Groundwater Level Dossier (GLD)
import matplotlib.pyplot as plt
import pandas as pd
import brodata
Download a GroundwaterLevelDossier with GroundwaterLevelDossier.from_bro_id(bro_id). The method returns an object containing the data from the BRO XML file.
gld = brodata.gld.GroundwaterLevelDossier.from_bro_id("GLD000000012893")
gld
GLD000000012893 contains 9781 duplicates (of 36460). Keeping only first values (sorted for importance).
GroundwaterLevelDossier(broId='GLD000000012893')
Observation data is available in the observation attribute as a pandas DataFrame.
df = gld.observation
df
| value | qualifier | status | observation_type | |
|---|---|---|---|---|
| time | ||||
| 2020-09-14 01:00:00 | 0.790 | goedgekeurd | voorlopig | reguliereMeting |
| 2020-09-14 02:00:00 | 0.790 | goedgekeurd | voorlopig | reguliereMeting |
| 2020-09-14 03:00:00 | 0.788 | goedgekeurd | voorlopig | reguliereMeting |
| 2020-09-14 04:00:03 | 0.787 | goedgekeurd | voorlopig | reguliereMeting |
| 2020-09-14 05:00:03 | 0.792 | goedgekeurd | voorlopig | reguliereMeting |
| ... | ... | ... | ... | ... |
| 2026-06-16 05:00:00 | 0.929 | goedgekeurd | voorlopig | reguliereMeting |
| 2026-06-16 06:00:00 | 0.925 | goedgekeurd | voorlopig | reguliereMeting |
| 2026-06-16 07:00:00 | 0.926 | goedgekeurd | voorlopig | reguliereMeting |
| 2026-06-24 14:00:50 | 0.925 | goedgekeurd | voorlopig | reguliereMeting |
| 2026-06-30 05:01:16 | 0.950 | goedgekeurd | voorlopig | reguliereMeting |
26679 rows × 4 columns
Create a time-series plot that uses different colors for each metadata combination. The helper function plot_series below implements this.
def plot_series(df):
f, ax = plt.subplots(figsize=(10, 8))
columns = ["qualifier", "status", "observation_type"]
for qualifier, status, observation_type in df[columns].drop_duplicates().values:
mask = df["qualifier"] == qualifier
if pd.isna(status):
mask = mask & pd.isna(df["status"])
else:
mask = mask & (df["status"] == status)
mask = mask & (df["observation_type"] == observation_type)
if status is None:
label = f"{observation_type} {qualifier}"
else:
label = f"{observation_type} {status} {qualifier}"
if mask.sum() > 100:
linestyle = "-"
marker = None
else:
linestyle = "none"
marker = "o"
df.loc[mask, "value"].plot(label=label, linestyle=linestyle, marker=marker)
ax.legend()
return f, ax
plot_series(df);
We can look at all other contents of this GroundwaterLevelDossier by using the to_dict() method.
gld_data = gld.to_dict()
gld_data.pop("observation")
gld_data
{'broId': 'GLD000000012893',
'corrected': 'nee',
'deliveryAccountableParty': '62251686',
'deregistered': 'nee',
'groundwaterMonitoringNet': 'GMN000000000163',
'groundwaterMonitoringWell': 'GMW000000036287',
'id': 'BRO_0267',
'latestAdditionTime': '2026-07-08T07:02:30+02:00',
'objectRegistrationTime': '2022-03-02T16:45:28+01:00',
'qualityRegime': 'IMBRO',
'registrationStatus': 'aangevuld',
'reregistered': 'nee',
'researchFirstDate': '2020-09-14',
'researchLastDate': '2026-06-30',
'tubeNumber': 1,
'underReview': 'nee'}
Multiple objects
All measurements of one tube
Download all GroundwaterLevelDossiers for a particular tube (piezometer) using brodata.gmw.get_tube_observations.
df = brodata.gmw.get_tube_observations("GMW000000017757", 1)
GMW000000017757_1 contains 185508 duplicates (of 274166). Keeping only first values (sorted for importance).
Plot the resulting pandas DataFrame again using the plot_series function defined above.
plot_series(df);
All measurements of one well
Download all measurements of a Groundwater Monitoring Well using brodata.gmw.get_observations(groundwaterMonitoringWell_id). The result is a GeoDataFrame where each row contains an observation DataFrame for a monitoring tube and a list of Groundwater Level Dossier BRO IDs.
gdf = brodata.gmw.get_observations("GMW000000017757")
gdf = gdf.set_index(["groundwaterMonitoringWell", "tubeNumber"]).sort_index()
GLD000000018830 contains 1656 duplicates (of 2693). Keeping only first values (sorted for importance).
