From df745a57b62e39bd1a67a06244d57d5d34e68689 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Tue, 11 Jul 2023 02:48:10 -0600 Subject: [PATCH 01/27] adding the links to all indexing materials --- workshops/scipy2023/README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/workshops/scipy2023/README.md b/workshops/scipy2023/README.md index 552c5c9f..cd0868be 100644 --- a/workshops/scipy2023/README.md +++ b/workshops/scipy2023/README.md @@ -63,7 +63,11 @@ Once your codespace is launched, the following happens: ``` ```{dropdown} Indexing +{doc}`../../fundamentals/02.1_indexing_Basic` + {doc}`../../intermediate/indexing/advanced-indexing` + +{doc}`../../intermediate/indexing/boolean-masking-indexing` ``` ```{dropdown} Computational Patterns From 155c810ba5568b4d566b96da2c66bf9daf067b2c Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Tue, 11 Jul 2023 02:55:33 -0600 Subject: [PATCH 02/27] typo fix + remove a redundant example. --- intermediate/indexing/advanced-indexing.ipynb | 51 ++++++++----------- 1 file changed, 20 insertions(+), 31 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 804af21c..efaf6556 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -17,7 +17,7 @@ "source": [ "## Overview\n", "\n", - "In the previous notebooks, we learned basic forms of indexing with xarray (positional and name based dimensions, integer and label based indexing), Datetime Indexing, and nearest neighbor lookups. In this tutorial, we will lean how Xarray indexing is different from Numpy and how to do vectorized/pointwise indexing using Xarray. \n", + "In the previous notebooks, we learned basic forms of indexing with xarray (positional and name based dimensions, integer and label based indexing), Datetime Indexing, and nearest neighbor lookups. In this tutorial, we will learn how Xarray indexing is different from Numpy and how to do vectorized/pointwise indexing using Xarray. \n", "First, let's import packages needed for this repository: " ] }, @@ -107,6 +107,18 @@ "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "target_lat = xr.DataArray([31, 41, 42, 42], dims=\"degrees_north\")\n", + "target_lon = xr.DataArray([200, 201, 202, 205], dims=\"degrees_east\")\n", + "\n", + "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -229,35 +241,6 @@ "```" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Analogously, label-based pointwise-indexing is also possible by the `.sel()` method:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "da = xr.DataArray(\n", - " np.random.rand(4, 3),\n", - " [\n", - " (\"time\", pd.date_range(\"2000-01-01\", periods=4)),\n", - " (\"space\", [\"IA\", \"IL\", \"IN\"]),\n", - " ],\n", - ")\n", - "times = xr.DataArray(pd.to_datetime([\"2000-01-03\", \"2000-01-02\", \"2000-01-01\"]), dims=\"new_time\")\n", - "\n", - "\n", - "# -- get data for each state and each time:\n", - "da.sel(space=xr.DataArray([\"IA\", \"IL\", \"IN\"], dims=[\"new_time\"]), time=times)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -269,6 +252,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -278,7 +266,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" + "pygments_lexer": "ipython3", + "version": "3.11.4" }, "toc": { "base_numbering": 1, From 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" + ], + "text/plain": [ + "\n", + "array([[ 1, 3, 5],\n", + " [11, 13, 15],\n", + " [21, 23, 25]])\n", + "Dimensions without coordinates: dim_0, dim_1" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da [[0,2,4],[0,2,4]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Orthogonal Indexing \n", + "The image below shows the difference between orthogonal and vectorized indexing for a 2D 5x5 array. \n", "\n", - "As we learned in the previous tutorial, positional indexing deviates from the behavior exhibited by NumPy when indexing with multiple arrays. However, Xarray pointwise indexing supports the indexing along multiple labeled dimensions using list-like objects similar to NumPy indexing behavior.\n", + "![Orthogonal vs. Vectorized Indexing](../../images/orthogonal_vs_vectorized.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Point-wise indexing, shown on the left, selects specific elements at given coordinates, resulting in an array of those individual elements. In the example shown, the indices `[0, 2, 4]`, `[0, 2, 4]` select the elements at positions (0, 0), (2, 2), and (4, 4), resulting in the values `[1, 13, 25]`. This is shown in NumPy indexing example. \n", "\n", - "If you only provide integers, slices, or unlabeled arrays (array without dimension names, such as `np.ndarray`, `list`, but not `DataArray()`) indexing can be understood as orthogonally (i.e. along independent axes, instead of using NumPy’s broadcasting rules to vectorize indexers). \n", "\n", - "*Orthogonal* or *outer* indexing considers one-dimensional arrays in the same way as slices when deciding the output shapes. The principle of outer or orthogonal indexing is that the result mirrors the effect of independently indexing along each dimension with integer or boolean arrays, treating both the indexed and indexing arrays as one-dimensional. This method of indexing is analogous to vector indexing in programming languages like MATLAB, Fortran, and R, where each indexer component *independently* selects along its corresponding dimension. \n", + " In contrast, **orthogonal indexing** uses the same indices to select entire rows and columns, forming a cross-product of the specified indices. This method results in subarrays that include all combinations of the selected rows and columns. The example demonstrates this by selecting rows 0, 2, and 4 and columns 0, 2, and 4, resulting in a subarray containing `[[1, 3, 5], [11, 13, 15], [21, 23, 25]]`. This is shown in Xarray indexing example.\n", + " \n", + " The output of orthogonal indexing is a 3x3 array, while the output of vectorized indexing is a 1D array." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The principle of outer or orthogonal indexing is that the result mirrors the effect of independently indexing along each dimension with integer or boolean arrays, treating both the indexed and indexing arrays as one-dimensional." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Orthogonal Indexing in Xarray\n", "\n", - "For example : " + "If you only provide integers, slices, or unlabeled arrays (array without dimension names, such as `np.ndarray`, `list`, but not `DataArray()`) indexing can be understood as orthogonally (i.e. along independent axes, instead of using NumPy’s broadcasting rules to vectorize indexers). In the example above we saw this behavior, but let's see it in action with a real dataset." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 2920, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", + "Attributes: (5)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "da.isel(time=0, lat=[2, 4, 10, 13], lon=[1, 6, 7]).plot(); # -- orthogonal indexing" + "import numpy as np\n", + "import pandas as pd\n", + "import xarray as xr\n", + "\n", + "\n", + "xr.set_options(display_expand_attrs=False)\n", + "np.set_printoptions(threshold=10, edgeitems=2)\n", + "\n", + "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", + "da_air = ds.air\n", + "ds\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "Attributes: (11)
