Use custom resolver for query and eval with nested frames.

docs/tutorials/data_loading_notebook.ipynb

@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With a valid Python environment, nested-pandas and it's dependencies are easy to install using the `pip` package manager. The following command can be used to install it:"
+    "With a valid Python environment, nested-pandas and its dependencies are easy to install using the `pip` package manager. The following command can be used to install it:"
    ]
   },
   {
@@ -47,7 +47,7 @@
     "\n",
     "We can use the `NestedFrame` constructor to create our base frame from a dictionary of our columns.\n",
     "\n",
-    "We can then create an addtional pandas dataframes and pack them into our `NestedFrame` with `NestedFrame.add_nested`"
+    "We can then create an addtional pandas dataframes and pack them into our `NestedFrame` with `NestedFrame.add_nested`."
    ]
   },
   {
@@ -97,7 +97,7 @@
     "# Note: that we use the `tempfile` module to create and then cleanup a temporary directory.\n",
     "# You can of course remove this and use your own directory and real files on your system.\n",
     "with tempfile.TemporaryDirectory() as temp_path:\n",
-    "    # Generates parquet files with random data within our temporary directorye.\n",
+    "    # Generates parquet files with random data within our temporary directory.\n",
     "    generate_parquet_file(10, {\"nested1\": 100, \"nested2\": 10}, temp_path, file_per_layer=True)\n",
     "\n",
     "    # Read each individual parquet file into its own dataframe.\n",
@@ -148,7 +148,7 @@
    "source": [
     "So inspect `nf`, a `NestedFrame` we created from our call to `read_parquet` with the `to_pack` argument, we're able to pack nested parquet files according to the shared index values with the index in `base.parquet`.\n",
     "\n",
-    "The resulting `NestedFrame` having the same number of rows as `base.parquet` and with `nested1.parquet` and `nested2.parquet` packed into the 'nested1' and 'nested2' columns respectively."
+    "The resulting `NestedFrame` having the same number of rows as `base.parquet` and with `nested1.parquet` and `nested2.parquet` packed into the `nested1` and `nested2` columns respectively."
    ]
   },
   {
@@ -164,7 +164,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Since we loaded each individual parquet file into its own dataframe, we can also verify that using `read_parquet` with the `to_pack` argument is equivalent to the following method of packing the dataframes directly with `NestedFrame.add_nested`"
+    "Since we loaded each individual parquet file into its own dataframe, we can also verify that using `read_parquet` with the `to_pack` argument is equivalent to the following method of packing the dataframes directly with `NestedFrame.add_nested`."
    ]
   },
   {
@@ -189,11 +189,11 @@
    "source": [
     "# Saving NestedFrames to Parquet Files\n",
     "\n",
-    "Additionally we can save an existing `NestedFrame` as one of more parquet files using `NestedFrame.to_parquet``\n",
+    "Additionally we can save an existing `NestedFrame` as one of more parquet files using `NestedFrame.to_parquet`.\n",
     "\n",
     "When `by_layer=True` we save each individual layer of the NestedFrame into its own parquet file in a specified output directory.\n",
     "\n",
-    "The base layer will be outputted to \"base.parquet\", and each nested layer will be written to a file based on its column name. So the nested layer in column `nested1` will be written to \"nested1.parquet\"."
+    "The base layer will be outputted to `base.parquet`, and each nested layer will be written to a file based on its column name. So the nested layer in column `nested1` will be written to `nested1.parquet`."
    ]
   },
   {
@@ -233,7 +233,7 @@
    "source": [
     "We also support saving a `NestedFrame` as a single parquet file where the packed layers are still packed in their respective columns.\n",
     "\n",
-    "Here we provide `NestedFrame.to_parquet` with the desired path of the *single* output file (rather than the path of a directory to store *multiple* output files) and use `per_layer=False'\n",
+    "Here we provide `NestedFrame.to_parquet` with the desired path of the *single* output file (rather than the path of a directory to store *multiple* output files) and use `per_layer=False`.\n",
     "\n",
     "Our `read_parquet` function can load a `NestedFrame` saved in this single file parquet without requiring any additional arguments. "
    ]

