{ "cells": [ { "cell_type": "markdown", "id": "a9570151", "metadata": {}, "source": [ "# File formats and modern data analysis" ] }, { "cell_type": "markdown", "id": "0302acfb", "metadata": {}, "source": [ "Most of the exciting developments in seamless data analysis, particularly for large Earth Science datasets are happening in python. In recent years, robust and open ecosystems have been developed around tools like xarray and Pandas which make it very easy to load in large data sets, manipulate them in various ways and produce beautiful plots. This notebook will serve as a basic overview of netcdf (in Python) manipulation with xarray and the Pandas/Geopandas packages. Most of this notebook is based on [Project Pythia by Brian Rose](https://foundations.projectpythia.org/) and the [Earth and Environmental Data Science Course](https://earth-env-data-science.github.io/). If you'd like to learn more, the tutorials at the above link are highly recommended." ] }, { "cell_type": "code", "execution_count": 2, "id": "ad4abfd3", "metadata": {}, "outputs": [], "source": [ "from datetime import timedelta\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import xarray as xr\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "markdown", "id": "a7149e4d", "metadata": {}, "source": [ "## xarray" ] }, { "cell_type": "code", "execution_count": 3, "id": "dbb2fc46", "metadata": {}, "outputs": [], "source": [ "data = 283 + 5 * np.random.randn(5, 3, 4) #make a random numpy array" ] }, { "cell_type": "markdown", "id": "cb9b42f7", "metadata": {}, "source": [ "The primary data type in xarray is a \"DataArray\". It works just like a numpy array, but also:\n", "1. Coordinate names and values are stored with the data, making slicing and indexing much more powerful.\n", "\n", "2. Attributes, similar to those in netCDF files, can be stored in a container built into the DataArray." ] }, { "cell_type": "code", "execution_count": 4, "id": "0fc889e8", "metadata": {}, "outputs": [], "source": [ "temp = xr.DataArray(data) #turn numpy array into DataArray" ] }, { "cell_type": "code", "execution_count": 5, "id": "a3f5f794", "metadata": {}, "outputs": [], "source": [ "temp = xr.DataArray(data, dims=['time', 'lat', 'lon']) #assigns names to the three dimensions" ] }, { "cell_type": "markdown", "id": "28ae014f", "metadata": {}, "source": [ "Most modern datasets that include temporal information (time and date when an observation was made) use a format for time called \"datetime\" ([see Project Pythia for detail](https://foundations.projectpythia.org/core/datetime/datetime.html)). What you should know is that datetime format has information as: YYYY-MM-DD HH:MM:SS. This can also be shortened if further precision (i.e. time or minutes) is not available, or can include arbitrarily high precision (decimal seconds). Converting from other time format to datetime can make many different ways of processing data much easier (especially when using Pandas, see later)." ] }, { "cell_type": "code", "execution_count": 6, "id": "55743f11", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',\n", " '2018-01-05'],\n", " dtype='datetime64[ns]', freq='D')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "times = pd.date_range('2018-01-01', periods=5)\n", "times" ] }, { "cell_type": "code", "execution_count": 7, "id": "072ce111", "metadata": {}, "outputs": [], "source": [ "lons = np.linspace(-120, -60, 4)\n", "lats = np.linspace(25, 55, 3)" ] }, { "cell_type": "code", "execution_count": 8, "id": "b0012f80", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2018-01-05\n",
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" ], "text/plain": [ "\n", "array([[[284.3731919 , 280.69406396, 272.63458131, 288.861379 ],\n", " [286.08371827, 293.92505478, 279.98117705, 284.09732089],\n", " [280.48544704, 276.70461341, 280.8361101 , 292.87753072]],\n", "\n", " [[285.18374146, 284.77539863, 277.84078267, 287.28971632],\n", " [288.62604636, 279.36089958, 280.94742249, 284.81648292],\n", " [284.3972094 , 285.39161043, 287.75959911, 278.08258108]],\n", "\n", " [[284.20285521, 273.86316932, 283.40986702, 273.06314051],\n", " [280.92411653, 281.67024583, 284.94829422, 282.34354215],\n", " [283.78733794, 273.81960044, 286.70009539, 284.55548309]],\n", "\n", " [[278.56755805, 269.94938932, 285.02154146, 285.52978898],\n", " [279.26592939, 284.28803678, 289.08551781, 273.03007754],\n", " [283.63122401, 291.03504506, 288.71806512, 286.0750452 ]],\n", "\n", " [[293.04821027, 284.54705147, 290.26563039, 276.23482923],\n", " [278.11692433, 290.20971507, 285.2343849 , 283.74721148],\n", " [278.20545212, 276.78642027, 284.64727083, 281.20318262]]])\n", "Coordinates:\n", " * time (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2018-01-05\n", " * lat (lat) float64 25.0 40.0 55.0\n", " * lon (lon) float64 -120.0 -100.0 -80.0 -60.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp = xr.DataArray(data, coords=[times, lats, lons], dims=['time', 'lat', 'lon'])\n", "temp" ] }, { "cell_type": "markdown", "id": "78a8b6d8", "metadata": {}, "source": [ "Just like we saw with netcdf files, attributes are useful information about variables (like units and names). We can add attributes to the DataArray to convey important metadata about the variables." ] }, { "cell_type": "code", "execution_count": 9, "id": "777ad813", "metadata": {}, "outputs": [], "source": [ "temp.attrs['units'] = 'kelvin'\n", "temp.attrs['standard_name'] = 'air_temperature'" ] }, { "cell_type": "markdown", "id": "93e1ddfa", "metadata": {}, "source": [ "When an operation is performed on a DataArray it loses its attributes in case they have changed (but it stay attached to the original data)" ] }, { "cell_type": "code", "execution_count": 10, "id": "2f09d034", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2018-01-05\n",
       "  * lat      (lat) float64 25.0 40.0 55.0\n",
       "  * lon      (lon) float64 -120.0 -100.0 -80.0 -60.0
" ], "text/plain": [ "\n", "array([[[11.2231919 , 7.54406396, -0.51541869, 15.711379 ],\n", " [12.93371827, 20.77505478, 6.83117705, 10.94732089],\n", " [ 7.33544704, 3.55461341, 7.6861101 , 19.72753072]],\n", "\n", " [[12.03374146, 11.62539863, 4.69078267, 14.13971632],\n", " [15.47604636, 6.21089958, 7.79742249, 11.66648292],\n", " [11.2472094 , 12.24161043, 14.60959911, 4.93258108]],\n", "\n", " [[11.05285521, 0.71316932, 10.25986702, -0.08685949],\n", " [ 7.77411653, 8.52024583, 11.79829422, 9.19354215],\n", " [10.63733794, 0.66960044, 13.55009539, 11.40548309]],\n", "\n", " [[ 5.41755805, -3.20061068, 11.87154146, 12.37978898],\n", " [ 6.11592939, 11.13803678, 15.93551781, -0.11992246],\n", " [10.48122401, 17.88504506, 15.56806512, 12.9250452 ]],\n", "\n", " [[19.89821027, 11.39705147, 17.11563039, 3.08482923],\n", " [ 4.96692433, 17.05971507, 12.0843849 , 10.59721148],\n", " [ 5.05545212, 3.63642027, 11.49727083, 8.05318262]]])\n", "Coordinates:\n", " * time (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2018-01-05\n", " * lat (lat) float64 25.0 40.0 55.0\n", " * lon (lon) float64 -120.0 -100.0 -80.0 -60.0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp_in_celsius = temp - 273.15\n", "temp_in_celsius" ] }, { "cell_type": "markdown", "id": "3d570534", "metadata": {}, "source": [ "A DataSet is an object type in xarray that can hold many different variables (sort of like a netCDF file, but within python's working memory). xarray can figure out automatically if the variables in a dataset share the same dimensions. Here we make one." ] }, { "cell_type": "code", "execution_count": 11, "id": "d299b943", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:      (time: 5, lat: 3, lon: 4)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2018-01-05\n",
       "  * lat          (lat) float64 25.0 40.0 55.0\n",
       "  * lon          (lon) float64 -120.0 -100.0 -80.0 -60.0\n",
       "Data variables:\n",
       "    Temperature  (time, lat, lon) float64 284.4 280.7 272.6 ... 284.6 281.2\n",
       "    Pressure     (time, lat, lon) float64 995.1 1.01e+03 ... 1.004e+03 998.7
" ], "text/plain": [ "\n", "Dimensions: (time: 5, lat: 3, lon: 4)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2018-01-05\n", " * lat (lat) float64 25.0 40.0 55.0\n", " * lon (lon) float64 -120.0 -100.0 -80.0 -60.0\n", "Data variables:\n", " Temperature (time, lat, lon) float64 284.4 280.7 272.6 ... 284.6 281.2\n", " Pressure (time, lat, lon) float64 995.1 1.01e+03 ... 1.004e+03 998.7" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pressure_data = 1000.0 + 5 * np.random.randn(5, 3, 4)\n", "pressure = xr.DataArray(\n", " pressure_data, coords=[times, lats, lons], dims=['time', 'lat', 'lon']\n", ")\n", "pressure.attrs['units'] = 'hPa'\n", "pressure.attrs['standard_name'] = 'air_pressure'\n", "\n", "ds = xr.Dataset(data_vars={'Temperature': temp, 'Pressure': pressure})\n", "ds\n", "#ds.Pressure #to select a variable" ] }, { "cell_type": "markdown", "id": "4dff939a", "metadata": {}, "source": [ "We can index and select data as in a numpy array using [], but also in other clever ways based on the dimensions." ] }, { "cell_type": "code", "execution_count": 12, "id": "8af38d1c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'Temperature' (lat: 3, lon: 4)>\n",
       "array([[285.18374146, 284.77539863, 277.84078267, 287.28971632],\n",
       "       [288.62604636, 279.36089958, 280.94742249, 284.81648292],\n",
       "       [284.3972094 , 285.39161043, 287.75959911, 278.08258108]])\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2018-01-02\n",
