	North America	USA	971	16.389758	1.803665e+13	321418820.0	2.512555
1540	North America	USA	412	16.389758	1.803665e+13	321418820.0	2.512555
1541	North America	USA	1203	16.389758	1.803665e+13	321418820.0	2.512555
1542	North America	USA	311	16.389758	1.803665e+13	321418820.0	2.512555
1543	North America	USA	148	16.389758	1.803665e+13	321418820.0	2.512555
1544	North America	USA	1338	16.389758	1.803665e+13	321418820.0	2.512555
1545	North America	USA	636	16.389758	1.803665e+13	321418820.0	2.512555
1546	North America	USA	1337	16.389758	1.803665e+13	321418820.0	2.512555
1547	North America	USA	224	16.389758	1.803665e+13	321418820.0	2.512555
1548	North America	USA	2292	16.389758	1.803665e+13	321418820.0	2.512555
1549	North America	USA	135	16.389758	1.803665e+13	321418820.0	2.512555
1550	North America	USA	1654	16.389758	1.803665e+13	321418820.0	2.512555
1551	North America	USA	786	16.389758	1.803665e+13	321418820.0	2.512555
1552	North America	USA	1153	16.389758	1.803665e+13	321418820.0	2.512555
1553	North America	USA	180	16.389758	1.803665e+13	321418820.0	2.512555
1554	North America	USA	2293	16.389758	1.803665e+13	321418820.0	2.512555
1555	North America	USA	366	16.389758	1.803665e+13	321418820.0	2.512555
1556	North America	USA	12	16.389758	1.803665e+13	321418820.0	2.512555
1557	North America	USA	1637	16.389758	1.803665e+13	321418820.0	2.512555
1558	North America	USA	519	16.389758	1.803665e+13	321418820.0	2.512555
1559	North America	USA	875	16.389758	1.803665e+13	321418820.0	2.512555
1560	North America	USA	848	16.389758	1.803665e+13	321418820.0	2.512555
1561	North America	USA	362	16.389758	1.803665e+13	321418820.0	2.512555
1562	North America	USA	856	16.389758	1.803665e+13	321418820.0	2.512555
1563	North America	USA	1068	16.389758	1.803665e+13	321418820.0	2.512555
1564	North America	USA	429	16.389758	1.803665e+13	321418820.0	2.512555
1565	North America	USA	430	16.389758	1.803665e+13	321418820.0	2.512555
1566	North America	USA	351	16.389758	1.803665e+13	321418820.0	2.512555
1567	North America	USA	776	16.389758	1.803665e+13	321418820.0	2.512555
1568	North America	USA	367	16.389758	1.803665e+13	321418820.0	2.512555
1569	North America	USA	1111	16.389758	1.803665e+13	321418820.0	2.512555
1570	North America	USA	775	16.389758	1.803665e+13	321418820.0	2.512555
1571	North America	USA	235	16.389758	1.803665e+13	321418820.0	2.512555
1572	North America	USA	288	16.389758	1.803665e+13	321418820.0	2.512555
1573	North America	USA	2291	16.389758	1.803665e+13	321418820.0	2.512555
1574	North America	USA	183	16.389758	1.803665e+13	321418820.0	2.512555
1575	North America	USA	1279	16.389758	1.803665e+13	321418820.0	2.512555
1576	North America	USA	525	16.389758	1.803665e+13	321418820.0	2.512555
1577	North America	USA	1524	16.389758	1.803665e+13	321418820.0	2.512555
1578	North America	USA	1081	16.389758	1.803665e+13	321418820.0	2.512555
1579	North America	USA	332	16.389758	1.803665e+13	321418820.0	2.512555
1580	North America	VIR	1393	NaN	NaN	103574.0	5.444845
1581	North America	VIR	1447	NaN	NaN	103574.0	5.444845
1582	North America	VIR	2118	NaN	NaN	103574.0	5.444845
1583	North America	VIR	2119	NaN	NaN	103574.0	5.444845
1584	South America	URY	433	2.231639	5.344270e+10	3431555.0	4.114019
1585	South America	URY	431	2.231639	5.344270e+10	3431555.0	4.114019
1586	South America	URY	434	2.231639	5.344270e+10	3431555.0	4.114019
1587	South America	URY	764	2.231639	5.344270e+10	3431555.0	4.114019
1588	South America	VEN	782	6.128009	NaN	31108083.0	2.496107
1589	South America	VEN	1053	6.128009	NaN	31108083.0	2.496107
1590	Oceania	WSM	1840	1.251930	7.610379e+08	193228.0	1.444597
1591	Asia	YEM	44	0.992680	3.773392e+10	26832215.0	1.581226

	ID	End	Missing Data Points	Start	Valid Data Points
0	1	2014	0	1985	30
1	1246	2014	0	1985	30
2	1242	2014	0	1985	30
3	1244	2014	0	1985	30
4	523	2014	0	1985	30
5	522	2014	0	1985	30
6	521	2014	0	1985	30
7	520	2014	0	1985	30
8	1245	2014	0	1985	30
9	518	2014	0	1985	30
10	513	2014	0	1985	30
11	510	2014	0	1985	30
12	489	2014	0	1985	30
13	509	2014	0	1985	30
14	508	2014	0	1985	30
15	1247	2014	0	1985	30
16	1252	2014	0	1985	30
17	499	2014	0	1985	30
18	498	2014	0	1985	30
19	497	2014	0	1985	30
20	1253	2014	0	1985	30
21	1263	2014	0	1985	30
22	526	2014	0	1985	30
23	1241	2014	0	1985	30
24	1239	2014	0	1985	30
25	538	2014	0	1985	30
26	595	2014	0	1985	30
27	1157	2014	0	1985	30
28	1158	2014	0	1985	30
29	567	2014	0	1985	30
30	566	2014	0	1985	30
31	1193	2014	0	1985	30
32	564	2014	0	1985	30
33	563	2014	0	1985	30
34	1209	2014	0	1985	30
35	1210	2014	0	1985	30
36	1211	2014	0	1985	30
37	1212	2014	0	1985	30
38	1213	2014	0	1985	30
39	1233	2014	0	1985	30
40	1234	2014	0	1985	30
41	1236	2014	0	1985	30
42	541	2014	0	1985	30
43	1237	2014	0	1985	30
44	539	2014	0	1985	30
45	1264	2014	0	1985	30
46	1265	2014	0	1985	30
47	394	2014	0	1985	30
48	412	2014	0	1985	30
49	431	2014	0	1985	30
50	430	2014	0	1985	30
51	429	2014	0	1985	30
52	1328	2014	0	1985	30
53	427	2014	0	1985	30
54	426	2014	0	1985	30
55	1347	2014	0	1985	30
