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of further and further
header levels

Each section in your notebook can also contain \(\LaTeX\) equations, enabled through MathJax. In the following example, we illustrate some sample MathJax equations. (Rendering instructions, as well as detailed information about MathJax, can be found in this documentation.)

-
-(1)\[\begin{align} +
+(1)\[\begin{align} \dot{x} & = \sigma(y-x) \\ \dot{y} & = \rho x - y - xz \\ \dot{z} & = -\beta z + xy diff --git a/_preview/469/core/cartopy/cartopy.html b/_preview/469/core/cartopy/cartopy.html index 7c5edba30..26d9bf03c 100644 --- a/_preview/469/core/cartopy/cartopy.html +++ b/_preview/469/core/cartopy/cartopy.html @@ -571,7 +571,7 @@

Create a map with a specified projection -
<cartopy.mpl.feature_artist.FeatureArtist at 0x7f6388800290>
+
<cartopy.mpl.feature_artist.FeatureArtist at 0x7f59bc33b740>
@@ -603,7 +603,7 @@

Add cartographic features to the map -
<cartopy.mpl.feature_artist.FeatureArtist at 0x7f63809f8ad0>
+
<cartopy.mpl.feature_artist.FeatureArtist at 0x7f59bc375f70>
diff --git a/_preview/469/core/data-formats/netcdf-cf.html b/_preview/469/core/data-formats/netcdf-cf.html index ec7c1a72e..606bd13be 100644 --- a/_preview/469/core/data-formats/netcdf-cf.html +++ b/_preview/469/core/data-formats/netcdf-cf.html @@ -564,7 +564,7 @@

Gridded Data -
/tmp/ipykernel_2588/2342439358.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+
/tmp/ipykernel_2511/2342439358.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
   start = datetime.utcnow().replace(hour=22, minute=0, second=0, microsecond=0)
 

 

@@ -610,7 +610,7 @@ 
Creating the file and dimensions
-
/tmp/ipykernel_2588/2008125275.py:5: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+
/tmp/ipykernel_2511/2008125275.py:5: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
   nc.history = str(datetime.utcnow()) + ' Python'
 

 

@@ -651,7 +651,7 @@ 
Creating the file and dimensions
 

 

-
1715804061.5780733
+
1715804423.3455348
 

 

 

@@ -614,7 +614,7 @@ 
time
 

 

-
The benchmark took 1.0002062320709229 seconds
+
The benchmark took 1.0002317428588867 seconds
 

 

 

@@ -815,8 +815,8 @@ 
Time Zone Naive Versus Time Zone Aware 
-
I am time zone naive 2024-05-15 20:14:22.615566.
-I am time zone aware 2024-05-15 20:14:22.615600+00:00.
+
I am time zone naive 2024-05-15 20:20:24.383078.
+I am time zone aware 2024-05-15 20:20:24.383111+00:00.
 

 

 

@@ -839,8 +839,8 @@ 
Full time zone support with the <
 

 

 

-
I am time zone naive: 2024-05-15 20:14:22.620300.
-I am time zone aware: 2024-05-15 14:14:22.630866-06:00.
+
I am time zone naive: 2024-05-15 20:20:24.387451.
+I am time zone aware: 2024-05-15 14:20:24.398482-06:00.
 

 

 

@@ -862,11 +862,11 @@ 
Print Time with a Different Time Zone
-
The UTC time is May 15, 2024, 8:14PM.
-The 'US/Mountain' time is May 15, 2024, 2:14PM.
+
The UTC time is May 15, 2024, 8:20PM.
+The 'US/Mountain' time is May 15, 2024, 2:20PM.
 

 

-
/tmp/ipykernel_2612/3080495102.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+
/tmp/ipykernel_2533/3080495102.py:1: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
   utc = dt.datetime.utcnow().replace(tzinfo=pytz.utc)
 

 

diff --git a/_preview/469/core/matplotlib/annotations-colorbars-layouts.html b/_preview/469/core/matplotlib/annotations-colorbars-layouts.html
index 69e773236..db7589f45 100644
--- a/_preview/469/core/matplotlib/annotations-colorbars-layouts.html
+++ b/_preview/469/core/matplotlib/annotations-colorbars-layouts.html
@@ -730,7 +730,7 @@ 
Basic Colorbars
-../../_images/e2fb70effcace6a7c3fab442e873db3e339d440734ebc834a72c3f248859e79d.png
+../../_images/63c336ba516a50c335f6f506d026535ea2c60c4b6458e054b95cab8634beda40.png
 

 

 
We can change which colormap to use by setting the keyword argument cmap = 'colormap_name' in the plotting function call.  This sets the colormap not only for the plot, but for the colorbar as well.  In this case, we use the magma colormap:

@@ -745,7 +745,7 @@ 
Basic Colorbars
-../../_images/ebca63cdc647170d478db7c8180b51906987e3b31ee12e34c8fc843113ec18c4.png
+../../_images/7cab4a86279c2bbd6b804a7d1e7106b9c6dd9e4d7289a36d04ab40d1d83cc7b6.png
 

 

 
@@ -764,10 +764,10 @@ 
Shared Colorbars
-
<matplotlib.colorbar.Colorbar at 0x7f3bd037acf0>
+
<matplotlib.colorbar.Colorbar at 0x7f3f05c26840>
 

 

-../../_images/ef1427315768d37e2c9b8e462de6ed0afbf00297a78a595efcf27eee49dbe5c7.png
+../../_images/99bb67ebc86a3b83aa758ba3ca4c13efb4d34a9fee572d99ee2d787d4e553dce.png
 

 

 
You may be wondering why the call to fig.colorbar uses the argument hist1[3]. The explanation is as follows: hist1 is a tuple returned by hist2d, and hist1[3] contains a matplotlib.collections.QuadMesh that points to the colormap for the first histogram. To make sure that both histograms are using the same colormap with the same range of values, vmax is set to 0.18 for both plots. This ensures that both histograms are using colormaps that represent values from 0 (the default for histograms) to 0.18. Because the same data values are used for both plots, it doesn’t matter whether we pass in hist1[3] or hist2[3] to fig.colorbar.
@@ -848,7 +848,7 @@ 
Custom Colorbars
-../../_images/37b08203bf43e80bc89e010f7ea09475d5a199de249f297cdfe51bc39e39fe10.png
+../../_images/c13784f5d3fbf2e7f5bd207f60b1b1de5ed7bf1520fd3e6a779a4689493cd653.png
 

 

 

@@ -865,7 +865,7 @@ 
Custom Colorbars
-../../_images/f9ab42e3e4bdc4b6693c265e0c5438260d00ceba84df39910d762187521e548b.png
+../../_images/47f9024f1062d5ffedece6a60710881d2dcaa07655c6ea9e5220ff6b12090f3d.png
 

 

 

@@ -902,7 +902,7 @@ 
Mosaic Subplots
-../../_images/b3926adcec06bf36b2d40a07f263f1f66a3c385557b4b4dd0a37f947efdf2721.png
+../../_images/f26c49f0c340b4ab92e459343b4207832f25e3766992e4e05b2539c1d747dcb2.png
 

 

 
You’ll notice there is not a colorbar plotted by default. When constructing the colorbar, we need to specify the following:

@@ -930,7 +930,7 @@ 
Mosaic Subplots
-../../_images/4deb6f700c9040dbd5ca974eb1452a2ca5e2968d45027346d83440654a7198b3.png
+../../_images/2132334a4e24113c264c7b73d26c7ca8322890e0d098aca0c212ac3a05099efc.png
diff --git a/_preview/469/core/numpy/numpy-broadcasting.html b/_preview/469/core/numpy/numpy-broadcasting.html index a35ee6854..b9b9e4751 100644 --- a/_preview/469/core/numpy/numpy-broadcasting.html +++ b/_preview/469/core/numpy/numpy-broadcasting.html @@ -1070,7 +1070,7 @@

Blocking

-
array([13.4, 18.6,  9.6,  9. , 17.7, 18.9, 16. , 18.3, 22.7, 25.2])
+
array([27.3, 10.3, 25.5, 19.3, 24.8, 17.6, 18.2, 24.3, 22.4, 26.1])
@@ -1093,8 +1093,8 @@

Blocking

-
array([ 0.        , 13.86666667, 12.4       , 12.1       , 15.2       ,
-       17.53333333, 17.73333333, 19.        , 22.06666667,  0.        ])
+
array([ 0.        , 21.03333333, 18.36666667, 23.2       , 20.56666667,
+       20.2       , 20.03333333, 21.63333333, 24.26666667,  0.        ])
@@ -1108,8 +1108,8 @@

Blocking

-
array([13.86666667, 12.4       , 12.1       , 15.2       , 17.53333333,
-       17.73333333, 19.        , 22.06666667])
+
array([21.03333333, 18.36666667, 23.2       , 20.56666667, 20.2       ,
+       20.03333333, 21.63333333, 24.26666667])
@@ -1122,7 +1122,7 @@

