Examine a dataset with pandas and seaborn\u00b6
\nConvert to a pandas DataFrame\u00b6
\n\n \n
\n \n [2]:\n@@ -932,15 +932,15 @@\n \n \n \n
\n [5]:\n \n
\n \n-<seaborn.axisgrid.PairGrid at 0xffff6edf2850>\n+<seaborn.axisgrid.PairGrid at 0xffff5e61fad0>\n \n
\n
\n \n ![\"../_images/examples_weather-data_9_1.png\"](\"../_images/examples_weather-data_9_1.png\")
\n@@ -1338,26 +1338,26 @@\n [0. , 0. , 0. ],\n [0. , 0. , 0. ],\n [0. , 0.01612903, 0. ],\n [0.33333333, 0.35 , 0.23333333],\n [0.93548387, 0.85483871, 0.82258065]])\n Coordinates:\n * location (location) <U2 'IA' 'IN' 'IL'\n- * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12
\n@@ -1338,26 +1338,26 @@\n [0. , 0. , 0. ],\n [0. , 0. , 0. ],\n [0. , 0.01612903, 0. ],\n [0.33333333, 0.35 , 0.23333333],\n [0.93548387, 0.85483871, 0.82258065]])\n Coordinates:\n * location (location) <U2 'IA' 'IN' 'IL'\n- * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12- location(location)<U2'IA' 'IN' 'IL'
array(['IA', 'IN', 'IL'], dtype='<U2')
- month(month)int641 2 3 4 5 6 7 8 9 10 11 12
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
- locationPandasIndex
PandasIndex(Index(['IA', 'IN', 'IL'], dtype='object', name='location'))
- monthPandasIndex
PandasIndex(Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))
\n \n \n \n@@ -1863,18 +1863,18 @@\n Dimensions: (time: 731, location: 3)\n Coordinates:\n * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2001-12-31\n * location (location) <U2 'IA' 'IN' 'IL'\n month (time) int64 1 1 1 1 1 1 1 1 1 ... 12 12 12 12 12 12 12 12 12\n Data variables:\n some_missing (time, location) float64 nan nan nan ... 2.063 -1.346 -4.544\n- filled (time, location) float64 -5.163 -4.216 ... -1.346 -4.544
\n \n [7]:\n freeze.to_pandas().plot()\n location
PandasIndex
PandasIndex(Index(['IA', 'IN', 'IL'], dtype='object', name='location'))
\n \n \n \"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (time: 731, location: 3)\\n\",\n \"Coordinates:\\n\",\n \" * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2001-12-31\\n\",\n \" * location (location) \\n\",\n@@ -587,18 +587,18 @@\n \"execution_count\": 3,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:47:32.682065Z\",\n \"start_time\": \"2020-01-27T15:47:32.652629Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-08T11:15:26.749643Z\",\n- \"iopub.status.busy\": \"2024-01-08T11:15:26.749351Z\",\n- \"iopub.status.idle\": \"2024-01-08T11:15:26.785774Z\",\n- \"shell.execute_reply\": \"2024-01-08T11:15:26.784993Z\"\n+ \"iopub.execute_input\": \"2024-01-08T11:27:47.125086Z\",\n+ \"iopub.status.busy\": \"2024-01-08T11:27:47.123079Z\",\n+ \"iopub.status.idle\": \"2024-01-08T11:27:47.155322Z\",\n+ \"shell.execute_reply\": \"2024-01-08T11:27:47.154745Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"array([[0.9516129 , 0.88709677, 0.93548387],\\n\",\n \" [0.84210526, 0.71929825, 0.77192982],\\n\",\n \" [0.24193548, 0.12903226, 0.16129032],\\n\",\n \" [0. , 0. , 0. ],\\n\",\n@@ -1236,18 +1236,18 @@\n \"execution_count\": 7,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:48:13.131247Z\",\n \"start_time\": \"2020-01-27T15:48:12.924985Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-08T11:15:29.221283Z\",\n- \"iopub.status.busy\": \"2024-01-08T11:15:29.221019Z\",\n- \"iopub.status.idle\": \"2024-01-08T11:15:29.393731Z\",\n- \"shell.execute_reply\": \"2024-01-08T11:15:29.393000Z\"\n+ \"iopub.execute_input\": \"2024-01-08T11:27:50.526915Z\",\n+ \"iopub.status.busy\": \"2024-01-08T11:27:50.524963Z\",\n+ \"iopub.status.idle\": \"2024-01-08T11:27:51.127790Z\",\n+ \"shell.execute_reply\": \"2024-01-08T11:27:51.127221Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n@@ -1284,18 +1284,18 @@\n \"execution_count\": 8,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:48:08.498259Z\",\n \"start_time\": \"2020-01-27T15:48:08.210890Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-08T11:15:29.397270Z\",\n- \"iopub.status.busy\": \"2024-01-08T11:15:29.397010Z\",\n- \"iopub.status.idle\": \"2024-01-08T11:15:29.897723Z\",\n- \"shell.execute_reply\": \"2024-01-08T11:15:29.896991Z\"\n+ \"iopub.execute_input\": \"2024-01-08T11:27:51.133965Z\",\n+ \"iopub.status.busy\": \"2024-01-08T11:27:51.131971Z\",\n+ \"iopub.status.idle\": \"2024-01-08T11:27:51.765721Z\",\n+ \"shell.execute_reply\": \"2024-01-08T11:27:51.765163Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n@@ -1349,18 +1349,18 @@\n \"execution_count\": 9,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:49:34.855086Z\",\n \"start_time\": \"2020-01-27T15:49:34.406439Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-08T11:15:29.901476Z\",\n- \"iopub.status.busy\": \"2024-01-08T11:15:29.901201Z\",\n- \"iopub.status.idle\": \"2024-01-08T11:15:30.501733Z\",\n- \"shell.execute_reply\": \"2024-01-08T11:15:30.500987Z\"\n+ \"iopub.execute_input\": \"2024-01-08T11:27:51.771830Z\",\n+ \"iopub.status.busy\": \"2024-01-08T11:27:51.769873Z\",\n+ \"iopub.status.idle\": \"2024-01-08T11:27:52.574271Z\",\n+ \"shell.execute_reply\": \"2024-01-08T11:27:52.573697Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n@@ -1408,18 +1408,18 @@\n \"execution_count\": 10,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:50:09.144586Z\",\n \"start_time\": \"2020-01-27T15:50:08.734682Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-08T11:15:30.505518Z\",\n- \"iopub.status.busy\": \"2024-01-08T11:15:30.505237Z\",\n- \"iopub.status.idle\": \"2024-01-08T11:15:31.233768Z\",\n- \"shell.execute_reply\": \"2024-01-08T11:15:31.233007Z\"\n+ \"iopub.execute_input\": \"2024-01-08T11:27:52.580501Z\",\n+ \"iopub.status.busy\": \"2024-01-08T11:27:52.578505Z\",\n+ \"iopub.status.idle\": \"2024-01-08T11:27:53.440792Z\",\n+ \"shell.execute_reply\": \"2024-01-08T11:27:53.440212Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n@@ -1477,18 +1477,18 @@\n \"execution_count\": 11,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:51:40.279299Z\",\n \"start_time\": \"2020-01-27T15:51:40.220342Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-08T11:15:31.237499Z\",\n- \"iopub.status.busy\": \"2024-01-08T11:15:31.237219Z\",\n- \"iopub.status.idle\": \"2024-01-08T11:15:31.309781Z\",\n- \"shell.execute_reply\": \"2024-01-08T11:15:31.309000Z\"\n+ \"iopub.execute_input\": \"2024-01-08T11:27:53.446903Z\",\n+ \"iopub.status.busy\": \"2024-01-08T11:27:53.444970Z\",\n+ \"iopub.status.idle\": \"2024-01-08T11:27:53.568470Z\",\n+ \"shell.execute_reply\": \"2024-01-08T11:27:53.567888Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \" \"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (time: 731, location: 3)\\n\",\n \"Coordinates:\\n\",\n \" * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2001-12-31\\n\",\n \" * location (location) \\n\",\n@@ -2052,18 +2052,18 @@\n \"execution_count\": 13,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:52:14.867866Z\",\n \"start_time\": \"2020-01-27T15:52:14.449684Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-08T11:15:31.349292Z\",\n- \"iopub.status.busy\": \"2024-01-08T11:15:31.349028Z\",\n- \"iopub.status.idle\": \"2024-01-08T11:15:32.121758Z\",\n- \"shell.execute_reply\": \"2024-01-08T11:15:32.120997Z\"\n+ \"iopub.execute_input\": \"2024-01-08T11:27:53.602327Z\",\n+ \"iopub.status.busy\": \"2024-01-08T11:27:53.600366Z\",\n+ \"iopub.status.idle\": \"2024-01-08T11:27:54.349229Z\",\n+ \"shell.execute_reply\": \"2024-01-08T11:27:54.348635Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n"}]}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/getting-started-guide/quick-overview.html", "source2": "./usr/share/doc/python-xarray-doc/html/getting-started-guide/quick-overview.html", "unified_diff": "@@ -310,15 +310,15 @@\n