" + ], + "text/plain": [ + "\n", + "array([[[296.6 , 296.6 , 296.19998, 296.4 ],\n", + " [293.1 , 293.1 , 293.29 , 293.29 ],\n", + " [284.6 , 284.6 , 284.9 , 284.19998]],\n", + "\n", + " [[296.4 , 296.4 , 295.9 , 296.19998],\n", + " [293.19998, 293.19998, 293.9 , 294.19998],\n", + " [283.29 , 283.29 , 285.19998, 285.19998]],\n", + "\n", + " ...,\n", + "\n", + " [[293.69 , 293.69 , 293.88998, 295.38998],\n", + " [288.29 , 288.29 , 289.19 , 290.79 ],\n", + " [282.09 , 282.09 , 281.59 , 282.38998]],\n", + "\n", + " [[293.79 , 293.79 , 293.69 , 295.09 ],\n", + " [289.49 , 289.49 , 290.38998, 291.59 ],\n", + " [282.09 , 282.09 , 281.99 , 283.09 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 20.0 30.0 40.0\n", + " lon (points) float32 200.0 200.0 202.5 205.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Dimensions without coordinates: points\n", + "Attributes: (11)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=[20, 30, 40], lon=lon_points, method=\"nearest\")" ] From 19c97f4818ea93ca6c91eb3e3c57ceeaeeb77aad Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Thu, 6 Jun 2024 13:10:09 -0600 Subject: [PATCH 04/27] updating advanced indexing --- intermediate/indexing/advanced-indexing.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index d6a06b3f..16b14d68 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -8,7 +8,7 @@ "\n", "## Learning Objectives\n", "\n", - "* Orthogonal vs. Vectorized and Pointwise Indexing" + "* Orthogonal vs. Pointwise Indexing" ] }, { @@ -17,7 +17,7 @@ "source": [ "## Overview\n", "\n", - "In the previous notebooks, we learned basic forms of indexing with Xarray (positional and name based dimensions, integer and label based indexing), datetime Indexing, and nearest neighbor lookups. Xarray positional indexing deviates from the NumPy when indexing with multiple arrays like arr[[0, 1], [0, 1]]. In this tutorial we learn about this difference and how to do vectorized/pointwise indexing using Xarray.\n", + "In the previous notebooks, we learned basic forms of indexing with Xarray (positional and name based dimensions, integer and label based indexing), datetime Indexing, and nearest neighbor lookups. Xarray positional indexing deviates from the NumPy when indexing with multiple arrays like `arr[[0, 1], [0, 1]]`. In this tutorial we learn about this difference and how to do vectorized/pointwise indexing using Xarray.\n", "\n", "For this notebook, first, we should learn about orthogonal (i.e. outer) and vectorized (i.e. pointwise) indexing concepts. \n", "\n", From f25af4c130254dfe423fac44cce2b867ef15da3e Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Thu, 6 Jun 2024 13:28:43 -0600 Subject: [PATCH 05/27] advanced indexing --- intermediate/indexing/advanced-indexing.ipynb | 30 ++++++++++--------- 1 file changed, 16 insertions(+), 14 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 16b14d68..43380b14 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -17,7 +17,9 @@ "source": [ "## Overview\n", "\n", - "In the previous notebooks, we learned basic forms of indexing with Xarray (positional and name based dimensions, integer and label based indexing), datetime Indexing, and nearest neighbor lookups. Xarray positional indexing deviates from the NumPy when indexing with multiple arrays like `arr[[0, 1], [0, 1]]`. In this tutorial we learn about this difference and how to do vectorized/pointwise indexing using Xarray.\n", + "In the previous notebooks, we learned basic forms of indexing with Xarray (positional and name based dimensions, integer and label based indexing), datetime Indexing, and nearest neighbor lookups. Xarray positional indexing deviates from the NumPy when indexing with multiple arrays like `arr[[0, 1], [0, 1]]`.\n", + "\n", + "In this tutorial we learn about this difference and how to do vectorized/pointwise indexing using Xarray.\n", "\n", "For this notebook, first, we should learn about orthogonal (i.e. outer) and vectorized (i.e. pointwise) indexing concepts. \n", "\n", @@ -31,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -44,7 +46,7 @@ " [21, 22, ..., 24, 25]])" ] }, - "execution_count": 42, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -66,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -441,11 +443,11 @@ " ...,\n", " [16, 17, ..., 19, 20],\n", " [21, 22, ..., 24, 25]])\n", - "Dimensions without coordinates: dim_0, dim_1
        • " ], "text/plain": [ "\n", @@ -457,7 +459,7 @@ "Dimensions without coordinates: dim_0, dim_1" ] }, - "execution_count": 43, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -470,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -479,7 +481,7 @@ "array([ 1, 13, 25])" ] }, - "execution_count": 44, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -490,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -863,9 +865,9 @@ "array([[ 1, 3, 5],\n", " [11, 13, 15],\n", " [21, 23, 25]])\n", - "Dimensions without coordinates: dim_0, dim_1
            • " ], "text/plain": [ "\n", @@ -875,7 +877,7 @@ "Dimensions without coordinates: dim_0, dim_1" ] }, - "execution_count": 45, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -888,7 +890,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The image below shows the difference between orthogonal and vectorized indexing for a 2D 5x5 array. \n", + "The image below summarizes the difference between orthogonal and vectorized indexing for a 2D 5x5 array. \n", "\n", "![Orthogonal vs. Vectorized Indexing](../../images/orthogonal_vs_vectorized.png)" ] From 9ff571abfb0801b1bcc56b7ed437fc3f155c5d16 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Thu, 6 Jun 2024 13:41:06 -0600 Subject: [PATCH 06/27] update indexing redundancies --- intermediate/indexing/advanced-indexing.ipynb | 31 ++++--------------- 1 file changed, 6 insertions(+), 25 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 43380b14..2ede75cd 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -907,13 +907,6 @@ " The output of orthogonal indexing is a 3x3 array, while the output of vectorized indexing is a 1D array." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The principle of outer or orthogonal indexing is that the result mirrors the effect of independently indexing along each dimension with integer or boolean arrays, treating both the indexed and indexing arrays as one-dimensional." - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1818,7 +1811,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 51, "metadata": { "tags": [] }, @@ -2216,7 +2209,7 @@ " lon (degrees_east) float32 200.0 200.0 202.5 205.0\n", " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", "Dimensions without coordinates: degrees_north, degrees_east\n", - "Attributes: (11)
          • long_name :
            4xDaily Air temperature at sigma level 995
            units :
            degK
            precision :
            2
            GRIB_id :
            11
            GRIB_name :
            TMP
            var_desc :
            Air temperature
            dataset :
            NMC Reanalysis
            level_desc :
            Surface
            statistic :
            Individual Obs
            parent_stat :
            Other
            actual_range :
            [185.16 322.1 ]