       "  * lat      (lat) float64 25.0 40.0 55.0\n",
       "  * lon      (lon) float64 -120.0 -100.0 -80.0 -60.0\n",
       "Attributes:\n",
       "    units:          kelvin\n",
       "    standard_name:  air_temperature
" ], "text/plain": [ "\n", "array([[285.18374146, 284.77539863, 277.84078267, 287.28971632],\n", " [288.62604636, 279.36089958, 280.94742249, 284.81648292],\n", " [284.3972094 , 285.39161043, 287.75959911, 278.08258108]])\n", "Coordinates:\n", " time datetime64[ns] 2018-01-02\n", " * lat (lat) float64 25.0 40.0 55.0\n", " * lon (lon) float64 -120.0 -100.0 -80.0 -60.0\n", "Attributes:\n", " units: kelvin\n", " standard_name: air_temperature" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "named_selection = ds.Temperature.sel(time='2018-01-02')\n", "named_selection" ] }, { "cell_type": "markdown", "id": "57a58b91", "metadata": {}, "source": [ "In this example, we are trying to sample a temporal data point within 2 days of the date 2018-01-07. Since the final date on our DataArray’s temporal axis is 2018-01-05, we can use .sel() to perform nearest-neighbor sampling, by setting the method keyword argument to ‘nearest’." ] }, { "cell_type": "code", "execution_count": 13, "id": "df432de9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray (lat: 3, lon: 4)>\n",
       "array([[293.04821027, 284.54705147, 290.26563039, 276.23482923],\n",
       "       [278.11692433, 290.20971507, 285.2343849 , 283.74721148],\n",
       "       [278.20545212, 276.78642027, 284.64727083, 281.20318262]])\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2018-01-05\n",
       "  * lat      (lat) float64 25.0 40.0 55.0\n",
       "  * lon      (lon) float64 -120.0 -100.0 -80.0 -60.0\n",
       "Attributes:\n",
       "    units:          kelvin\n",
       "    standard_name:  air_temperature
" ], "text/plain": [ "\n", "array([[293.04821027, 284.54705147, 290.26563039, 276.23482923],\n", " [278.11692433, 290.20971507, 285.2343849 , 283.74721148],\n", " [278.20545212, 276.78642027, 284.64727083, 281.20318262]])\n", "Coordinates:\n", " time datetime64[ns] 2018-01-05\n", " * lat (lat) float64 25.0 40.0 55.0\n", " * lon (lon) float64 -120.0 -100.0 -80.0 -60.0\n", "Attributes:\n", " units: kelvin\n", " standard_name: air_temperature" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp.sel(time='2018-01-07', method='nearest', tolerance=timedelta(days=2))" ] }, { "cell_type": "markdown", "id": "4d9a9f24", "metadata": {}, "source": [ "In this example, we are trying to extract a timeseries at 40°N latitude and 105°W longitude. Our DataArray does not contain a longitude data value of -105, so in order to retrieve this timeseries, we must interpolate between data points. .interp() allows us to retrieve data from any latitude and longitude by means of interpolation. This method uses coordinate-value selection, similarly to .sel()." ] }, { "cell_type": "code", "execution_count": 14, "id": "5bf609ab", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray (time: 5)>\n",
       "array([291.96472065, 281.67718627, 281.48371351, 283.03250993,\n",
       "       287.18651738])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2018-01-05\n",
       "    lon      int64 -105\n",
       "    lat      int64 40\n",
       "Attributes:\n",
       "    units:          kelvin\n",
       "    standard_name:  air_temperature
" ], "text/plain": [ "\n", "array([291.96472065, 281.67718627, 281.48371351, 283.03250993,\n", " 287.18651738])\n", "Coordinates:\n", " * time (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2018-01-05\n", " lon int64 -105\n", " lat int64 40\n", "Attributes:\n", " units: kelvin\n", " standard_name: air_temperature" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp.interp(lon=-105, lat=40)" ] }, { "cell_type": "markdown", "id": "f4001acb", "metadata": {}, "source": [ "A slice is a range of data along one or more coordinates. To slice data, you create a slice object and then use it to index a dataset." ] }, { "cell_type": "code", "execution_count": 15, "id": "61375954", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray (time: 3, lat: 2, lon: 2)>\n",
       "array([[[280.69406396, 272.63458131],\n",
       "        [293.92505478, 279.98117705]],\n",
       "\n",
       "       [[284.77539863, 277.84078267],\n",
       "        [279.36089958, 280.94742249]],\n",
       "\n",
       "       [[273.86316932, 283.40986702],\n",
       "        [281.67024583, 284.94829422]]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2018-01-01 2018-01-02 2018-01-03\n",
       "  * lat      (lat) float64 25.0 40.0\n",
       "  * lon      (lon) float64 -100.0 -80.0\n",
       "Attributes:\n",
       "    units:          kelvin\n",
       "    standard_name:  air_temperature
" ], "text/plain": [ "\n", "array([[[280.69406396, 272.63458131],\n", " [293.92505478, 279.98117705]],\n", "\n", " [[284.77539863, 277.84078267],\n", " [279.36089958, 280.94742249]],\n", "\n", " [[273.86316932, 283.40986702],\n", " [281.67024583, 284.94829422]]])\n", "Coordinates:\n", " * time (time) datetime64[ns] 2018-01-01 2018-01-02 2018-01-03\n", " * lat (lat) float64 25.0 40.0\n", " * lon (lon) float64 -100.0 -80.0\n", "Attributes:\n", " units: kelvin\n", " standard_name: air_temperature" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp.sel(time=slice('2018-01-01', '2018-01-03'), lon=slice(-110, -70), lat=slice(25, 45))" ] }, { "cell_type": "markdown", "id": "bfcc86d4", "metadata": {}, "source": [ "## Loading netCDF in Python with xarray" ] }, { "cell_type": "markdown", "id": "405a123c", "metadata": {}, "source": [ "Xarray can easily open netCDF datasets, provided they conform to certain limitations (for example, 1-dimensional coordinates)" ] }, { "cell_type": "code", "execution_count": 16, "id": "a805a774", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:    (longitude: 1440, latitude: 721, time: 6)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * time       (time) datetime64[ns] 2022-01-01 2022-02-01 ... 2022-06-01\n",
       "Data variables:\n",
       "    t2m        (time, latitude, longitude) float32 ...\n",
       "    sp         (time, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2023-08-16 15:10:53 GMT by grib_to_netcdf-2.25.1: /opt/ecmw...
" ], "text/plain": [ "\n", "Dimensions: (longitude: 1440, latitude: 721, time: 6)\n", "Coordinates:\n", " * longitude (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n", " * latitude (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n", " * time (time) datetime64[ns] 2022-01-01 2022-02-01 ... 2022-06-01\n", "Data variables:\n", " t2m (time, latitude, longitude) float32 ...\n", " sp (time, latitude, longitude) float32 ...\n", "Attributes:\n", " Conventions: CF-1.6\n", " history: 2023-08-16 15:10:53 GMT by grib_to_netcdf-2.25.1: /opt/ecmw..." ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = xr.open_dataset('ERA5_2mtemp_SLP_2022.nc')\n", "ds" ] }, { "cell_type": "markdown", "id": "4e0fc24b", "metadata": {}, "source": [ "We can select slices based on coordinates as before, and xarray will select in for all DataArrays within the DataSet" ] }, { "cell_type": "code", "execution_count": 17, "id": "27ba5ade", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:    (longitude: 1440, latitude: 721)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "    time       datetime64[ns] 2022-02-01\n",
       "Data variables:\n",
       "    t2m        (latitude, longitude) float32 ...\n",
       "    sp         (latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2023-08-16 15:10:53 GMT by grib_to_netcdf-2.25.1: /opt/ecmw...
" ], "text/plain": [ "\n", "Dimensions: (longitude: 1440, latitude: 721)\n", "Coordinates:\n", " * longitude (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n", " * latitude (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n", " time datetime64[ns] 2022-02-01\n", "Data variables:\n", " t2m (latitude, longitude) float32 ...\n", " sp (latitude, longitude) float32 ...\n", "Attributes:\n", " Conventions: CF-1.6\n", " history: 2023-08-16 15:10:53 GMT by grib_to_netcdf-2.25.1: /opt/ecmw..." ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_feb = ds.sel(time='2022-02-01')\n", "ds_feb" ] }, { "cell_type": "markdown", "id": "90ed347a", "metadata": {}, "source": [ "You can use named dimensions in an Xarray Dataset to manually slice and index data. However, these dimension names also serve an additional purpose: you can use them to specify dimensions to aggregate on. There are many different aggregation operations available; in this example, we focus on std (standard deviation). Here I am first selecting one variable (temperature) and then taking the mean over time, giving back a 2D DataArray with the mean at each lat/lon coordinate" ] }, { "cell_type": "code", "execution_count": 18, "id": "2dfac716", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 't2m' (latitude: 721, longitude: 1440)>\n",
       "array([[256.01334, 256.01334, 256.01334, ..., 256.01334, 256.01334,\n",
       "        256.01334],\n",
       "       [256.00876, 256.00906, 256.00876, ..., 256.00876, 256.00876,\n",
       "        256.00906],\n",
       "       [256.06802, 256.06827, 256.06854, ..., 256.06775, 256.06802,\n",
       "        256.06827],\n",
       "       ...,\n",
       "       [228.11365, 228.11528, 228.11772, ..., 228.10957, 228.11177,\n",
       "        228.11339],\n",
