Using dataframes to explore data and make plots

In this example we’ll see some ways to explore data that you can get from pygeodes and make plots

Imports

from pygeodes import Geodes

Configuration

geodes = Geodes()

Searching items

Let’s start by searching recent items in Africa :

from pygeodes.utils.datetime_utils import complete_datetime_from_str

date = complete_datetime_from_str("2024-07-20")

items, dataframe = geodes.search_items(
    query={
        "continent_code": {"eq": "AF"},
        "end_datetime": {"gte": date},
    },
    return_df=True,
    get_all=False,
)

/work/scratch/data/fournih/test_env/lib/python3.11/site-packages/urllib3/connectionpool.py:1099: InsecureRequestWarning: Unverified HTTPS request is being made to host 'geodes-portal.cnes.fr'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#tls-warnings
  warnings.warn(

Found 361266 items matching your query, returning 80 as get_all parameter is set to False
80 item(s) found for query : {'continent_code': {'eq': 'AF'}, 'end_datetime': {'gte': '2024-07-20T00:00:00.000000Z'}}

Let’s add to our result dataframe the column eo:cloud_cover :

from pygeodes.utils.formatting import format_items

dataframe = format_items(dataframe, {"eo:cloud_cover"})

Exploring data and adding colors in function of a parameter

With numerical data

Now we can explore our data on a map and add colors corresponding to cloud cover :

dataframe.explore(column="eo:cloud_cover", cmap="Blues")

Make this Notebook Trusted to load map: File -> Trust Notebook

With literal data

It can also work with literal data, like processing:level :

dataframe = format_items(dataframe, {"processing:level"})

dataframe.explore(column="processing:level", cmap="Dark2")

Make this Notebook Trusted to load map: File -> Trust Notebook

Plotting data

You can also plot without the map canvas, for instance :

import matplotlib.pyplot as plt

dataframe.plot(column="processing:level", legend=True)

<Axes: >

Or even plot without geometry, let’s see :

dataframe.plot(kind="hist", column="eo:cloud_cover", range=(0, 100))

<Axes: ylabel='Frequency'>