\n \n [12]:\n df = both.sel(time="2000").mean("location").reset_coords(drop=True).to_dataframe()\n df.head()\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -157,15 +157,15 @@\n \n [../_images/examples_weather-data_7_1.png]\n \n **** Visualize using seaborn\u00c2\u00b6 ****\n [5]:\n sns.pairplot(df.reset_index(), vars=ds.data_vars)\n [5]:\n-\n+\n [../_images/examples_weather-data_9_1.png]\n \n ***** Probability of freeze by calendar month\u00c2\u00b6 *****\n [6]:\n freeze = (ds[\"tmin\"] <= 0).groupby(\"time.month\").mean(\"time\")\n freeze\n [6]:\n"}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/examples/weather-data.ipynb.gz", "source2": "./usr/share/doc/python-xarray-doc/html/examples/weather-data.ipynb.gz", "unified_diff": null, "details": [{"source1": "weather-data.ipynb", "source2": "weather-data.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9992323118860381%", "Differences: {\"'cells'\": \"{1: {'metadata': {'execution': {'iopub.execute_input': '2024-01-08T11:27:42.590472Z', \"", " \"'iopub.status.busy': '2024-01-08T11:27:42.590156Z', 'iopub.status.idle': \"", " \"'2024-01-08T11:27:47.081164Z', 'shell.execute_reply': \"", " \"'2024-01-08T11:27:47.080493Z'}}, 'outputs': {0: {'data': {'text/html': {insert: \"", " '[(370, \" tmax (time, location) float64 12.98 3.31 6.779 ... 6.636 3.343 '", " \"3.805 \\n\",\n@@ -701,18 +701,18 @@\n \"execution_count\": 4,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:47:34.617042Z\",\n \"start_time\": \"2020-01-27T15:47:34.282605Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-08T11:15:26.789808Z\",\n- \"iopub.status.busy\": \"2024-01-08T11:15:26.789529Z\",\n- \"iopub.status.idle\": \"2024-01-08T11:15:27.309771Z\",\n- \"shell.execute_reply\": \"2024-01-08T11:15:27.309004Z\"\n+ \"iopub.execute_input\": \"2024-01-08T11:27:47.161513Z\",\n+ \"iopub.status.busy\": \"2024-01-08T11:27:47.159577Z\",\n+ \"iopub.status.idle\": \"2024-01-08T11:27:47.853962Z\",\n+ \"shell.execute_reply\": \"2024-01-08T11:27:47.853344Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n@@ -749,25 +749,25 @@\n \"execution_count\": 5,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:47:37.643175Z\",\n \"start_time\": \"2020-01-27T15:47:37.202479Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-08T11:15:27.313549Z\",\n- \"iopub.status.busy\": \"2024-01-08T11:15:27.313268Z\",\n- \"iopub.status.idle\": \"2024-01-08T11:15:29.145020Z\",\n- \"shell.execute_reply\": \"2024-01-08T11:15:29.128989Z\"\n+ \"iopub.execute_input\": \"2024-01-08T11:27:47.857335Z\",\n+ \"iopub.status.busy\": \"2024-01-08T11:27:47.857050Z\",\n+ \"iopub.status.idle\": \"2024-01-08T11:27:50.429039Z\",\n+ \"shell.execute_reply\": \"2024-01-08T11:27:50.428463Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n@@ -797,18 +797,18 @@\n \"execution_count\": 6,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:48:11.241224Z\",\n \"start_time\": \"2020-01-27T15:48:11.211156Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-08T11:15:29.155616Z\",\n- \"iopub.status.busy\": \"2024-01-08T11:15:29.155336Z\",\n- \"iopub.status.idle\": \"2024-01-08T11:15:29.217731Z\",\n- \"shell.execute_reply\": \"2024-01-08T11:15:29.216996Z\"\n+ \"iopub.execute_input\": \"2024-01-08T11:27:50.435278Z\",\n+ \"iopub.status.busy\": \"2024-01-08T11:27:50.433305Z\",\n+ \"iopub.status.idle\": \"2024-01-08T11:27:50.520934Z\",\n+ \"shell.execute_reply\": \"2024-01-08T11:27:50.520363Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ],\n \"text/plain\": [\n \"Plotting\u00b6