56	1349	2014	0	1985	30
57	413	2014	0	1985	30
58	1351	2014	0	1985	30
59	487	2014	0	1985	30
60	808	2014	0	1985	30
61	1353	2014	0	1985	30
62	1354	2014	0	1985	30
63	407	2014	0	1985	30
64	405	2014	0	1985	30
65	1365	2014	0	1985	30
66	1370	2014	0	1985	30
67	1371	2014	0	1985	30
68	396	2014	0	1985	30
69	1327	2014	0	1985	30
70	1325	2014	0	1985	30
71	1324	2014	0	1985	30
72	437	2014	0	1985	30
73	486	2014	0	1985	30
74	485	2014	0	1985	30
75	484	2014	0	1985	30
76	483	2014	0	1985	30
77	1269	2014	0	1985	30
78	1272	2014	0	1985	30
79	1274	2014	0	1985	30
80	1293	2014	0	1985	30
81	467	2014	0	1985	30
82	1294	2014	0	1985	30
83	1295	2014	0	1985	30
84	1299	2014	0	1985	30
85	453	2014	0	1985	30
86	1320	2014	0	1985	30
87	448	2014	0	1985	30
88	447	2014	0	1985	30
89	1321	2014	0	1985	30
90	445	2014	0	1985	30
91	1322	2014	0	1985	30
92	1155	2014	0	1985	30
93	1154	2014	0	1985	30
94	1153	2014	0	1985	30
95	934	2014	0	1985	30
96	724	2014	0	1985	30
97	723	2014	0	1985	30
98	722	2014	0	1985	30
99	721	2014	0	1985	30
...	...	...	...	...	...
426	368	2014	2	1985	28
427	255	2012	2	1985	28
428	1549	2014	2	1985	28
430	98	2012	0	1985	28
432	1444	2014	2	1985	28
433	82	2012	0	1985	28
435	113	2012	0	1985	28
437	1032	2013	1	1985	28
438	956	2014	2	1985	28
440	132	2014	2	1985	28
442	173	2012	0	1985	28
445	1640	2014	2	1985	28
446	120	2012	0	1985	28
447	1392	2013	1	1985	28
448	81	2012	0	1985	28
449	703	2012	0	1985	28
450	80	2012	0	1985	28
451	91	2012	0	1985	28
452	729	2012	0	1985	28
453	1115	2014	2	1985	28
454	468	2014	2	1985	28
455	470	2012	2	1985	28
456	76	2012	0	1985	28
457	1489	2012	0	1985	28
459	762	2012	0	1985	28
460	89	2012	0	1985	28
461	1070	2012	0	1985	28
465	410	2014	2	1985	28
466	810	2012	0	1985	28
467	1801	2014	2	1985	28
468	1197	2012	1	1985	27
470	1149	2011	3	1985	27
471	1215	2011	0	1985	27
472	1266	2011	0	1985	27
474	446	2012	3	1985	27
477	635	2011	1	1985	27
478	640	2011	0	1985	27
479	1345	2011	0	1985	27
480	1364	2011	1	1985	27
481	1346	2011	2	1985	27
483	1232	2011	0	1985	27
484	131	2011	1	1985	27
486	1448	2014	3	1985	27
487	820	2014	3	1985	27
488	910	2014	3	1985	27
491	61	2014	3	1985	27
492	1468	2014	3	1985	27
493	828	2011	0	1985	27
494	118	2011	0	1985	27
497	685	2011	0	1985	27
498	205	2012	2	1985	26
499	601	2012	2	1985	26
501	425	2014	4	1985	26
503	1329	2012	2	1985	26
507	1473	2014	4	1985	26
508	1156	2014	4	1985	26
511	593	2012	4	1985	26
515	543	2012	2	1985	26
516	1421	2014	4	1985	26
517	374	2014	4	1985	26
518	5	2014	4	1985	26
520	7	2010	0	1985	26
521	258	2014	4	1985	26
525	1637	2014	4	1985	26
526	1192	2014	4	1985	26
527	1036	2010	0	1985	26
528	1037	2010	0	1985	26
529	569	2010	0	1985	26
531	826	2014	5	1985	25
535	1297	2014	5	1985	25
536	1270	2012	3	1985	25
538	471	2014	5	1985	25
542	634	2011	2	1985	25
543	137	2014	5	1985	25
544	136	2014	5	1985	25
546	1638	2014	5	1985	25
548	397	2014	5	1985	25
549	1395	2012	3	1985	25
551	438	2012	3	1985	25
563	1161	2012	4	1985	24
565	409	2014	6	1985	24
568	605	2013	5	1985	24
570	51	2014	6	1985	24
571	1469	2014	6	1985	24
577	411	2012	6	1985	24
578	213	2014	6	1985	24
579	71	2014	6	1985	24
584	375	2010	3	1985	23
588	414	2012	5	1985	23
589	1628	2014	7	1985	23
602	600	2013	6	1985	23
614	1195	2014	7	1985	23
617	836	2014	8	1985	22
640	369	2012	6	1985	22
641	1643	2014	8	1985	22
650	469	2014	8	1985	22
658	936	2014	9	1985	21
667	474	2014	9	1985	21
671	612	2012	7	1985	21
677	2021	2014	9	1985	21

	ID	End	Missing Data Points	Start	Valid Data Points	tide	flag for attention	timestamp	Location	Latitude	Longitude	GLOSS ID	Country	Date	Coastline	Station	Flag
0	1	2015	19	1807	190	6905.0	010	1807-01-01	Brest	48.383	-4.495	242.0	FRA	30/08/2016	190	91	N
1	1	2015	19	1807	190	6931.0	010	1807-02-01	Brest	48.383	-4.495	242.0	FRA	30/08/2016	190	91	N
2	1	2015	19	1807	190	6896.0	010	1807-03-01	Brest	48.383	-4.495	242.0	FRA	30/08/2016	190	91	N
3	1	2015	19	1807	190	6953.0	010	1807-04-01	Brest	48.383	-4.495	242.0	FRA	30/08/2016	190	91	N
4	1	2015	19	1807	190	7043.0	010	1807-05-01	Brest	48.383	-4.495	242.0	FRA	30/08/2016	190	91	N

	ID	First	Last	Percent Change	rise	Location	Latitude	Longitude	GLOSS ID	Country	Date	Coastline	Station	id	Flag
0	1	6943.233333	7139.430303	2.825729	up	Brest	48.383	-4.495	242.0	FRA	30/08/2016	190	91	1	N
1	5	6934.850000	7083.570000	2.144531	up	Holyhead	53.314	-4.620	NaN	GBR	31/05/2016	170	191	5	Y
2	7	6822.250000	7149.583333	4.798026	up	Cuxhaven 2	53.867	8.717	284.0	DEU	27/01/2016	140	12	7	N
3	8	6897.683333	7093.300000	2.835976	up	Wismar 2	53.899	11.458	NaN	DEU	18/11/2015	120	22	8	N
4	9	6690.750000	6946.533333	3.822940	up	Maassluis	51.918	4.250	NaN	NLD	14/06/2016	150	61	9	N

	timestamp	ID	End	Missing Data Points	Start	Valid Data Points	tide	Location	Latitude	Longitude	GLOSS ID	Country	Date	Coastline	Station	Flag
0	1970-01-01	1	2015	19	1807	190	7250.0	Brest	48.383	-4.495	242.0	FRA	30/08/2016	190	91	N
1	1970-02-01	1	2015	19	1807	190	7096.0	Brest	48.383	-4.495	242.0	FRA	30/08/2016	190	91	N
2	1970-03-01	1	2015	19	1807	190	6989.0	Brest	48.383	-4.495	242.0	FRA	30/08/2016	190	91	N