GLD000000018832 contains 183662 duplicates (of 271473). Keeping only first values (sorted for importance).
GLD000000018833 contains 1646 duplicates (of 2691). Keeping only first values (sorted for importance).
GLD000000018834 contains 219197 duplicates (of 306986). Keeping only first values (sorted for importance).
GLD000000018856 contains 1630 duplicates (of 2692). Keeping only first values (sorted for importance).
GLD000000018835 contains 203701 duplicates (of 291490). Keeping only first values (sorted for importance).
GLD000000018836 contains 653 duplicates (of 1333). Keeping only first values (sorted for importance).
GLD000000018838 contains 1651 duplicates (of 2692). Keeping only first values (sorted for importance).
GLD000000018839 contains 169151 duplicates (of 257575). Keeping only first values (sorted for importance).
GLD000000018846 contains 1608 duplicates (of 2669). Keeping only first values (sorted for importance).
GLD000000018840 contains 184735 duplicates (of 272102). Keeping only first values (sorted for importance).
GLD000000018841 contains 1611 duplicates (of 2673). Keeping only first values (sorted for importance).
GLD000000018842 contains 260924 duplicates (of 348696). Keeping only first values (sorted for importance).
gdf
| corrected | deliveryAccountableParty | deregistered | id | latestAdditionTime | latestCorrectionTime | objectRegistrationTime | observation | qualityRegime | registrationStatus | reregistered | researchFirstDate | researchLastDate | underReview | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| groundwaterMonitoringWell | tubeNumber | ||||||||||||||
| GMW000000017757 | 1 | ja | 17278718 | nee | BRO_0018 | 2025-11-11T12:18:27+01:00 | 2026-04-08T11:53:44+02:00 | 2022-07-14T04:17:41+02:00 | value qualifier st... | IMBRO/A | aangevuld | nee | 1983-01-25 | 2011-04-06 | nee |
| 1 | nee | 17278718 | nee | BRO_1914 | 2026-07-08T00:48:55+02:00 | NaN | 2022-07-14T04:36:52+02:00 | value qualifier st... | IMBRO | aangevuld | nee | 2010-08-18 | 2026-07-07 | nee | |
| 2 | ja | 17278718 | nee | BRO_0018 | 2025-11-11T15:15:16+01:00 | 2026-05-08T11:27:10+02:00 | 2022-07-14T04:37:25+02:00 | value qualifier st... | IMBRO/A | aangevuld | nee | 1983-01-25 | 2011-04-12 | nee | |
| 2 | ja | 17278718 | nee | BRO_1916 | 2026-07-07T22:25:28+02:00 | 2026-05-08T11:27:59+02:00 | 2022-07-14T04:58:55+02:00 | value qualifier ... | IMBRO | aangevuld | nee | 2010-08-18 | 2026-07-07 | nee | |
| 3 | ja | 17278718 | nee | BRO_0018 | 2025-11-11T14:40:23+01:00 | 2026-04-08T11:56:15+02:00 | 2022-07-14T06:59:31+02:00 | value qualifier st... | IMBRO/A | aangevuld | nee | 1983-01-25 | 2011-04-30 | nee | |
| 3 | ja | 17278718 | nee | BRO_1886 | 2026-07-07T19:43:36+02:00 | 2026-05-16T20:21:14+02:00 | 2022-07-14T04:59:30+02:00 | value qualifier ... | IMBRO | aangevuld | nee | 2010-08-18 | 2026-07-06 | nee | |
| 4 | ja | 17278718 | nee | BRO_0018 | 2025-11-11T12:33:52+01:00 | 2026-04-08T11:56:36+02:00 | 2022-07-14T05:00:06+02:00 | value qualifier st... | IMBRO/A | aangevuld | nee | 1983-01-25 | 2007-03-14 | nee | |
| 4 | nee | 17278718 | nee | BRO_0004 | 2025-07-29T09:29:15+02:00 | NaN | 2022-07-14T05:01:25+02:00 | value qualifier status... | IMBRO | aangevuld | nee | 2016-09-21 | 2016-09-22 | nee | |
| 5 | ja | 17278718 | nee | BRO_0018 | 2025-11-11T10:52:39+01:00 | 2026-04-08T11:57:53+02:00 | 2022-07-14T05:21:44+02:00 | value qualifier st... | IMBRO/A | aangevuld | nee | 1983-01-25 | 2011-04-12 | nee | |
| 5 | ja | 17278718 | nee | BRO_1880 | 2026-07-08T00:25:33+02:00 | 2026-04-25T20:04:19+02:00 | 2022-07-14T05:22:25+02:00 | value qualifier st... | IMBRO | aangevuld | nee | 1983-01-28 | 2026-07-07 | nee | |
| 6 | ja | 17278718 | nee | BRO_0018 | 2025-11-04T23:08:30+01:00 | 2026-04-08T11:59:10+02:00 | 2022-07-14T06:00:35+02:00 | value qualifier st... | IMBRO/A | aangevuld | nee | 1983-01-25 | 2011-05-12 | nee | |
| 6 | nee | 17278718 | nee | BRO_1912 | 2026-07-07T23:19:55+02:00 | NaN | 2022-07-14T05:26:08+02:00 | value qualifier ... | IMBRO | aangevuld | nee | 2010-08-18 | 2026-07-07 | nee | |
| 7 | ja | 17278718 | nee | BRO_0018 | 2025-11-07T21:53:19+01:00 | 2026-04-12T08:05:09+02:00 | 2022-07-14T05:27:05+02:00 | value qualifier status obse... | IMBRO/A | aangevuld | nee | 1983-01-25 | 2011-05-09 | nee | |
| 7 | ja | 17278718 | nee | BRO_1884 | 2026-07-07T23:49:34+02:00 | 2026-04-12T08:06:45+02:00 | 2022-07-14T05:39:17+02:00 | value qualifier st... | IMBRO | aangevuld | nee | 2010-08-19 | 2026-07-07 | nee |
f, ax = plt.subplots(figsize=(10, 8))
for index in gdf.index.unique():
observations = [x for x in gdf.loc[[index], "observation"] if not x.empty]
df = pd.concat(observations).sort_index()
for qualifier in df["qualifier"].unique():
if pd.isna(qualifier):
continue
mask = df["qualifier"] == qualifier
label = f"{index[0]}_{index[1]} {qualifier}"
if mask.sum() > 100:
linestyle = "-"
marker = None
else:
linestyle = "none"
marker = "o"
df.loc[mask, "value"].plot(label=label, linestyle=linestyle, marker=marker)
plt.legend();
All measurements within an extent (option 1: brodata.gm.get_data_in_extent)
Download groundwater level data within a bounding box using brodata.gm.get_data_in_extent.
This method returns a GeoDataFrame containing metadata for monitoring tubes. Measurements are in the observation column (a DataFrame per tube), and the BRO IDs of the groundwater level dossiers are provided as a list in the groundwaterLevelDossier column.
extent = [118200, 118400, 439700, 440000]
gdf_gm = brodata.gm.get_data_in_extent(extent)
GLD000000012892 contains 20206 duplicates (of 60849). Keeping only first values (sorted for importance).
GLD000000012893 contains 9781 duplicates (of 36460). Keeping only first values (sorted for importance).
gdf_gm.T
| gmw_bro_id | GMW000000036287 | GMW000000036328 | GMW000000088164 |
|---|---|---|---|
| tube_number | 1 | 1 | 1 |
| geometry | POINT (118300.36532391477 439767.46430370974) | POINT (118342.32107156981 439964.2012157428) | POINT (118206.55880049393 439792.29601374094) |
| gm_gmw_monitoringtube_pk | 36386 | 36427 | 101157 |
| gm_gmw.href | https://api.pdok.nl/tno/bro-grondwatermonitori... | https://api.pdok.nl/tno/bro-grondwatermonitori... | https://api.pdok.nl/tno/bro-grondwatermonitori... |
| gm_gmw_fk | 27656 | 27697 | 75834 |
| tube_status | gebruiksklaar | gebruiksklaar | gebruiksklaar |
| tube_in_use | ja | ja | ja |
| tube_top_diameter | 63 | 63 | 40 |
| screen_top_position | 0.142 | 0.795 | 0.86 |
| screen_bottom_position | -0.858 | -0.205 | -0.14 |
| electrode_top_position | None | None | None |
| electrode_bottom_position | None | None | None |
| observation | value qualifier st... | value qualifier st... | value qualifier st... |
| groundwaterLevelDossier | [GLD000000012893] | [GLD000000012892] | [GLD000000153600] |
gdf_gm['observation'].iloc[0]
| value | qualifier | status | observation_type | |
|---|---|---|---|---|
| time | ||||
| 2020-09-14 01:00:00 | 0.790 | goedgekeurd | voorlopig | reguliereMeting |
| 2020-09-14 02:00:00 | 0.790 | goedgekeurd | voorlopig | reguliereMeting |
| 2020-09-14 03:00:00 | 0.788 | goedgekeurd | voorlopig | reguliereMeting |
| 2020-09-14 04:00:03 | 0.787 | goedgekeurd | voorlopig | reguliereMeting |
| 2020-09-14 05:00:03 | 0.792 | goedgekeurd | voorlopig | reguliereMeting |
| ... | ... | ... | ... | ... |
| 2026-06-16 05:00:00 | 0.929 | goedgekeurd | voorlopig | reguliereMeting |
| 2026-06-16 06:00:00 | 0.925 | goedgekeurd | voorlopig | reguliereMeting |
| 2026-06-16 07:00:00 | 0.926 | goedgekeurd | voorlopig | reguliereMeting |
| 2026-06-24 14:00:50 | 0.925 | goedgekeurd | voorlopig | reguliereMeting |
| 2026-06-30 05:01:16 | 0.950 | goedgekeurd | voorlopig | reguliereMeting |
26679 rows × 4 columns
Plot the GeoDataFrame on a map using its plot method.
f, ax = plt.subplots()
ax.axis("scaled")
ax.axis(extent)
gdf_gm.plot(ax=ax);
All measurements within an extent (option 2: brodata.gmw.get_data_in_extent)
The alternative method brodata.gmw.get_data_in_extent first downloads well characteristics, then each GroundwaterMonitoringWell to retrieve tube metadata, and uses the BRO relations API to obtain the relevant Groundwater Level Dossiers (GLD).
Both methods place measurements in the observation column and list the corresponding GLD BRO IDs in groundwaterLevelDossier. The two methods differ in how tube metadata is retrieved.
Because brodata.gmw.get_data_in_extent requires multiple BRO requests (characteristics, GroundwaterMonitoringWell, relations, GroundwaterLevelDossier), the BRO developed a PDOK web service that replaces the first three requests and is faster and less error-prone. For most use cases, brodata.gm.get_data_in_extent (option 1) is the preferred method because it is simpler and returns the key information (tube horizontal and vertical positions).
extent = [118200, 118400, 439700, 440000]
gdf_gmw = brodata.gmw.get_data_in_extent(
extent=extent, kind="gld", combine=True, as_csv=True
)
GLD000000012893 contains 9781 duplicates (of 36460). Keeping only first values (sorted for importance).
GLD000000012892 contains 20206 duplicates (of 60849). Keeping only first values (sorted for importance).
gdf_gmw.T
| groundwaterMonitoringWell | GMW000000036287 | GMW000000036328 | GMW000000088164 |
|---|---|---|---|
| tubeNumber | 1 | 1 | 1 |
| constructionStandard | STOWAgwst | STOWAgwst | NEN5104 |
| coordinateTransformation | RDNAPTRANS2008 | RDNAPTRANS2008 | RDNAPTRANS2018 |
| corrected | nee | nee | nee |
| deliveredLocation | POINT (118300.381 439767.501) | POINT (118342.335 439964.237) | POINT (118206.574 439792.333) |
| deliveryAccountableParty | 62251686 | 62251686 | 62251686 |
| deliveryContext | publiekeTaak | publiekeTaak | publiekeTaak |
| deregistered | nee | nee | nee |
| groundLevelPosition | 1.932 | 2.215 | 2.62 |
| groundLevelPositioningMethod | RTKGPS0tot4cm | RTKGPS0tot4cm | RTKGPS0tot4cm |
| groundLevelStable | ja | ja | ja |
| horizontalPositioningMethod | RTKGPS0tot2cm | RTKGPS0tot2cm | RTKGPS0tot2cm |
| id | BRO_0003 | BRO_0003 | BRO_0003 |
| initialFunction | stand | stand | kwaliteit |
| localVerticalReferencePoint | NAP | NAP | NAP |
| numberOfMonitoringTubes | 1 | 1 | 1 |
| objectRegistrationTime | 2020-09-15T10:17:39+02:00 | 2020-09-15T10:18:09+02:00 | 2025-01-14T11:34:29+01:00 |
| offset | 0.0 | 0.0 | 0.0 |
| owner | 62251686 | 62251686 | 62251686 |
| qualityRegime | IMBRO | IMBRO | IMBRO |
| registrationStatus | geregistreerd | geregistreerd | geregistreerd |
| removed | nee | nee | nee |
| reregistered | nee | nee | nee |
| standardizedLocation | POINT (4.85343324 51.94514556) | POINT (4.85402244 51.94691658) | POINT (4.85206629 51.945362545) |
| underReview | nee | nee | nee |
| verticalDatum | NAP | NAP | NAP |