            • " ], "text/plain": [ "\n", @@ -2283,7 +2276,7 @@ "Attributes: (11)" ] }, - "execution_count": 41, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -2295,18 +2288,6 @@ "da_air.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target_lat = xr.DataArray([31, 41, 42, 42], dims=\"degrees_north\")\n", - "target_lon = xr.DataArray([200, 201, 202, 205], dims=\"degrees_east\")\n", - "\n", - "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing" - ] - }, { "cell_type": "markdown", "metadata": {}, From 64d714c328518deb6072445c1c48da02e1de9fdc Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Thu, 6 Jun 2024 14:39:59 -0600 Subject: [PATCH 07/27] adding excercise --- intermediate/indexing/advanced-indexing.ipynb | 156 ++++++++++-------- 1 file changed, 88 insertions(+), 68 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 2ede75cd..a5a8095e 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -33,20 +33,20 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 1, 2, ..., 4, 5],\n", - " [ 6, 7, ..., 9, 10],\n", - " ...,\n", - " [16, 17, ..., 19, 20],\n", - " [21, 22, ..., 24, 25]])" + "array([[ 1, 2, 3, 4, 5],\n", + " [ 6, 7, 8, 9, 10],\n", + " [11, 12, 13, 14, 15],\n", + " [16, 17, 18, 19, 20],\n", + " [21, 22, 23, 24, 25]])" ] }, - "execution_count": 47, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -437,42 +437,42 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
            <xarray.DataArray (dim_0: 5, dim_1: 5)>\n",
+       "
            <xarray.DataArray (x: 5, y: 5)>\n",
        "array([[ 1,  2, ...,  4,  5],\n",
        "       [ 6,  7, ...,  9, 10],\n",
        "       ...,\n",
        "       [16, 17, ..., 19, 20],\n",
        "       [21, 22, ..., 24, 25]])\n",
-       "Dimensions without coordinates: dim_0, dim_1
                • " ], "text/plain": [ - "\n", + "\n", "array([[ 1, 2, ..., 4, 5],\n", " [ 6, 7, ..., 9, 10],\n", " ...,\n", " [16, 17, ..., 19, 20],\n", " [21, 22, ..., 24, 25]])\n", - "Dimensions without coordinates: dim_0, dim_1" + "Dimensions without coordinates: x, y" ] }, - "execution_count": 48, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import xarray as xr\n", - "da = xr.DataArray(np_array)\n", + "da = xr.DataArray(np_array, dims=[\"x\", \"y\"])\n", "da" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -481,7 +481,7 @@ "array([ 1, 13, 25])" ] }, - "execution_count": 49, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -492,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -865,9 +865,9 @@ "array([[ 1, 3, 5],\n", " [11, 13, 15],\n", " [21, 23, 25]])\n", - "Dimensions without coordinates: dim_0, dim_1
                    • " ], "text/plain": [ "\n", @@ -877,7 +877,7 @@ "Dimensions without coordinates: dim_0, dim_1" ] }, - "execution_count": 50, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -918,7 +918,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 5, "metadata": { "tags": [] }, @@ -1297,17 +1297,17 @@ " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", "Data variables:\n", " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", - "Attributes: (5)" ], "text/plain": [ @@ -1366,7 +1366,7 @@ "Attributes: (5)" ] }, - "execution_count": 46, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1387,7 +1387,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1765,10 +1765,10 @@ " * lat (lat) float32 70.0 65.0 50.0 42.5\n", " * lon (lon) float32 202.5 215.0 217.5\n", " time datetime64[ns] 2013-01-01\n", - "Attributes: (11)
                    • lat
                      (lat)
                      float32
                      70.0 65.0 50.0 42.5
                      standard_name :
                      latitude
                      long_name :
                      Latitude
                      units :
                      degrees_north
                      axis :
                      Y
                      array([70. , 65. , 50. , 42.5], dtype=float32)
                    • lon
                      (lon)
                      float32
                      202.5 215.0 217.5
                      standard_name :
                      longitude
                      long_name :
                      Longitude
                      units :
                      degrees_east
                      axis :
                      X
                      array([202.5, 215. , 217.5], dtype=float32)
                    • time
                      ()
                      datetime64[ns]
                      2013-01-01
                      standard_name :
                      time
                      long_name :
                      Time
                      array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
                    • lat
                      PandasIndex
                      PandasIndex(Index([70.0, 65.0, 50.0, 42.5], dtype='float32', name='lat'))
                    • lon
                      PandasIndex
                      PandasIndex(Index([202.5, 215.0, 217.5], dtype='float32', name='lon'))
                  • long_name :
                    4xDaily Air temperature at sigma level 995
                    units :
                    degK
                    precision :
                    2
                    GRIB_id :
                    11
                    GRIB_name :
                    TMP
                    var_desc :
                    Air temperature
                    dataset :
                    NMC Reanalysis
                    level_desc :
                    Surface
                    statistic :
                    Individual Obs
                    parent_stat :
                    Other
                    actual_range :
                    [185.16 322.1 ]
                    • " ], "text/plain": [ "\n", @@ -1783,7 +1783,7 @@ "Attributes: (11)" ] }, - "execution_count": 39, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1811,7 +1811,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 7, "metadata": { "tags": [] }, @@ -2209,7 +2209,7 @@ " lon (degrees_east) float32 200.0 200.0 202.5 205.0\n", " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", "Dimensions without coordinates: degrees_north, degrees_east\n", - "Attributes: (11)
                  • long_name :
                    4xDaily Air temperature at sigma level 995
                    units :
                    degK
                    precision :
                    2
                    GRIB_id :
                    11
                    GRIB_name :
                    TMP
                    var_desc :
                    Air temperature
                    dataset :
                    NMC Reanalysis
                    level_desc :
                    Surface
                    statistic :
                    Individual Obs
                    parent_stat :
                    Other
                    actual_range :
                    [185.16 322.1 ]
                    • " ], "text/plain": [ "\n", @@ -2276,7 +2276,7 @@ "Attributes: (11)" ] }, - "execution_count": 51, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -2304,7 +2304,7 @@ "\n", "But what if we would like to find the information from the nearest grid cell to a collection of specified points (for example, weather stations or tower data)?\n", "\n", - "## Vectorized or Pointwise Indexing\n", + "## Vectorized or Pointwise Indexing in Xarray\n", "\n", "Like NumPy and pandas, Xarray supports indexing many array elements at once in a *vectorized* manner. \n", "\n", @@ -2319,7 +2319,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 44, "metadata": { "tags": [] }, @@ -2692,7 +2692,7 @@ "}\n", "