Blocking

-
array([13.4, 18.6,  9.6,  9. , 17.7, 18.9, 16. , 18.3, 22.7, 25.2])
+
array([27.3, 10.3, 25.5, 19.3, 24.8, 17.6, 18.2, 24.3, 22.4, 26.1])
@@ -1141,14 +1141,14 @@

Blocking

-
array([[13.4, 18.6,  9.6],
-       [18.6,  9.6,  9. ],
-       [ 9.6,  9. , 17.7],
-       [ 9. , 17.7, 18.9],
-       [17.7, 18.9, 16. ],
-       [18.9, 16. , 18.3],
-       [16. , 18.3, 22.7],
-       [18.3, 22.7, 25.2]])
+
array([[27.3, 10.3, 25.5],
+       [10.3, 25.5, 19.3],
+       [25.5, 19.3, 24.8],
+       [19.3, 24.8, 17.6],
+       [24.8, 17.6, 18.2],
+       [17.6, 18.2, 24.3],
+       [18.2, 24.3, 22.4],
+       [24.3, 22.4, 26.1]])
@@ -1161,8 +1161,8 @@

Blocking

-
array([13.86666667, 12.4       , 12.1       , 15.2       , 17.53333333,
-       17.73333333, 19.        , 22.06666667])
+
array([21.03333333, 18.36666667, 23.2       , 20.56666667, 20.2       ,
+       20.03333333, 21.63333333, 24.26666667])
@@ -1176,14 +1176,14 @@

Blocking

-
array([[  13.4,   18.6, 2000. ],
-       [  18.6, 2000. ,    9. ],
-       [2000. ,    9. ,   17.7],
-       [   9. ,   17.7,   18.9],
-       [  17.7,   18.9,   16. ],
-       [  18.9,   16. ,   18.3],
-       [  16. ,   18.3,   22.7],
-       [  18.3,   22.7,   25.2]])
+
array([[  27.3,   10.3, 2000. ],
+       [  10.3, 2000. ,   19.3],
+       [2000. ,   19.3,   24.8],
+       [  19.3,   24.8,   17.6],
+       [  24.8,   17.6,   18.2],
+       [  17.6,   18.2,   24.3],
+       [  18.2,   24.3,   22.4],
+       [  24.3,   22.4,   26.1]])
@@ -1210,13 +1210,13 @@

Finding the difference between min and max -
array([[6, 7, 5, ..., 7, 6, 7],
-       [7, 6, 7, ..., 7, 7, 7],
-       [7, 7, 7, ..., 6, 8, 7],
+
array([[7, 7, 7, ..., 6, 6, 6],
+       [5, 7, 6, ..., 7, 7, 7],
+       [7, 6, 7, ..., 6, 7, 7],
        ...,
-       [7, 6, 6, ..., 8, 8, 7],
-       [6, 7, 7, ..., 7, 5, 7],
-       [6, 7, 7, ..., 7, 7, 7]])
+       [7, 6, 7, ..., 7, 6, 6],
+       [6, 7, 7, ..., 7, 7, 7],
+       [7, 7, 6, ..., 7, 6, 7]])
@@ -1241,13 +1241,13 @@

Finding the difference between min and max -
array([[775. , 737.5, 812.5, ..., 737.5, 775. , 737.5],
-       [737.5, 775. , 737.5, ..., 737.5, 737.5, 737.5],
-       [737.5, 737.5, 737.5, ..., 775. , 700. , 737.5],
+
array([[737.5, 737.5, 737.5, ..., 775. , 775. , 775. ],
+       [812.5, 737.5, 775. , ..., 737.5, 737.5, 737.5],
+       [737.5, 775. , 737.5, ..., 775. , 737.5, 737.5],
        ...,
-       [737.5, 775. , 775. , ..., 700. , 700. , 737.5],
-       [775. , 737.5, 737.5, ..., 737.5, 812.5, 737.5],
-       [775. , 737.5, 737.5, ..., 737.5, 737.5, 737.5]])
+       [737.5, 775. , 737.5, ..., 737.5, 775. , 775. ],
+       [775. , 737.5, 737.5, ..., 737.5, 737.5, 737.5],
+       [737.5, 737.5, 775. , ..., 737.5, 775. , 737.5]])
@@ -1276,19 +1276,19 @@

Finding the difference between min and max -
array([[-11.02006574, -15.39073793,  -5.52332143, ...,  -8.50269148,
-         -9.51650345,  -8.61185863],
-       [ -8.86455499,  -8.10451488, -10.70314401, ..., -12.32352392,
-        -13.79478923, -12.05621114],
-       [ -9.0304711 ,  -7.98813533, -10.76465621, ...,  -8.88330733,
-        -13.40957636, -12.27633651],
+
array([[-11.30945709, -13.30950638,  -8.91172625, ...,  -9.23622578,
+        -10.25733406, -11.59151859],
+       [ -5.35679931, -10.215725  ,  -9.82230787, ...,  -9.74594194,
+        -10.18426911, -13.71841054],
+       [ -9.47387578,  -6.67506719,  -5.04708377, ...,  -9.77360955,
+         -9.50864457,  -8.57108264],
        ...,
-       [-11.70497609,  -9.44607957,  -7.99503061, ..., -10.84665035,
-        -17.16883503,  -8.15202088],
-       [ -9.73293976, -12.90670918,  -6.18857571, ...,  -9.94927784,
-         -4.93405623, -11.13110698],
-       [ -8.06983933, -11.81048769, -10.56012605, ...,  -8.04649128,
-        -14.81127037,  -7.72338292]])
+       [ -6.43517574,  -8.06258415,  -9.43323769, ..., -11.65130542,
+         -9.61503225, -10.6434578 ],
+       [-13.44451631, -12.03811827, -12.58467017, ...,  -9.31814649,
+        -10.89243121,  -8.20274499],
+       [ -8.03807801,  -7.58997168, -13.36913286, ..., -11.65776466,
+         -8.02325848, -12.38611754]])
@@ -1332,19 +1332,19 @@

Finding the difference between min and max -
array([[-11.02006574, -15.39073793,  -5.52332143, ...,  -8.50269148,
-         -9.51650345,  -8.61185863],
-       [ -8.86455499,  -8.10451488, -10.70314401, ..., -12.32352392,
-        -13.79478923, -12.05621114],
-       [ -9.0304711 ,  -7.98813533, -10.76465621, ...,  -8.88330733,
-        -13.40957636, -12.27633651],
+
array([[-11.30945709, -13.30950638,  -8.91172625, ...,  -9.23622578,
+        -10.25733406, -11.59151859],
+       [ -5.35679931, -10.215725  ,  -9.82230787, ...,  -9.74594194,
+        -10.18426911, -13.71841054],
+       [ -9.47387578,  -6.67506719,  -5.04708377, ...,  -9.77360955,
+         -9.50864457,  -8.57108264],
        ...,
-       [-11.70497609,  -9.44607957,  -7.99503061, ..., -10.84665035,
-        -17.16883503,  -8.15202088],
-       [ -9.73293976, -12.90670918,  -6.18857571, ...,  -9.94927784,
-         -4.93405623, -11.13110698],
-       [ -8.06983933, -11.81048769, -10.56012605, ...,  -8.04649128,
-        -14.81127037,  -7.72338292]])
+       [ -6.43517574,  -8.06258415,  -9.43323769, ..., -11.65130542,
+         -9.61503225, -10.6434578 ],
+       [-13.44451631, -12.03811827, -12.58467017, ...,  -9.31814649,
+        -10.89243121,  -8.20274499],
+       [ -8.03807801,  -7.58997168, -13.36913286, ..., -11.65776466,
+         -8.02325848, -12.38611754]])
diff --git a/_preview/469/core/pandas/pandas.html b/_preview/469/core/pandas/pandas.html index 59d31adf4..2ccb3736a 100644 --- a/_preview/469/core/pandas/pandas.html +++ b/_preview/469/core/pandas/pandas.html @@ -1098,7 +1098,7 @@

Indexing a Seri

-
/tmp/ipykernel_2797/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+
/tmp/ipykernel_2723/737336773.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
   nino34_series[3]
 

 

@@ -1449,7 +1449,7 @@ 
Extending to the 
-
/tmp/ipykernel_2797/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+
/tmp/ipykernel_2723/541596450.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
   df["Nino34"][3]
 

 

@@ -3645,7 +3645,7 @@ 
Resampling
-
/tmp/ipykernel_2797/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
+
/tmp/ipykernel_2723/233158901.py:1: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
   df.Nino34.resample('1Y').mean().plot();
 

 

diff --git a/_preview/469/core/xarray/computation-masking.html b/_preview/469/core/xarray/computation-masking.html
index 1aee61d74..d9d8ada50 100644
--- a/_preview/469/core/xarray/computation-masking.html
+++ b/_preview/469/core/xarray/computation-masking.html
@@ -943,7 +943,7 @@ 
Data Setup
@@ -1576,7 +1576,7 @@ 
Arithmetic Operations
+      dtype='float64', name='lon', length=360))

    • In addition, there are many other arithmetic operations that can be performed on DataArrays. In this example, we demonstrate squaring the original Celsius values of our air temperature data:

    @@ -2246,7 +2246,7 @@

    Arithmetic Operations + dtype='float64', name='lon', length=360))

    • @@ -2876,7 +2876,7 @@

    Aggregation Methodsdim keyword argument; this means that the function was applied over all of the dataset’s dimensions. In other words, the aggregation method computed the mean of every element of the temperature dataset across every temporal and spatial data point. However, if a dimension name is used with the dim keyword argument, the aggregation method computes an aggregation along the given dimension. In this next example, we use aggregation to calculate the temporal mean across all spatial data; this is performed by providing the dimension name 'time' to the dim keyword argument:

    @@ -3276,7 +3276,7 @@

    Aggregation Methods @@ -3717,7 +3717,7 @@

    Aggregation Methods @@ -4325,7 +4325,7 @@

    Aggregation Methods @@ -4892,7 +4892,7 @@

    Compute climatologyDataArray containing the climatology data, we can plot the data in different ways. In this example, we plot the climatology at a specific latitude-longitude point:

    @@ -5412,7 +5412,7 @@

    Compute anomaly
    @@ -6095,7 +6095,7 @@

    Compute anomaly.weighted() method and the newly loaded CESM2 dataset to weight the grid cell data as described above:

    @@ -6598,7 +6598,7 @@

    Other high level computation functionalityrolling method to compute averages in a moving window of 5 months of data:

    @@ -7114,7 +7114,7 @@

    Other high level computation functionality
    @@ -7783,7 +7783,7 @@

    Masking Data

    Using where with one condition

    @@ -8421,15 +8421,15 @@

    Using where + dtype='float64', name='lon', length=360))

  • cell_measures :
    area: areacello
    cell_methods :
    area: mean where sea time: mean
    comment :
    Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
    description :
    This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
    frequency :
    mon
    id :
    tos
    long_name :
    Sea Surface Temperature
    mipTable :
    Omon
    out_name :
    tos
    prov :
    Omon ((isd.003))
    realm :
    ocean
    standard_name :
    sea_surface_temperature
    time :
    time
    time_label :
    time-mean
    time_title :
    Temporal mean
    title :
    Sea Surface Temperature
    type :
    real
    units :
    degC
    variable_id :
    tos

    • As shown in the previous example, methods like .isel() and .sel() return data of a different shape than the original data provided to them. However, .where() preserves the shape of the original data by masking the values with a Boolean condition. Data values for which the condition is True are returned identical to the values passed in. On the other hand, data values for which the condition is False are returned as a preset example value. (This example value defaults to nan, but can be set to other values as well.)

    Before testing .where(), it is helpful to look at the official documentation. As stated above, the .where() method takes a Boolean condition. (Boolean conditions use operators such as less-than, greater-than, and equal-to, and return a value of True or False.) Most uses of .where() check whether or not specific data values are less than or greater than a constant value. As stated in the documentation, the data values specified in the Boolean condition of .where() can be any of the following:

    @@ -8864,7 +8864,7 @@

    Using where + dtype='float64', name='lon', length=360))
  • cell_measures :
    area: areacello
    cell_methods :
    area: mean where sea time: mean
    comment :
    Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
    description :
    This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
    frequency :
    mon
    id :
    tos
    long_name :
    Sea Surface Temperature
    mipTable :
    Omon
    out_name :
    tos
    prov :
    Omon ((isd.003))
    realm :
    ocean
    standard_name :
    sea_surface_temperature
    time :
    time
    time_label :
    time-mean
    time_title :
    Temporal mean
    title :
    Sea Surface Temperature
    type :
    real
    units :
    degC
    variable_id :
    tos

    • In this example, we use Matplotlib to plot the original, unmasked data, as well as the masked data created in the previous example.

    diff --git a/_preview/469/core/xarray/dask-arrays-xarray.html b/_preview/469/core/xarray/dask-arrays-xarray.html index e54b9efbf..1c8ca3376 100644 --- a/_preview/469/core/xarray/dask-arrays-xarray.html +++ b/_preview/469/core/xarray/dask-arrays-xarray.html @@ -568,91 +568,91 @@

    Create a dask.a

    -
    array([[[0.42619585, 0.3460787 , 0.70212984, ..., 0.48105343,
-         0.68892914, 0.61161118],
-        [0.60714903, 0.21387138, 0.31264833, ..., 0.41828523,
-         0.36530337, 0.11563184],
-        [0.80690735, 0.48943936, 0.12829131, ..., 0.71241428,
-         0.10929086, 0.49277564],
+
    array([[[0.50129255, 0.12170979, 0.4823771 , ..., 0.1171057 ,
+         0.45503716, 0.07804539],
+        [0.48194159, 0.96575481, 0.48930575, ..., 0.48938636,
+         0.88043439, 0.9713421 ],
+        [0.87146974, 0.6794744 , 0.09736247, ..., 0.85600927,
+         0.39258694, 0.0934675 ],
         ...,
-        [0.79437761, 0.86203153, 0.27549624, ..., 0.36676001,
-         0.72596153, 0.47562752],
-        [0.99318622, 0.41793571, 0.84353615, ..., 0.79432612,
-         0.79933505, 0.34578859],
-        [0.6297163 , 0.21976961, 0.07774188, ..., 0.24356169,
-         0.73588426, 0.67582006]],
-
-       [[0.14075869, 0.36484711, 0.70363903, ..., 0.84024515,
-         0.96586779, 0.27715433],
-        [0.19058993, 0.45925603, 0.30252367, ..., 0.20554363,
-         0.16240525, 0.72766255],
-        [0.74412481, 0.56040744, 0.62414631, ..., 0.11383181,
-         0.54231686, 0.22334335],
+        [0.35123015, 0.94305833, 0.63955098, ..., 0.515642  ,
+         0.5916941 , 0.71237481],
+        [0.81589098, 0.08791992, 0.43822532, ..., 0.96868279,
+         0.3014953 , 0.14146411],
+        [0.65072238, 0.88178701, 0.72214194, ..., 0.14991292,
+         0.12428558, 0.71978217]],
+
+       [[0.2945843 , 0.58190069, 0.06567647, ..., 0.10771692,
+         0.4656152 , 0.87549448],
+        [0.66203288, 0.25977193, 0.59163841, ..., 0.42418254,
+         0.513806  , 0.67740073],
+        [0.99307403, 0.66818184, 0.82612966, ..., 0.47637769,
+         0.66800734, 0.06002842],
         ...,
-        [0.88422507, 0.45308131, 0.77458962, ..., 0.68637992,
-         0.11265889, 0.43630416],
-        [0.21112711, 0.25631919, 0.37390342, ..., 0.43258578,
-         0.43820432, 0.28678117],
-        [0.6278334 , 0.37738011, 0.92017037, ..., 0.9450499 ,
-         0.38574193, 0.66003659]],
-
-       [[0.24615892, 0.70447701, 0.20479162, ..., 0.13018228,
-         0.63449086, 0.13711861],
-        [0.17568184, 0.56132821, 0.4603529 , ..., 0.5996156 ,
-         0.74353839, 0.35994547],
-        [0.67880917, 0.35877394, 0.06871422, ..., 0.27812217,
-         0.55220094, 0.47480306],
+        [0.35584311, 0.9401825 , 0.93194724, ..., 0.59611877,
+         0.02216099, 0.86972338],
+        [0.49672944, 0.13113269, 0.95823488, ..., 0.25836428,
+         0.8646987 , 0.67833181],
+        [0.56935623, 0.99929225, 0.79268213, ..., 0.26782241,
+         0.86166941, 0.15650059]],
+
+       [[0.59137874, 0.59546995, 0.03759033, ..., 0.838337  ,
+         0.20889574, 0.42644245],
+        [0.23324162, 0.34029668, 0.96497438, ..., 0.21990532,
+         0.27924336, 0.76667751],
+        [0.78420866, 0.16506527, 0.77192297, ..., 0.6114422 ,
+         0.76322341, 0.61931358],
         ...,
-        [0.22274867, 0.33590431, 0.97711209, ..., 0.9144557 ,
-         0.26473722, 0.80258083],
-        [0.71261017, 0.42653901, 0.83356844, ..., 0.17961502,
-         0.9276977 , 0.23783074],
-        [0.41089129, 0.95130338, 0.86978586, ..., 0.9140639 ,
-         0.15353737, 0.01253671]],
+        [0.74233335, 0.71062548, 0.08062059, ..., 0.38670771,
+         0.78420202, 0.88637306],
+        [0.63585926, 0.50219253, 0.87121342, ..., 0.58678452,
+         0.33564125, 0.67089288],
+        [0.8276186 , 0.21154281, 0.01396797, ..., 0.95112473,
+         0.42799596, 0.5517961 ]],
 
        ...,
 
-       [[0.67903025, 0.17489637, 0.08324747, ..., 0.33922201,
-         0.63953105, 0.26750945],
-        [0.98153673, 0.56552277, 0.96594569, ..., 0.84674109,
-         0.39312139, 0.15294754],
-        [0.24752702, 0.9459115 , 0.07983537, ..., 0.49408528,
-         0.16383513, 0.50539386],
+       [[0.36548177, 0.8124403 , 0.98302885, ..., 0.16221743,
+         0.31696554, 0.05521503],
+        [0.21044976, 0.49266523, 0.82979493, ..., 0.76736092,
+         0.77400551, 0.02562802],
+        [0.37917563, 0.67170857, 0.82232536, ..., 0.53168069,
+         0.78606023, 0.38887112],
         ...,
-        [0.87801373, 0.97310603, 0.47329974, ..., 0.44647724,
-         0.274614  , 0.3340663 ],
-        [0.46119499, 0.4050515 , 0.26859605, ..., 0.05971933,
-         0.70586876, 0.65798148],
-        [0.18646554, 0.06725249, 0.28496924, ..., 0.97594642,
-         0.00710785, 0.88032036]],
-
-       [[0.58650894, 0.35310113, 0.90393813, ..., 0.28316129,
-         0.36489762, 0.86573707],
-        [0.01697008, 0.25901842, 0.49447514, ..., 0.13938943,
-         0.32705863, 0.27436511],
-        [0.69975508, 0.33497713, 0.95225859, ..., 0.22648317,
-         0.00186771, 0.41287448],
+        [0.61131742, 0.39183678, 0.58678986, ..., 0.12118291,
+         0.88815621, 0.30250744],
+        [0.18190165, 0.49205168, 0.93850341, ..., 0.84258232,
+         0.12207021, 0.08583647],
+        [0.31806248, 0.33154385, 0.40594319, ..., 0.44955817,
+         0.2446685 , 0.75114996]],
+
+       [[0.37252423, 0.83874324, 0.40274604, ..., 0.27167212,
+         0.64681993, 0.04431818],
+        [0.94896414, 0.16955608, 0.04567061, ..., 0.21889054,
+         0.50111575, 0.91115928],
+        [0.07953844, 0.40079749, 0.85497659, ..., 0.55662617,
+         0.11984294, 0.49493556],
         ...,
-        [0.94575658, 0.42493895, 0.13350709, ..., 0.80746073,
-         0.3605811 , 0.59630113],
-        [0.48763946, 0.36949871, 0.11096313, ..., 0.25782538,
-         0.35392457, 0.20190832],
-        [0.37260902, 0.69682549, 0.17793745, ..., 0.87804855,
-         0.92775986, 0.92836787]],
-
-       [[0.88785624, 0.63056509, 0.85095943, ..., 0.70954772,
-         0.74707012, 0.14563673],
-        [0.52718367, 0.80369536, 0.97749093, ..., 0.47084097,
-         0.75500911, 0.73108591],
-        [0.20517313, 0.31264406, 0.19515458, ..., 0.3887044 ,
-         0.17821843, 0.73959291],
+        [0.20769167, 0.67627594, 0.545316  , ..., 0.19526661,
+         0.01273605, 0.75151812],
+        [0.0938911 , 0.78829473, 0.16116177, ..., 0.87163685,
+         0.00636033, 0.84051121],
+        [0.12142223, 0.11751318, 0.40008057, ..., 0.51826842,
+         0.79502155, 0.79471742]],
+
+       [[0.03393967, 0.14304421, 0.58478203, ..., 0.33549936,
+         0.172149  , 0.24527774],
+        [0.60344338, 0.44849003, 0.2743809 , ..., 0.39053084,
+         0.04644772, 0.3881293 ],
+        [0.08313059, 0.13929276, 0.69234803, ..., 0.04451855,
+         0.22467704, 0.66854978],
         ...,
-        [0.32597474, 0.02341073, 0.86163965, ..., 0.86879983,
-         0.61371226, 0.41784847],
-        [0.16944696, 0.99562056, 0.3013229 , ..., 0.10752478,
-         0.82700023, 0.91398824],
-        [0.70175585, 0.75304761, 0.5102735 , ..., 0.69517215,
-         0.21953989, 0.46159043]]])
+        [0.82708562, 0.86163999, 0.04971098, ..., 0.79550766,
+         0.39569401, 0.76924608],
+        [0.75823461, 0.65411879, 0.43274064, ..., 0.77002936,
+         0.13612392, 0.20823894],
+        [0.68794281, 0.62137479, 0.89932778, ..., 0.14320067,
+         0.35456247, 0.85885081]]])
    @@ -872,11 +872,11 @@

    Compute the result -
    CPU times: user 403 ms, sys: 28.6 ms, total: 431 ms
-Wall time: 245 ms
+
    CPU times: user 395 ms, sys: 32.3 ms, total: 427 ms
+Wall time: 226 ms
 
    
 
    
-
    12000065.34764746
+
    12000727.753660701
 
    
 
    
 
    
@@ -974,7 +974,7 @@ 
    Exercise with d
    -../../_images/a1927d08cb5b70901df577b99c1cebea9b83a0672ce6103bafc305b12b7f2027.png +../../_images/a75f3ba474e4d57105fe3c2cdb9111b3716c368c48feaa67dbaf0068330e50ca.png

    @@ -1106,7 +1106,7 @@

    Testing a bigger calculation -../../_images/5ae78cf502e800bf5a7265a705a4af1240c72f831651c81e172d06ac239acc35.png +../../_images/a648db3a2629e85815e466ef5e59afbb9533919ef45f2dffd6a5d141177d0c5a.png
    @@ -1117,37 +1117,37 @@

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@@ -1857,7 +1851,7 @@ 
    Reading data with 
@@ -1971,7 +1965,7 @@ 
    Reading data with chunks keyword argument, this technique does not specify how to chunk the data, and therefore the resulting Dask Array contains only one chunk.
    
 
    Correct usage of the chunks keyword argument specifies how many values in each dimension are contained in a single chunk. In this example, specifying the chunks keyword argument as chunks={'time':90} indicates to Xarray and Dask that 90 time slices are allocated to each chunk on the temporal axis.
    
@@ -2586,7 +2580,7 @@ 
    Reading data with 
@@ -2666,7 +2660,7 @@ 
    Reading data with .chunks method on an Xarray DataArray. In this example, we show that the tos DataArray now contains two chunks on the time dimension, with each chunk containing 90 time slices.
    
 
    
@@ -3287,7 +3281,7 @@ 
    Parallel and lazy computation using 
@@ -3342,7 +3336,7 @@ 
    Parallel and lazy computation using 
  • lon
    (lon)
    float64
    0.5 1.5 2.5 ... 357.5 358.5 359.5
    array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5])
    • lat
      PandasIndex
      PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
+        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])
    • lon
      (lon)
      float64
      0.5 1.5 2.5 ... 357.5 358.5 359.5
      array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5])
    • lat
      PandasIndex
      PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
        ...
         80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
-      dtype='float64', name='lat', length=180))
    • lon
      PandasIndex
      PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
+      dtype='float64', name='lat', length=180))
    • lon
      PandasIndex
      PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
        ...
        350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],
-      dtype='float64', name='lon', length=360))
    • 
+      dtype='float64', name='lon', length=360))
    • 
 
    
 
    As shown in the above example, the result of the applied operations is an Xarray DataArray that contains a Dask Array, an identical object type to the object that the operations were performed on. This is true for any operations that can be applied to Xarray DataArrays, including subsetting operations; this next example illustrates this:
    
 
    
@@ -3742,7 +3736,7 @@ 
    Parallel and lazy computation using 
@@ -3797,10 +3791,10 @@ 
    Parallel and lazy computation using dask.visualize() function, as shown in this example:
    
 
    
@@ -3822,10 +3816,10 @@ 
    Parallel and lazy computation using 
-
    [                                        ] | 0% Completed | 129.02 us
+
    [                                        ] | 0% Completed | 127.07 us
 
    
 
    
-
    [########################################] | 100% Completed | 101.17 ms
+
    [########################################] | 100% Completed | 101.39 ms
 
    
 
    
 
    
diff --git a/_preview/469/core/xarray/enso-xarray.html b/_preview/469/core/xarray/enso-xarray.html
index 32ddcb63d..012e29118 100644
--- a/_preview/469/core/xarray/enso-xarray.html
+++ b/_preview/469/core/xarray/enso-xarray.html
@@ -958,7 +958,7 @@ 
    The Niño 3.4 Index
@@ -1590,7 +1590,7 @@ 
    Select the Niño 3.4 regionwhere() method:
    
 
    
@@ -2190,7 +2190,7 @@ 
    Select the Niño 3.4 region
@@ -3015,7 +3015,7 @@ 
    Compute the anomalies
    <xarray.DataArray 'tos' ()> Size: 4B
-array(1.8436452, dtype=float32)
    
+array(1.8436452, dtype=float32)
    
 
    
 
    The final step of the Niño 3.4 index calculation involves normalizing the data. In this example, we perform this normalization by dividing the smoothed anomaly data by the standard deviation calculated above:
    
 
    
diff --git a/_preview/469/core/xarray/xarray-intro.html b/_preview/469/core/xarray/xarray-intro.html
index b5942df1d..369876cb3 100644
--- a/_preview/469/core/xarray/xarray-intro.html
+++ b/_preview/469/core/xarray/xarray-intro.html
@@ -583,25 +583,25 @@ 
    Generate a random numpy array
-
    array([[[273.80442217, 289.87496223, 283.43025556, 279.52287757],
-        [290.42309981, 281.464434  , 287.40603837, 278.43287441],
-        [282.62005989, 292.38893315, 288.19165787, 283.35910849]],
+
    array([[[289.44705173, 288.57153159, 275.44047043, 294.25838396],
+        [287.85161575, 283.50611783, 277.53595954, 279.06417585],
+        [286.12350092, 273.21540816, 277.66062654, 280.28457805]],
 
-       [[287.8422476 , 281.86675526, 281.66114533, 286.51895117],
-        [274.72806155, 279.52345451, 280.4543508 , 284.10403691],
-        [282.79579667, 286.43994545, 275.587234  , 292.30626036]],
+       [[295.03260826, 279.59313303, 290.98832877, 279.77499799],
+        [278.95918871, 272.71420309, 287.82516859, 278.99590275],
+        [283.80477841, 291.16324141, 276.13038116, 283.905546  ]],
 
-       [[280.49928862, 276.18696897, 279.02877455, 280.06627735],
-        [279.6443178 , 285.98499909, 279.87552066, 275.67146669],
-        [291.20774029, 283.25765874, 283.6759564 , 274.68438846]],
+       [[289.3198786 , 271.32639859, 286.92079794, 294.56036398],
+        [282.54834705, 280.54989636, 274.87552029, 280.56905816],
+        [292.25692774, 269.05323758, 286.37231473, 281.75710318]],
 
-       [[283.37776298, 284.59586384, 283.27852928, 287.67374542],
-        [282.3938179 , 278.58383709, 277.52597722, 279.40061209],
-        [283.87371819, 293.89142085, 294.98215092, 282.8231519 ]],
+       [[274.77795265, 281.41918854, 281.85682779, 279.88194864],
+        [296.47598817, 286.58650518, 285.29480791, 275.58603747],
+        [284.04359783, 286.26999408, 275.64153335, 279.19894639]],
 
-       [[288.12378065, 286.20004961, 271.76515987, 286.68613421],
-        [281.49914175, 271.89767102, 278.9359245 , 281.18729189],
-        [279.86577066, 278.41230452, 289.89084998, 283.11860462]]])
+       [[292.10654386, 281.09526564, 291.18475062, 281.27542066],
+        [276.52719491, 280.14139858, 276.34463485, 284.29537389],
+        [281.36781571, 275.64069397, 284.71280549, 278.71279454]]])
 
    
 
    
 
    
@@ -982,44 +982,44 @@ 
    Wrap the array: first attempt
@@ -1402,44 +1402,44 @@ 
    Assign dimension names
    <xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[273.80442217, 289.87496223, 283.43025556, 279.52287757],
-        [290.42309981, 281.464434  , 287.40603837, 278.43287441],
-        [282.62005989, 292.38893315, 288.19165787, 283.35910849]],
-
-       [[287.8422476 , 281.86675526, 281.66114533, 286.51895117],
-        [274.72806155, 279.52345451, 280.4543508 , 284.10403691],
-        [282.79579667, 286.43994545, 275.587234  , 292.30626036]],
-
-       [[280.49928862, 276.18696897, 279.02877455, 280.06627735],
-        [279.6443178 , 285.98499909, 279.87552066, 275.67146669],
-        [291.20774029, 283.25765874, 283.6759564 , 274.68438846]],
-
-       [[283.37776298, 284.59586384, 283.27852928, 287.67374542],
-        [282.3938179 , 278.58383709, 277.52597722, 279.40061209],
-        [283.87371819, 293.89142085, 294.98215092, 282.8231519 ]],
-
-       [[288.12378065, 286.20004961, 271.76515987, 286.68613421],
-        [281.49914175, 271.89767102, 278.9359245 , 281.18729189],
-        [279.86577066, 278.41230452, 289.89084998, 283.11860462]]])
-Dimensions without coordinates: time, lat, lon
    
+array([[[289.44705173, 288.57153159, 275.44047043, 294.25838396],
+        [287.85161575, 283.50611783, 277.53595954, 279.06417585],
+        [286.12350092, 273.21540816, 277.66062654, 280.28457805]],
+
+       [[295.03260826, 279.59313303, 290.98832877, 279.77499799],
+        [278.95918871, 272.71420309, 287.82516859, 278.99590275],
+        [283.80477841, 291.16324141, 276.13038116, 283.905546  ]],
+
+       [[289.3198786 , 271.32639859, 286.92079794, 294.56036398],
+        [282.54834705, 280.54989636, 274.87552029, 280.56905816],
+        [292.25692774, 269.05323758, 286.37231473, 281.75710318]],
+
+       [[274.77795265, 281.41918854, 281.85682779, 279.88194864],
+        [296.47598817, 286.58650518, 285.29480791, 275.58603747],
+        [284.04359783, 286.26999408, 275.64153335, 279.19894639]],
+
+       [[292.10654386, 281.09526564, 291.18475062, 281.27542066],
+        [276.52719491, 280.14139858, 276.34463485, 284.29537389],
+        [281.36781571, 275.64069397, 284.71280549, 278.71279454]]])
+Dimensions without coordinates: time, lat, lon
    
 
    
 
    This DataArray is already an improvement over a NumPy array; the DataArray contains names for each of the dimensions (or axes in NumPy parlance). An additional improvement is the association of coordinate-value arrays with data upon creation of a DataArray. In the next example, we illustrate the creation of NumPy arrays representing the coordinate values for each dimension of the DataArray, and how to associate these coordinate arrays with the data in our DataArray.
    
 
@@ -1849,51 +1849,51 @@ 
    Initialize the 
   fill: currentColor;
 }
 
    <xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[273.80442217, 289.87496223, 283.43025556, 279.52287757],
-        [290.42309981, 281.464434  , 287.40603837, 278.43287441],
-        [282.62005989, 292.38893315, 288.19165787, 283.35910849]],
+array([[[289.44705173, 288.57153159, 275.44047043, 294.25838396],
+        [287.85161575, 283.50611783, 277.53595954, 279.06417585],
+        [286.12350092, 273.21540816, 277.66062654, 280.28457805]],
 
-       [[287.8422476 , 281.86675526, 281.66114533, 286.51895117],
-        [274.72806155, 279.52345451, 280.4543508 , 284.10403691],
-        [282.79579667, 286.43994545, 275.587234  , 292.30626036]],
+       [[295.03260826, 279.59313303, 290.98832877, 279.77499799],
+        [278.95918871, 272.71420309, 287.82516859, 278.99590275],
+        [283.80477841, 291.16324141, 276.13038116, 283.905546  ]],
 
-       [[280.49928862, 276.18696897, 279.02877455, 280.06627735],
-        [279.6443178 , 285.98499909, 279.87552066, 275.67146669],
-        [291.20774029, 283.25765874, 283.6759564 , 274.68438846]],
+       [[289.3198786 , 271.32639859, 286.92079794, 294.56036398],
+        [282.54834705, 280.54989636, 274.87552029, 280.56905816],
+        [292.25692774, 269.05323758, 286.37231473, 281.75710318]],
 
-       [[283.37776298, 284.59586384, 283.27852928, 287.67374542],
-        [282.3938179 , 278.58383709, 277.52597722, 279.40061209],
-        [283.87371819, 293.89142085, 294.98215092, 282.8231519 ]],
+       [[274.77795265, 281.41918854, 281.85682779, 279.88194864],
+        [296.47598817, 286.58650518, 285.29480791, 275.58603747],
+        [284.04359783, 286.26999408, 275.64153335, 279.19894639]],
 
-       [[288.12378065, 286.20004961, 271.76515987, 286.68613421],
-        [281.49914175, 271.89767102, 278.9359245 , 281.18729189],
-        [279.86577066, 278.41230452, 289.89084998, 283.11860462]]])
+       [[292.10654386, 281.09526564, 291.18475062, 281.27542066],
+        [276.52719491, 280.14139858, 276.34463485, 284.29537389],
+        [281.36781571, 275.64069397, 284.71280549, 278.71279454]]])
 Coordinates:
   * time     (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
   * lat      (lat) float64 24B 25.0 40.0 55.0
-  * lon      (lon) float64 32B -120.0 -100.0 -80.0 -60.0
    
+              dtype='datetime64[ns]', name='time', freq='D'))
  • PandasIndex
    PandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
  • PandasIndex
    PandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
    • 
 
    
 
 
    
@@ -2273,54 +2273,54 @@ 
    Set useful attributes
    <xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[273.80442217, 289.87496223, 283.43025556, 279.52287757],
-        [290.42309981, 281.464434  , 287.40603837, 278.43287441],
-        [282.62005989, 292.38893315, 288.19165787, 283.35910849]],
+array([[[289.44705173, 288.57153159, 275.44047043, 294.25838396],
+        [287.85161575, 283.50611783, 277.53595954, 279.06417585],
+        [286.12350092, 273.21540816, 277.66062654, 280.28457805]],
 
-       [[287.8422476 , 281.86675526, 281.66114533, 286.51895117],
-        [274.72806155, 279.52345451, 280.4543508 , 284.10403691],
-        [282.79579667, 286.43994545, 275.587234  , 292.30626036]],
+       [[295.03260826, 279.59313303, 290.98832877, 279.77499799],
+        [278.95918871, 272.71420309, 287.82516859, 278.99590275],
+        [283.80477841, 291.16324141, 276.13038116, 283.905546  ]],
 
-       [[280.49928862, 276.18696897, 279.02877455, 280.06627735],
-        [279.6443178 , 285.98499909, 279.87552066, 275.67146669],
-        [291.20774029, 283.25765874, 283.6759564 , 274.68438846]],
+       [[289.3198786 , 271.32639859, 286.92079794, 294.56036398],
+        [282.54834705, 280.54989636, 274.87552029, 280.56905816],
+        [292.25692774, 269.05323758, 286.37231473, 281.75710318]],
 
-       [[283.37776298, 284.59586384, 283.27852928, 287.67374542],
-        [282.3938179 , 278.58383709, 277.52597722, 279.40061209],
-        [283.87371819, 293.89142085, 294.98215092, 282.8231519 ]],
+       [[274.77795265, 281.41918854, 281.85682779, 279.88194864],
+        [296.47598817, 286.58650518, 285.29480791, 275.58603747],
+        [284.04359783, 286.26999408, 275.64153335, 279.19894639]],
 
-       [[288.12378065, 286.20004961, 271.76515987, 286.68613421],
-        [281.49914175, 271.89767102, 278.9359245 , 281.18729189],
-        [279.86577066, 278.41230452, 289.89084998, 283.11860462]]])
+       [[292.10654386, 281.09526564, 291.18475062, 281.27542066],
+        [276.52719491, 280.14139858, 276.34463485, 284.29537389],
+        [281.36781571, 275.64069397, 284.71280549, 278.71279454]]])
 Coordinates:
   * time     (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
   * lat      (lat) float64 24B 25.0 40.0 55.0
   * lon      (lon) float64 32B -120.0 -100.0 -80.0 -60.0
 Attributes:
     units:          kelvin
-    standard_name:  air_temperature
    
+              dtype='datetime64[ns]', name='time', freq='D'))
  • PandasIndex
    PandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
  • PandasIndex
    PandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
  • units :
    kelvin
    standard_name :
    air_temperature
    • 
 
    
 
 
    
@@ -2699,51 +2699,51 @@ 
    Issues with preservation of attributesdocumentation page.
    
 
    
@@ -3134,54 +3134,54 @@ 
    Create a pressure 
@@ -3566,48 +3566,48 @@ 
    Create a Datase
   * lat          (lat) float64 24B 25.0 40.0 55.0
   * lon          (lon) float64 32B -120.0 -100.0 -80.0 -60.0
 Data variables:
-    Temperature  (time, lat, lon) float64 480B 273.8 289.9 283.4 ... 289.9 283.1
-    Pressure     (time, lat, lon) float64 480B 1.002e+03 994.3 ... 1.004e+03
    
+              dtype='datetime64[ns]', name='time', freq='D'))
  • lat
    PandasIndex
    PandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
  • lon
    PandasIndex
    PandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
    • 
 
    
 
    As listed in the rich display above, the new Dataset object is aware that both DataArrays share the same coordinate axes. (Please note that if this page is not run as a Jupyter Notebook, the rich display may be unavailable.)
    
 
@@ -3986,54 +3986,54 @@ 
    Access Data variables and Coordinates in a DataArrays through a dictionary syntax, as shown in this example:
    
 
    
@@ -4407,54 +4407,54 @@ 
    Access Data variables and Coordinates in a Dataset objects are mainly used for loading data from files, which will be covered later in this tutorial.
    
 
@@ -4838,18 +4838,18 @@ 
    NumPy-like selection
    <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[287.8422476 , 281.86675526, 281.66114533, 286.51895117],
-       [274.72806155, 279.52345451, 280.4543508 , 284.10403691],
-       [282.79579667, 286.43994545, 275.587234  , 292.30626036]])
+array([[295.03260826, 279.59313303, 290.98832877, 279.77499799],
+       [278.95918871, 272.71420309, 287.82516859, 278.99590275],
+       [283.80477841, 291.16324141, 276.13038116, 283.905546  ]])
 Coordinates:
     time     datetime64[ns] 8B 2018-01-02
   * lat      (lat) float64 24B 25.0 40.0 55.0
   * lon      (lon) float64 32B -120.0 -100.0 -80.0 -60.0
 Attributes:
     units:          kelvin
-    standard_name:  air_temperature
    
+    standard_name:  air_temperature
    
 
    
 
    This example reveals one of the major shortcomings of index selection. In order to retrieve the correct data using index selection, anyone using a DataArray must have precise knowledge of the axes in the DataArray, including the order of the axes and the meaning of their indices.
    
 
    By using named coordinates, as shown in the next set of examples, we can avoid this cumbersome burden.
    
@@ -5229,18 +5229,18 @@ 
    Selecting with 
   fill: currentColor;
 }
 
    <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[287.8422476 , 281.86675526, 281.66114533, 286.51895117],
-       [274.72806155, 279.52345451, 280.4543508 , 284.10403691],
-       [282.79579667, 286.43994545, 275.587234  , 292.30626036]])
+array([[295.03260826, 279.59313303, 290.98832877, 279.77499799],
+       [278.95918871, 272.71420309, 287.82516859, 278.99590275],
+       [283.80477841, 291.16324141, 276.13038116, 283.905546  ]])
 Coordinates:
     time     datetime64[ns] 8B 2018-01-02
   * lat      (lat) float64 24B 25.0 40.0 55.0
   * lon      (lon) float64 32B -120.0 -100.0 -80.0 -60.0
 Attributes:
     units:          kelvin
-    standard_name:  air_temperature
    
+    standard_name:  air_temperature
    
 
    
 
    This method yields the same result as the index selection, however:
    
 
    
+    standard_name:  air_temperature
    
 
    
 
    This selection technique is similar to NumPy’s index-based selection, as shown below:
    
 
    temp[1,:,:]
@@ -7208,29 +7208,29 @@ 
    One more selection method: 
    <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[289.87496223, 283.43025556],
-        [281.464434  , 287.40603837]],
+array([[[288.57153159, 275.44047043],
+        [283.50611783, 277.53595954]],
 
-       [[281.86675526, 281.66114533],
-        [279.52345451, 280.4543508 ]],
+       [[279.59313303, 290.98832877],
+        [272.71420309, 287.82516859]],
 
-       [[276.18696897, 279.02877455],
-        [285.98499909, 279.87552066]]])
+       [[271.32639859, 286.92079794],
+        [280.54989636, 274.87552029]]])
 Coordinates:
   * time     (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
   * lat      (lat) float64 16B 25.0 40.0
   * lon      (lon) float64 16B -100.0 -80.0
 Attributes:
     units:          kelvin
-    standard_name:  air_temperature
    
+       [[271.32639859, 286.92079794],
+        [280.54989636, 274.87552029]]])
    • time
      (time)
      datetime64[ns]
      2018-01-01 2018-01-02 2018-01-03
      array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+       '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
    • lat
      (lat)
      float64
      25.0 40.0
      array([25., 40.])
    • lon
      (lon)
      float64
      -100.0 -80.0
      array([-100.,  -80.])
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
    • lat
      PandasIndex
      PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
    • lon
      PandasIndex
      PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
  • units :
    kelvin
    standard_name :
    air_temperature
    • 
 
    
 
    In contrast with the previous example, this example shows a useful advantage of using the .loc attribute. When using the .loc attribute, you can specify data slices using a syntax similar to NumPy in addition to, or instead of, using the slice function.  Both of these slicing techniques are illustrated below:
    
 
    
@@ -7604,29 +7604,29 @@ 
    One more selection method: 
    <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[289.87496223, 283.43025556],
-        [281.464434  , 287.40603837]],
+array([[[288.57153159, 275.44047043],
+        [283.50611783, 277.53595954]],
 
-       [[281.86675526, 281.66114533],
-        [279.52345451, 280.4543508 ]],
+       [[279.59313303, 290.98832877],
+        [272.71420309, 287.82516859]],
 
-       [[276.18696897, 279.02877455],
-        [285.98499909, 279.87552066]]])
+       [[271.32639859, 286.92079794],
+        [280.54989636, 274.87552029]]])
 Coordinates:
   * time     (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
   * lat      (lat) float64 16B 25.0 40.0
   * lon      (lon) float64 16B -100.0 -80.0
 Attributes:
     units:          kelvin
-    standard_name:  air_temperature
    
+       [[271.32639859, 286.92079794],
+        [280.54989636, 274.87552029]]])
    • time
      (time)
      datetime64[ns]
      2018-01-01 2018-01-02 2018-01-03
      array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
+       '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
    • lat
      (lat)
      float64
      25.0 40.0
      array([25., 40.])
    • lon
      (lon)
      float64
      -100.0 -80.0
      array([-100.,  -80.])
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
    • lat
      PandasIndex
      PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
    • lon
      PandasIndex
      PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
  • units :
    kelvin
    standard_name :
    air_temperature
    • 
 