       "       [228.01544, 228.0168 , 228.01843, ..., 228.0125 , 228.01355,\n",
       "        228.0141 ],\n",
       "       [227.6132 , 227.6132 , 227.6132 , ..., 227.6132 , 227.6132 ,\n",
       "        227.6132 ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0
" ], "text/plain": [ "\n", "array([[256.01334, 256.01334, 256.01334, ..., 256.01334, 256.01334,\n", " 256.01334],\n", " [256.00876, 256.00906, 256.00876, ..., 256.00876, 256.00876,\n", " 256.00906],\n", " [256.06802, 256.06827, 256.06854, ..., 256.06775, 256.06802,\n", " 256.06827],\n", " ...,\n", " [228.11365, 228.11528, 228.11772, ..., 228.10957, 228.11177,\n", " 228.11339],\n", " [228.01544, 228.0168 , 228.01843, ..., 228.0125 , 228.01355,\n", " 228.0141 ],\n", " [227.6132 , 227.6132 , 227.6132 , ..., 227.6132 , 227.6132 ,\n", " 227.6132 ]], dtype=float32)\n", "Coordinates:\n", " * longitude (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n", " * latitude (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t2m = ds['t2m']\n", "t2m.mean(dim=['time'])" ] }, { "cell_type": "markdown", "id": "f7ebf4ca", "metadata": {}, "source": [ "Xarray greatly simplifies plotting of data stored as DataArrays and Datasets. One advantage of this is that many common plot elements, such as axis labels, are automatically generated and optimized for the data being plotted. Xarray includes a built-in plotting interface, which makes use of Matplotlib behind the scenes. In order to use this interface, you can call the .plot() method, which is included in every DataArray." ] }, { "cell_type": "code", "execution_count": 19, "id": "97d1caa6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds.t2m.sel(latitude=33.0,time='2022-02-01').plot()" ] }, { "cell_type": "markdown", "id": "0c3fd55f", "metadata": {}, "source": [ "## Pandas" ] }, { "cell_type": "markdown", "id": "fe4043ed", "metadata": {}, "source": [ "Pandas is a very powerful library for working with tabular data (e.g., spreadsheets, comma-separated-value files, or database printouts; all of these are quite common for geoscientific data). It allows us to use labels for our data; this, in turn, allows us to write expressive and robust code to manipulate the data.\n", "\n", "Key features of Pandas are the abilities to read in tabular data and to slice and dice data, as well as exploratory analysis tools native to the library." ] }, { "cell_type": "markdown", "id": "262a4659", "metadata": {}, "source": [ "A Series represents a one-dimensional array of data. The main difference between a Series and numpy array is that a Series has an index. The index contains the labels that we use to access the data." ] }, { "cell_type": "code", "execution_count": 23, "id": "872dc429", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Mercury 3.000000e+23\n", "Venus 4.870000e+24\n", "Earth 5.970000e+24\n", "dtype: float64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "names = ['Mercury', 'Venus', 'Earth']\n", "values = [0.3e24, 4.87e24, 5.97e24]\n", "masses = pd.Series(values, index=names)\n", "masses" ] }, { "cell_type": "markdown", "id": "47539444", "metadata": {}, "source": [ "Like in xarray, series have built-in plotting methods" ] }, { "cell_type": "code", "execution_count": 24, "id": "5d5cc030", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "masses.plot(kind='bar')" ] }, { "cell_type": "markdown", "id": "0a54906f", "metadata": {}, "source": [ "Arithmetic operations and most numpy function can be applied to Series. An important point is that the Series keep their index during such operations." ] }, { "cell_type": "code", "execution_count": 25, "id": "3c8d9149", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Mercury 6.006452e-46\n", "Venus 2.396820e-48\n", "Earth 1.600655e-48\n", "dtype: float64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.log(masses) / masses**2" ] }, { "cell_type": "markdown", "id": "8d29bce3", "metadata": {}, "source": [ "We can index the series using strings, which is very handy" ] }, { "cell_type": "code", "execution_count": 26, "id": "22039a65", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5.97e+24" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "masses.loc['Earth']" ] }, { "cell_type": "code", "execution_count": 27, "id": "db492501", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Venus 4.870000e+24\n", "Earth 5.970000e+24\n", "dtype: float64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "masses.loc[['Venus', 'Earth']]" ] }, { "cell_type": "code", "execution_count": 28, "id": "7a4316e5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Mercury 3.000000e+23\n", "Venus 4.870000e+24\n", "Earth 5.970000e+24\n", "dtype: float64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "masses.loc['Mercury':'Earth'] #slicing" ] }, { "cell_type": "markdown", "id": "c5d03605", "metadata": {}, "source": [ "There is a lot more to Series, but they are limit to a single “column”. A more useful Pandas data structure is the DataFrame. A DataFrame is basically a bunch of series that share the same index. It’s a lot like a table in a spreadsheet." ] }, { "cell_type": "code", "execution_count": 29, "id": "cf80036a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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massdiameterrotation_period
Mercury3.000000e+234879000.01407.6
Venus4.870000e+2412104000.0NaN
Earth5.970000e+2412756000.023.9
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" ], "text/plain": [ " mass diameter rotation_period\n", "Mercury 3.000000e+23 4879000.0 1407.6\n", "Venus 4.870000e+24 12104000.0 NaN\n", "Earth 5.970000e+24 12756000.0 23.9" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# first we create a dictionary\n", "data = {'mass': [0.3e24, 4.87e24, 5.97e24], # kg\n", " 'diameter': [4879e3, 12_104e3, 12_756e3], # m\n", " 'rotation_period': [1407.6, np.nan, 23.9] # h\n", " }\n", "df = pd.DataFrame(data, index=['Mercury', 'Venus', 'Earth'])\n", "df" ] }, { "cell_type": "markdown", "id": "02eedd21", "metadata": {}, "source": [ "We can do many different arithmetic or statistical operations on a DataFrame" ] }, { "cell_type": "code", "execution_count": 30, "id": "3df61ca6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "mass 3.000000e+23\n", "diameter 4.879000e+06\n", "rotation_period 2.390000e+01\n", "dtype: float64" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.min()" ] }, { "cell_type": "code", "execution_count": 31, "id": "6080d8e9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "mass 3.713333e+24\n", "diameter 9.913000e+06\n", "rotation_period 7.157500e+02\n", "dtype: float64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.mean()" ] }, { "cell_type": "code", "execution_count": 32, "id": "9287ee6d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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massdiameterrotation_period
count3.000000e+003.000000e+002.000000
mean3.713333e+249.913000e+06715.750000
std3.006765e+244.371744e+06978.423653
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75%5.420000e+241.243000e+071061.675000
max5.970000e+241.275600e+071407.600000
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" ], "text/plain": [ " mass diameter rotation_period\n", "count 3.000000e+00 3.000000e+00 2.000000\n", "mean 3.713333e+24 9.913000e+06 715.750000\n", "std 3.006765e+24 4.371744e+06 978.423653\n", "min 3.000000e+23 4.879000e+06 23.900000\n", "25% 2.585000e+24 8.491500e+06 369.825000\n", "50% 4.870000e+24 1.210400e+07 715.750000\n", "75% 5.420000e+24 1.243000e+07 1061.675000\n", "max 5.970000e+24 1.275600e+07 1407.600000" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "id": "04c08aae", "metadata": {}, "source": [ "Indexing is made very easy with dataframes" ] }, { "cell_type": "code", "execution_count": 33, "id": "4d1804c1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Mercury 3.000000e+23\n", "Venus 4.870000e+24\n", "Earth 5.970000e+24\n", "Name: mass, dtype: float64" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['mass']" ] }, { "cell_type": "code", "execution_count": 34, "id": "70e905c2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "mass 5.970000e+24\n", "diameter 1.275600e+07\n", "rotation_period 2.390000e+01\n", "Name: Earth, dtype: float64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['Earth']" ] }, { "cell_type": "code", "execution_count": 35, "id": "612e3b7c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5.97e+24" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['Earth', 'mass']" ] }, { "cell_type": "markdown", "id": "cd86d0bc", "metadata": {}, "source": [ "Adding new columns is as easy as referencing an undefined column" ] }, { "cell_type": "code", "execution_count": 36, "id": "ec260144", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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massdiameterrotation_perioddensity
Mercury3.000000e+234879000.01407.64933.216530
Venus4.870000e+2412104000.0NaN5244.977070
Earth5.970000e+2412756000.023.95493.285577
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" ], "text/plain": [ " mass diameter rotation_period density\n", "Mercury 3.000000e+23 4879000.0 1407.6 4933.216530\n", "Venus 4.870000e+24 12104000.0 NaN 5244.977070\n", "Earth 5.970000e+24 12756000.0 23.9 5493.285577" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['density'] = df.mass / (4/3 * np.pi * (df.diameter/2)**3)\n", "df" ] }, { "cell_type": "markdown", "id": "57a668bb", "metadata": {}, "source": [ "New series or dataframes can be added to existing dataframes" ] }, { "cell_type": "code", "execution_count": 37, "id": "5db0759e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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massdiameterrotation_perioddensitytemperature
Mercury3.000000e+234879000.01407.64933.216530167
Venus4.870000e+2412104000.0NaN5244.977070464
Earth5.970000e+2412756000.023.95493.28557715
MarsNaNNaNNaNNaN-65
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" ], "text/plain": [ " mass diameter rotation_period density temperature\n", "Mercury 3.000000e+23 4879000.0 1407.6 4933.216530 167\n", "Venus 4.870000e+24 12104000.0 NaN 5244.977070 464\n", "Earth 5.970000e+24 12756000.0 23.9 5493.285577 15\n", "Mars NaN NaN NaN NaN -65" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temperature = pd.Series([167, 464, 15, -65],\n", " index=['Mercury', 'Venus', 'Earth', 'Mars'],\n", " name='temperature')\n", "df.join(temperature, how='right')" ] }, { "cell_type": "code", "execution_count": 38, "id": "81df8730", "metadata": {}, "outputs": [], "source": [ "everyone = df.reindex(['Mercury', 'Venus', 'Earth', 'Mars'])" ] }, { "cell_type": "markdown", "id": "5cc179cd", "metadata": {}, "source": [ "Indexing with a boolean is easy" ] }, { "cell_type": "code", "execution_count": 39, "id": "29d039b5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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massdiameterrotation_perioddensity
Venus4.870000e+2412104000.0NaN5244.977070
Earth5.970000e+2412756000.023.95493.285577
\n", "
" ], "text/plain": [ " mass diameter rotation_period density\n", "Venus 4.870000e+24 12104000.0 NaN 5244.977070\n", "Earth 5.970000e+24 12756000.0 23.9 5493.285577" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bigguys = df[df.mass > 4e24]\n", "bigguys" ] }, { "cell_type": "markdown", "id": "2395de5a", "metadata": {}, "source": [ "As in xarray, plotting functions from matplotlib are build directly into Pandas" ] }, { "cell_type": "code", "execution_count": 40, "id": "a05d3c34", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.plot(kind='scatter', x='mass', y='diameter', grid=True)" ] }, { "cell_type": "code", "execution_count": 41, "id": "cc5bd1b7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.plot(kind='bar')" ] }, { "cell_type": "markdown", "id": "d3c58a9d", "metadata": {}, "source": [ "Also like xarray, we can use datetime to index data. Plotting with datetime is very nice" ] }, { "cell_type": "code", "execution_count": 42, "id": "7f91742c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "two_years = pd.date_range(start='2014-01-01', end='2016-01-01', freq='D')\n", "timeseries = pd.Series(np.sin(2 *np.pi *two_years.dayofyear / 365),\n", " index=two_years)\n", "timeseries.plot()" ] }, { "cell_type": "code", "execution_count": 43, "id": "3cd77e66", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "timeseries.loc['2015-01-01':'2015-07-01'].plot() #slicing" ] }, { "cell_type": "markdown", "id": "3a50e696", "metadata": {}, "source": [ "Loading uncompressed data directly into a dataframe makes manipulating the data much easier. Here we need to know that the file is space-delimited, not comma delimited which is the default assumption of the read_csv function." ] }, { "cell_type": "code", "execution_count": 44, "id": "e751946b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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365 rows × 28 columns

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" ], "text/plain": [ " WBANNO LST_DATE CRX_VN LONGITUDE LATITUDE T_DAILY_MAX T_DAILY_MIN \\\n", "0 64756 20170101 2.422 -73.74 41.79 6.6 -5.4 \n", "1 64756 20170102 2.422 -73.74 41.79 4.0 -6.8 \n", "2 64756 20170103 2.422 -73.74 41.79 4.9 0.7 \n", "3 64756 20170104 2.422 -73.74 41.79 8.7 -1.6 \n", "4 64756 20170105 2.422 -73.74 41.79 -0.5 -4.6 \n", ".. ... ... ... ... ... ... ... \n", "360 64756 20171227 2.622 -73.74 41.79 -6.7 -19.3 \n", "361 64756 20171228 2.622 -73.74 41.79 -10.3 -21.5 \n", "362 64756 20171229 2.622 -73.74 41.79 -9.4 -19.0 \n", "363 64756 20171230 2.622 -73.74 41.79 -7.1 -18.9 \n", "364 64756 20171231 2.622 -73.74 41.79 -12.3 -21.8 \n", "\n", " T_DAILY_MEAN T_DAILY_AVG P_DAILY_CALC ... SOIL_MOISTURE_5_DAILY \\\n", "0 0.6 2.2 0.0 ... -99.000 \n", "1 -1.4 -1.2 0.0 ... -99.000 \n", "2 2.8 2.7 13.1 ... -99.000 \n", "3 3.6 3.5 1.3 ... -99.000 \n", "4 -2.5 -2.8 0.0 ... -99.000 \n", ".. ... ... ... ... ... \n", "360 -13.0 -12.9 0.0 ... 0.258 \n", "361 -15.9 -15.8 0.0 ... 0.235 \n", "362 -14.2 -14.7 0.0 ... -99.000 \n", "363 -13.0 -13.6 1.1 ... -99.000 \n", "364 -17.0 -16.7 0.0 ... -99.000 \n", "\n", " SOIL_MOISTURE_10_DAILY SOIL_MOISTURE_20_DAILY SOIL_MOISTURE_50_DAILY \\\n", "0 -99.000 0.207 0.152 \n", "1 -99.000 0.205 0.151 \n", "2 -99.000 0.205 0.150 \n", "3 -99.000 0.215 0.153 \n", "4 -99.000 0.215 0.154 \n", ".. ... ... ... \n", "360 0.238 0.215 0.166 \n", "361 0.244 0.211 0.165 \n", "362 -99.000 0.207 0.163 \n", "363 -99.000 0.203 0.161 \n", "364 -99.000 0.200 0.160 \n", "\n", " SOIL_MOISTURE_100_DAILY SOIL_TEMP_5_DAILY SOIL_TEMP_10_DAILY \\\n", "0 0.175 -0.1 0.0 \n", "1 0.173 -0.2 0.0 \n", "2 0.173 -0.1 0.0 \n", "3 0.174 -0.1 0.0 \n", "4 0.177 -0.1 0.0 \n", ".. ... ... ... \n", "360 0.170 0.8 1.0 \n", "361 0.168 0.4 0.6 \n", "362 0.167 0.1 0.4 \n", "363 0.166 0.0 0.2 \n", "364 0.165 -0.2 0.1 \n", "\n", " SOIL_TEMP_20_DAILY SOIL_TEMP_50_DAILY SOIL_TEMP_100_DAILY \n", "0 0.6 1.5 3.4 \n", "1 0.6 1.5 3.3 \n", "2 0.5 1.5 3.3 \n", "3 0.5 1.5 3.2 \n", "4 0.5 1.4 3.1 \n", ".. ... ... ... \n", "360 1.7 3.1 4.6 \n", "361 1.4 2.8 4.5 \n", "362 1.1 2.6 4.3 \n", "363 0.9 2.4 4.1 \n", "364 0.8 2.2 4.0 \n", "\n", "[365 rows x 28 columns]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('station_data.txt', sep='\\s+')\n", "df" ] }, { "cell_type": "code", "execution_count": 45, "id": "d4b743e8", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('station_data.txt', sep='\\s+', na_values=[-9999.0, -99.0]) #we tell it what missing values look like" ] }, { "cell_type": "code", "execution_count": 46, "id": "278c3061", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 365 entries, 0 to 364\n", "Data columns (total 28 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 WBANNO 365 non-null int64 \n", " 1 LST_DATE 365 non-null int64 \n", " 2 CRX_VN 365 non-null float64\n", " 3 LONGITUDE 365 non-null float64\n", " 4 LATITUDE 365 non-null float64\n", " 5 T_DAILY_MAX 364 non-null float64\n", " 6 T_DAILY_MIN 364 non-null float64\n", " 7 T_DAILY_MEAN 364 non-null float64\n", " 8 T_DAILY_AVG 364 non-null float64\n", " 9 P_DAILY_CALC 364 non-null float64\n", " 10 SOLARAD_DAILY 364 non-null float64\n", " 11 SUR_TEMP_DAILY_TYPE 365 non-null object \n", " 12 SUR_TEMP_DAILY_MAX 364 non-null float64\n", " 13 SUR_TEMP_DAILY_MIN 364 non-null float64\n", " 14 SUR_TEMP_DAILY_AVG 364 non-null float64\n", " 15 RH_DAILY_MAX 364 non-null float64\n", " 16 RH_DAILY_MIN 364 non-null float64\n", " 17 RH_DAILY_AVG 364 non-null float64\n", " 18 SOIL_MOISTURE_5_DAILY 317 non-null float64\n", " 19 SOIL_MOISTURE_10_DAILY 317 non-null float64\n", " 20 SOIL_MOISTURE_20_DAILY 336 non-null float64\n", " 21 SOIL_MOISTURE_50_DAILY 364 non-null float64\n", " 22 SOIL_MOISTURE_100_DAILY 359 non-null float64\n", " 23 SOIL_TEMP_5_DAILY 364 non-null float64\n", " 24 SOIL_TEMP_10_DAILY 364 non-null float64\n", " 25 SOIL_TEMP_20_DAILY 364 non-null float64\n", " 26 SOIL_TEMP_50_DAILY 364 non-null float64\n", " 27 SOIL_TEMP_100_DAILY 364 non-null float64\n", "dtypes: float64(25), int64(2), object(1)\n", "memory usage: 80.0+ KB\n" ] } ], "source": [ "df.info() #dump info about dataframe" ] }, { "cell_type": "code", "execution_count": 47, "id": "47e6502f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 365 entries, 0 to 364\n", "Data columns (total 28 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 WBANNO 365 non-null int64 \n", " 1 LST_DATE 365 non-null datetime64[ns]\n", " 2 CRX_VN 365 non-null float64 \n", " 3 LONGITUDE 365 non-null float64 \n", " 4 LATITUDE 365 non-null float64 \n", " 5 T_DAILY_MAX 364 non-null float64 \n", " 6 T_DAILY_MIN 364 non-null float64 \n", " 7 T_DAILY_MEAN 364 non-null float64 \n", " 8 T_DAILY_AVG 364 non-null float64 \n", " 9 P_DAILY_CALC 364 non-null float64 \n", " 10 SOLARAD_DAILY 364 non-null float64 \n", " 11 SUR_TEMP_DAILY_TYPE 365 non-null object \n", " 12 SUR_TEMP_DAILY_MAX 364 non-null float64 \n", " 13 SUR_TEMP_DAILY_MIN 364 non-null float64 \n", " 14 SUR_TEMP_DAILY_AVG 364 non-null float64 \n", " 15 RH_DAILY_MAX 364 non-null float64 \n", " 16 RH_DAILY_MIN 364 non-null float64 \n", " 17 RH_DAILY_AVG 364 non-null float64 \n", " 18 SOIL_MOISTURE_5_DAILY 317 non-null float64 \n", " 19 SOIL_MOISTURE_10_DAILY 317 non-null float64 \n", " 20 SOIL_MOISTURE_20_DAILY 336 non-null float64 \n", " 21 SOIL_MOISTURE_50_DAILY 364 non-null float64 \n", " 22 SOIL_MOISTURE_100_DAILY 359 non-null float64 \n", " 23 SOIL_TEMP_5_DAILY 364 non-null float64 \n", " 24 SOIL_TEMP_10_DAILY 364 non-null float64 \n", " 25 SOIL_TEMP_20_DAILY 364 non-null float64 \n", " 26 SOIL_TEMP_50_DAILY 364 non-null float64 \n", " 27 SOIL_TEMP_100_DAILY 364 non-null float64 \n", "dtypes: datetime64[ns](1), float64(25), int64(1), object(1)\n", "memory usage: 80.0+ KB\n" ] } ], "source": [ "df = pd.read_csv('station_data.txt', sep='\\s+',na_values=[-9999.0, -99.0],parse_dates=[1]) #make sure it knows which column is datatime formated\n", "df.info()" ] }, { "cell_type": "code", "execution_count": 48, "id": "43f717da", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LST_DATE
2017-01-01647562.422-73.7441.796.6-5.40.62.20.08.68...NaNNaN0.2070.1520.175-0.10.00.61.53.4
2017-01-02647562.422-73.7441.794.0-6.8-1.4-1.20.02.08...NaNNaN0.2050.1510.173-0.20.00.61.53.3
2017-01-03647562.422-73.7441.794.90.72.82.713.10.68...NaNNaN0.2050.1500.173-0.10.00.51.53.3
2017-01-04647562.422-73.7441.798.7-1.63.63.51.32.85...NaNNaN0.2150.1530.174-0.10.00.51.53.2
2017-01-05647562.422-73.7441.79-0.5-4.6-2.5-2.80.04.90...NaNNaN0.2150.1540.177-0.10.00.51.43.1
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2017-12-27647562.622-73.7441.79-6.7-19.3-13.0-12.90.08.36...0.2580.2380.2150.1660.1700.81.01.73.14.6
2017-12-28647562.622-73.7441.79-10.3-21.5-15.9-15.80.08.46...0.2350.2440.2110.1650.1680.40.61.42.84.5
2017-12-29647562.622-73.7441.79-9.4-19.0-14.2-14.70.07.09...NaNNaN0.2070.1630.1670.10.41.12.64.3
2017-12-30647562.622-73.7441.79-7.1-18.9-13.0-13.61.11.10...NaNNaN0.2030.1610.1660.00.20.92.44.1
2017-12-31647562.622-73.7441.79-12.3-21.8-17.0-16.70.03.77...NaNNaN0.2000.1600.165-0.20.10.82.24.0
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365 rows × 27 columns

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" ], "text/plain": [ " WBANNO CRX_VN LONGITUDE LATITUDE T_DAILY_MAX T_DAILY_MIN \\\n", "LST_DATE \n", "2017-01-01 64756 2.422 -73.74 41.79 6.6 -5.4 \n", "2017-01-02 64756 2.422 -73.74 41.79 4.0 -6.8 \n", "2017-01-03 64756 2.422 -73.74 41.79 4.9 0.7 \n", "2017-01-04 64756 2.422 -73.74 41.79 8.7 -1.6 \n", "2017-01-05 64756 2.422 -73.74 41.79 -0.5 -4.6 \n", "... ... ... ... ... ... ... \n", "2017-12-27 64756 2.622 -73.74 41.79 -6.7 -19.3 \n", "2017-12-28 64756 2.622 -73.74 41.79 -10.3 -21.5 \n", "2017-12-29 64756 2.622 -73.74 41.79 -9.4 -19.0 \n", "2017-12-30 64756 2.622 -73.74 41.79 -7.1 -18.9 \n", "2017-12-31 64756 2.622 -73.74 41.79 -12.3 -21.8 \n", "\n", " T_DAILY_MEAN T_DAILY_AVG P_DAILY_CALC SOLARAD_DAILY ... \\\n", "LST_DATE ... \n", "2017-01-01 0.6 2.2 0.0 8.68 ... \n", "2017-01-02 -1.4 -1.2 0.0 2.08 ... \n", "2017-01-03 2.8 2.7 13.1 0.68 ... \n", "2017-01-04 3.6 3.5 1.3 2.85 ... \n", "2017-01-05 -2.5 -2.8 0.0 4.90 ... \n", "... ... ... ... ... ... \n", "2017-12-27 -13.0 -12.9 0.0 8.36 ... \n", "2017-12-28 -15.9 -15.8 0.0 8.46 ... \n", "2017-12-29 -14.2 -14.7 0.0 7.09 ... \n", "2017-12-30 -13.0 -13.6 1.1 1.10 ... \n", "2017-12-31 -17.0 -16.7 0.0 3.77 ... \n", "\n", " SOIL_MOISTURE_5_DAILY SOIL_MOISTURE_10_DAILY \\\n", "LST_DATE \n", "2017-01-01 NaN NaN \n", "2017-01-02 NaN NaN \n", "2017-01-03 NaN NaN \n", "2017-01-04 NaN NaN \n", "2017-01-05 NaN NaN \n", "... ... ... \n", "2017-12-27 0.258 0.238 \n", "2017-12-28 0.235 0.244 \n", "2017-12-29 NaN NaN \n", "2017-12-30 NaN NaN \n", "2017-12-31 NaN NaN \n", "\n", " SOIL_MOISTURE_20_DAILY SOIL_MOISTURE_50_DAILY \\\n", "LST_DATE \n", "2017-01-01 0.207 0.152 \n", "2017-01-02 0.205 0.151 \n", "2017-01-03 0.205 0.150 \n", "2017-01-04 0.215 0.153 \n", "2017-01-05 0.215 0.154 \n", "... ... ... \n", "2017-12-27 0.215 0.166 \n", "2017-12-28 0.211 0.165 \n", "2017-12-29 0.207 0.163 \n", "2017-12-30 0.203 0.161 \n", "2017-12-31 0.200 0.160 \n", "\n", " SOIL_MOISTURE_100_DAILY SOIL_TEMP_5_DAILY SOIL_TEMP_10_DAILY \\\n", "LST_DATE \n", "2017-01-01 0.175 -0.1 0.0 \n", "2017-01-02 0.173 -0.2 0.0 \n", "2017-01-03 0.173 -0.1 0.0 \n", "2017-01-04 0.174 -0.1 0.0 \n", "2017-01-05 0.177 -0.1 0.0 \n", "... ... ... ... \n", "2017-12-27 0.170 0.8 1.0 \n", "2017-12-28 0.168 0.4 0.6 \n", "2017-12-29 0.167 0.1 0.4 \n", "2017-12-30 0.166 0.0 0.2 \n", "2017-12-31 0.165 -0.2 0.1 \n", "\n", " SOIL_TEMP_20_DAILY SOIL_TEMP_50_DAILY SOIL_TEMP_100_DAILY \n", "LST_DATE \n", "2017-01-01 0.6 1.5 3.4 \n", "2017-01-02 0.6 1.5 3.3 \n", "2017-01-03 0.5 1.5 3.3 \n", "2017-01-04 0.5 1.5 3.2 \n", "2017-01-05 0.5 1.4 3.1 \n", "... ... ... ... \n", "2017-12-27 1.7 3.1 4.6 \n", "2017-12-28 1.4 2.8 4.5 \n", "2017-12-29 1.1 2.6 4.3 \n", "2017-12-30 0.9 2.4 4.1 \n", "2017-12-31 0.8 2.2 4.0 \n", "\n", "[365 rows x 27 columns]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.set_index('LST_DATE') #how to index (see on left)\n", "df" ] }, { "cell_type": "code", "execution_count": 49, "id": "a9ae805c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "WBANNO 64756\n", "CRX_VN 2.422\n", "LONGITUDE -73.74\n", "LATITUDE 41.79\n", "T_DAILY_MAX 19.3\n", "T_DAILY_MIN 12.3\n", "T_DAILY_MEAN 15.8\n", "T_DAILY_AVG 16.3\n", "P_DAILY_CALC 4.9\n", "SOLARAD_DAILY 3.93\n", "SUR_TEMP_DAILY_TYPE C\n", "SUR_TEMP_DAILY_MAX 22.3\n", "SUR_TEMP_DAILY_MIN 11.9\n", "SUR_TEMP_DAILY_AVG 17.7\n", "RH_DAILY_MAX 94.7\n", "RH_DAILY_MIN 76.4\n", "RH_DAILY_AVG 89.5\n", "SOIL_MOISTURE_5_DAILY 0.148\n", "SOIL_MOISTURE_10_DAILY 0.113\n", "SOIL_MOISTURE_20_DAILY 0.094\n", "SOIL_MOISTURE_50_DAILY 0.114\n", "SOIL_MOISTURE_100_DAILY 0.151\n", "SOIL_TEMP_5_DAILY 21.4\n", "SOIL_TEMP_10_DAILY 21.7\n", "SOIL_TEMP_20_DAILY 22.1\n", "SOIL_TEMP_50_DAILY 22.2\n", "SOIL_TEMP_100_DAILY 21.5\n", "Name: 2017-08-07 00:00:00, dtype: object" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['2017-08-07']" ] }, { "cell_type": "code", "execution_count": 50, "id": "d5e9fe71", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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WBANNOCRX_VNLONGITUDELATITUDET_DAILY_MAXT_DAILY_MINT_DAILY_MEANT_DAILY_AVGP_DAILY_CALCSOLARAD_DAILY...SOIL_MOISTURE_5_DAILYSOIL_MOISTURE_10_DAILYSOIL_MOISTURE_20_DAILYSOIL_MOISTURE_50_DAILYSOIL_MOISTURE_100_DAILYSOIL_TEMP_5_DAILYSOIL_TEMP_10_DAILYSOIL_TEMP_20_DAILYSOIL_TEMP_50_DAILYSOIL_TEMP_100_DAILY
LST_DATE
2017-07-01647562.422-73.7441.7928.019.723.923.80.219.28...0.1570.1360.1440.1290.16325.725.423.721.919.9
2017-07-02647562.422-73.7441.7929.818.424.123.74.027.67...0.1460.1350.1430.1290.16226.826.424.522.320.1
2017-07-03647562.422-73.7441.7928.315.021.721.40.027.08...0.1410.1320.1390.1280.16226.426.324.822.820.3
2017-07-04647562.422-73.7441.7926.812.619.720.00.029.45...0.1310.1260.1360.1260.16125.925.824.622.920.6
2017-07-05647562.422-73.7441.7928.011.920.020.70.026.90...0.1160.1140.1310.1250.16125.325.324.222.820.7
2017-07-06647562.422-73.7441.7925.714.320.020.30.019.03...0.1050.1040.1260.1240.16024.724.723.922.720.9
2017-07-07647562.422-73.7441.7925.816.821.320.011.513.88...0.1140.1000.1230.1230.16024.224.223.422.420.8
2017-07-08647562.422-73.7441.7929.015.322.121.50.021.92...0.1300.1060.1220.1230.15925.525.323.922.420.8
2017-07-09647562.422-73.7441.7926.310.918.619.40.029.72...0.1190.1030.1190.1210.15824.824.823.822.520.8
2017-07-10647562.422-73.7441.7927.611.819.721.30.023.67...0.1050.0960.1130.1200.15824.724.723.622.520.9
2017-07-11647562.422-73.7441.7927.419.223.322.68.517.79...0.1060.0930.1100.1200.15725.625.424.122.620.9
2017-07-12647562.422-73.7441.7929.418.523.923.11.916.27...0.1080.0940.1080.1180.15725.825.624.222.821.0
2017-07-13647562.422-73.7441.7929.518.323.923.423.313.61...0.1340.1100.1080.1180.15625.725.724.423.021.0
2017-07-14647562.422-73.7441.7918.515.917.217.54.15.36...0.1940.1510.1140.1200.15523.023.323.422.921.2
2017-07-15647562.422-73.7441.7926.616.521.521.00.821.13...0.1900.1630.1190.1220.15524.624.423.222.221.2
2017-07-16647562.422-73.7441.7927.913.320.621.00.027.03...0.1710.1540.1230.1230.15525.425.323.922.621.1
2017-07-17647562.422-73.7441.7929.216.122.622.90.020.47...0.1550.1430.1240.1220.15625.725.624.422.921.2
2017-07-18647562.422-73.7441.7930.319.324.824.70.024.99...0.1420.1320.1220.1220.15627.026.724.923.221.3
2017-07-19647562.422-73.7441.7931.219.125.125.00.027.69...0.1260.1180.1180.1220.15627.627.425.623.721.5
2017-07-20647562.422-73.7441.7931.816.624.223.40.721.53...0.1110.1030.1140.1210.15627.027.025.624.021.7
2017-07-21647562.422-73.7441.7930.616.623.623.60.025.55...0.1000.0930.1080.1200.15527.127.025.524.021.9
2017-07-22647562.422-73.7441.7927.715.621.721.20.516.04...0.0920.0860.1040.1190.15625.926.125.324.122.0
2017-07-23647562.422-73.7441.7926.418.522.522.20.019.03...0.0870.0820.1000.1180.15526.026.024.923.822.1
2017-07-24647562.422-73.7441.7919.414.817.116.729.29.10...0.1450.1180.1020.1170.15423.123.623.923.522.1
2017-07-25647562.422-73.7441.7918.613.716.216.20.07.35...0.1670.1330.1070.1160.15321.922.222.422.521.9
2017-07-26647562.422-73.7441.7924.711.218.018.30.022.22...0.1550.1280.1080.1180.15222.923.022.322.021.7
2017-07-27647562.422-73.7441.7924.215.219.719.50.08.28...0.1440.1220.1090.1180.15422.522.722.422.021.4
2017-07-28647562.422-73.7441.7926.516.921.720.90.021.06...0.1370.1170.1100.1190.15424.124.122.822.021.3
2017-07-29647562.422-73.7441.7924.210.417.318.10.021.28...0.1260.1080.1080.1180.15423.323.623.022.221.3
2017-07-30647562.422-73.7441.7925.58.216.817.30.027.68...0.1130.0990.1040.1170.15422.823.022.422.021.3
2017-07-31647562.422-73.7441.7929.410.119.720.10.025.49...0.1010.0900.0990.1160.15323.823.822.721.921.2
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31 rows × 27 columns