\nVisualizing your datasets is quick and convenient:
\nIn [37]: data.plot()\n-Out[37]: <matplotlib.collections.QuadMesh at 0xffff43f65c90>\n+Out[37]: <matplotlib.collections.QuadMesh at 0xffff67537a10>\n ![\"../_images/plotting_quick_overview.png\"](\"../_images/plotting_quick_overview.png\")
\n Note the automatic labeling with names and units. Our effort in adding metadata attributes has paid off! Many aspects of these figures are customizable: see Plotting.
\npandas\u00b6
\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -269,15 +269,15 @@\n Coordinates:\n * x (x) int64 10 20\n Dimensions without coordinates: y\n \n ***** Plotting\u00c2\u00b6 *****\n Visualizing your datasets is quick and convenient:\n In [37]: data.plot()\n-Out[37]:\n
\n Note
\nThis method replicates the behavior of scipy.optimize.curve_fit().
In [3]: ds.temperature\n Out[3]: \n <xarray.DataArray 'temperature' (time: 30, latitude: 180, longitude: 180)>\n-dask.array<open_dataset-78c250975acbcfcf8c779b7811a8c27ctemperature, shape=(30, 180, 180), dtype=float64, chunksize=(10, 180, 180), chunktype=numpy.ndarray>\n+dask.array<open_dataset-4dbe4737ce0678ff5049460d084186c6temperature, shape=(30, 180, 180), dtype=float64, chunksize=(10, 180, 180), chunktype=numpy.ndarray>\n Coordinates:\n * time (time) datetime64[ns] 2015-01-01 2015-01-02 ... 2015-01-30\n * longitude (longitude) int64 0 1 2 3 4 5 6 7 ... 173 174 175 176 177 178 179\n * latitude (latitude) float64 89.5 88.5 87.5 86.5 ... -87.5 -88.5 -89.5\n Once you\u2019ve manipulated a Dask array, you can still write a dataset too big to\n@@ -138,16 +138,16 @@\n # or distributed.progress when using the distributed scheduler\n In [6]: delayed_obj = ds.to_netcdf("manipulated-example-data.nc", compute=False)\n \n In [7]: with ProgressBar():\n ...: results = delayed_obj.compute()\n ...: \n \n-[ ] | 0% Completed | 9.97 ms\n-[########################################] | 100% Completed | 114.03 ms\n+[ ] | 0% Completed | 10.73 ms\n+[########################################] | 100% Completed | 111.72 ms\n \n \n
\n \n \n
\n \n Note
\nWhen using Dask\u2019s distributed scheduler to write NETCDF4 files,\n it may be necessary to set the environment variable HDF5_USE_FILE_LOCKING=FALSE\n to avoid competing locks within the HDF5 SWMR file locking scheme. Note that\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -76,15 +76,15 @@\n You\u00e2\u0080\u0099ll notice that printing a dataset still shows a preview of array values,\n even if they are actually Dask arrays. We can do this quickly with Dask because\n we only need to compute the first few values (typically from the first block).\n To reveal the true nature of an array, print a DataArray:\n In [3]: ds.temperature\n Out[3]:\n ds.pipe(func)) instead of\n simply calling it (e.g., func(ds)). This allows you to write pipelines for\n transforming your data (using \u201cmethod chaining\u201d) instead of writing hard to\n follow nested function calls:
# these lines are equivalent, but with pipe we can make the logic flow\n # entirely from left to right\n In [60]: plt.plot((2 * ds.temperature.sel(x=0)).mean("y"))\n-Out[60]: [<matplotlib.lines.Line2D at 0xffff35426dd0>]\n+Out[60]: [<matplotlib.lines.Line2D at 0xffff5c9f11d0>]\n \n In [61]: (ds.temperature.sel(x=0).pipe(lambda x: 2 * x).mean("y").pipe(plt.plot))\n-Out[61]: [<matplotlib.lines.Line2D at 0xffff35336650>]\n+Out[61]: [<matplotlib.lines.Line2D at 0xffff5c93c9d0>]\n Both pipe and assign replicate the pandas methods of the same names\n (DataFrame.pipe and\n DataFrame.assign).
With xarray, there is no performance penalty for creating new datasets, even if\n variables are lazily loaded from a file on disk. Creating new objects instead\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -574,19 +574,19 @@\n There is also the pipe() method that allows you to use a method call with an\n external function (e.g., ds.pipe(func)) instead of simply calling it (e.g.,\n func(ds)). This allows you to write pipelines for transforming your data (using\n \u00e2\u0080\u009cmethod chaining\u00e2\u0080\u009d) instead of writing hard to follow nested function calls:\n # these lines are equivalent, but with pipe we can make the logic flow\n # entirely from left to right\n In [60]: plt.plot((2 * ds.temperature.sel(x=0)).mean(\"y\"))\n-Out[60]: [
![\"../_images/interpolation_sample1.png\"](\"../_images/interpolation_sample1.png\")
\n Additional keyword arguments can be passed to scipy\u2019s functions.
\n# fill 0 for the outside of the original coordinates.\n In [21]: da.interp(x=np.linspace(-0.5, 1.5, 10), kwargs={"fill_value": 0.0})\n Out[21]: \n@@ -615,15 +615,15 @@\n 858 f"Dimensions {invalid} do not exist. Expected one or more of {dims}"\n 859 )\n 861 return indexers\n 863 elif missing_dims == "warn":\n 864 \n 865 # don't modify input\n \n-ValueError: Dimensions {'lon', 'lat'} do not exist. Expected one or more of Frozen({'x': 3, 'y': 4})\n+ValueError: Dimensions {'lat', 'lon'} do not exist. Expected one or more of Frozen({'x': 3, 'y': 4})\n \n In [63]: dsi.air.plot(ax=axes[1])\n ---------------------------------------------------------------------------\n NameError Traceback (most recent call last)\n Cell In [63], line 1\n ----> 1 dsi.air.plot(ax=axes[1])\n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -165,26 +165,26 @@\n ....: np.sin(np.linspace(0, 2 * np.pi, 10)),\n ....: dims=\"x\",\n ....: coords={\"x\": np.linspace(0, 1, 10)},\n ....: )\n ....:\n \n In [17]: da.plot.line(\"o\", label=\"original\")\n-Out[17]: []\n+Out[17]: []\n \n In [18]: da.interp(x=np.linspace(0, 1, 100)).plot.line(label=\"linear\n (default)\")\n-Out[18]: []\n+Out[18]: []\n \n In [19]: da.interp(x=np.linspace(0, 1, 100), method=\"cubic\").plot.line\n (label=\"cubic\")\n-Out[19]: []\n+Out[19]: []\n \n In [20]: plt.legend()\n-Out[20]: \n+Out[20]: \n [../_images/interpolation_sample1.png]\n Additional keyword arguments can be passed to scipy\u00e2\u0080\u0099s functions.\n # fill 0 for the outside of the original coordinates.\n In [21]: da.interp(x=np.linspace(-0.5, 1.5, 10), kwargs={\"fill_value\": 0.0})\n Out[21]:\n \n array([ 0. , 0. , 0. , 0.814, 0.604, -0.604, -0.814, 0. , 0. ,\n@@ -558,15 +558,15 @@\n of {dims}\"\n 859 )\n 861 return indexers\n 863 elif missing_dims == \"warn\":\n 864\n 865 # don't modify input\n \n-ValueError: Dimensions {'lon', 'lat'} do not exist. Expected one or more of\n+ValueError: Dimensions {'lat', 'lon'} do not exist. Expected one or more of\n Frozen({'x': 3, 'y': 4})\n \n In [63]: dsi.air.plot(ax=axes[1])\n ---------------------------------------------------------------------------\n NameError Traceback (most recent call last)\n Cell In [63], line 1\n ----> 1 dsi.air.plot(ax=axes[1])\n"}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/user-guide/plotting.html", "source2": "./usr/share/doc/python-xarray-doc/html/user-guide/plotting.html", "unified_diff": "@@ -643,15 +643,15 @@\n --> 186 raise KeyError(key)\n 188 ref_name, var_name = split_key\n 189 ref_var = variables[ref_name]\n \n KeyError: 'lat'\n \n In [51]: b.plot()\n-Out[51]: [<matplotlib.lines.Line2D at 0xffff83223890>]\n+Out[51]: [<matplotlib.lines.Line2D at 0xffffaa97b2d0>]\n ![\"../_images/plotting_nonuniform_coords.png\"](\"../_images/plotting_nonuniform_coords.png\")
\n ![\"../_images/da_A_y.png\"](\"../_images/da_A_y.png\")
\n ![\"../_images/ds_A_y.png\"](\"../_images/ds_A_y.png\")
\n ![\"../_images/ds_simple_scatter.png\"](\"../_images/ds_simple_scatter.png\")
\n ![\"../_images/ds_hue_scatter.png\"](\"../_images/ds_hue_scatter.png\")
\n ![\"../_images/ds_discrete_legend_hue_scatter.png\"](\"../_images/ds_discrete_legend_hue_scatter.png\")
\n ![\"../_images/ds_discrete_colorbar_hue_scatter.png\"](\"../_images/ds_discrete_colorbar_hue_scatter.png\")
\n ![\"../_images/ds_hue_size_scatter.png\"](\"../_images/ds_hue_size_scatter.png\")
\n ![\"../_images/ds_hue_size_scatter_z.png\"](\"../_images/ds_hue_size_scatter_z.png\")
\n ![\"../_images/ds_facet_scatter.png\"](\"../_images/ds_facet_scatter.png\")
\n ![\"../_images/ds_facet_scatter_z.png\"](\"../_images/ds_facet_scatter_z.png\")
\n ![\"../_images/ds_simple_quiver.png\"](\"../_images/ds_simple_quiver.png\")
\n ![\"../_images/ds_facet_quiver.png\"](\"../_images/ds_facet_quiver.png\")
\n ![\"../_images/ds_simple_streamplot.png\"](\"../_images/ds_simple_streamplot.png\")
\n ![\"../_images/ds_facet_streamplot.png\"](\"../_images/ds_facet_streamplot.png\")
\n ![\"../_images/plotting_ways_to_use.png\"](\"../_images/plotting_ways_to_use.png\")
![\"../_images/plotting_example_2d_simple.png\"](\"../_images/plotting_example_2d_simple.png\")
![\"../_images/plotting_example_2d_irreg.png\"](\"../_images/plotting_example_2d_irreg.png\")
![\"_build/html/_static/plotting_example_2d_irreg_map.png\"](\"_build/html/_static/plotting_example_2d_irreg_map.png\")
![\"_build/html/_static/plotting_example_2d_irreg_map_infer.png\"](\"_build/html/_static/plotting_example_2d_irreg_map_infer.png\")
![\"../_images/plotting_example_2d_hue_xy.png\"](\"../_images/plotting_example_2d_hue_xy.png\")
\n ![\"_images/where_example.png\"](\"_images/where_example.png\")