3	1970-04-01	1	2015	19	1807	190	6983.0	Brest	48.383	-4.495	242.0	FRA	30/08/2016	190	91	N
4	1970-05-01	1	2015	19	1807	190	6971.0	Brest	48.383	-4.495	242.0	FRA	30/08/2016	190	91	N

	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

	timestamp	ID	tide	location	latitude	longitude	country	year
0	1985-12-31	1	7069.0	Brest	48.383	-4.495	FRA	1985
1	1986-12-31	1	7068.0	Brest	48.383	-4.495	FRA	1986
2	1987-12-31	1	7073.0	Brest	48.383	-4.495	FRA	1987
3	1988-12-31	1	7078.0	Brest	48.383	-4.495	FRA	1988
4	1989-12-31	1	7084.0	Brest	48.383	-4.495	FRA	1989
5	1990-12-31	1	7085.0	Brest	48.383	-4.495	FRA	1990
6	1991-12-31	1	7084.0	Brest	48.383	-4.495	FRA	1991
7	1992-12-31	1	7077.0	Brest	48.383	-4.495	FRA	1992
8	1993-12-31	1	7074.0	Brest	48.383	-4.495	FRA	1993
9	1994-12-31	1	7076.0	Brest	48.383	-4.495	FRA	1994
10	1995-12-31	1	7082.0	Brest	48.383	-4.495	FRA	1995
11	1996-12-31	1	7093.0	Brest	48.383	-4.495	FRA	1996
12	1997-12-31	1	7104.0	Brest	48.383	-4.495	FRA	1997
13	1998-12-31	1	7107.0	Brest	48.383	-4.495	FRA	1998
14	1999-12-31	1	7106.0	Brest	48.383	-4.495	FRA	1999
15	2000-12-31	1	7110.0	Brest	48.383	-4.495	FRA	2000
16	2001-12-31	1	7114.0	Brest	48.383	-4.495	FRA	2001
17	2002-12-31	1	7119.0	Brest	48.383	-4.495	FRA	2002
18	2003-12-31	1	7124.0	Brest	48.383	-4.495	FRA	2003
19	2004-12-31	1	7125.0	Brest	48.383	-4.495	FRA	2004
20	2005-12-31	1	7113.0	Brest	48.383	-4.495	FRA	2005
21	2006-12-31	1	7109.0	Brest	48.383	-4.495	FRA	2006
22	2007-12-31	1	7103.0	Brest	48.383	-4.495	FRA	2007
23	2008-12-31	1	7105.0	Brest	48.383	-4.495	FRA	2008
24	2009-12-31	1	7110.0	Brest	48.383	-4.495	FRA	2009
25	2010-12-31	1	7127.0	Brest	48.383	-4.495	FRA	2010
26	2011-12-31	1	7128.0	Brest	48.383	-4.495	FRA	2011
27	2012-12-31	1	7132.0	Brest	48.383	-4.495	FRA	2012
28	2013-12-31	1	7134.0	Brest	48.383	-4.495	FRA	2013
29	2014-12-31	1	7146.0	Brest	48.383	-4.495	FRA	2014
30	2015-12-31	1	7139.0	Brest	48.383	-4.495	FRA	2015
31	1985-12-31	5	7007.0	Holyhead	53.314	-4.620	GBR	1985
32	1986-12-31	5	7002.0	Holyhead	53.314	-4.620	GBR	1986
33	1987-12-31	5	6999.0	Holyhead	53.314	-4.620	GBR	1987
34	1988-12-31	5	7000.0	Holyhead	53.314	-4.620	GBR	1988
35	1989-12-31	5	7011.0	Holyhead	53.314	-4.620	GBR	1989
36	1990-12-31	5	7016.0	Holyhead	53.314	-4.620	GBR	1990
37	1991-12-31	5	7010.0	Holyhead	53.314	-4.620	GBR	1991
38	1992-12-31	5	7011.0	Holyhead	53.314	-4.620	GBR	1992
39	1993-12-31	5	7008.0	Holyhead	53.314	-4.620	GBR	1993
40	1994-12-31	5	6988.0	Holyhead	53.314	-4.620	GBR	1994
41	1995-12-31	5	6977.0	Holyhead	53.314	-4.620	GBR	1995
42	1996-12-31	5	6997.0	Holyhead	53.314	-4.620	GBR	1996
43	1997-12-31	5	7002.0	Holyhead	53.314	-4.620	GBR	1997
44	1998-12-31	5	7002.0	Holyhead	53.314	-4.620	GBR	1998
45	1999-12-31	5	7010.0	Holyhead	53.314	-4.620	GBR	1999
46	2000-12-31	5	7024.0	Holyhead	53.314	-4.620	GBR	2000
47	2001-12-31	5	7034.0	Holyhead	53.314	-4.620	GBR	2001
48	2002-12-31	5	7046.0	Holyhead	53.314	-4.620	GBR	2002
49	2003-12-31	5	7042.0	Holyhead	53.314	-4.620	GBR	2003
50	2004-12-31	5	7044.0	Holyhead	53.314	-4.620	GBR	2004
51	2005-12-31	5	7037.0	Holyhead	53.314	-4.620	GBR	2005
52	2006-12-31	5	7047.0	Holyhead	53.314	-4.620	GBR	2006
53	2007-12-31	5	7041.0	Holyhead	53.314	-4.620	GBR	2007
54	2008-12-31	5	7040.0	Holyhead	53.314	-4.620	GBR	2008
55	2009-12-31	5	7043.0	Holyhead	53.314	-4.620	GBR	2009
56	2010-12-31	5	7055.0	Holyhead	53.314	-4.620	GBR	2010
57	2011-12-31	5	7057.0	Holyhead	53.314	-4.620	GBR	2011
58	2012-12-31	5	7074.0	Holyhead	53.314	-4.620	GBR	2012
59	2013-12-31	5	7078.0	Holyhead	53.314	-4.620	GBR	2013
60	2014-12-31	5	7088.0	Holyhead	53.314	-4.620	GBR	2014
61	2015-12-31	5	7084.0	Holyhead	53.314	-4.620	GBR	2015
62	1985-12-31	7	7088.0	Cuxhaven 2	53.867	8.717	DEU	1985
63	1986-12-31	7	7072.0	Cuxhaven 2	53.867	8.717	DEU	1986
64	1987-12-31	7	7064.0	Cuxhaven 2	53.867	8.717	DEU	1987
65	1988-12-31	7	7059.0	Cuxhaven 2	53.867	8.717	DEU	1988
66	1989-12-31	7	7075.0	Cuxhaven 2	53.867	8.717	DEU	1989
67	1990-12-31	7	7104.0	Cuxhaven 2	53.867	8.717	DEU	1990
68	1991-12-31	7	7111.0	Cuxhaven 2	53.867	8.717	DEU	1991
69	1992-12-31	7	7116.0	Cuxhaven 2	53.867	8.717	DEU	1992
70	1993-12-31	7	7097.0	Cuxhaven 2	53.867	8.717	DEU	1993
71	1994-12-31	7	7105.0	Cuxhaven 2	53.867	8.717	DEU	1994
72	1995-12-31	7	7099.0	Cuxhaven 2	53.867	8.717	DEU	1995
73	1996-12-31	7	7078.0	Cuxhaven 2	53.867	8.717	DEU	1996
74	1997-12-31	7	7077.0	Cuxhaven 2	53.867	8.717	DEU	1997
75	1998-12-31	7	7104.0	Cuxhaven 2	53.867	8.717	DEU	1998
76	1999-12-31	7	7108.0	Cuxhaven 2	53.867	8.717	DEU	1999
77	2000-12-31	7	7108.0	Cuxhaven 2	53.867	8.717	DEU	2000
78	2001-12-31	7	7141.0	Cuxhaven 2	53.867	8.717	DEU	2001
79	2002-12-31	7	7151.0	Cuxhaven 2	53.867	8.717	DEU	2002
80	2003-12-31	7	7142.0	Cuxhaven 2	53.867	8.717	DEU	2003
81	2004-12-31	7	7143.0	Cuxhaven 2	53.867	8.717	DEU	2004
82	2005-12-31	7	7138.0	Cuxhaven 2	53.867	8.717	DEU	2005
83	2006-12-31	7	7138.0	Cuxhaven 2	53.867	8.717	DEU	2006
84	2007-12-31	7	7156.0	Cuxhaven 2	53.867	8.717	DEU	2007
85	2008-12-31	7	7167.0	Cuxhaven 2	53.867	8.717	DEU	2008
86	2009-12-31	7	7155.0	Cuxhaven 2	53.867	8.717	DEU	2009
87	2010-12-31	7	7150.0	Cuxhaven 2	53.867	8.717	DEU	2010
88	1985-12-31	8	7048.0	Wismar 2	53.899	11.458	DEU	1985
89	1986-12-31	8	7037.0	Wismar 2	53.899	11.458	DEU	1986
90	1987-12-31	8	7047.0	Wismar 2	53.899	11.458	DEU	1987
91	1988-12-31	8	7048.0	Wismar 2	53.899	11.458	DEU	1988
92	1989-12-31	8	7073.0	Wismar 2	53.899	11.458	DEU	1989
93	1990-12-31	8	7082.0	Wismar 2	53.899	11.458	DEU	1990
94	1991-12-31	8	7077.0	Wismar 2	53.899	11.458	DEU	1991
95	1992-12-31	8	7076.0	Wismar 2	53.899	11.458	DEU	1992
96	1993-12-31	8	7066.0	Wismar 2	53.899	11.458	DEU	1993
97	1994-12-31	8	7046.0	Wismar 2	53.899	11.458	DEU	1994
98	1995-12-31	8	7051.0	Wismar 2	53.899	11.458	DEU	1995
99	1996-12-31	8	7049.0	Wismar 2	53.899	11.458	DEU	1996
...	...	...	...	...	...	...	...	...
15452	2008-12-31	2261	6947.0	Iquique Ii	-20.204	-70.148	CHL	2008
15453	2009-12-31	2261	6948.0	Iquique Ii	-20.204	-70.148	CHL	2009
15454	2010-12-31	2261	6951.0	Iquique Ii	-20.204	-70.148	CHL	2010
15455	2011-12-31	2261	6965.0	Iquique Ii	-20.204	-70.148	CHL	2011
15456	2012-12-31	2261	6972.0	Iquique Ii	-20.204	-70.148	CHL	2012
15457	2013-12-31	2261	6957.0	Iquique Ii	-20.204	-70.148	CHL	2013
15458	2014-12-31	2261	6951.0	Iquique Ii	-20.204	-70.148	CHL	2014
15459	2015-12-31	2261	6967.0	Iquique Ii	-20.204	-70.148	CHL	2015
15460	1985-12-31	2262	6981.0	Corral Ii	-39.878	-73.423	CHL	1985
15461	1986-12-31	2262	6961.0	Corral Ii	-39.878	-73.423	CHL	1986
15462	1987-12-31	2262	6961.0	Corral Ii	-39.878	-73.423	CHL	1987
15463	1988-12-31	2262	6926.0	Corral Ii	-39.878	-73.423	CHL	1988
15464	1989-12-31	2262	6924.0	Corral Ii	-39.878	-73.423	CHL	1989
15465	1990-12-31	2262	6914.0	Corral Ii	-39.878	-73.423	CHL	1990
15466	1991-12-31	2262	6916.0	Corral Ii	-39.878	-73.423	CHL	1991
15467	1992-12-31	2262	6911.0	Corral Ii	-39.878	-73.423	CHL	1992
15468	1993-12-31	2262	6935.0	Corral Ii	-39.878	-73.423	CHL	1993
15469	1994-12-31	2262	6948.0	Corral Ii	-39.878	-73.423	CHL	1994
15470	1995-12-31	2262	6949.0	Corral Ii	-39.878	-73.423	CHL	1995
15471	1996-12-31	2262	6926.0	Corral Ii	-39.878	-73.423	CHL	1996
15472	1997-12-31	2262	6943.0	Corral Ii	-39.878	-73.423	CHL	1997
15473	1998-12-31	2262	6928.0	Corral Ii	-39.878	-73.423	CHL	1998
15474	1999-12-31	2262	6903.0	Corral Ii	-39.878	-73.423	CHL	1999
15475	2000-12-31	2262	6901.0	Corral Ii	-39.878	-73.423	CHL	2000
15476	2001-12-31	2262	6928.0	Corral Ii	-39.878	-73.423	CHL	2001
15477	2002-12-31	2262	6920.0	Corral Ii	-39.878	-73.423	CHL	2002
15478	2003-12-31	2262	6918.0	Corral Ii	-39.878	-73.423	CHL	2003
15479	2004-12-31	2262	6915.0	Corral Ii	-39.878	-73.423	CHL	2004
15480	2005-12-31	2262	6919.0	Corral Ii	-39.878	-73.423	CHL	2005
15481	2006-12-31	2262	6873.0	Corral Ii	-39.878	-73.423	CHL	2006
15482	2007-12-31	2262	6840.0	Corral Ii	-39.878	-73.423	CHL	2007
15483	2008-12-31	2262	6854.0	Corral Ii	-39.878	-73.423	CHL	2008
15484	2009-12-31	2262	6865.0	Corral Ii	-39.878	-73.423	CHL	2009
15485	2010-12-31	2262	6840.0	Corral Ii	-39.878	-73.423	CHL	2010
15486	2011-12-31	2262	6866.0	Corral Ii	-39.878	-73.423	CHL	2011
15487	2012-12-31	2262	6881.0	Corral Ii	-39.878	-73.423	CHL	2012
15488	2013-12-31	2262	6868.0	Corral Ii	-39.878	-73.423	CHL	2013
15489	2014-12-31	2262	6869.0	Corral Ii	-39.878	-73.423	CHL	2014
15490	2015-12-31	2262	6893.0	Corral Ii	-39.878	-73.423	CHL	2015
15491	1985-12-31	2295	7055.0	Beaufort, North Carolina	34.720	-76.670	USA	1985
15492	1986-12-31	2295	7071.0	Beaufort, North Carolina	34.720	-76.670	USA	1986
15493	1987-12-31	2295	7080.0	Beaufort, North Carolina	34.720	-76.670	USA	1987
15494	1988-12-31	2295	7067.0	Beaufort, North Carolina	34.720	-76.670	USA	1988
15495	1989-12-31	2295	7059.0	Beaufort, North Carolina	34.720	-76.670	USA	1989
15496	1990-12-31	2295	7054.0	Beaufort, North Carolina	34.720	-76.670	USA	1990
15497	1991-12-31	2295	7056.0	Beaufort, North Carolina	34.720	-76.670	USA	1991
15498	1992-12-31	2295	7064.0	Beaufort, North Carolina	34.720	-76.670	USA	1992
15499	1993-12-31	2295	7078.0	Beaufort, North Carolina	34.720	-76.670	USA	1993
15500	1994-12-31	2295	7084.0	Beaufort, North Carolina	34.720	-76.670	USA	1994
15501	1995-12-31	2295	7096.0	Beaufort, North Carolina	34.720	-76.670	USA	1995
15502	1996-12-31	2295	7094.0	Beaufort, North Carolina	34.720	-76.670	USA	1996
15503	1997-12-31	2295	7096.0	Beaufort, North Carolina	34.720	-76.670	USA	1997
15504	1998-12-31	2295	7100.0	Beaufort, North Carolina	34.720	-76.670	USA	1998
15505	1999-12-31	2295	7113.0	Beaufort, North Carolina	34.720	-76.670	USA	1999
15506	2000-12-31	2295	7112.0	Beaufort, North Carolina	34.720	-76.670	USA	2000
15507	2001-12-31	2295	7111.0	Beaufort, North Carolina	34.720	-76.670	USA	2001
15508	2002-12-31	2295	7106.0	Beaufort, North Carolina	34.720	-76.670	USA	2002
15509	2003-12-31	2295	7100.0	Beaufort, North Carolina	34.720	-76.670	USA	2003
15510	2004-12-31	2295	7088.0	Beaufort, North Carolina	34.720	-76.670	USA	2004
15511	2005-12-31	2295	7097.0	Beaufort, North Carolina	34.720	-76.670	USA	2005
15512	2006-12-31	2295	7103.0	Beaufort, North Carolina	34.720	-76.670	USA	2006
15513	2007-12-31	2295	7106.0	Beaufort, North Carolina	34.720	-76.670	USA	2007
15514	2008-12-31	2295	7109.0	Beaufort, North Carolina	34.720	-76.670	USA	2008
15515	2009-12-31	2295	7124.0	Beaufort, North Carolina	34.720	-76.670	USA	2009
15516	2010-12-31	2295	7122.0	Beaufort, North Carolina	34.720	-76.670	USA	2010
15517	2011-12-31	2295	7128.0	Beaufort, North Carolina	34.720	-76.670	USA	2011
15518	2012-12-31	2295	7141.0	Beaufort, North Carolina	34.720	-76.670	USA	2012
15519	2013-12-31	2295	7149.0	Beaufort, North Carolina	34.720	-76.670	USA	2013
15520	2014-12-31	2295	7160.0	Beaufort, North Carolina	34.720	-76.670	USA	2014
15521	2015-12-31	2295	7169.0	Beaufort, North Carolina	34.720	-76.670	USA	2015
15522	1985-12-31	2316	7055.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	1985
15523	1986-12-31	2316	7051.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	1986
15524	1987-12-31	2316	7050.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	1987
15525	1988-12-31	2316	7052.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	1988
15526	1989-12-31	2316	7058.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	1989
15527	1990-12-31	2316	7064.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	1990
15528	1991-12-31	2316	7066.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	1991
15529	1992-12-31	2316	7068.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	1992
15530	1993-12-31	2316	7054.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	1993
15531	1994-12-31	2316	7039.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	1994
15532	1995-12-31	2316	7024.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	1995
15533	1996-12-31	2316	7022.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	1996
15534	1997-12-31	2316	7011.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	1997
15535	1998-12-31	2316	7039.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	1998
15536	1999-12-31	2316	7055.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	1999
15537	2000-12-31	2316	7072.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	2000
15538	2001-12-31	2316	7091.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	2001
15539	2002-12-31	2316	7106.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	2002
15540	2003-12-31	2316	7095.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	2003
15541	2004-12-31	2316	7089.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	2004
15542	2005-12-31	2316	7084.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	2005
15543	2006-12-31	2316	7070.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	2006
15544	2007-12-31	2316	7070.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	2007
15545	2008-12-31	2316	7074.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	2008
15546	2009-12-31	2316	7081.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	2009
15547	2010-12-31	2316	7084.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	2010
15548	2011-12-31	2316	7098.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	2011
15549	2012-12-31	2316	7106.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	2012
15550	2013-12-31	2316	7123.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	2013
15551	2014-12-31	2316	7122.0	Port Stephens (Tomaree)	-32.715	152.182	AUS	2014

	tide	missing day	flag for attention	timestamp	ID	Location	Latitude	Longitude	GLOSS ID	Country	Date	Coastline	Station	Flag
221346	5999.0	0	000	1874-01-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221347	5939.0	0	000	1874-02-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221348	5929.0	0	000	1874-03-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221349	5999.0	0	000	1874-04-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221350	6099.0	0	000	1874-05-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221351	6249.0	0	000	1874-06-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221352	6149.0	0	000	1874-07-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221353	6049.0	0	000	1874-08-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221354	5919.0	0	000	1874-09-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221355	5829.0	0	000	1874-10-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221356	5819.0	0	000	1874-11-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221357	6009.0	0	000	1874-12-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221358	6059.0	0	000	1875-01-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221359	6099.0	0	000	1875-02-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221360	6099.0	0	000	1875-03-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221361	6129.0	0	000	1875-04-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221362	6179.0	0	000	1875-05-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221363	6219.0	0	000	1875-06-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221364	