                    <xarray.DataArray (points: 4)>\n",
        "array([31, 41, 42, 42])\n",
-       "Dimensions without coordinates: points
                    " + "Dimensions without coordinates: points" ], "text/plain": [ "\n", @@ -2700,7 +2700,7 @@ "Dimensions without coordinates: points" ] }, - "execution_count": 22, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -2713,7 +2713,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 45, "metadata": { "tags": [] }, @@ -3086,7 +3086,7 @@ "}\n", "
                    <xarray.DataArray (points: 4)>\n",
        "array([200, 201, 202, 205])\n",
-       "Dimensions without coordinates: points
                    " + "Dimensions without coordinates: points" ], "text/plain": [ "\n", @@ -3094,7 +3094,7 @@ "Dimensions without coordinates: points" ] }, - "execution_count": 26, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -3113,7 +3113,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 23, "metadata": { "tags": [] }, @@ -3495,14 +3495,14 @@ " lon (points) float32 200.0 200.0 202.5 205.0\n", " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", "Dimensions without coordinates: points\n", - "Attributes: (11)
                  • long_name :
                    4xDaily Air temperature at sigma level 995
                    units :
                    degK
                    precision :
                    2
                    GRIB_id :
                    11
                    GRIB_name :
                    TMP
                    var_desc :
                    Air temperature
                    dataset :
                    NMC Reanalysis
                    level_desc :
                    Surface
                    statistic :
                    Individual Obs
                    parent_stat :
                    Other
                    actual_range :
                    [185.16 322.1 ]
                    • " ], "text/plain": [ "\n", @@ -3530,13 +3530,13 @@ "Attributes: (11)" ] }, - "execution_count": 27, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\")" + "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\")" ] }, { @@ -3548,7 +3548,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 24, "metadata": { "tags": [] }, @@ -3559,13 +3559,13 @@ "('time', 'points')" ] }, - "execution_count": 28, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\").dims" + "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\").dims" ] }, { @@ -3581,7 +3581,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 43, "metadata": { "tags": [] }, @@ -3975,7 +3975,7 @@ " lon (points) float32 200.0 200.0 202.5 205.0\n", " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", "Dimensions without coordinates: points\n", - "Attributes: (11)
                  • long_name :
                    4xDaily Air temperature at sigma level 995
                    units :
                    degK
                    precision :
                    2
                    GRIB_id :
                    11
                    GRIB_name :
                    TMP
                    var_desc :
                    Air temperature
                    dataset :
                    NMC Reanalysis
                    level_desc :
                    Surface
                    statistic :
                    Individual Obs
                    parent_stat :
                    Other
                    actual_range :
                    [185.16 322.1 ]
                    • " ], "text/plain": [ "\n", @@ -4034,22 +4034,42 @@ "Attributes: (11)" ] }, - "execution_count": 29, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "da.sel(lat=[20, 30, 40], lon=lon_points, method=\"nearest\")" + "da_air.sel(lat=[20, 30, 40], lon=lon_points, method=\"nearest\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "```{warning}\n", - "If an indexer is a `DataArray()`, its coordinates should not conflict with the selected subpart of the target array (except for the explicitly indexed dimensions with `.loc`/`.sel`). Otherwise, `IndexError` will be raised!\n", - "```" + "## Excersises\n", + "\n", + "```{exercise}\n", + ":label: indexing-1\n", + "\n", + "In the simple 2D 5x5 Xarray data array above, select the sub-array containing (0,0),(2,2),(4,4) : \n", + "```\n", + "\n", + "\n", + "\n", + "````{solution} indexing-1\n", + ":class: dropdown\n", + "```python\n", + "\n", + "indices = np.array([0, 2, 4])\n", + "\n", + "xs_da = xr.DataArray(indices, dims=\"points\")\n", + "ys_da = xr.DataArray(indices, dims=\"points\")\n", + "\n", + "subset_da = da.sel(x=xs_da, y=xs_da)\n", + "subset_da\n", + "```\n", + "````\n" ] }, { From 8999e25372168f943d9d81d8cdd37a750445a803 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 6 Jun 2024 20:43:55 +0000 Subject: [PATCH 08/27] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- intermediate/indexing/advanced-indexing.ipynb | 3823 +---------------- 1 file changed, 29 insertions(+), 3794 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index a5a8095e..9950e621 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -33,24 +33,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1, 2, 3, 4, 5],\n", - " [ 6, 7, 8, 9, 10],\n", - " [11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25]])" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "\n", @@ -68,822 +53,32 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" + "👆 please notice how the output if the indexing example above resulted in an array of 3x4. " ] }, { From 5b686307d5b323c645cefe5389c5503e510196ab Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Thu, 6 Jun 2024 14:56:21 -0600 Subject: [PATCH 10/27] updating header --- intermediate/indexing/advanced-indexing.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 1d0fecfb..1dd3bebf 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -190,7 +190,7 @@ "\n", "But what if we would like to find the information from the nearest grid cell to a collection of specified points (for example, weather stations or tower data)?\n", "\n", - "## Vectorized or Pointwise Indexing in Xarray\n", + "## Pointwise Indexing in Xarray\n", "\n", "Like NumPy and pandas, Xarray supports indexing many array elements at once in a *vectorized* manner. \n", "\n", From e7bd5c3bf9a08a17514c54d98f38c4464bcfbce8 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Thu, 6 Jun 2024 14:57:53 -0600 Subject: [PATCH 11/27] updating header --- intermediate/indexing/advanced-indexing.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 1dd3bebf..0ad3a9cd 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -190,7 +190,7 @@ "\n", "But what if we would like to find the information from the nearest grid cell to a collection of specified points (for example, weather stations or tower data)?\n", "\n", - "## Pointwise Indexing in Xarray\n", + "## Vectorized or Pointwise Indexing in Xarray\n", "\n", "Like NumPy and pandas, Xarray supports indexing many array elements at once in a *vectorized* manner. \n", "\n", @@ -293,14 +293,14 @@ "## Excersises\n", "\n", "```{exercise}\n", - ":label: indexing-1\n", + ":label: indexing-advanced-1\n", "\n", "In the simple 2D 5x5 Xarray data array above, select the sub-array containing (0,0),(2,2),(4,4) : \n", "```\n", "\n", "\n", "\n", - "````{solution} indexing-1\n", + "````{solution} indexing-advanced-1\n", ":class: dropdown\n", "```python\n", "\n", From fd4e2401910074ec92ef54ac19a61e506f63b728 Mon Sep 17 00:00:00 2001 From: Scott Henderson Date: Mon, 24 Jun 2024 19:43:35 -0700 Subject: [PATCH 12/27] align with new exercise syntax --- intermediate/indexing/advanced-indexing.ipynb | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 0ad3a9cd..89c17773 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -292,15 +292,12 @@ "source": [ "## Excersises\n", "\n", - "```{exercise}\n", - ":label: indexing-advanced-1\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", "In the simple 2D 5x5 Xarray data array above, select the sub-array containing (0,0),(2,2),(4,4) : \n", - "```\n", - "\n", - "\n", "\n", - "````{solution} indexing-advanced-1\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "```python\n", "\n", @@ -312,7 +309,8 @@ "subset_da = da.sel(x=xs_da, y=xs_da)\n", "subset_da\n", "```\n", - "````\n" + ":::\n", + "::::" ] }, { From 473ac50bfbc6bc4503d9b6d82c0de8dba2041d3a Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Mon, 1 Jul 2024 17:46:03 -0600 Subject: [PATCH 13/27] adding advanced indexing --- intermediate/indexing/advanced-indexing.ipynb | 161 +++++++++++++----- 1 file changed, 119 insertions(+), 42 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 89c17773..b6e8af62 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -8,7 +8,8 @@ "\n", "## Learning Objectives\n", "\n", - "* Orthogonal vs. Pointwise Indexing" + "* Orthogonal vs. Pointwise (Vectorized) Indexing\n", + "* Pointwise indexing in Xarray to extract data at a collection of points." ] }, { @@ -17,18 +18,9 @@ "source": [ "## Overview\n", "\n", - "In the previous notebooks, we learned basic forms of indexing with Xarray (positional and name based dimensions, integer and label based indexing), datetime Indexing, and nearest neighbor lookups. Xarray positional indexing deviates from the NumPy when indexing with multiple arrays like `arr[[0, 1], [0, 1]]`.\n", + "In the previous notebooks, we learned basic forms of indexing with Xarray, including positional and label-based indexing, datetime indexing, and nearest neighbor lookups. We also learned that indexing an Xarray DataArray directly works (mostly) like it does for NumPy arrays; however, Xarray indexing behvaior deviates from NumPy when using multiple arrays for indexing, like `arr[[0, 1], [0, 1]]`.\n", "\n", - "In this tutorial we learn about this difference and how to do vectorized/pointwise indexing using Xarray.\n", - "\n", - "For this notebook, first, we should learn about orthogonal (i.e. outer) and vectorized (i.e. pointwise) indexing concepts. \n", - "\n", - "* *Orthogonal* or *outer* indexing allows for indexing along each dimension independently, treating the indexers as one-dimensional arrays. The principle of outer or orthogonal indexing is that the result mirrors the effect of independently indexing along each dimension with integer or boolean arrays, treating both the indexed and indexing arrays as one-dimensional. This method of indexing is analogous to vector indexing in programming languages like MATLAB, Fortran, and R, where each indexer component *independently* selects along its corresponding dimension. This is the default behavior in Xarray.\n", - "\n", - "* *Vectorized* indexing is a more general form of indexing that allows for arbitrary combinations of indexing arrays. This method of indexing is analogous to the broadcasting rules in NumPy, where the dimensions of the indexers are aligned and the result is determined by the shape of the indexers. This is the default behavior in NumPy. \n", - "\n", - "\n", - "We can better understand this with an example: " + "To better understand this difference, let's take a look at an example of 2D 5x5 array:" ] }, { @@ -63,6 +55,13 @@ "da" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's see how the indexing behavior is different between NumPy array and Xarray DataArray when indexing with mutliple arrays:" + ] + }, { "cell_type": "code", "execution_count": null, @@ -85,7 +84,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The image below summarizes the difference between orthogonal and vectorized indexing for a 2D 5x5 array. \n", + "The image below summarizes the difference between vectorized and orthogonal indexing for a 2D 5x5 NumPy array and Xarray DataArray:\n", "\n", "\n", "\n", @@ -96,12 +95,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " Point-wise indexing, shown on the left, selects specific elements at given coordinates, resulting in an array of those individual elements. In the example shown, the indices `[0, 2, 4]`, `[0, 2, 4]` select the elements at positions (0, 0), (2, 2), and (4, 4), resulting in the values `[1, 13, 25]`. This is shown in NumPy indexing example. \n", + "**Pointwise** or **Vectorized indexing**, shown on the left, selects specific elements at given coordinates, resulting in an array of those individual elements. In the example shown, the indices `[0, 2, 4]`, `[0, 2, 4]` select the elements at positions `(0, 0)`, `(2, 2)`, and `(4, 4)`, resulting in the values `[1, 13, 25]`. This is the default behavior of NumPy arrays.\n", + " \n", + "In contrast, **orthogonal indexing** uses the same indices to select entire rows and columns, forming a cross-product of the specified indices. This method results in sub-arrays that include all combinations of the selected rows and columns. The example demonstrates this by selecting rows 0, 2, and 4 and columns 0, 2, and 4, resulting in a subarray containing `[[1, 3, 5], [11, 13, 15], [21, 23, 25]]`. This is Xarray DataArray's default behavior.\n", + " \n", + "The output of vectorized indexing is a `1D array`, while the output of orthogonal indexing is a `3x3` array. \n", "\n", "\n", - " In contrast, **orthogonal indexing** uses the same indices to select entire rows and columns, forming a cross-product of the specified indices. This method results in subarrays that include all combinations of the selected rows and columns. The example demonstrates this by selecting rows 0, 2, and 4 and columns 0, 2, and 4, resulting in a subarray containing `[[1, 3, 5], [11, 13, 15], [21, 23, 25]]`. This is shown in Xarray indexing example.\n", - " \n", - " The output of orthogonal indexing is a 3x3 array, while the output of vectorized indexing is a 1D array." + ":::{tip} To Summarize: \n", + "\n", + "- *Pointwise* or *vectorized* indexing is a more general form of indexing that allows for arbitrary combinations of indexing arrays. This method of indexing is analogous to the broadcasting rules in NumPy, where the dimensions of the indexers are aligned and the result is determined by the shape of the indexers. This is the default behavior in NumPy.\n", + "\n", + "- *Orthogonal* or *outer* indexing allows for indexing along each dimension independently, treating the indexers as one-dimensional arrays. The principle of outer or orthogonal indexing is that the result mirrors the effect of independently indexing along each dimension with integer or boolean arrays, treating both the indexed and indexing arrays as one-dimensional. This method of indexing is analogous to vector indexing in programming languages like MATLAB, Fortran, and R, where each indexer component independently selects along its corresponding dimension. This is the default behavior in Xarray.\n", + "\n", + "\n", + ":::" ] }, { @@ -110,7 +118,9 @@ "source": [ "## Orthogonal Indexing in Xarray\n", "\n", - "If you only provide integers, slices, or unlabeled arrays (array without dimension names, such as `np.ndarray`, `list`, but not `DataArray()`) indexing can be understood as orthogonally (i.e. along independent axes, instead of using NumPy’s broadcasting rules to vectorize indexers). In the example above we saw this behavior, but let's see it in action with a real dataset." + "As explained earlier, when you use only integers, slices, or unlabeled arrays (arrays without dimension names, such as `np.ndarray` or `list`, but not `DataArray`) to index an `Xarray DataArray`, Xarray interprets these indexers orthogonally. This means it indexes along independent axes, rather than using NumPy's broadcasting rules to vectorize the indexers. \n", + "\n", + "In the example above we saw this behavior, but let's see this behavior in action with a real dataset. Here we’ll use `air temperature` data from the National Center for Environmental Prediction:" ] }, { @@ -122,7 +132,6 @@ "outputs": [], "source": [ "import numpy as np\n", - "import pandas as pd\n", "import xarray as xr\n", "\n", "\n", @@ -148,7 +157,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "👆 please notice how the output if the indexing example above resulted in an array of 3x4. " + "👆 please notice how the output of the indexing example above resulted in an array of size `3x4`" ] }, { @@ -178,7 +187,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In the above example, you can see how the output shape is `time` x `lats` x `lons`. " + "In the above example, you can see how the output shape is `time` x `lats` x `lons`.\n", + "\n", + "```{attention}\n", + "Please note that slices or sequences/arrays without named-dimensions are treated as if they have the same dimension which is indexed along.\n", + "```\n", + "\n", + "For example:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da_air.sel(lat=[20, 30, 40], lon=target_lon, method=\"nearest\")" ] }, { @@ -188,19 +213,62 @@ }, "source": [ "\n", - "But what if we would like to find the information from the nearest grid cell to a collection of specified points (for example, weather stations or tower data)?\n", + "But what if we'd like to find the nearest climate model grid cell to a collection of specified points (for example observation sites, or weather stations)?\n", "\n", "## Vectorized or Pointwise Indexing in Xarray\n", "\n", "Like NumPy and pandas, Xarray supports indexing many array elements at once in a *vectorized* manner. \n", "\n", - "**Vectorized indexing** or **Pointwise Indexing** using `DataArrays()` can be used to extract information from the nearest grid cells of interest, for example, the nearest climate model grid cells to a collection of specified weather station latitudes and longitudes.\n", + "**Vectorized indexing** or **Pointwise Indexing** using `DataArrays()` can be used to extract information from the nearest grid cells of interest, for example, the nearest climate model grid cells to a collection of specified observation tower data latitudes and longitudes.\n", "\n", "```{hint}\n", - "To trigger vectorized indexing behavior, you will need to provide the selection dimensions with a different name than the original dimensions. This dimension name will be used in the output array.\n", + "To trigger vectorized indexing behavior, you will need to provide the selection dimensions with a new **shared** output dimension name. This means that the dimensions of both indexers must be the same, and the output will have the same dimension name as the indexers.\n", "```\n", "\n", - "In the example below, the selections of the closest latitude and longitude are renamed to an output dimension named `points`:" + "Let's see how this works with an example. A researcher wants to find the nearest climate model grid cell to a collection of observation sites. She has the latitude and longitude of the observation sites as following:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "obs_lats = [31.81,\n", + " 41.26,\n", + " 22.59,\n", + " 44.47,\n", + " 28.57]\n", + "\n", + "obs_lons = [200.16,\n", + " 201.57,\n", + " 305.54,\n", + " 210.56,\n", + " 226.59]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the researcher use the lists to index the DataArray, they will get the orthogonal indexing behavior, which is not what they wants." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da_air.sel(lat=obs_lats, lon=obs_lats, method=\"nearest\") # -- orthogonal indexing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To trigger the pointwise indexing, they need to create DataArray objects with the same dimension name, and then use them to index the DataArray. \n", + "For example, the code below first create DataArray objects for the latitude and longitude of the observation sites using a shared dimension name `points`, and then use them to index the DataArray `air_temperature`:" ] }, { @@ -211,8 +279,8 @@ }, "outputs": [], "source": [ - "# Define target latitude and longitude (where weather stations might be)\n", - "lat_points = xr.DataArray([31, 41, 42, 42], dims=\"points\")\n", + "## latitudes of weather stations with a dimension of \"points\"\n", + "lat_points = xr.DataArray(obs_lats, dims=\"points\")\n", "lat_points" ] }, @@ -224,7 +292,8 @@ }, "outputs": [], "source": [ - "lon_points = xr.DataArray([200, 201, 202, 205], dims=\"points\")\n", + "## longitudes of weather stations with a dimension of \"points\"\n", + "lon_points = xr.DataArray(obs_lons, dims=\"points\")\n", "lon_points" ] }, @@ -243,7 +312,7 @@ }, "outputs": [], "source": [ - "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\")" + "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\") # -- pointwise indexing" ] }, { @@ -268,34 +337,29 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "```{attention}\n", - "Please note that slices or sequences/arrays without named-dimensions are treated as if they have the same dimension which is indexed along.\n", - "```\n", - "\n", - "For example:" + "Now, let's plot the data for all stations." ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "tags": [] - }, + "metadata": {}, "outputs": [], "source": [ - "da_air.sel(lat=[20, 30, 40], lon=lon_points, method=\"nearest\")" + "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\").plot(x='time', hue='points');\n", + "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Excersises\n", + "## Exercises\n", "\n", "::::{admonition} Exercise\n", ":class: tip\n", "\n", - "In the simple 2D 5x5 Xarray data array above, select the sub-array containing (0,0),(2,2),(4,4) : \n", + "In the simple 2D 5x5 Xarray data array above, select the sub-array containing (0,0),(2,2),(4,4):\n", "\n", ":::{admonition} Solution\n", ":class: dropdown\n", @@ -319,11 +383,23 @@ "source": [ "## Additional Resources\n", "\n", - "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)\n" + "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)\n", + "\n", + "\n", + ":::{seealso}\n", + "- [NumPy Fancy Indexing](https://numpy.org/doc/stable/reference/arrays.indexing.html#fancy-indexing)\n", + "- [Xarray Indexing](indexing.md)\n", + "\n", + ":::\n" ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -333,7 +409,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" + "pygments_lexer": "ipython3", + "version": "3.9.0" }, "toc": { "base_numbering": 1, From f6706055b65e138bbf0a84e53a6b79eefd7c1144 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 1 Jul 2024 23:46:17 +0000 Subject: [PATCH 14/27] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- intermediate/indexing/advanced-indexing.ipynb | 27 +++++-------------- 1 file changed, 6 insertions(+), 21 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index b6e8af62..c52754cc 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -234,17 +234,9 @@ "metadata": {}, "outputs": [], "source": [ - "obs_lats = [31.81,\n", - " 41.26,\n", - " 22.59,\n", - " 44.47,\n", - " 28.57]\n", + "obs_lats = [31.81, 41.26, 22.59, 44.47, 28.57]\n", "\n", - "obs_lons = [200.16,\n", - " 201.57,\n", - " 305.54,\n", - " 210.56,\n", - " 226.59]" + "obs_lons = [200.16, 201.57, 305.54, 210.56, 226.59]" ] }, { @@ -260,7 +252,7 @@ "metadata": {}, "outputs": [], "source": [ - "da_air.sel(lat=obs_lats, lon=obs_lats, method=\"nearest\") # -- orthogonal indexing" + "da_air.sel(lat=obs_lats, lon=obs_lats, method=\"nearest\") # -- orthogonal indexing" ] }, { @@ -312,7 +304,7 @@ }, "outputs": [], "source": [ - "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\") # -- pointwise indexing" + "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\") # -- pointwise indexing" ] }, { @@ -346,8 +338,7 @@ "metadata": {}, "outputs": [], "source": [ - "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\").plot(x='time', hue='points');\n", - "\n" + "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\").plot(x='time', hue='points');" ] }, { @@ -395,11 +386,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -409,8 +395,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" + "pygments_lexer": "ipython3" }, "toc": { "base_numbering": 1, From 2a5ffb200819acbad795b7809cd7cacd0414116a Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Mon, 1 Jul 2024 17:56:21 -0600 Subject: [PATCH 15/27] add numpy advanced indexing --- intermediate/indexing/advanced-indexing.ipynb | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index c52754cc..827c9be6 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -379,10 +379,15 @@ "\n", ":::{seealso}\n", "- [NumPy Fancy Indexing](https://numpy.org/doc/stable/reference/arrays.indexing.html#fancy-indexing)\n", - "- [Xarray Indexing](indexing.md)\n", + "- [NumPy Advanced Indexing](https://numpy.org/doc/stable/user/basics.indexing.html#advanced-indexing)\n", "\n", ":::\n" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { From 82d7bf1dc070735dc046a21d7f6fc2251baf499a Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Mon, 1 Jul 2024 18:04:57 -0600 Subject: [PATCH 16/27] update learning objectives --- intermediate/indexing/advanced-indexing.ipynb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 827c9be6..91cdfd3f 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -8,8 +8,9 @@ "\n", "## Learning Objectives\n", "\n", - "* Orthogonal vs. Pointwise (Vectorized) Indexing\n", - "* Pointwise indexing in Xarray to extract data at a collection of points." + "* Orthogonal vs. Pointwise (Vectorized) Indexing.\n", + "* Pointwise indexing in Xarray to extract data at a collection of points.\n", + "* Understand the difference between NumPy and Xarray indexing behavior." ] }, { From 5e3ef9ba8331a27ac68f87cdd90d21810100477f Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Mon, 1 Jul 2024 18:18:50 -0600 Subject: [PATCH 17/27] few minor updates and wording changes --- intermediate/indexing/advanced-indexing.ipynb | 4276 ++++++++++++++++- 1 file changed, 4250 insertions(+), 26 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 91cdfd3f..a49c12a7 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -26,9 +26,24 @@ }, { "cell_type": "code", - 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                    " - ], - "text/plain": [ - "\n", - "array([[290.19998, 283.19998, ..., 280.29 , 290.4 ],\n", - " [290.19998, 282.79 , ..., 280.1 , 290.4 ],\n", - " ...,\n", - " [286.69 , 280.38998, ..., 283.38998, 292.49 ],\n", - " [287.59 , 280.59 , ..., 283.59 , 292.38998]], dtype=float32)\n", - "Coordinates:\n", - " lat (degrees_north) float32 32.5 42.5 22.5 45.0 27.5\n", - " lon (degrees_north) float32 200.0 202.5 305.0 210.0 227.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Dimensions without coordinates: degrees_north\n", - "Attributes: (11)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\") # -- pointwise indexing" ] From 24462e4f801496d53bafee7686cb0c33b2bf9401 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Mon, 1 Jul 2024 18:22:45 -0600 Subject: [PATCH 19/27] update indexing docs --- intermediate/indexing/advanced-indexing.ipynb | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index e69dfec7..2516f1f6 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -379,16 +379,11 @@ "\n", "\n", ":::{seealso}\n", - "- [NumPy Fancy Indexing](https://numpy.org/doc/stable/reference/arrays.indexing.html#fancy-indexing)\n", - "- [NumPy Advanced Indexing](https://numpy.org/doc/stable/user/basics.indexing.html#advanced-indexing)\n", + "- [Introductions to Fancy Indexing](https://jakevdp.github.io/PythonDataScienceHandbook/02.07-fancy-indexing.html)\n", + "- [NumPy Docs - Advanced Indexing](https://numpy.org/doc/stable/user/basics.indexing.html#advanced-indexing)\n", "\n", ":::\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { From 792fc33ffec68010c916b20093ee189f25502770 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Mon, 1 Jul 2024 18:25:26 -0600 Subject: [PATCH 20/27] quick merge conflict resolve --- intermediate/indexing/advanced-indexing.ipynb | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 2516f1f6..1d87a9d1 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -387,6 +387,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "dask-tutorial", + "language": "python", + "name": "python3" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -396,7 +401,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" + "pygments_lexer": "ipython3", + "version": "3.9.18" }, "toc": { "base_numbering": 1, From c22c825df3a81ff425c856d0ea42a6ab2ecaf7cd Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 2 Jul 2024 00:25:38 +0000 Subject: [PATCH 21/27] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- intermediate/indexing/advanced-indexing.ipynb | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 1d87a9d1..2516f1f6 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -387,11 +387,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "dask-tutorial", - "language": "python", - "name": "python3" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -401,8 +396,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" + "pygments_lexer": "ipython3" }, "toc": { "base_numbering": 1, From 11e2aa4f1210edc8bf806d458ef3c8477c2e634e Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Mon, 1 Jul 2024 18:29:38 -0600 Subject: [PATCH 22/27] update docs --- intermediate/indexing/advanced-indexing.