docs/tutorials/data_manipulation.ipynb

@@ -49,7 +49,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First, we can directly fetch a column from our nested column (aptly called \"nested\"). For example, below we can fetch the time column, \"t\", by specifying `\"nested.t\"` as the column to retrieve. This returns a \"flat\" view of the nested t column, where all rows from all dataframes are present in one dataframe."
+    "First, we can directly fetch a column from our nested column (aptly called \"nested\"). For example, below we can fetch the time column, \"t\", by specifying `\"nested.t\"` as the column to retrieve. This returns a \"flat\" view of the nested `t` column, where all rows from all dataframes are present in one dataframe."
    ]
   },
   {
@@ -170,7 +170,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is functionally equivalent to using `add_nested`"
+    "This is functionally equivalent to using `add_nested`:"
    ]
   },
   {

docs/tutorials/low_level.ipynb

@@ -8,7 +8,7 @@
     "# Lower-level interface for performance and flexibility\n",
     "## Reveal the hidden power of nested Series\n",
     "\n",
-    "This section is for users looking to optimize the performance, both computationally and in memory-usage, of their workflows. This section also details a broader suite of data representations usable within `nested-pandas`.\n",
+    "This section is for users looking to optimize both the compute and memory performance of their workflows. This section also details a broader suite of data representations usable within `nested-pandas`.\n",
     "It shows how to deal with individual nested columns: add, remove, and modify data using both \"flat-array\" and \"list-array\" representations.\n",
     "It also demonstrates how to convert nested Series to and from different data types, like `pd.ArrowDtype`d Series, flat dataframes, list-array dataframes, and collections of nested elements."
    ]
@@ -36,7 +36,7 @@
    "source": [
     "## Generate some data and get a Series of `NestedDtype` type\n",
     "\n",
-    "We are going to use built-in data generator to get a `NestedFrame` with a \"nested\" column being a `Series` of `NestedDtype` type.\n",
+    "We are going to use the built-in data generator to get a `NestedFrame` with a \"nested\" column being a `Series` of `NestedDtype` type.\n",
     "This column would represent [light curves](https://en.wikipedia.org/wiki/Light_curve) of some astronomical objects. "
    ]
   },
@@ -94,7 +94,7 @@
    "id": "33d8caacf0bf042e",
    "metadata": {},
    "source": [
-    "You can also get a list of fields with `.fields` attribute"
+    "You can also get a list of fields with `.fields` attribute:"
    ]
   },
   {
@@ -130,7 +130,7 @@
    "id": "7167f5a9c947d96f",
    "metadata": {},
    "source": [
-    "You can also get a subset of nested columns as a new nested Series"
+    "You can also get a subset of nested columns as a new nested Series:"
    ]
   },
   {
@@ -479,7 +479,7 @@
    "source": [
     "#### pd.Series from an array\n",
     "\n",
-    "Construction with `pyarrow` struct arrays is the cheapest way to create a nested Series. It is very semilliar to initialisation of a `pd.Series` of `pd.ArrowDtype` type."
+    "Construction with `pyarrow` struct arrays is the cheapest way to create a nested Series. It is very similar to the initialization of a `pd.Series` of `pd.ArrowDtype` type."
    ]
   },
   {
@@ -611,21 +611,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "pygments_lexer": "ipython3",
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,

docs/tutorials/nested_spectra.ipynb

@@ -79,7 +79,7 @@
     "flux = np.array([])\n",
     "err = np.array([])\n",
     "index = np.array([])\n",
-    "# Loop over each spectrum, adding it's data to the arrays\n",
+    "# Loop over each spectrum, adding its data to the arrays\n",
     "for i, hdu in enumerate(sp):\n",
     "    wave = np.append(wave, 10 ** hdu[\"COADD\"].data.loglam)  # * u.angstrom\n",
     "    flux = np.append(flux, hdu[\"COADD\"].data.flux * 1e-17)  # * u.erg/u.second/u.centimeter**2/u.angstrom\n",
@@ -115,7 +115,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And we can see that each object now has the \"coadd_spectrum\" nested column with the full spectrum available."
+    "And we can see that each object now has the `coadd_spectrum` nested column with the full spectrum available."
    ]
   },
   {
@@ -161,7 +161,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -175,9 +175,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }