title: Earth Engine / Python / EEException: User memory limit exceeded
body: <p>I am trying to load and plot the daily windspeed at a specified location from the GFS0P25 dataset.
  I get the following error &quot;EEException: User memory limit exceeded.&quot; at the line &quot;wind.select('u_component_of_wind_10m_above_ground').filterDate(i_date,f_date)&quot;.</p>
  <p>I am aware of the memory limit of earth engine. <strong>How can I improve the query so that I can load the daily average wind speed and overcome the memory limit?</strong></p>
  <p>The problem is there are many-many rows of data for each location at each time - I am already doing a daily-mean calculation later on in the code, but it doesn't address the memory problem.</p>
  <p>Thanks for your help!</p>
  <p>Note: i've hidden the service account and credentials - please use your own login, thanks!</p>
  <pre><code>import ee
  import pandas as pd
  
  #service_account = 'xxx'
  #credentials = ee.ServiceAccountCredentials(service_account, 'C:/Data/ee-xxxx.json')
  #ee.Initialize(credentials)
  
  # # Trigger the authentication flow.
  ee.Authenticate()
  
  # # Initialize the library.
  ee.Initialize()
  
  wind = ee.ImageCollection('NOAA/GFS0P25')
                                                  
  i_date = '2022-01-01'
  f_date = '2022-07-01'
  wind=wind.select('u_component_of_wind_10m_above_ground').filterDate(i_date,f_date) ####TRACEBACK HERE
  
  u_lon = 21.450520
  u_lat = 63.941972
  u_poi = ee.Geometry.Point(u_lon, u_lat)
  scale = 1000  # scale in meters
  wind_u = wind.getRegion(u_poi, scale).getInfo()
  wind_u[:5]
  
  df = pd.DataFrame(wind_u)
  headers = df.iloc[0]
  df = pd.DataFrame(df.values[1:], columns=headers)
  df['u_component_of_wind_10m_above_ground'] = pd.to_numeric(df['u_component_of_wind_10m_above_ground'], errors='coerce')
  
  df['id'] = df['id'].str.slice(0,8)
  df['id'] = pd.to_datetime(df['id'], format='%Y%m%d')
  # Keep the columns of interest.
  df = df[['id','u_component_of_wind_10m_above_ground']]
  df=df.groupby('id').mean().reset_index()
  
  import plotly.express as px
  import webbrowser
  fig = px.scatter(df, x=&quot;id&quot;, y=&quot;u_component_of_wind_10m_above_ground&quot;)
  fig.show()
  fig.write_html(&quot;c:/data/windchart.html&quot;)
  webbrowser.open(&quot;c:/data/windchart.html&quot;)
  </code></pre>