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 2516f1f6..ada9a69b 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -19,7 +19,7 @@ "source": [ "## Overview\n", "\n", - "In the previous notebooks, we learned basic forms of indexing with Xarray, including positional and label-based indexing, datetime indexing, and nearest neighbor lookups. We also learned that indexing an Xarray DataArray directly works (mostly) like it does for NumPy arrays; however, Xarray indexing behvaior deviates from NumPy when using multiple arrays for indexing, like `arr[[0, 1], [0, 1]]`.\n", + "In the previous notebooks, we learned basic forms of indexing with Xarray, including positional and label-based indexing, datetime indexing, and nearest neighbor lookups. We also learned that indexing an Xarray DataArray directly works (mostly) like it does for NumPy arrays; however, Xarray indexing behavior deviates from NumPy when using multiple arrays for indexing, like `arr[[0, 1], [0, 1]]`.\n", "\n", "To better understand this difference, let's take a look at an example of 2D 5x5 array:" ] @@ -60,7 +60,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, let's see how the indexing behavior is different between NumPy array and Xarray DataArray when indexing with mutliple arrays:" + "Now, let's see how the indexing behavior is different between NumPy array and Xarray DataArray when indexing with multiple arrays:" ] }, { From 7d70d22b6cb27ef301a0948257ecf5fcc269d19b Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Tue, 2 Jul 2024 17:39:21 -0600 Subject: [PATCH 23/27] adding np.ix_ --- intermediate/indexing/advanced-indexing.ipynb | 21 ++++++++++++++++++- 1 file changed, 20 insertions(+), 1 deletion(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index ada9a69b..7029124b 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -110,6 +110,18 @@ "- *Orthogonal* or *outer* indexing allows for indexing along each dimension independently, treating the indexers as one-dimensional arrays. The principle of outer or orthogonal indexing is that the result mirrors the effect of independently indexing along each dimension with integer or boolean arrays, treating both the indexed and indexing arrays as one-dimensional. This method of indexing is analogous to vector indexing in programming languages like MATLAB, Fortran, and R, where each indexer component independently selects along its corresponding dimension. This is the default behavior in Xarray.\n", "\n", "\n", + ":::\n", + "\n", + ":::{note} Orthogonal indexing with NumPy\n", + ":class: dropdown\n", + "\n", + "While Pointwise indexing is the default behavior in NumPy, you can achieve orthogonal indexing by using the [`np.ix_` function](https://numpy.org/doc/stable/reference/generated/numpy.ix_.html). This function constructs an open mesh from multiple arrays, allowing you to index along each dimension independently similar to Xarray indexig behavior. For example: \n", + "\n", + "```python\n", + "ixgrid = np.ix_([0, 2, 4], [0, 2, 4])\n", + "np_array[ixgrid]\n", + "```\n", + "\n", ":::" ] }, @@ -138,6 +150,7 @@ "\n", "xr.set_options(display_expand_attrs=False)\n", "np.set_printoptions(threshold=10, edgeitems=2)\n", + "%config InlineBackend.figure_format='retina'\n", "\n", "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "da_air = ds.air\n", @@ -387,6 +400,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -396,7 +414,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" + "pygments_lexer": "ipython3", + "version": "3.12.3" }, "toc": { "base_numbering": 1, From 89459e565f2e160a6ebd5043bee67635433aaa60 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 2 Jul 2024 23:39:34 +0000 Subject: [PATCH 24/27] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- intermediate/indexing/advanced-indexing.ipynb | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 7029124b..d8553475 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -400,11 +400,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -414,8 +409,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" + "pygments_lexer": "ipython3" }, "toc": { "base_numbering": 1, From 5f18954578daa51b2f09410f8826ba5cbef1667c Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Tue, 2 Jul 2024 17:44:48 -0600 Subject: [PATCH 25/27] fix merge --- intermediate/indexing/advanced-indexing.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index d8553475..3f350941 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -115,7 +115,7 @@ ":::{note} Orthogonal indexing with NumPy\n", ":class: dropdown\n", "\n", - "While Pointwise indexing is the default behavior in NumPy, you can achieve orthogonal indexing by using the [`np.ix_` function](https://numpy.org/doc/stable/reference/generated/numpy.ix_.html). This function constructs an open mesh from multiple arrays, allowing you to index along each dimension independently similar to Xarray indexig behavior. For example: \n", + "While Pointwise indexing is the default behavior in NumPy, you can achieve orthogonal indexing by using the [`np.ix_` function](https://numpy.org/doc/stable/reference/generated/numpy.ix_.html). This function constructs an open mesh from multiple arrays, allowing you to index along each dimension independently similar to Xarray indexing behavior. For example: \n", "\n", "```python\n", "ixgrid = np.ix_([0, 2, 4], [0, 2, 4])\n", @@ -410,6 +410,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3" + "version": "3.9.18" }, "toc": { "base_numbering": 1, From 3081b360a17781694a07f83fa431f1a4f83cdd4e Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Tue, 2 Jul 2024 17:47:32 -0600 Subject: [PATCH 26/27] typo fix --- intermediate/indexing/advanced-indexing.ipynb | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 3f350941..6cfd9d0f 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -115,7 +115,7 @@ ":::{note} Orthogonal indexing with NumPy\n", ":class: dropdown\n", "\n", - "While Pointwise indexing is the default behavior in NumPy, you can achieve orthogonal indexing by using the [`np.ix_` function](https://numpy.org/doc/stable/reference/generated/numpy.ix_.html). This function constructs an open mesh from multiple arrays, allowing you to index along each dimension independently similar to Xarray indexing behavior. For example: \n", + "While pointwise indexing is the default behavior in NumPy, you can achieve orthogonal indexing by using the [`np.ix_` function](https://numpy.org/doc/stable/reference/generated/numpy.ix_.html). This function constructs an open mesh from multiple arrays, allowing you to index along each dimension independently similar to Xarray indexing behavior. For example: \n", "\n", "```python\n", "ixgrid = np.ix_([0, 2, 4], [0, 2, 4])\n", @@ -400,6 +400,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -409,7 +414,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" + "pygments_lexer": "ipython3", "version": "3.9.18" }, "toc": { From 383eeae6263bf4db3ee4db17e4657231ba42fd12 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 2 Jul 2024 23:47:45 +0000 Subject: [PATCH 27/27] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- intermediate/indexing/advanced-indexing.ipynb | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 6cfd9d0f..ca3f2d5c 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -400,11 +400,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -414,8 +409,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" + "pygments_lexer": "ipython3" }, "toc": { "base_numbering": 1,