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diff --git a/face-recognition/source_code_step_2/training/elton_john/httpimggalpmdstaticnetfithttpAFFwwwEgalaEfrFvarFgalFstorageFimagesFmediaFmultiuploaddufevrierFeltonjohnFfreFRFeltonjohnEjpgxqualityeltonjohnjpg.jpg b/face-recognition/source_code_step_2/training/elton_john/httpimggalpmdstaticnetfithttpAFFwwwEgalaEfrFvarFgalFstorageFimagesFmediaFmultiuploaddufevrierFeltonjohnFfreFRFeltonjohnEjpgxqualityeltonjohnjpg.jpg
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diff --git a/face-recognition/source_code_step_6/training/elton_john/httpimggalpmdstaticnetfithttpAFFwwwEgalaEfrFvarFgalFstorageFimagesFmediaFmultiuploaddufevrierFeltonjohnFfreFRFeltonjohnEjpgxqualityeltonjohnjpg.jpg b/face-recognition/source_code_step_6/training/elton_john/httpimggalpmdstaticnetfithttpAFFwwwEgalaEfrFvarFgalFstorageFimagesFmediaFmultiuploaddufevrierFeltonjohnFfreFRFeltonjohnEjpgxqualityeltonjohnjpg.jpg
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diff --git a/polars-vs-pandas/DataFrame_Plots.ipynb b/polars-vs-pandas/DataFrame_Plots.ipynb
new file mode 100644
index 0000000000..f55e66e654
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+++ b/polars-vs-pandas/DataFrame_Plots.ipynb
@@ -0,0 +1,205 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-08-15T20:56:04.701160Z",
+     "start_time": "2025-08-15T20:56:04.678795Z"
+    }
+   },
+   "source": [
+    "from data_generation import generate_data\n",
+    "\n",
+    "sales_data = generate_data(50)"
+   ],
+   "outputs": [],
+   "execution_count": 7
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-15T20:56:04.881866Z",
+     "start_time": "2025-08-15T20:56:04.723927Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import polars as pl\n",
+    "\n",
+    "orders_polars = pl.DataFrame(sales_data)\n",
+    "\n",
+    "(\n",
+    "    orders_polars.group_by(\"region\")\n",
+    "    .agg(total_sales=pl.col(\"sales_income\").sum())\n",
+    "    .plot.bar(x=\"region\", y=\"total_sales\")\n",
+    "    .properties(width=200, height=200, title=\"Total Sales per Region ($)\")\n",
+    ")"
+   ],
+   "id": "832f13776b5db08e",
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-8a03b6246b5e4d8ea3b883efc4869c50.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-8a03b6246b5e4d8ea3b883efc4869c50.vega-embed details,\n",
+       "  #altair-viz-8a03b6246b5e4d8ea3b883efc4869c50.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-8a03b6246b5e4d8ea3b883efc4869c50\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-8a03b6246b5e4d8ea3b883efc4869c50\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-8a03b6246b5e4d8ea3b883efc4869c50\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-89be832c5d820f6cbea042bc76c1e103\"}, \"mark\": {\"type\": \"bar\", \"tooltip\": true}, \"encoding\": {\"x\": {\"field\": \"region\", \"type\": \"nominal\"}, \"y\": {\"field\": \"total_sales\", \"type\": \"quantitative\"}}, \"height\": 200, \"params\": [{\"name\": \"param_3\", \"select\": {\"type\": \"interval\", \"encodings\": [\"x\", \"y\"]}, \"bind\": \"scales\"}], \"title\": \"Total Sales per Region ($)\", \"width\": 200, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.20.1.json\", \"datasets\": {\"data-89be832c5d820f6cbea042bc76c1e103\": [{\"region\": \"North\", \"total_sales\": 34620}, {\"region\": \"East\", \"total_sales\": 30668}, {\"region\": \"West\", \"total_sales\": 22286}, {\"region\": \"South\", \"total_sales\": 41821}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 3840x2880 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-15T20:56:05.442276Z",
+     "start_time": "2025-08-15T20:56:05.109097Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "orders_pandas = pd.DataFrame(sales_data)\n",
+    "\n",
+    "(\n",
+    "    orders_pandas.groupby(\n",
+    "        [\n",
+    "            \"region\",\n",
+    "        ]\n",
+    "    )[\"sales_income\"]\n",
+    "    .sum()\n",
+    "    .plot(kind=\"bar\", title=\"Total Sales per Region ($)\", ylabel=\"total_sales\")\n",
+    ")"
+   ],
+   "id": "4199475a83ffc612",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Total Sales per Region ($)'}, xlabel='region', ylabel='total_sales'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 3840x2880 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 9
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/polars-vs-pandas/Online_Retail.parquet b/polars-vs-pandas/Online_Retail.parquet
new file mode 100644
index 0000000000..30f6193e23
Binary files /dev/null and b/polars-vs-pandas/Online_Retail.parquet differ
diff --git a/polars-vs-pandas/README.md b/polars-vs-pandas/README.md
new file mode 100644
index 0000000000..4c8b92b1d0
--- /dev/null
+++ b/polars-vs-pandas/README.md
@@ -0,0 +1,14 @@
+The materials contained in this download are designed to complement the RealPython tutorial [Polars vs pandas - What's the Difference](https://realpython.com/polars-vs-pandas-difference/).
+
+You should create a new folder named marimo on your computer and place each of these files inside it. You may also consider creating a [Python virtual environment](https://realpython.com/python-virtual-environments-a-primer/) within this folder.
+
+Your download bundle contains the following files:
+
+Online_Retail.parquet		 	- This parquet file contains retail data used in some of the queries.
+data_generation.py		 	- This script contains the generate_data() function used to generate different quantities of data.
+dataframe_and_lazyframe_time_tests.py	- This script performs time tests for DataFrames and a LazyFrame.
+streaming_test.py	                - This script performs time tests for a LazyFrame with streaming enabled.
+
+dataframe_conversions.py	 	- This file contains the code used to convert between pandas and Polars DataFrames, plus a Narwhals example.
+sample_pandas_and_polars_code.py 	- This file contains the code used to illustrate the differences between pandas and Polars syntax.
+DataFrame_Plots.ipynb			- This Jupyter Notebook file contains the plotting code to demonstrate default plotting capabilities.
diff --git a/polars-vs-pandas/data_generation.py b/polars-vs-pandas/data_generation.py
new file mode 100644
index 0000000000..108ab6c308
--- /dev/null
+++ b/polars-vs-pandas/data_generation.py
@@ -0,0 +1,19 @@
+import numpy as np
+
+
+def generate_data(number_of_rows):
+    rng = np.random.default_rng()
+
+    return {
+        "order_id": range(1, number_of_rows + 1),
+        "region": rng.choice(
+            ["North", "South", "East", "West"], size=number_of_rows
+        ),
+        "sales_person": rng.choice(
+            ["Armstrong", "Aldrin", "Collins"], size=number_of_rows
+        ),
+        "product": rng.choice(
+            ["Helmet", "Oxygen", "Boots", "Gloves"], size=number_of_rows
+        ),
+        "sales_income": rng.integers(1, 5001, size=number_of_rows),
+    }
diff --git a/polars-vs-pandas/dataframe_and_lazyframe_time_tests.py b/polars-vs-pandas/dataframe_and_lazyframe_time_tests.py
new file mode 100644
index 0000000000..e5b427c820
--- /dev/null
+++ b/polars-vs-pandas/dataframe_and_lazyframe_time_tests.py
@@ -0,0 +1,93 @@
+import functools
+import sys
+from timeit import Timer
+
+import pandas as pd
+import polars as pl
+from data_generation import generate_data
+
+
+def create_pandas_dataframe(test_data):
+    return pd.DataFrame(test_data).convert_dtypes(dtype_backend="pyarrow")
+
+
+def create_polars_dataframe(test_data):
+    return pl.DataFrame(test_data)
+
+
+def create_polars_lazyframe(test_data):
+    return pl.LazyFrame(test_data)
+
+
+def analyze_pandas_dataframe(pandas_df):
+    return pandas_df.groupby(["region", "product", "sales_person"])[
+        "sales_income"
+    ].sum()
+
+
+def analyze_polars_dataframe(polars_df):
+    return polars_df.group_by(["region", "product", "sales_person"]).agg(
+        total_sales=pl.col("sales_income").sum()
+    )
+
+
+def analyze_polars_lazyframe(polars_lf):
+    return (
+        polars_lf.group_by(["region", "product", "sales_person"])
+        .agg(total_sales=pl.col("sales_income").sum())
+        .collect()
+    )
+
+
+test_data = generate_data(int(sys.argv[1]))
+
+print("Creating Dataframes...")
+print(f"Pandas dataframe creation time for {int(sys.argv[1])} rows:")
+print(Timer(functools.partial(create_pandas_dataframe, test_data)).timeit(100))
+print(f"\nPolars dataframe creation time for {int(sys.argv[1])} rows:")
+print(Timer(functools.partial(create_polars_dataframe, test_data)).timeit(100))
+print(f"\nPolars lazyframe creation time for {int(sys.argv[1])} rows:")
+print(Timer(functools.partial(create_polars_lazyframe, test_data)).timeit(100))
+
+pandas_df = create_pandas_dataframe(test_data)
+polars_df = create_polars_dataframe(test_data)
+polars_lf = create_polars_lazyframe(test_data)
+
+print("\nAnalyzing Dataframes...")
+print(f"Pandas dataframe analysis time for {int(sys.argv[1])} rows:")
+print(
+    Timer(functools.partial(analyze_pandas_dataframe, pandas_df)).timeit(100)
+)
+
+print(f"\nPolars dataframe analysis time for {int(sys.argv[1])} rows:")
+print(
+    Timer(functools.partial(analyze_polars_dataframe, polars_df)).timeit(100)
+)
+
+print(f"\nPolars lazyframe analysis time for {int(sys.argv[1])} rows:")
+print(
+    Timer(functools.partial(analyze_polars_lazyframe, polars_lf)).timeit(100)
+)
+
+print("\nShow Boots sales in the East region for pandas DataFrame")
+print(analyze_pandas_dataframe(pandas_df)["East"]["Boots"])
+
+print("\nShow Boots sales in the East region for polars DataFrame")
+print(
+    (
+        analyze_polars_dataframe(polars_df).filter(
+            pl.col("region") == "East",
+            pl.col("product") == "Boots",
+        )
+    )
+)
+
+print("\nShow Boots sales in the East region for pandas LazyFrame")
+print(
+    (
+        analyze_polars_lazyframe(polars_lf).filter(
+            pl.col("region") == "East",
+            pl.col("product") == "Boots",
+        )
+    )
+)
diff --git a/polars-vs-pandas/sample_pandas_and_polars_code.py b/polars-vs-pandas/sample_pandas_and_polars_code.py
new file mode 100644
index 0000000000..46d7b5cb02
--- /dev/null
+++ b/polars-vs-pandas/sample_pandas_and_polars_code.py
@@ -0,0 +1,32 @@
+import pandas as pd
+import polars as pl
+
+# Pandas index-based syntax
+orders_pandas = pd.read_parquet("online_retail.parquet")
+
+orders_pandas["Total"] = orders_pandas["Quantity"] * orders_pandas["UnitPrice"]
+
+orders_pandas[["InvoiceNo", "Quantity", "UnitPrice", "Total"]][
+    orders_pandas["Total"] > 100
+].head(3)
+
+# Pandas method chaining syntax
+orders_pandas = pd.read_parquet("online_retail.parquet")
+
+(
+    orders_pandas.assign(
+        Total=orders_pandas["Quantity"] * orders_pandas["UnitPrice"]
+    )
+    .filter(["InvoiceNo", "Quantity", "UnitPrice", "Total"])
+    .query("Total > 100")
+).head(3)
+
+# Polars method chaining syntax
+orders_polars = pl.read_parquet("online_retail.parquet")
+
+(
+    orders_polars.select(
+        pl.col(["InvoiceNo", "Quantity", "UnitPrice"]),
+        total=pl.col("Quantity") * pl.col("UnitPrice"),
+    ).filter(pl.col("total") > 100)
+).head(3)
diff --git a/polars-vs-pandas/streaming_test.py b/polars-vs-pandas/streaming_test.py
new file mode 100644
index 0000000000..58b783570a
--- /dev/null
+++ b/polars-vs-pandas/streaming_test.py
@@ -0,0 +1,37 @@
+import functools
+import sys
+from timeit import Timer
+
+import polars as pl
+from data_generation import generate_data
+
+
+def create_polars_lazyframe(test_data):
+    return pl.LazyFrame(test_data)
+
+
+def analyze_polars_lazyframe(polars_lf):
+    polars_lf.group_by(["region", "product", "sales_person"]).agg(
+        total_sales=pl.col("sales_income").sum()
+    ).collect()
+
+
+def analyze_polars_streaming(polars_lf):
+    polars_lf.group_by(["region", "product", "sales_person"]).agg(
+        total_sales=pl.col("sales_income").sum()
+    ).collect(engine="streaming")
+
+
+test_data = generate_data(int(sys.argv[1]))
+
+polars_lf = create_polars_lazyframe(test_data)
+
+print(f"Polars lazyframe analysis time for {int(sys.argv[1])} rows:")
+print(
+    Timer(functools.partial(analyze_polars_lazyframe, polars_lf)).timeit(100)
+)
+
+print(f"\nPolars streaming analysis time for {int(sys.argv[1])} rows:")
+print(
+    Timer(functools.partial(analyze_polars_streaming, polars_lf)).timeit(100)
+)