148 chart data table

DataTable Charts

Uncomment the following line to install geemap if needed.

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# %pip install -U geemap

# %pip install -U geemap

Import libraries¶

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import ee
import geemap
from geemap import chart
import pandas as pd

import ee import geemap from geemap import chart import pandas as pd

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geemap.ee_initialize()

geemap.ee_initialize()

Manual DataTable chart¶

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data = {
    "State": ["CA", "NY", "IL", "MI", "OR"],
    "Population": [37253956, 19378102, 12830632, 9883640, 3831074],
}

df = pd.DataFrame(data)
df

data = { "State": ["CA", "NY", "IL", "MI", "OR"], "Population": [37253956, 19378102, 12830632, 9883640, 3831074], } df = pd.DataFrame(data) df

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fig = chart.Chart(
    df,
    x_cols=["State"],
    y_cols=["Population"],
    chart_type="ColumnChart",
    colors=["#1d6b99"],
    title="State Population (US census, 2010)",
    x_label="State",
    y_label="Population",
)
fig

fig = chart.Chart( df, x_cols=["State"], y_cols=["Population"], chart_type="ColumnChart", colors=["#1d6b99"], title="State Population (US census, 2010)", x_label="State", y_label="Population", ) fig

Computed DataTable chart¶

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# Import the example feature collection and subset the forest feature.
forest = ee.FeatureCollection("projects/google/charts_feature_example").filter(
    ee.Filter.eq("label", "Forest")
)

# Load MODIS vegetation indices data and subset a decade of images.
veg_indices = (
    ee.ImageCollection("MODIS/061/MOD13A1")
    .filter(ee.Filter.date("2010-01-01", "2020-01-01"))
    .select(["NDVI", "EVI"])
)

# Build a feature collection where each feature has a property that represents
# a DataFrame row.


def aggregate(img):
    # Reduce the image to the mean of pixels intersecting the forest ecoregion.
    stat = img.reduceRegion(
        **{"reducer": ee.Reducer.mean(), "geometry": forest, "scale": 500}
    )

    # Extract the reduction results along with the image date.
    date = geemap.image_date(img)
    evi = stat.get("EVI")
    ndvi = stat.get("NDVI")

    # Make a list of observation attributes to define a row in the DataTable.
    row = ee.List([date, evi, ndvi])

    # Return the row as a property of an ee.Feature.
    return ee.Feature(None, {"row": row})


reduction_table = veg_indices.map(aggregate)

# Aggregate the 'row' property from all features in the new feature collection
# to make a server-side 2-D list (DataTable).
data_table_server = reduction_table.aggregate_array("row")

# Define column names and properties for the DataTable. The order should
# correspond to the order in the construction of the 'row' property above.
column_header = ee.List([["Date", "EVI", "NDVI"]])

# Concatenate the column header to the table.
data_table_server = column_header.cat(data_table_server)

# Import the example feature collection and subset the forest feature. forest = ee.FeatureCollection("projects/google/charts_feature_example").filter( ee.Filter.eq("label", "Forest") ) # Load MODIS vegetation indices data and subset a decade of images. veg_indices = ( ee.ImageCollection("MODIS/061/MOD13A1") .filter(ee.Filter.date("2010-01-01", "2020-01-01")) .select(["NDVI", "EVI"]) ) # Build a feature collection where each feature has a property that represents # a DataFrame row. def aggregate(img): # Reduce the image to the mean of pixels intersecting the forest ecoregion. stat = img.reduceRegion( **{"reducer": ee.Reducer.mean(), "geometry": forest, "scale": 500} ) # Extract the reduction results along with the image date. date = geemap.image_date(img) evi = stat.get("EVI") ndvi = stat.get("NDVI") # Make a list of observation attributes to define a row in the DataTable. row = ee.List([date, evi, ndvi]) # Return the row as a property of an ee.Feature. return ee.Feature(None, {"row": row}) reduction_table = veg_indices.map(aggregate) # Aggregate the 'row' property from all features in the new feature collection # to make a server-side 2-D list (DataTable). data_table_server = reduction_table.aggregate_array("row") # Define column names and properties for the DataTable. The order should # correspond to the order in the construction of the 'row' property above. column_header = ee.List([["Date", "EVI", "NDVI"]]) # Concatenate the column header to the table. data_table_server = column_header.cat(data_table_server)

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data_table = chart.DataTable(data_table_server, date_column="Date")
data_table.head()

data_table = chart.DataTable(data_table_server, date_column="Date") data_table.head()

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fig = chart.Chart(
    data_table,
    chart_type="LineChart",
    x_cols="Date",
    y_cols=["EVI", "NDVI"],
    colors=["#e37d05", "#1d6b99"],
    title="Average Vegetation Index Value by Date for Forest",
    x_label="Date",
    y_label="Vegetation index (x1e4)",
    stroke_width=3,
    legend_location="right",
)
fig

fig = chart.Chart( data_table, chart_type="LineChart", x_cols="Date", y_cols=["EVI", "NDVI"], colors=["#e37d05", "#1d6b99"], title="Average Vegetation Index Value by Date for Forest", x_label="Date", y_label="Vegetation index (x1e4)", stroke_width=3, legend_location="right", ) fig

Interval chart¶

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# Define a point to extract an NDVI time series for.
geometry = ee.Geometry.Point([-121.679, 36.479])

# Define a band of interest (NDVI), import the MODIS vegetation index dataset,
# and select the band.
band = "NDVI"
ndvi_col = ee.ImageCollection("MODIS/061/MOD13Q1").select(band)

# Map over the collection to add a day of year (doy) property to each image.


def set_doy(img):
    doy = ee.Date(img.get("system:time_start")).getRelative("day", "year")
    # Add 8 to day of year number so that the doy label represents the middle of
    # the 16-day MODIS NDVI composite.
    return img.set("doy", ee.Number(doy).add(8))


ndvi_col = ndvi_col.map(set_doy)

# Join all coincident day of year observations into a set of image collections.
distinct_doy = ndvi_col.filterDate("2013-01-01", "2014-01-01")
filter = ee.Filter.equals(**{"leftField": "doy", "rightField": "doy"})
join = ee.Join.saveAll("doy_matches")
join_col = ee.ImageCollection(join.apply(distinct_doy, ndvi_col, filter))

# Calculate the absolute range, interquartile range, and median for the set
# of images composing each coincident doy observation group. The result is
# an image collection with an image representative per unique doy observation
# with bands that describe the 0, 25, 50, 75, 100 percentiles for the set of
# coincident doy images.


def cal_percentiles(img):
    doyCol = ee.ImageCollection.fromImages(img.get("doy_matches"))

    return doyCol.reduce(
        ee.Reducer.percentile([0, 25, 50, 75, 100], ["p0", "p25", "p50", "p75", "p100"])
    ).set({"doy": img.get("doy")})


comp = ee.ImageCollection(join_col.map(cal_percentiles))

# Extract the inter-annual NDVI doy percentile statistics for the
# point of interest per unique doy representative. The result is
# is a feature collection where each feature is a doy representative that
# contains a property (row) describing the respective inter-annual NDVI
# variance, formatted as a list of values.


def order_percentiles(img):
    stats = ee.Dictionary(
        img.reduceRegion(
            **{"reducer": ee.Reducer.first(), "geometry": geometry, "scale": 250}
        )
    )

    # Order the percentile reduction elements according to how you want columns
    # in the DataTable arranged (x-axis values need to be first).
    row = ee.List(
        [
            img.get("doy"),
            stats.get(band + "_p50"),
            stats.get(band + "_p0"),
            stats.get(band + "_p25"),
            stats.get(band + "_p75"),
            stats.get(band + "_p100"),
        ]
    )

    # Return the row as a property of an ee.Feature.
    return ee.Feature(None, {"row": row})


reduction_table = comp.map(order_percentiles)

# Aggregate the 'row' properties to make a server-side 2-D array (DataTable).
data_table_server = reduction_table.aggregate_array("row")

# Define column names and properties for the DataTable. The order should
# correspond to the order in the construction of the 'row' property above.
column_header = ee.List([["DOY", "median", "p0", "p25", "p75", "p100"]])

# Concatenate the column header to the table.
data_table_server = column_header.cat(data_table_server)

# Define a point to extract an NDVI time series for. geometry = ee.Geometry.Point([-121.679, 36.479]) # Define a band of interest (NDVI), import the MODIS vegetation index dataset, # and select the band. band = "NDVI" ndvi_col = ee.ImageCollection("MODIS/061/MOD13Q1").select(band) # Map over the collection to add a day of year (doy) property to each image. def set_doy(img): doy = ee.Date(img.get("system:time_start")).getRelative("day", "year") # Add 8 to day of year number so that the doy label represents the middle of # the 16-day MODIS NDVI composite. return img.set("doy", ee.Number(doy).add(8)) ndvi_col = ndvi_col.map(set_doy) # Join all coincident day of year observations into a set of image collections. distinct_doy = ndvi_col.filterDate("2013-01-01", "2014-01-01") filter = ee.Filter.equals(**{"leftField": "doy", "rightField": "doy"}) join = ee.Join.saveAll("doy_matches") join_col = ee.ImageCollection(join.apply(distinct_doy, ndvi_col, filter)) # Calculate the absolute range, interquartile range, and median for the set # of images composing each coincident doy observation group. The result is # an image collection with an image representative per unique doy observation # with bands that describe the 0, 25, 50, 75, 100 percentiles for the set of # coincident doy images. def cal_percentiles(img): doyCol = ee.ImageCollection.fromImages(img.get("doy_matches")) return doyCol.reduce( ee.Reducer.percentile([0, 25, 50, 75, 100], ["p0", "p25", "p50", "p75", "p100"]) ).set({"doy": img.get("doy")}) comp = ee.ImageCollection(join_col.map(cal_percentiles)) # Extract the inter-annual NDVI doy percentile statistics for the # point of interest per unique doy representative. The result is # is a feature collection where each feature is a doy representative that # contains a property (row) describing the respective inter-annual NDVI # variance, formatted as a list of values. def order_percentiles(img): stats = ee.Dictionary( img.reduceRegion( **{"reducer": ee.Reducer.first(), "geometry": geometry, "scale": 250} ) ) # Order the percentile reduction elements according to how you want columns # in the DataTable arranged (x-axis values need to be first). row = ee.List( [ img.get("doy"), stats.get(band + "_p50"), stats.get(band + "_p0"), stats.get(band + "_p25"), stats.get(band + "_p75"), stats.get(band + "_p100"), ] ) # Return the row as a property of an ee.Feature. return ee.Feature(None, {"row": row}) reduction_table = comp.map(order_percentiles) # Aggregate the 'row' properties to make a server-side 2-D array (DataTable). data_table_server = reduction_table.aggregate_array("row") # Define column names and properties for the DataTable. The order should # correspond to the order in the construction of the 'row' property above. column_header = ee.List([["DOY", "median", "p0", "p25", "p75", "p100"]]) # Concatenate the column header to the table. data_table_server = column_header.cat(data_table_server)

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df = chart.DataTable(data_table_server)
df.head()

df = chart.DataTable(data_table_server) df.head()

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fig = chart.Chart(
    df,
    chart_type="IntervalChart",
    x_cols="DOY",
    y_cols=["p0", "p25", "median", "p75", "p100"],
    title="Annual NDVI Time Series with Inter-Annual Variance",
    x_label="Day of Year",
    y_label="Vegetation index (x1e4)",
    stroke_width=1,
    fill="between",
    fill_colors=["#b6d1c6", "#83b191", "#83b191", "#b6d1c6"],
    fill_opacities=[0.6] * 4,
    labels=["p0", "p25", "median", "p75", "p100"],
    display_legend=True,
    legend_location="top-right",
    ylim=(0, 10000),
)
fig

fig = chart.Chart( df, chart_type="IntervalChart", x_cols="DOY", y_cols=["p0", "p25", "median", "p75", "p100"], title="Annual NDVI Time Series with Inter-Annual Variance", x_label="Day of Year", y_label="Vegetation index (x1e4)", stroke_width=1, fill="between", fill_colors=["#b6d1c6", "#83b191", "#83b191", "#b6d1c6"], fill_opacities=[0.6] * 4, labels=["p0", "p25", "median", "p75", "p100"], display_legend=True, legend_location="top-right", ylim=(0, 10000), ) fig