6189.0	0	000	1875-07-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221365	6199.0	0	000	1875-08-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221366	6099.0	0	000	1875-09-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221367	6059.0	0	000	1875-10-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221368	6189.0	0	000	1875-11-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221369	6229.0	0	000	1875-12-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221370	6069.0	0	000	1876-01-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221371	6029.0	0	000	1876-02-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221372	6179.0	0	000	1876-03-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221373	6159.0	0	000	1876-04-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221374	6269.0	0	000	1876-05-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221375	6239.0	0	000	1876-06-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221376	6259.0	0	000	1876-07-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221377	6149.0	0	000	1876-08-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221378	6099.0	0	000	1876-09-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221379	6029.0	0	000	1876-10-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221380	6159.0	0	000	1876-11-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221381	6219.0	0	000	1876-12-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221382	6219.0	0	000	1877-01-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221383	6199.0	0	000	1877-02-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221384	6229.0	0	000	1877-03-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221385	6369.0	0	000	1877-04-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221386	6429.0	0	000	1877-05-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221387	6439.0	0	000	1877-06-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221388	6359.0	0	000	1877-07-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221389	6259.0	0	000	1877-08-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221390	6129.0	0	000	1877-09-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221391	6049.0	0	000	1877-10-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221392	6049.0	0	000	1877-11-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221393	6089.0	0	000	1877-12-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221394	6229.0	0	000	1878-01-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221395	6099.0	0	000	1878-02-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221396	6169.0	0	000	1878-03-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221397	6409.0	0	000	1878-04-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221398	6379.0	0	000	1878-05-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221399	6359.0	0	000	1878-06-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221400	6339.0	0	000	1878-07-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221401	6249.0	0	000	1878-08-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221402	6199.0	0	000	1878-09-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221403	6059.0	0	000	1878-10-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221404	6069.0	0	000	1878-11-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221405	6239.0	0	000	1878-12-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221406	6359.0	0	000	1879-01-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221407	6299.0	0	000	1879-02-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221408	6359.0	0	000	1879-03-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221409	6369.0	0	000	1879-04-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221410	6379.0	0	000	1879-05-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221411	6399.0	0	000	1879-06-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221412	6329.0	0	000	1879-07-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221413	6179.0	0	000	1879-08-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221414	6159.0	0	000	1879-09-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221415	6119.0	0	000	1879-10-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221416	6119.0	0	000	1879-11-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221417	6309.0	0	000	1879-12-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221418	6109.0	0	000	1880-01-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221419	6059.0	0	000	1880-02-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221420	6029.0	0	000	1880-03-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221421	6219.0	0	000	1880-04-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221422	6299.0	0	000	1880-05-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221423	6329.0	0	000	1880-06-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221424	6289.0	0	000	1880-07-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221425	6299.0	0	000	1880-08-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221426	6179.0	0	000	1880-09-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221427	6169.0	0	000	1880-10-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221428	6219.0	0	000	1880-11-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221429	6119.0	0	000	1880-12-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221430	6279.0	0	000	1881-01-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221431	6349.0	0	000	1881-02-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221432	6369.0	0	000	1881-03-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221433	6469.0	0	000	1881-04-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221434	6459.0	0	000	1881-05-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221435	6439.0	0	000	1881-06-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221436	6459.0	0	000	1881-07-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221437	6289.0	0	000	1881-08-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221438	6239.0	0	000	1881-09-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221439	6229.0	0	000	1881-10-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221440	6139.0	0	000	1881-11-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221441	6109.0	0	000	1881-12-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221442	6189.0	0	000	1882-01-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221443	6089.0	0	000	1882-02-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221444	6129.0	0	000	1882-03-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
221445	6219.0	0	000	1882-04-01	41	POTI	42.167	41.683	NaN	GEO	21/06/2016	305	21	N
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
645922	6904.0	0	000	2007-09-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645923	6954.0	0	000	2007-10-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645924	7074.0	0	000	2007-11-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645925	7124.0	0	000	2007-12-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645926	7154.0	0	000	2008-01-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645927	7174.0	0	000	2008-02-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645928	7204.0	0	000	2008-03-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645929	7124.0	0	000	2008-04-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645930	7014.0	0	000	2008-05-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645931	6954.0	0	000	2008-06-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645932	6914.0	0	000	2008-07-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645933	6924.0	0	000	2008-08-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645934	6934.0	0	000	2008-09-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645935	6994.0	0	000	2008-10-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645936	7014.0	0	000	2008-11-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645937	7084.0	0	000	2008-12-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645938	7144.0	0	000	2009-01-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645939	7154.0	0	000	2009-02-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645940	7144.0	0	000	2009-03-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645941	7094.0	0	000	2009-04-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645942	6994.0	0	000	2009-05-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645943	6894.0	0	000	2009-06-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645944	6874.0	0	000	2009-07-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645945	6904.0	0	000	2009-08-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645946	6924.0	0	000	2009-09-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645947	6924.0	0	000	2009-10-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645948	7064.0	0	000	2009-11-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645949	7104.0	0	000	2009-12-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645950	7164.0	0	000	2010-01-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645951	7144.0	0	000	2010-02-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645952	7124.0	0	000	2010-03-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645953	7084.0	0	000	2010-04-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645954	7004.0	0	000	2010-05-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645955	6864.0	0	000	2010-06-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645956	6864.0	0	000	2010-07-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645957	6854.0	0	000	2010-08-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645958	6944.0	0	000	2010-09-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645959	7074.0	0	000	2010-10-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645960	6984.0	0	000	2010-11-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645961	