    
 
    As described above, the arguments to .loc must be in the order of the DataArray’s dimensions.  Attempting to slice data without ordering arguments properly can cause errors, as shown below:
    
 
    
@@ -8059,10 +8059,10 @@ 
    Access netCDF data with 
  • Originating_or_generating_Center :
    US National Weather Service, National Centres for Environmental Prediction (NCEP)
    Originating_or_generating_Subcenter :
    North American Regional Reanalysis Project
    GRIB_table_version :
    0,131
    Generating_process_or_model :
    North American Regional Reanalysis (NARR)
    Conventions :
    CF-1.6
    history :
    Read using CDM IOSP GribCollection v3
    featureType :
    GRID
    History :
    Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2)
 Original Dataset = DatasetScan#narr-a_221_19930313_0000_000.grb; Translation Date = 2018-01-04T23:57:16.043Z
    geospatial_lat_min :
    10.753308882144761
    geospatial_lat_max :
    46.8308828962289
    geospatial_lon_min :
    -153.88242040519995
    geospatial_lon_max :
    -42.666108129242815
    • 
 
    
 
@@ -8504,17 +8504,17 @@ 
    Subsetting the 
     positive:                down
     Grib_level_type:         100
     _CoordinateAxisType:     Pressure
-    _CoordinateZisPositive:  down
    
+      dtype='float32', name='isobaric1'))
  • units :
    hPa
    long_name :
    Isobaric surface
    positive :
    down
    Grib_level_type :
    100
    _CoordinateAxisType :
    Pressure
    _CoordinateZisPositive :
    down
    • 
 
    
 
    (As described earlier in this tutorial, we can also use dictionary syntax to select specific DataArrays; in this case, we would write ds['isobaric1'].)
    
 
    Many of the subsetting operations usable on DataArrays can also be used on Datasets.  However, when used on Datasets, these operations are performed on every DataArray in the Dataset, as shown below:
    
@@ -8916,7 +8916,7 @@ 
    Subsetting the 
     geospatial_lat_min:                   10.753308882144761
     geospatial_lat_max:                   46.8308828962289
     geospatial_lon_min:                   -153.88242040519995
-    geospatial_lon_max:                   -42.666108129242815
  • Originating_or_generating_Center :
    US National Weather Service, National Centres for Environmental Prediction (NCEP)
    Originating_or_generating_Subcenter :
    North American Regional Reanalysis Project
    GRIB_table_version :
    0,131
    Generating_process_or_model :
    North American Regional Reanalysis (NARR)
    Conventions :
    CF-1.6
    history :
    Read using CDM IOSP GribCollection v3
    featureType :
    GRID
    History :
    Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2)
 Original Dataset = DatasetScan#narr-a_221_19930313_0000_000.grb; Translation Date = 2018-01-04T23:57:16.043Z
    geospatial_lat_min :
    10.753308882144761
    geospatial_lat_max :
    46.8308828962289
    geospatial_lon_min :
    -153.88242040519995
    geospatial_lon_max :
    -42.666108129242815
    • 
 
    
 
    As shown above, the subsetting operation performed on the Dataset returned a new Dataset.  If only a single DataArray is needed from this new Dataset, it can be retrieved using the familiar dot notation:
    
@@ -9356,7 +9356,7 @@ 
    Subsetting the 
     Grib1_TableVersion:  131
     Grib1_Parameter:     11
     Grib1_Level_Type:    100
-    Grib1_Level_Desc:    Isobaric surface
    
+      dtype='float32', name='x', length=268))
  • long_name :
    Temperature @ Isobaric surface
    units :
    K
    description :
    Temperature
    grid_mapping :
    LambertConformal_Projection
    Grib_Variable_Id :
    VAR_7-15-131-11_L100
    Grib1_Center :
    7
    Grib1_Subcenter :
    15
    Grib1_TableVersion :
    131
    Grib1_Parameter :
    11
    Grib1_Level_Type :
    100
    Grib1_Level_Desc :
    Isobaric surface
    • 
 
    
 
 
    
@@ -9790,19 +9790,19 @@ 
    Aggregation operations
+      dtype='float32', name='isobaric1'))
    • 
 
    
 
    
 
    Info
    
@@ -10196,19 +10196,19 @@ 
    Aggregation operations
+      dtype='float32', name='isobaric1'))

    • @@ -10226,7 +10226,7 @@

    Simple visualization with
    -
    [<matplotlib.lines.Line2D at 0x7fc1f65cbe90>]
+
    [<matplotlib.lines.Line2D at 0x7f2dcc1e81a0>]
 
    
 
    
 ../../_images/4ab1942c85eedfd46edda089377ea3fb0aeff64b36813c5fe50a9d7190fe57ae.png
@@ -10250,7 +10250,7 @@ 
    Customizing the plot
    -
    [<matplotlib.lines.Line2D at 0x7fc1e8bafcb0>]
+
    [<matplotlib.lines.Line2D at 0x7f2d968affb0>]
 
    
 
    
 ../../_images/1dbed8c14d7aad1a5b5210f39f76f6256fdb5b6e3ab323da00548fccb1f7b63d.png
@@ -10268,7 +10268,7 @@ 
    Plotting 2-D data
-
    <matplotlib.collections.QuadMesh at 0x7fc1e8b556a0>
+
    <matplotlib.collections.QuadMesh at 0x7f2d96912d80>
 
    
 
    
 ../../_images/0eb5a1e40ec32e805469d633b51ec3aa0d297f83004e65925cbcea02bf555092.png
diff --git a/_preview/469/foundations/conda.html b/_preview/469/foundations/conda.html
index 61053ee44..c07684127 100644
--- a/_preview/469/foundations/conda.html
+++ b/_preview/469/foundations/conda.html
@@ -619,7 +619,7 @@ 
    Resources and References
 
  • Linux commands
    • 
 
  • Conda documentation
    • 
-
  • Conda cheat sheet
    • 
+
  • Conda cheat sheet
    • 
 
  • Anaconda
    • 
 
  • Miniconda
    • 
 
diff --git a/_preview/469/searchindex.js b/_preview/469/searchindex.js
index 48cf12846..4b583b068 100644
--- a/_preview/469/searchindex.js
+++ b/_preview/469/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"2nd Derivative": [[15, "nd-derivative"]], "A community learning resource for Python-based computing in the geosciences": [[48, "a-community-learning-resource-for-python-based-computing-in-the-geosciences"]], "A content subsection": [[1, "a-content-subsection"]], "A figure with two different regional maps": [[3, "a-figure-with-two-different-regional-maps"]], "A little history and nomenclature": [[28, "a-little-history-and-nomenclature"]], "A simple query of just the year:": [[7, "a-simple-query-of-just-the-year"]], "A very first Python program": [[45, "a-very-first-python-program"]], "About version control and git": [[28, "about-version-control-and-git"]], "Access Data variables and Coordinates in a Dataset": [[23, "access-data-variables-and-coordinates-in-a-dataset"]], "Access netCDF data with xr.open_dataset": [[23, "access-netcdf-data-with-xr-open-dataset"]], "Add a Box Around the Text": [[9, "add-a-box-around-the-text"]], "Add a fancy background image to the map.": [[3, "add-a-fancy-background-image-to-the-map"]], "Add cartographic features to the map": [[3, "add-cartographic-features-to-the-map"]], "Add in the cartographic shapefiles": [[3, "add-in-the-cartographic-shapefiles"]], "Add some predefined features": [[3, "add-some-predefined-features"]], "Adding Annotations": [[9, "adding-annotations"]], "Adding labels and a grid": [[11, "adding-labels-and-a-grid"]], "Adding labels to an Axes object": [[11, "adding-labels-to-an-axes-object"]], "Adding titles to each subplot": [[11, "adding-titles-to-each-subplot"]], "Additional Resources": [[10, "additional-resources"]], "Advanced GitHub Topics": [[31, "advanced-github-topics"]], "Aggregation Methods": [[20, "aggregation-methods"]], "Aggregation operations": [[23, "aggregation-operations"]], "An example of plotting data": [[3, "an-example-of-plotting-data"]], "Animations": [[10, "animations"]], "Annotations, Colorbars, and Advanced Layouts": [[9, "annotations-colorbars-and-advanced-layouts"]], "Another content subsection": [[1, "another-content-subsection"]], "Apply & Combine": [[20, "apply-combine"]], "Applying operations to a DataFrame": [[18, "applying-operations-to-a-dataframe"]], "Approximate selection and interpolation": [[23, "approximate-selection-and-interpolation"]], "Arithmetic": [[14, "arithmetic"]], "Arithmetic Operations": [[20, "arithmetic-operations"]], "Array comparisons": [[13, "array-comparisons"]], "Array math functions": [[14, "array-math-functions"]], "Arrays of numbers with NumPy": [[45, "arrays-of-numbers-with-numpy"]], "Assign dimension names": [[23, "assign-dimension-names"]], "Auxiliary Coordinates": [[5, "auxiliary-coordinates"]], "Basic Colorbars": [[9, "basic-colorbars"]], "Basic Line Plots": [[11, "basic-line-plots"]], "Basic Python data types": [[45, "basic-python-data-types"]], "Basic Statistics": [[18, "basic-statistics"]], "Basic Version Control with git": [[28, "basic-version-control-with-git"]], "Basic concepts: map projections and GeoAxes": [[3, "basic-concepts-map-projections-and-geoaxes"]], "Basic flow control": [[45, "basic-flow-control"]], "Blocked algorithms": [[21, "blocked-algorithms"]], "Blocking": [[15, "blocking"]], "Building the book locally": [[0, "building-the-book-locally"]], "Building the site": [[0, "building-the-site"]], "Calculating ENSO with Xarray": [[22, "calculating-enso-with-xarray"]], "Calculating coastal tides with the timedelta class": [[7, "calculating-coastal-tides-with-the-timedelta-class"]], "Cartopy": [[2, "cartopy"]], "Cartopy\u2019s set_extent method": [[3, "cartopys-set-extent-method"]], "Cell Bounds": [[5, "cell-bounds"]], "Character strings (str)": [[45, "character-strings-str"]], "Checking out a previous commit": [[28, "checking-out-a-previous-commit"]], "Choosing a Python Platform": [[40, "choosing-a-python-platform"]], "Classes of colormaps": [[9, "classes-of-colormaps"]], "Clone, branch, change, commit, push": [[37, "clone-branch-change-commit-push"]], "Cloning a repository": [[32, "cloning-a-repository"]], "Cloning and Forking a Repository": [[32, "cloning-and-forking-a-repository"]], "Cloning and forking": [[32, "cloning-and-forking"]], "Cloning the remote repository": [[37, "cloning-the-remote-repository"]], "Code Cells": [[42, "code-cells"]], "Colormap Overview": [[9, "colormap-overview"]], "Command Mode Shortcuts": [[42, "command-mode-shortcuts"]], "Commands we used in this tutorial": [[28, "commands-we-used-in-this-tutorial"]], "Committing": [[28, "committing"]], "Compare across branches": [[28, "compare-across-branches"]], "Compare across commits": [[28, "compare-across-commits"]], "Comparing versions": [[28, "comparing-versions"]], "Complete Workflow": [[30, "complete-workflow"]], "Computations and Masks with Xarray": [[20, "computations-and-masks-with-xarray"]], "Compute anomaly": [[20, "compute-anomaly"]], "Compute climatology": [[20, "compute-climatology"]], "Compute the anomalies": [[22, "compute-the-anomalies"]], "Compute the result": [[21, "compute-the-result"]], "Configuring Your GitHub Account": [[36, "configuring-your-github-account"]], "Consoles": [[42, "consoles"]], "Constants": [[14, "constants"]], "Content section": [[31, "content-section"]], "Contour and Filled Contour Plots": [[11, "contour-and-filled-contour-plots"]], "Contribute to Project Pythia via GitHub": [[29, "contribute-to-project-pythia-via-github"]], "Contributing a new Jupyter Notebook": [[0, "contributing-a-new-jupyter-notebook"]], "Controlling the output format with strftime()": [[7, "controlling-the-output-format-with-strftime"]], "Coordinate System Information": [[5, "coordinate-system-information"]], "Coordinate variables": [[5, "coordinate-variables"]], "Core libraries": [[16, "core-libraries"]], "Create Some Sample Data": [[9, "create-some-sample-data"]], "Create a DataArray with named Coordinates": [[23, "create-a-dataarray-with-named-coordinates"]], "Create a Dataset object": [[23, "create-a-dataset-object"]], "Create a Pull Request": [[29, "create-a-pull-request"]], "Create a conda environment": [[0, "create-a-conda-environment"]], "Create a dask.array object": [[21, "create-a-dask-array-object"]], "Create a map with a specified projection": [[3, "create-a-map-with-a-specified-projection"]], "Create a new branch": [[37, "create-a-new-branch"]], "Create a new feature branch": [[28, "create-a-new-feature-branch"]], "Create a pressure DataArray using the same coordinates": [[23, "create-a-pressure-dataarray-using-the-same-coordinates"]], "Create a regional map centered over New York State": [[3, "create-a-regional-map-centered-over-new-york-state"]], "Create an array of \u2018data\u2019": [[14, "create-an-array-of-data"]], "Create regional maps": [[3, "create-regional-maps"]], "Creating a Conda Environment": [[24, "creating-a-conda-environment"]], "Creating a New Branch": [[30, "creating-a-new-branch"]], "Creating and filling a variable": [[5, "creating-and-filling-a-variable"]], "Creating the file and dimensions": [[5, "creating-the-file-and-dimensions"]], "Creation and basic setup": [[5, "creation-and-basic-setup"]], "Creation of a DataArray object": [[23, "creation-of-a-dataarray-object"]], "Custom Colorbars": [[9, "custom-colorbars"]], "Customize your Plot": [[18, "customize-your-plot"]], "Customizing colors": [[11, "customizing-colors"]], "Customizing the plot": [[23, "customizing-the-plot"]], "Danger": [[1, null], [5, null], [18, null], [42, null]], "Dask Arrays with Xarray": [[21, "dask-arrays-with-xarray"], [21, "id1"]], "Dask Shortcomings": [[21, "dask-shortcomings"]], "Data Formats": [[4, "data-formats"]], "Data Setup": [[20, "data-setup"]], "Datetime": [[6, "datetime"]], "Dealing with Time Zones": [[7, "dealing-with-time-zones"]], "Deleting Branches": [[30, "deleting-branches"]], "Dictionaries (dict)": [[45, "dictionaries-dict"]], "Did you know?": [[38, null], [39, null], [39, null]], "Discussions": [[33, "discussions"]], "Displaying Images": [[11, "displaying-images"]], "Domain-specific libraries": [[16, "domain-specific-libraries"]], "Email notifications": [[36, "email-notifications"]], "Example usage of the datetime object": [[7, "example-usage-of-the-datetime-object"]], "Executing Jupyter": [[26, "executing-jupyter"]], "Exercise with dask.arrays": [[21, "exercise-with-dask-arrays"]], "Exploratory Data Analysis": [[18, "exploratory-data-analysis"]], "Explore some of Cartopy\u2019s map projections": [[3, "explore-some-of-cartopys-map-projections"]], "Extend Matplotlib\u2019s axes into georeferenced GeoAxes": [[3, "extend-matplotlibs-axes-into-georeferenced-geoaxes"]], "Extending to higher dimensions": [[15, "extending-to-higher-dimensions"]], "Extending to the DataFrame": [[18, "extending-to-the-dataframe"]], "FOSS + VCS + Web = GitHub": [[39, "foss-vcs-web-github"]], "FOSS and VCS on the Internet": [[39, "foss-and-vcs-on-the-internet"]], "Figure and Axes": [[11, "figure-and-axes"]], "Finding the difference between min and max": [[15, "finding-the-difference-between-min-and-max"]], "Floating point numbers (float)": [[45, "floating-point-numbers-float"]], "Forking Workflow": [[37, "forking-workflow"]], "Forking a repository": [[32, "forking-a-repository"]], "Forking the upstream repository": [[37, "forking-the-upstream-repository"]], "Formatted Text in the Notebook with Markdown": [[43, "formatted-text-in-the-notebook-with-markdown"]], "Free \\neq open source!": [[39, null]], "Free and open-source software (FOSS)": [[39, "free-and-open-source-software-foss"]], "Full time zone support with the pytz module": [[7, "full-time-zone-support-with-the-pytz-module"]], "Generate a random numpy array": [[23, "generate-a-random-numpy-array"]], "Generate a secure personal access token for https": [[36, "generate-a-secure-personal-access-token-for-https"]], "Generate an SSH public/private keypair": [[36, "generate-an-ssh-public-private-keypair"]], "Generate test data using NumPy": [[11, "generate-test-data-using-numpy"]], "Generation": [[14, "generation"]], "Get a Quick Look at the Beginning/End of your DataFrame": [[18, "get-a-quick-look-at-the-beginning-end-of-your-dataframe"]], "Getting Started with GitHub": [[25, "getting-started-with-github"]], "Getting Started with Jupyter": [[26, "getting-started-with-jupyter"]], "Getting Started with Python": [[27, "getting-started-with-python"]], "Git Branches": [[30, "git-branches"]], "Git Feature Branch Workflow": [[37, "git-feature-branch-workflow"]], "Git commands mini-reference": [[28, "git-commands-mini-reference"]], "Git is a distributed VCS": [[28, "git-is-a-distributed-vcs"]], "Git is not GitHub": [[28, "git-is-not-github"]], "GitHub Repositories": [[35, "github-repositories"]], "GitHub Workflows": [[34, "github-workflows"], [37, "github-workflows"]], "GitHub notifications": [[36, "github-notifications"]], "GitHub secure key generation": [[36, "github-secure-key-generation"]], "GitHub workflows": [[37, 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\ No newline at end of file