| wellCode | GMW38B125170 | GMW38B125211 | GMW38B147347 |
| wellConstructionDate | NaT | NaT | NaT |
| wellHeadProtector | potNietWaterdicht | potNietWaterdicht | potNietWaterdicht |
| withPrehistory | nee | nee | nee |
| tubeType | standaardbuis | standaardbuis | standaardbuis |
| artesianWellCapPresent | nee | nee | nee |
| sedimentSumpPresent | nee | nee | nee |
| numberOfGeoOhmCables | 0 | 0 | 0 |
| tubeTopDiameter | 63.0 | 63.0 | 40.0 |
| variableDiameter | nee | nee | nee |
| tubeStatus | gebruiksklaar | gebruiksklaar | gebruiksklaar |
| tubeTopPosition | 1.802 | 2.065 | 2.5 |
| tubeTopPositioningMethod | RTKGPS0tot4cm | RTKGPS0tot4cm | RTKGPS0tot4cm |
| tubePartInserted | nee | nee | nee |
| tubeInUse | ja | ja | ja |
| tubePackingMaterial | bentoniet | bentoniet | bentoniet |
| tubeMaterial | peHighDensity | peHighDensity | pvc |
| glue | geen | geen | geen |
| screenLength | 1.0 | 1.0 | 1.0 |
| sockMaterial | nylon | nylon | nylon |
| screenTopPosition | 0.142 | 0.795 | 0.86 |
| screenBottomPosition | -0.858 | -0.205 | -0.14 |
| plainTubePartLength | 1.66 | 1.27 | 1.64 |
| observation | value qualifier st... | value qualifier st... | value qualifier st... |
| groundwaterLevelDossier | [GLD000000012893] | [GLD000000012892] | [GLD000000153600] |
f, ax = plt.subplots()
ax.axis("scaled")
ax.axis(extent)
gdf_gmw.plot(ax=ax);
Observations summary
Use brodata.gld.get_observations_summary to download a summary of the observations within a Groundwater Level Dossier.
brodata.gld.get_observations_summary("GLD000000012893")
| startDate | endDate | observationType | observationProcessId | observationStatus | definitionProcessExists | |
|---|---|---|---|---|---|---|
| observationId | ||||||
| _b2c67e6a-023e-40f2-b35c-9c084e64fde3 | 2021-06-01 | 2021-09-01 | reguliereMeting | _a70fde89-e249-46e2-b53e-20d2261f9c52 | voorlopig | ja |
| _c1567684-9a7b-443e-8b58-4b2c9ab7d80c | 2021-03-01 | 2021-06-01 | reguliereMeting | _de73efa3-540e-4248-ab69-65dbc403e3bd | voorlopig | ja |
| _c02cf139-8f3e-41c1-abbd-95e56cc1f3fa | 2021-01-01 | 2021-03-01 | reguliereMeting | _d207e131-f660-481c-90a9-d2e970863b7c | voorlopig | ja |
| _b5bffdc6-45ea-4ada-b8e7-d9a0814d4efe | 2020-09-14 | 2021-01-01 | reguliereMeting | _cbf4d0d5-e32c-41bb-8baa-04b4e4dc8795 | voorlopig | ja |
| _0bd66762-5821-40d2-9ff0-4bb74edbb695 | 2022-01-02 | 2022-02-26 | reguliereMeting | _ad212724-16f0-4855-becf-a7ee8e4b422c | voorlopig | ja |
| ... | ... | ... | ... | ... | ... | ... |
| _0421f266-586c-4c65-9e9e-099bcecf1871 | 2023-06-01 | 2023-07-01 | reguliereMeting | _e5fc63d2-02c5-4011-a219-ea5f662be0b3 | voorlopig | ja |
| _1f6bcbed-a4c9-4551-b377-18bf38e8dfb6 | 2023-07-01 | 2023-07-21 | reguliereMeting | _51fc6399-d3cd-400d-a29b-291b28ba930e | voorlopig | ja |
| _0fc0319f-59f9-438a-b18e-a58ea0bc24fc | 2023-08-09 | 2023-08-29 | reguliereMeting | _1dd761d0-6d24-4fec-8548-1c9ca4afda48 | voorlopig | ja |
| _f66b7f05-f86b-46d6-a0a1-c933cdc02607 | 2026-06-24 | 2026-06-24 | reguliereMeting | _37c14390-7c4d-4c98-9523-7167707bc347 | voorlopig | ja |
| _a969ead0-5888-4f9f-83d1-12339056f5f3 | 2026-06-30 | 2026-06-30 | reguliereMeting | _9e16720b-8e5a-41a8-8b99-30867f744916 | voorlopig | ja |
132 rows × 6 columns
Objects as csv
The XML representations of GroundwaterLevelDossier objects can become large and may be relatively slow to parse. To improve performance, the data is also available in CSV format. You can retrieve the data as CSV using the method brodata.gld.get_objects_as_csv(). This method returns a pandas.DataFrame equivalent to the GroundwaterLevelDossier.observation attribute.
You can set the parameter as_csv=True when calling brodata.gm.get_data_in_extent() or brodata.gmw.get_data_in_extent() (described in the previous sections). When enabled, these methods download and process the data using the CSV endpoint instead of the XML files.
df = brodata.gld.get_objects_as_csv("GLD000000012893")
df
GLD000000012893 contains 9781 duplicates (of 36460). Keeping only first values (sorted for importance).
| value | qualifier | status | observation_type | |
|---|---|---|---|---|
| time | ||||
| 2020-09-14 01:00:00 | 0.790 | goedgekeurd | voorlopig | reguliereMeting |
| 2020-09-14 02:00:00 | 0.790 | goedgekeurd | voorlopig | reguliereMeting |
| 2020-09-14 03:00:00 | 0.788 | goedgekeurd | voorlopig | reguliereMeting |
| 2020-09-14 04:00:03 | 0.787 | goedgekeurd | voorlopig | reguliereMeting |
| 2020-09-14 05:00:03 | 0.792 | goedgekeurd | voorlopig | reguliereMeting |
| ... | ... | ... | ... | ... |
| 2026-06-16 05:00:00 | 0.929 | goedgekeurd | voorlopig | reguliereMeting |
| 2026-06-16 06:00:00 | 0.925 | goedgekeurd | voorlopig | reguliereMeting |
| 2026-06-16 07:00:00 | 0.926 | goedgekeurd | voorlopig | reguliereMeting |
| 2026-06-24 14:00:50 | 0.925 | goedgekeurd | voorlopig | reguliereMeting |
| 2026-06-30 05:01:16 | 0.950 | goedgekeurd | voorlopig | reguliereMeting |
26679 rows × 4 columns
plot_series(df);
Series as csv
An alternative is the method brodata.gld.get_series_as_csv. This method retrieves a table with measurements for different observation types (regulier_voorlopig, regulier_beoordeeld, controle en onbekend) as columns, and is intended for applications such as the graphical visualization of groundwater levels.
brodata.gld.get_series_as_csv() returns a different data structure for the observations in the pandas.DataFrame compared to brodata.gld.get_objects_as_csv() or GroundwaterLevelDossier.observation. For this reason, its use is generally not recommended.
df = brodata.gld.get_series_as_csv("GLD000000012893")
df
| Voorlopige Waarde [m] | Voorlopige Opmerking | Beoordeelde Waarde [m] | Beoordeelde Opmerking | Controle Waarde [m] | Controle Opmerking | Onbekend Waarde [m] | Onbekend Opmerking | |
|---|---|---|---|---|---|---|---|---|
| Tijdstip | ||||||||
| 2020-09-14 00:00:00 | 0.790 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2020-09-14 01:00:00 | 0.790 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2020-09-14 02:00:00 | 0.788 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2020-09-14 03:00:03 | 0.787 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2020-09-14 04:00:03 | 0.792 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2026-06-16 04:00:00 | 0.929 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2026-06-16 05:00:00 | 0.925 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2026-06-16 06:00:00 | 0.926 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2026-06-24 13:00:50 | 0.925 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2026-06-30 04:01:16 | 0.950 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
26679 rows × 8 columns
df[["Voorlopige Waarde [m]", "Beoordeelde Waarde [m]", "Controle Waarde [m]", "Onbekend Waarde [m]"]].plot(figsize=(10, 8));