7024.0	0	000	2010-12-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645962	7094.0	0	000	2011-01-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645963	7134.0	0	000	2011-02-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645964	7094.0	0	000	2011-03-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645965	7114.0	0	000	2011-04-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645966	7024.0	0	000	2011-05-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645967	6934.0	0	000	2011-06-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645968	6864.0	0	000	2011-07-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645969	6914.0	0	000	2011-08-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645970	6944.0	0	000	2011-09-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645971	7004.0	0	000	2011-10-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645972	7094.0	0	000	2011-11-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645973	7124.0	0	000	2011-12-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645974	7124.0	0	000	2012-01-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645975	7114.0	0	000	2012-02-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645976	7124.0	0	000	2012-03-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645977	6974.0	0	000	2012-04-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645978	6984.0	0	000	2012-05-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645979	6894.0	0	000	2012-06-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645980	6894.0	0	000	2012-07-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645981	6934.0	0	000	2012-08-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645982	6904.0	0	000	2012-09-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645983	6924.0	0	000	2012-10-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645984	6954.0	0	000	2012-11-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645985	6974.0	0	000	2012-12-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645986	6984.0	0	000	2013-01-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645987	6954.0	0	000	2013-02-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645988	6984.0	0	000	2013-03-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645989	6944.0	0	000	2013-04-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645990	6914.0	0	000	2013-05-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645991	6904.0	0	000	2013-06-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645992	6924.0	0	000	2013-07-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645993	6894.0	0	000	2013-08-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645994	6934.0	0	000	2013-09-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645995	6914.0	0	000	2013-10-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645996	6904.0	0	000	2013-11-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645997	6974.0	0	000	2013-12-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645998	6914.0	0	000	2014-01-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
645999	6894.0	0	000	2014-02-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646000	6944.0	0	000	2014-03-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646001	6914.0	0	000	2014-04-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646002	6874.0	0	000	2014-05-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646003	6894.0	0	000	2014-06-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646004	6894.0	0	000	2014-07-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646005	6964.0	0	000	2014-08-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646006	6944.0	0	000	2014-09-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646007	6904.0	0	000	2014-10-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646008	6954.0	0	000	2014-11-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646009	6934.0	0	000	2014-12-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646010	6944.0	0	000	2015-01-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646011	6934.0	0	000	2015-02-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646012	6944.0	0	000	2015-03-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646013	6944.0	0	000	2015-04-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646014	6904.0	0	000	2015-05-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646015	6924.0	0	000	2015-06-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646016	6894.0	0	000	2015-07-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646017	6924.0	0	000	2015-08-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646018	6884.0	0	000	2015-09-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646019	6954.0	0	000	2015-10-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646020	6964.0	0	000	2015-11-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y
646021	6974.0	0	000	2015-12-01	51	BATUMI	41.633	41.700	NaN	GEO	06/07/2016	305	31	Y