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" ], "text/plain": [ " WBANNO CRX_VN LONGITUDE LATITUDE T_DAILY_MAX T_DAILY_MIN \\\n", "LST_DATE \n", "2017-07-01 64756 2.422 -73.74 41.79 28.0 19.7 \n", "2017-07-02 64756 2.422 -73.74 41.79 29.8 18.4 \n", "2017-07-03 64756 2.422 -73.74 41.79 28.3 15.0 \n", "2017-07-04 64756 2.422 -73.74 41.79 26.8 12.6 \n", "2017-07-05 64756 2.422 -73.74 41.79 28.0 11.9 \n", "2017-07-06 64756 2.422 -73.74 41.79 25.7 14.3 \n", "2017-07-07 64756 2.422 -73.74 41.79 25.8 16.8 \n", "2017-07-08 64756 2.422 -73.74 41.79 29.0 15.3 \n", "2017-07-09 64756 2.422 -73.74 41.79 26.3 10.9 \n", "2017-07-10 64756 2.422 -73.74 41.79 27.6 11.8 \n", "2017-07-11 64756 2.422 -73.74 41.79 27.4 19.2 \n", "2017-07-12 64756 2.422 -73.74 41.79 29.4 18.5 \n", "2017-07-13 64756 2.422 -73.74 41.79 29.5 18.3 \n", "2017-07-14 64756 2.422 -73.74 41.79 18.5 15.9 \n", "2017-07-15 64756 2.422 -73.74 41.79 26.6 16.5 \n", "2017-07-16 64756 2.422 -73.74 41.79 27.9 13.3 \n", "2017-07-17 64756 2.422 -73.74 41.79 29.2 16.1 \n", "2017-07-18 64756 2.422 -73.74 41.79 30.3 19.3 \n", "2017-07-19 64756 2.422 -73.74 41.79 31.2 19.1 \n", "2017-07-20 64756 2.422 -73.74 41.79 31.8 16.6 \n", "2017-07-21 64756 2.422 -73.74 41.79 30.6 16.6 \n", "2017-07-22 64756 2.422 -73.74 41.79 27.7 15.6 \n", "2017-07-23 64756 2.422 -73.74 41.79 26.4 18.5 \n", "2017-07-24 64756 2.422 -73.74 41.79 19.4 14.8 \n", "2017-07-25 64756 2.422 -73.74 41.79 18.6 13.7 \n", "2017-07-26 64756 2.422 -73.74 41.79 24.7 11.2 \n", "2017-07-27 64756 2.422 -73.74 41.79 24.2 15.2 \n", "2017-07-28 64756 2.422 -73.74 41.79 26.5 16.9 \n", "2017-07-29 64756 2.422 -73.74 41.79 24.2 10.4 \n", "2017-07-30 64756 2.422 -73.74 41.79 25.5 8.2 \n", "2017-07-31 64756 2.422 -73.74 41.79 29.4 10.1 \n", "\n", " T_DAILY_MEAN T_DAILY_AVG P_DAILY_CALC SOLARAD_DAILY ... \\\n", "LST_DATE ... \n", "2017-07-01 23.9 23.8 0.2 19.28 ... \n", "2017-07-02 24.1 23.7 4.0 27.67 ... \n", "2017-07-03 21.7 21.4 0.0 27.08 ... \n", "2017-07-04 19.7 20.0 0.0 29.45 ... \n", "2017-07-05 20.0 20.7 0.0 26.90 ... \n", "2017-07-06 20.0 20.3 0.0 19.03 ... \n", "2017-07-07 21.3 20.0 11.5 13.88 ... \n", "2017-07-08 22.1 21.5 0.0 21.92 ... \n", "2017-07-09 18.6 19.4 0.0 29.72 ... \n", "2017-07-10 19.7 21.3 0.0 23.67 ... \n", "2017-07-11 23.3 22.6 8.5 17.79 ... \n", "2017-07-12 23.9 23.1 1.9 16.27 ... \n", "2017-07-13 23.9 23.4 23.3 13.61 ... \n", "2017-07-14 17.2 17.5 4.1 5.36 ... \n", "2017-07-15 21.5 21.0 0.8 21.13 ... \n", "2017-07-16 20.6 21.0 0.0 27.03 ... \n", "2017-07-17 22.6 22.9 0.0 20.47 ... \n", "2017-07-18 24.8 24.7 0.0 24.99 ... \n", "2017-07-19 25.1 25.0 0.0 27.69 ... \n", "2017-07-20 24.2 23.4 0.7 21.53 ... \n", "2017-07-21 23.6 23.6 0.0 25.55 ... \n", "2017-07-22 21.7 21.2 0.5 16.04 ... \n", "2017-07-23 22.5 22.2 0.0 19.03 ... \n", "2017-07-24 17.1 16.7 29.2 9.10 ... \n", "2017-07-25 16.2 16.2 0.0 7.35 ... \n", "2017-07-26 18.0 18.3 0.0 22.22 ... \n", "2017-07-27 19.7 19.5 0.0 8.28 ... \n", "2017-07-28 21.7 20.9 0.0 21.06 ... \n", "2017-07-29 17.3 18.1 0.0 21.28 ... \n", "2017-07-30 16.8 17.3 0.0 27.68 ... \n", "2017-07-31 19.7 20.1 0.0 25.49 ... \n", "\n", " SOIL_MOISTURE_5_DAILY SOIL_MOISTURE_10_DAILY \\\n", "LST_DATE \n", "2017-07-01 0.157 0.136 \n", "2017-07-02 0.146 0.135 \n", "2017-07-03 0.141 0.132 \n", "2017-07-04 0.131 0.126 \n", "2017-07-05 0.116 0.114 \n", "2017-07-06 0.105 0.104 \n", "2017-07-07 0.114 0.100 \n", "2017-07-08 0.130 0.106 \n", "2017-07-09 0.119 0.103 \n", "2017-07-10 0.105 0.096 \n", "2017-07-11 0.106 0.093 \n", "2017-07-12 0.108 0.094 \n", "2017-07-13 0.134 0.110 \n", "2017-07-14 0.194 0.151 \n", "2017-07-15 0.190 0.163 \n", "2017-07-16 0.171 0.154 \n", "2017-07-17 0.155 0.143 \n", "2017-07-18 0.142 0.132 \n", "2017-07-19 0.126 0.118 \n", "2017-07-20 0.111 0.103 \n", "2017-07-21 0.100 0.093 \n", "2017-07-22 0.092 0.086 \n", "2017-07-23 0.087 0.082 \n", "2017-07-24 0.145 0.118 \n", "2017-07-25 0.167 0.133 \n", "2017-07-26 0.155 0.128 \n", "2017-07-27 0.144 0.122 \n", "2017-07-28 0.137 0.117 \n", "2017-07-29 0.126 0.108 \n", "2017-07-30 0.113 0.099 \n", "2017-07-31 0.101 0.090 \n", "\n", " SOIL_MOISTURE_20_DAILY SOIL_MOISTURE_50_DAILY \\\n", "LST_DATE \n", "2017-07-01 0.144 0.129 \n", "2017-07-02 0.143 0.129 \n", "2017-07-03 0.139 0.128 \n", "2017-07-04 0.136 0.126 \n", "2017-07-05 0.131 0.125 \n", "2017-07-06 0.126 0.124 \n", "2017-07-07 0.123 0.123 \n", "2017-07-08 0.122 0.123 \n", "2017-07-09 0.119 0.121 \n", "2017-07-10 0.113 0.120 \n", "2017-07-11 0.110 0.120 \n", "2017-07-12 0.108 0.118 \n", "2017-07-13 0.108 0.118 \n", "2017-07-14 0.114 0.120 \n", "2017-07-15 0.119 0.122 \n", "2017-07-16 0.123 0.123 \n", "2017-07-17 0.124 0.122 \n", "2017-07-18 0.122 0.122 \n", "2017-07-19 0.118 0.122 \n", "2017-07-20 0.114 0.121 \n", "2017-07-21 0.108 0.120 \n", "2017-07-22 0.104 0.119 \n", "2017-07-23 0.100 0.118 \n", "2017-07-24 0.102 0.117 \n", "2017-07-25 0.107 0.116 \n", "2017-07-26 0.108 0.118 \n", "2017-07-27 0.109 0.118 \n", "2017-07-28 0.110 0.119 \n", "2017-07-29 0.108 0.118 \n", "2017-07-30 0.104 0.117 \n", "2017-07-31 0.099 0.116 \n", "\n", " SOIL_MOISTURE_100_DAILY SOIL_TEMP_5_DAILY SOIL_TEMP_10_DAILY \\\n", "LST_DATE \n", "2017-07-01 0.163 25.7 25.4 \n", "2017-07-02 0.162 26.8 26.4 \n", "2017-07-03 0.162 26.4 26.3 \n", "2017-07-04 0.161 25.9 25.8 \n", "2017-07-05 0.161 25.3 25.3 \n", "2017-07-06 0.160 24.7 24.7 \n", "2017-07-07 0.160 24.2 24.2 \n", "2017-07-08 0.159 25.5 25.3 \n", "2017-07-09 0.158 24.8 24.8 \n", "2017-07-10 0.158 24.7 24.7 \n", "2017-07-11 0.157 25.6 25.4 \n", "2017-07-12 0.157 25.8 25.6 \n", "2017-07-13 0.156 25.7 25.7 \n", "2017-07-14 0.155 23.0 23.3 \n", "2017-07-15 0.155 24.6 24.4 \n", "2017-07-16 0.155 25.4 25.3 \n", "2017-07-17 0.156 25.7 25.6 \n", "2017-07-18 0.156 27.0 26.7 \n", "2017-07-19 0.156 27.6 27.4 \n", "2017-07-20 0.156 27.0 27.0 \n", "2017-07-21 0.155 27.1 27.0 \n", "2017-07-22 0.156 25.9 26.1 \n", "2017-07-23 0.155 26.0 26.0 \n", "2017-07-24 0.154 23.1 23.6 \n", "2017-07-25 0.153 21.9 22.2 \n", "2017-07-26 0.152 22.9 23.0 \n", "2017-07-27 0.154 22.5 22.7 \n", "2017-07-28 0.154 24.1 24.1 \n", "2017-07-29 0.154 23.3 23.6 \n", "2017-07-30 0.154 22.8 23.0 \n", "2017-07-31 0.153 23.8 23.8 \n", "\n", " SOIL_TEMP_20_DAILY SOIL_TEMP_50_DAILY SOIL_TEMP_100_DAILY \n", "LST_DATE \n", "2017-07-01 23.7 21.9 19.9 \n", "2017-07-02 24.5 22.3 20.1 \n", "2017-07-03 24.8 22.8 20.3 \n", "2017-07-04 24.6 22.9 20.6 \n", "2017-07-05 24.2 22.8 20.7 \n", "2017-07-06 23.9 22.7 20.9 \n", "2017-07-07 23.4 22.4 20.8 \n", "2017-07-08 23.9 22.4 20.8 \n", "2017-07-09 23.8 22.5 20.8 \n", "2017-07-10 23.6 22.5 20.9 \n", "2017-07-11 24.1 22.6 20.9 \n", "2017-07-12 24.2 22.8 21.0 \n", "2017-07-13 24.4 23.0 21.0 \n", "2017-07-14 23.4 22.9 21.2 \n", "2017-07-15 23.2 22.2 21.2 \n", "2017-07-16 23.9 22.6 21.1 \n", "2017-07-17 24.4 22.9 21.2 \n", "2017-07-18 24.9 23.2 21.3 \n", "2017-07-19 25.6 23.7 21.5 \n", "2017-07-20 25.6 24.0 21.7 \n", "2017-07-21 25.5 24.0 21.9 \n", "2017-07-22 25.3 24.1 22.0 \n", "2017-07-23 24.9 23.8 22.1 \n", "2017-07-24 23.9 23.5 22.1 \n", "2017-07-25 22.4 22.5 21.9 \n", "2017-07-26 22.3 22.0 21.7 \n", "2017-07-27 22.4 22.0 21.4 \n", "2017-07-28 22.8 22.0 21.3 \n", "2017-07-29 23.0 22.2 21.3 \n", "2017-07-30 22.4 22.0 21.3 \n", "2017-07-31 22.7 21.9 21.2 \n", "\n", "[31 rows x 27 columns]" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['2017-07-01':'2017-07-31'] #slicing" ] }, { "cell_type": "code", "execution_count": 51, "id": "3eb20d4c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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WBANNOCRX_VNLONGITUDELATITUDET_DAILY_MAXT_DAILY_MINT_DAILY_MEANT_DAILY_AVGP_DAILY_CALCSOLARAD_DAILY...SOIL_MOISTURE_5_DAILYSOIL_MOISTURE_10_DAILYSOIL_MOISTURE_20_DAILYSOIL_MOISTURE_50_DAILYSOIL_MOISTURE_100_DAILYSOIL_TEMP_5_DAILYSOIL_TEMP_10_DAILYSOIL_TEMP_20_DAILYSOIL_TEMP_50_DAILYSOIL_TEMP_100_DAILY
count365.0365.0000003.650000e+023.650000e+02364.000000364.000000364.000000364.000000364.000000364.000000...317.000000317.000000336.000000364.000000359.000000364.000000364.000000364.000000364.000000364.000000
mean64756.02.470767-7.374000e+014.179000e+0115.7200554.0379129.8763749.9901102.79780213.068187...0.1894980.1839910.1654700.1401920.16063012.31263712.32060412.06016511.97802211.915659
std0.00.0859975.265234e-133.842198e-1310.5020879.4606769.7274519.6191687.2386287.953074...0.0520310.0541130.0439890.0204950.0160119.3900349.3381768.7677528.0783467.187317
min64756.02.422000-7.374000e+014.179000e+01-12.300000-21.800000-17.000000-16.7000000.0000000.100000...0.0750000.0780000.0870000.1010000.117000-0.700000-0.4000000.2000000.9000001.900000
25%64756.02.422000-7.374000e+014.179000e+016.900000-2.7750002.1000002.2750000.0000006.225000...0.1520000.1390000.1187500.1180000.1540002.2250002.0000002.4750003.3000004.100000
50%64756.02.422000-7.374000e+014.179000e+0117.4500004.35000010.85000011.0500000.00000012.865000...0.1920000.1980000.1830000.1475000.16500013.30000013.35000013.10000012.85000011.600000
75%64756.02.422000-7.374000e+014.179000e+0124.85000011.90000018.15000018.4500001.40000019.740000...0.2340000.2270000.2030000.1570000.17300021.02500021.12500020.40000019.80000019.325000
max64756.02.622000-7.374000e+014.179000e+0133.40000020.70000025.70000026.70000065.70000029.910000...0.2960000.3210000.2350000.1820000.19200027.60000027.40000025.60000024.10000022.100000
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8 rows × 26 columns

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" ], "text/plain": [ " WBANNO CRX_VN LONGITUDE LATITUDE T_DAILY_MAX \\\n", "count 365.0 365.000000 3.650000e+02 3.650000e+02 364.000000 \n", "mean 64756.0 2.470767 -7.374000e+01 4.179000e+01 15.720055 \n", "std 0.0 0.085997 5.265234e-13 3.842198e-13 10.502087 \n", "min 64756.0 2.422000 -7.374000e+01 4.179000e+01 -12.300000 \n", "25% 64756.0 2.422000 -7.374000e+01 4.179000e+01 6.900000 \n", "50% 64756.0 2.422000 -7.374000e+01 4.179000e+01 17.450000 \n", "75% 64756.0 2.422000 -7.374000e+01 4.179000e+01 24.850000 \n", "max 64756.0 2.622000 -7.374000e+01 4.179000e+01 33.400000 \n", "\n", " T_DAILY_MIN T_DAILY_MEAN T_DAILY_AVG P_DAILY_CALC SOLARAD_DAILY \\\n", "count 364.000000 364.000000 364.000000 364.000000 364.000000 \n", "mean 4.037912 9.876374 9.990110 2.797802 13.068187 \n", "std 9.460676 9.727451 9.619168 7.238628 7.953074 \n", "min -21.800000 -17.000000 -16.700000 0.000000 0.100000 \n", "25% -2.775000 2.100000 2.275000 0.000000 6.225000 \n", "50% 4.350000 10.850000 11.050000 0.000000 12.865000 \n", "75% 11.900000 18.150000 18.450000 1.400000 19.740000 \n", "max 20.700000 25.700000 26.700000 65.700000 29.910000 \n", "\n", " ... SOIL_MOISTURE_5_DAILY SOIL_MOISTURE_10_DAILY \\\n", "count ... 317.000000 317.000000 \n", "mean ... 0.189498 0.183991 \n", "std ... 0.052031 0.054113 \n", "min ... 0.075000 0.078000 \n", "25% ... 0.152000 0.139000 \n", "50% ... 0.192000 0.198000 \n", "75% ... 0.234000 0.227000 \n", "max ... 0.296000 0.321000 \n", "\n", " SOIL_MOISTURE_20_DAILY SOIL_MOISTURE_50_DAILY \\\n", "count 336.000000 364.000000 \n", "mean 0.165470 0.140192 \n", "std 0.043989 0.020495 \n", "min 0.087000 0.101000 \n", "25% 0.118750 0.118000 \n", "50% 0.183000 0.147500 \n", "75% 0.203000 0.157000 \n", "max 0.235000 0.182000 \n", "\n", " SOIL_MOISTURE_100_DAILY SOIL_TEMP_5_DAILY SOIL_TEMP_10_DAILY \\\n", "count 359.000000 364.000000 364.000000 \n", "mean 0.160630 12.312637 12.320604 \n", "std 0.016011 9.390034 9.338176 \n", "min 0.117000 -0.700000 -0.400000 \n", "25% 0.154000 2.225000 2.000000 \n", "50% 0.165000 13.300000 13.350000 \n", "75% 0.173000 21.025000 21.125000 \n", "max 0.192000 27.600000 27.400000 \n", "\n", " SOIL_TEMP_20_DAILY SOIL_TEMP_50_DAILY SOIL_TEMP_100_DAILY \n", "count 364.000000 364.000000 364.000000 \n", "mean 12.060165 11.978022 11.915659 \n", "std 8.767752 8.078346 7.187317 \n", "min 0.200000 0.900000 1.900000 \n", "25% 2.475000 3.300000 4.100000 \n", "50% 13.100000 12.850000 11.600000 \n", "75% 20.400000 19.800000 19.325000 \n", "max 25.600000 24.100000 22.100000 \n", "\n", "[8 rows x 26 columns]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 52, "id": "9f21269c", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(ncols=2, nrows=2, figsize=(14,14))\n", "\n", "df.iloc[:, 4:8].boxplot(ax=ax[0,0])\n", "df.iloc[:, 10:14].boxplot(ax=ax[0,1])\n", "df.iloc[:, 14:17].boxplot(ax=ax[1,0])\n", "df.iloc[:, 18:22].boxplot(ax=ax[1,1])\n", "\n", "\n", "ax[1, 1].set_xticklabels(ax[1, 1].get_xticklabels(), rotation=90);" ] }, { "cell_type": "code", "execution_count": 53, "id": "0c6a694c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df[['T_DAILY_MIN', 'T_DAILY_MEAN', 'T_DAILY_MAX']].plot()" ] }, { "cell_type": "markdown", "id": "7af94a1d", "metadata": {}, "source": [ "Pandas can quickly resample data" ] }, { "cell_type": "code", "execution_count": 54, "id": "f996f04e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_mm = df.resample('MS').mean() #resample monthly and take a mean\n", "df_mm[['T_DAILY_MIN', 'T_DAILY_MEAN', 'T_DAILY_MAX']].plot()" ] }, { "cell_type": "markdown", "id": "df8a5a08", "metadata": {}, "source": [ "One of the most powerful functions of Pandas is groupby, which allows you to do operations on data grouped by different attributes. It takes practice to get \"good\" at groupby, but worth it. First lets pull in some large earthquake catalogue data" ] }, { "cell_type": "code", "execution_count": 56, "id": "a8e853c2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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timelatitudelongitudedepthmagmagTypenstgapdminrmsnetupdatedplacetypecountry
id
usc000mqlp2014-01-31 23:08:03.660-4.9758153.9466110.184.2mbNaN98.01.9400.61us2014-04-08T01:43:19.000Z115km ESE of Taron, Papua New GuineaearthquakePapua New Guinea
usc000mqln2014-01-31 22:54:32.970-28.1775-177.905895.844.3mbNaN104.01.0631.14us2014-04-08T01:43:19.000Z120km N of Raoul Island, New ZealandearthquakeNew Zealand
usc000mqls2014-01-31 22:49:49.740-23.1192179.1174528.344.4mbNaN80.05.4390.95us2014-04-08T01:43:19.000ZSouth of the Fiji IslandsearthquakeSouth of the Fiji Islands
usc000mf1x2014-01-31 22:19:44.33051.1569-178.091037.504.2mbNaNNaNNaN0.83us2014-04-08T01:43:19.000Z72km E of Amatignak Island, AlaskaearthquakeAlaska
usc000mqlm2014-01-31 21:56:44.320-4.8800153.8434112.664.3mbNaN199.01.8080.79us2014-04-08T01:43:19.000Z100km ESE of Taron, Papua New GuineaearthquakePapua New Guinea
................................................
usc000t6yh2014-12-01 02:56:07.95021.2031143.548411.054.4mbNaN107.05.9960.87us2015-02-24T00:35:14.040Z158km WNW of Farallon de Pajaros, Northern Mar...earthquakeNorthern Mariana Islands
usc000t6y22014-12-01 01:50:23.380-7.8798106.427552.104.3mbNaN119.01.4121.50us2015-02-24T00:35:14.040Z57km SSW of Cibungur, IndonesiaearthquakeIndonesia
usc000t6y12014-12-01 01:04:17.8907.1429126.8844176.674.3mbNaN134.01.2970.87us2015-02-24T00:35:14.040Z37km ESE of Santiago, PhilippinesearthquakePhilippines
usb000t1gp2014-12-01 00:40:02.72037.209671.945895.574.2mbNaN125.01.0970.91us2015-02-24T00:35:14.040Z11km ESE of Roshtqal'a, TajikistanearthquakeTajikistan
usc000t6yn2014-12-01 00:24:33.140-24.6340-179.6018470.864.5mbNaN131.010.5470.74us2015-02-24T00:35:14.040ZSouth of the Fiji IslandsearthquakeSouth of the Fiji Islands
\n", "

16371 rows × 15 columns

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" ], "text/plain": [ " time latitude longitude depth mag magType \\\n", "id \n", "usc000mqlp 2014-01-31 23:08:03.660 -4.9758 153.9466 110.18 4.2 mb \n", "usc000mqln 2014-01-31 22:54:32.970 -28.1775 -177.9058 95.84 4.3 mb \n", "usc000mqls 2014-01-31 22:49:49.740 -23.1192 179.1174 528.34 4.4 mb \n", "usc000mf1x 2014-01-31 22:19:44.330 51.1569 -178.0910 37.50 4.2 mb \n", "usc000mqlm 2014-01-31 21:56:44.320 -4.8800 153.8434 112.66 4.3 mb \n", "... ... ... ... ... ... ... \n", "usc000t6yh 2014-12-01 02:56:07.950 21.2031 143.5484 11.05 4.4 mb \n", "usc000t6y2 2014-12-01 01:50:23.380 -7.8798 106.4275 52.10 4.3 mb \n", "usc000t6y1 2014-12-01 01:04:17.890 7.1429 126.8844 176.67 4.3 mb \n", "usb000t1gp 2014-12-01 00:40:02.720 37.2096 71.9458 95.57 4.2 mb \n", "usc000t6yn 2014-12-01 00:24:33.140 -24.6340 -179.6018 470.86 4.5 mb \n", "\n", " nst gap dmin rms net updated \\\n", "id \n", "usc000mqlp NaN 98.0 1.940 0.61 us 2014-04-08T01:43:19.000Z \n", "usc000mqln NaN 104.0 1.063 1.14 us 2014-04-08T01:43:19.000Z \n", "usc000mqls NaN 80.0 5.439 0.95 us 2014-04-08T01:43:19.000Z \n", "usc000mf1x NaN NaN NaN 0.83 us 2014-04-08T01:43:19.000Z \n", "usc000mqlm NaN 199.0 1.808 0.79 us 2014-04-08T01:43:19.000Z \n", "... ... ... ... ... .. ... \n", "usc000t6yh NaN 107.0 5.996 0.87 us 2015-02-24T00:35:14.040Z \n", "usc000t6y2 NaN 119.0 1.412 1.50 us 2015-02-24T00:35:14.040Z \n", "usc000t6y1 NaN 134.0 1.297 0.87 us 2015-02-24T00:35:14.040Z \n", "usb000t1gp NaN 125.0 1.097 0.91 us 2015-02-24T00:35:14.040Z \n", "usc000t6yn NaN 131.0 10.547 0.74 us 2015-02-24T00:35:14.040Z \n", "\n", " place type \\\n", "id \n", "usc000mqlp 115km ESE of Taron, Papua New Guinea earthquake \n", "usc000mqln 120km N of Raoul Island, New Zealand earthquake \n", "usc000mqls South of the Fiji Islands earthquake \n", "usc000mf1x 72km E of Amatignak Island, Alaska earthquake \n", "usc000mqlm 100km ESE of Taron, Papua New Guinea earthquake \n", "... ... ... \n", "usc000t6yh 158km WNW of Farallon de Pajaros, Northern Mar... earthquake \n", "usc000t6y2 57km SSW of Cibungur, Indonesia earthquake \n", "usc000t6y1 37km ESE of Santiago, Philippines earthquake \n", "usb000t1gp 11km ESE of Roshtqal'a, Tajikistan earthquake \n", "usc000t6yn South of the Fiji Islands earthquake \n", "\n", " country \n", "id \n", "usc000mqlp Papua New Guinea \n", "usc000mqln New Zealand \n", "usc000mqls South of the Fiji Islands \n", "usc000mf1x Alaska \n", "usc000mqlm Papua New Guinea \n", "... ... \n", "usc000t6yh Northern Mariana Islands \n", "usc000t6y2 Indonesia \n", "usc000t6y1 Philippines \n", "usb000t1gp Tajikistan \n", "usc000t6yn South of the Fiji Islands \n", "\n", "[16371 rows x 15 columns]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('http://www.ldeo.columbia.edu/~rpa/usgs_earthquakes_2014.csv', parse_dates=['time'], index_col='id')\n", "df['country'] = df.place.str.split(', ').str[-1]\n", "df_small = df[df.mag<4]\n", "df = df[df.mag>4]\n", "df" ] }, { "cell_type": "markdown", "id": "927a0084", "metadata": {}, "source": [ "The workflow with groupby can be divided into three general steps:\n", "\n", "1. Split: Partition the data into different groups based on some criterion.\n", "\n", "2. Apply: Do some caclulation within each group. Different types of “apply” steps might be\n", "\n", " (a) Aggregation: Get the mean or max within the group.\n", "\n", " (b) Transformation: Normalize all the values within a group\n", "\n", " (c) Filtration: Eliminate some groups based on a criterion.\n", "\n", "3. Combine: Put the results back together into a single object." ] }, { "cell_type": "markdown", "id": "7cb8a9bd", "metadata": {}, "source": [ "Here we split by country name" ] }, { "cell_type": "code", "execution_count": 62, "id": "8508cb31", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['',\n", " 'Afghanistan',\n", " 'Alaska',\n", " 'Albania',\n", " 'Algeria',\n", " 'American Samoa',\n", " 'Angola',\n", " 'Anguilla',\n", " 'Antarctica',\n", " 'Argentina',\n", " 'Arizona',\n", " 'Aruba',\n", " 'Ascension Island region',\n", " 'Australia',\n", " 'Azerbaijan',\n", " 'Azores Islands region',\n", " 'Azores-Cape St. Vincent Ridge',\n", " 'Balleny Islands region',\n", " 'Banda Sea',\n", " 'Bangladesh',\n", " 'Barbados',\n", " 'Barbuda',\n", " 'Bay of Bengal',\n", " 'Bermuda',\n", " 'Bhutan',\n", " 'Bolivia',\n", " 'Bosnia and Herzegovina',\n", " 'Bouvet Island',\n", " 'Bouvet Island region',\n", " 'Brazil',\n", " 'British Indian Ocean Territory',\n", " 'British Virgin Islands',\n", " 'Burma',\n", " 'Burundi',\n", " 'California',\n", " 'Canada',\n", " 'Cape Verde',\n", " 'Carlsberg Ridge',\n", " 'Cayman Islands',\n", " 'Celebes Sea',\n", " 'Central East Pacific Rise',\n", " 'Central Mid-Atlantic Ridge',\n", " 'Chagos Archipelago region',\n", " 'Chile',\n", " 'China',\n", " 'Christmas Island',\n", " 'Colombia',\n", " 'Comoros',\n", " 'Cook Islands',\n", " 'Costa Rica',\n", " 'Crozet Islands region',\n", " 'Cuba',\n", " 'Cyprus',\n", " 'Davis Strait',\n", " 'Democratic Republic of the Congo',\n", " 'Djibouti',\n", " 'Dominica',\n", " 'Dominican Republic',\n", " 'Drake Passage',\n", " 'East Timor',\n", " 'East of Severnaya Zemlya',\n", " 'East of the Kuril Islands',\n", " 'East of the North Island of New Zealand',\n", " 'East of the Philippine Islands',\n", " 'East of the South Sandwich Islands',\n", " 'Easter Island region',\n", " 'Eastern Greenland',\n", " 'Ecuador',\n", " 'Ecuador region',\n", " 'Egypt',\n", " 'El Salvador',\n", " 'Eritrea',\n", " 'Ethiopia',\n", " 'Falkland Islands region',\n", " 'Federated States of Micronesia region',\n", " 'Fiji',\n", " 'Fiji region',\n", " 'France',\n", " 'French Polynesia',\n", " 'French Southern Territories',\n", " 'Galapagos Triple Junction region',\n", " 'Georgia',\n", " 'Greece',\n", " 'Greenland',\n", " 'Greenland Sea',\n", " 'Guadeloupe',\n", " 'Guam',\n", " 'Guatemala',\n", " 'Gulf of Alaska',\n", " 'Haiti',\n", " 'Hawaii',\n", " 'Honduras',\n", " 'Iceland',\n", " 'Idaho',\n", " 'India',\n", " 'India region',\n", " 'Indian Ocean Triple Junction',\n", " 'Indonesia',\n", " 'Iran',\n", " 'Iraq',\n", " 'Italy',\n", " 'Japan',\n", " 'Japan region',\n", " 'Jordan',\n", " 'Kansas',\n", " 'Kazakhstan',\n", " 'Kermadec Islands region',\n", " 'Kosovo',\n", " 'Kuril Islands',\n", " 'Kyrgyzstan',\n", " 'Labrador Sea',\n", " 'Laptev Sea',\n", " 'Macedonia',\n", " 'Macquarie Island region',\n", " 'Malawi',\n", " 'Malaysia',\n", " 'Mariana Islands region',\n", " 'Martinique',\n", " 'Mauritania',\n", " 'Mauritius',\n", " 'Mauritius - Reunion region',\n", " 'Mexico',\n", " 'Micronesia',\n", " 'Mid-Indian Ridge',\n", " 'Molucca Sea',\n", " 'Mongolia',\n", " 'Montana',\n", " 'Montenegro',\n", " 'Morocco',\n", " 'Mozambique',\n", " 'Mozambique Channel',\n", " 'Nepal',\n", " 'New Caledonia',\n", " 'New Mexico',\n", " 'New Zealand',\n", " 'Nicaragua',\n", " 'Niue',\n", " 'North Atlantic Ocean',\n", " 'North Indian Ocean',\n", " 'North Korea',\n", " 'North of Ascension Island',\n", " 'North of Franz Josef Land',\n", " 'North of New Zealand',\n", " 'North of Severnaya Zemlya',\n", " 'North of Svalbard',\n", " 'Northern East Pacific Rise',\n", " 'Northern Mariana Islands',\n", " 'Northern Mid-Atlantic Ridge',\n", " 'Northwest of Australia',\n", " 'Norway',\n", " 'Norwegian Sea',\n", " 'Off the coast of Central America',\n", " 'Off the coast of Ecuador',\n", " 'Off the coast of Oregon',\n", " 'Off the east coast of the North Island of New Zealand',\n", " 'Off the south coast of Australia',\n", " 'Off the west coast of northern Sumatra',\n", " 'Oklahoma',\n", " 'Oman',\n", " 'Oregon',\n", " 'Owen Fracture Zone region',\n", " 'Pacific-Antarctic Ridge',\n", " 'Pakistan',\n", " 'Palau',\n", " 'Palau region',\n", " 'Panama',\n", " 'Papua New Guinea',\n", " 'Peru',\n", " 'Peru-Ecuador border region',\n", " 'Philippine Islands region',\n", " 'Philippines',\n", " 'Poland',\n", " 'Portugal',\n", " 'Portugal region',\n", " 'Prince Edward Islands',\n", " 'Prince Edward Islands region',\n", " 'Puerto Rico',\n", " 'Republic of the Congo',\n", " 'Reykjanes Ridge',\n", " 'Romania',\n", " 'Russia',\n", " 'Russia region',\n", " 'Saint Helena',\n", " 'Saint Lucia',\n", " 'Saint Vincent and the Grenadines',\n", " 'Samoa',\n", " 'Santa Cruz Islands region',\n", " 'Saudi Arabia',\n", " 'Scotia Sea',\n", " 'Sea of Okhotsk',\n", " 'Serbia',\n", " 'Slovenia',\n", " 'Socotra region',\n", " 'Solomon Islands',\n", " 'Somalia',\n", " 'South Africa',\n", " 'South Atlantic Ocean',\n", " 'South Carolina',\n", " 'South Georgia Island region',\n", " 'South Georgia and the South Sandwich Islands',\n", " 'South Indian Ocean',\n", " 'South Napa Earthquake',\n", " 'South Sandwich Islands',\n", " 'South Sandwich Islands region',\n", " 'South Shetland Islands',\n", " 'South Sudan',\n", " 'South of Africa',\n", " 'South of Australia',\n", " 'South of Panama',\n", " 'South of Tasmania',\n", " 'South of Tonga',\n", " 'South of the Fiji Islands',\n", " 'South of the Kermadec Islands',\n", " 'South of the Mariana Islands',\n", " 'Southeast Indian Ridge',\n", " 'Southeast central Pacific Ocean',\n", " 'Southeast of Easter Island',\n", " 'Southern East Pacific Rise',\n", " 'Southern Mid-Atlantic Ridge',\n", " 'Southern Pacific Ocean',\n", " 'Southwest Indian Ridge',\n", " 'Southwest of Africa',\n", " 'Southwest of Australia',\n", " 'Southwestern Atlantic Ocean',\n", " 'Spain',\n", " 'Sudan',\n", " 'Svalbard and Jan Mayen',\n", " 'Sweden',\n", " 'Syria',\n", " 'Taiwan',\n", " 'Tajikistan',\n", " 'Tanzania',\n", " 'Thailand',\n", " 'Tonga',\n", " 'Tonga region',\n", " 'Trinidad and Tobago',\n", " 'Tristan da Cunha region',\n", " 'Turkey',\n", " 'Turkmenistan',\n", " 'Uganda',\n", " 'Ukraine',\n", " 'United Kingdom',\n", " 'Utah',\n", " 'Uzbekistan',\n", " 'Vanuatu',\n", " 'Vanuatu region',\n", " 'Venezuela',\n", " 'Vietnam',\n", " 'Wallis and Futuna',\n", " 'West Chile Rise',\n", " 'West of Australia',\n", " 'West of Macquarie Island',\n", " 'West of Vancouver Island',\n", " 'West of the Galapagos Islands',\n", " 'Western Australia',\n", " 'Western Indian-Antarctic Ridge',\n", " 'Yemen',\n", " 'Zambia',\n", " 'north of Ascension Island',\n", " 'northern Mid-Atlantic Ridge',\n", " 'south of Panama',\n", " 'western Xizang']" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb = df.groupby('country') \n", "list(gb.groups.keys())" ] }, { "cell_type": "markdown", "id": "2de3db3b", "metadata": {}, "source": [ "Then we aggregate - in this case we take the magnitude and find the the ten countries with the largest Earthquakes (by MM). Doing groupby first ensures that we don't just get the ten largest earthquakes. We groupby, find the max for each group, then find the then largest." ] }, { "cell_type": "code", "execution_count": 63, "id": "1b6ae400", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "country\n", "Chile 8.2\n", "Alaska 7.9\n", "Solomon Islands 7.6\n", "Papua New Guinea 7.5\n", "El Salvador 7.3\n", "Mexico 7.2\n", "Fiji 7.1\n", "Indonesia 7.1\n", "Southern East Pacific Rise 7.0\n", " 6.9\n", "Name: mag, dtype: float64" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.mag.max().nlargest(10)" ] }, { "cell_type": "markdown", "id": "830d534f", "metadata": {}, "source": [ "Multiple functions can also be done at once" ] }, { "cell_type": "code", "execution_count": 65, "id": "4df37764", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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aminamaxmean
country
4.16.94.582544
Afghanistan4.15.64.410656
Alaska4.17.94.515025
Albania4.15.04.391667
Algeria4.15.54.583333
............
Zambia4.15.34.457143
north of Ascension Island4.44.44.400000
northern Mid-Atlantic Ridge4.74.74.700000
south of Panama4.14.14.100000
western Xizang4.54.54.500000
\n", "

262 rows × 3 columns

\n", "
" ], "text/plain": [ " amin amax mean\n", "country \n", " 4.1 6.9 4.582544\n", "Afghanistan 4.1 5.6 4.410656\n", "Alaska 4.1 7.9 4.515025\n", "Albania 4.1 5.0 4.391667\n", "Algeria 4.1 5.5 4.583333\n", "... ... ... ...\n", "Zambia 4.1 5.3 4.457143\n", "north of Ascension Island 4.4 4.4 4.400000\n", "northern Mid-Atlantic Ridge 4.7 4.7 4.700000\n", "south of Panama 4.1 4.1 4.100000\n", "western Xizang 4.5 4.5 4.500000\n", "\n", "[262 rows x 3 columns]" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.mag.aggregate([np.min, np.max, np.mean])" ] }, { "cell_type": "markdown", "id": "d5f5c13a", "metadata": {}, "source": [ "This can be combined with a plotting function all in a single line of code" ] }, { "cell_type": "code", "execution_count": 67, "id": "5e37da7c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.groupby('country').mag.count().nlargest(20).plot(kind='bar', figsize=(12,6)) #bar graph of countries by number of earthquakes" ] }, { "cell_type": "markdown", "id": "a5a1e130", "metadata": {}, "source": [ "The key difference between aggregation and transformation is that aggregation returns a smaller object than the original, indexed by the group keys, while transformation returns an object with the same index (and same size) as the original object. Groupby + transformation is used when applying an operation that requires information about the whole group. Here we normalize earthquakes by magnitude within country grouping" ] }, { "cell_type": "code", "execution_count": 69, "id": "27482dbc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id\n", "usc000mqlp -0.915774\n", "usc000mqln -0.675696\n", "usc000mqls -0.282385\n", "usc000mf1x -0.684915\n", "usc000mqlm -0.666807\n", " ... \n", "usc000t6yh -0.281723\n", "usc000t6y2 -0.451617\n", "usc000t6y1 -0.627247\n", "usb000t1gp -0.629262\n", "usc000t6yn 0.043277\n", "Name: mag, Length: 16371, dtype: float64" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def normalize(x):\n", " return (x - x.mean())/x.std()\n", "\n", "mag_normalized_by_country = gb.mag.transform(normalize)\n", "mag_normalized_by_country" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }