diff --git a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block.ipynb b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block.ipynb
index f6b10d38..13724a46 100644
--- a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block.ipynb
+++ b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "header",
@@ -45,8 +45,7 @@
     "\n",
     "## Key links to documentation:\n",
     "* NRTL Model - https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n",
-    "* parmest - https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/index.html\n",
-    ""
+    "* parmest - https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/index.html\n"
    ]
   },
   {
@@ -61,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "exercise"
@@ -76,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "solution"
@@ -100,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -121,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -152,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "exercise"
@@ -198,7 +197,12 @@
     "    m.fs.state_block.initialize(outlvl=idaeslog.INFO)\n",
     "\n",
     "    # Fix at actual temperature\n",
-    "    m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n",
+    "    if isinstance(data, dict) or isinstance(data, pd.Series):\n",
+    "        m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n",
+    "    elif isinstance(data, pd.DataFrame):\n",
+    "        m.fs.state_block.temperature.fix(float(data.iloc[0][\"temperature\"]))\n",
+    "    else:\n",
+    "        raise ValueError(\"Unrecognized data type.\") \n",
     "\n",
     "    # Set bounds on variables to be estimated\n",
     "    m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n",
@@ -213,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "solution"
@@ -264,7 +268,13 @@
     "    m.fs.state_block.initialize(outlvl=idaeslog.INFO_LOW)\n",
     "\n",
     "    # Fix at actual temperature\n",
-    "    m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n",
+    "    # Fix at actual temperature\n",
+    "    if isinstance(data, dict) or isinstance(data, pd.Series):\n",
+    "        m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n",
+    "    elif isinstance(data, pd.DataFrame):\n",
+    "        m.fs.state_block.temperature.fix(float(data.iloc[0][\"temperature\"]))\n",
+    "    else:\n",
+    "        raise ValueError(\"Unrecognized data type.\") \n",
     "\n",
     "    # Set bounds on variables to be estimated\n",
     "    m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n",
@@ -279,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "testing"
@@ -349,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "exercise"
@@ -362,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "solution"
@@ -393,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -453,10 +463,10 @@
     "    # and vapor phase. For example, the squared error for the vapor phase is:\n",
     "    # (float(data[\"vap_benzene\"]) - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"])**2\n",
     "    expr = (\n",
-    "        float(data[\"vap_benzene\"])\n",
+    "        float(data.iloc[0][\"vap_benzene\"])\n",
     "        - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"]\n",
     "    ) ** 2 + (\n",
-    "        float(data[\"liq_benzene\"])\n",
+    "        float(data.iloc[0][\"liq_benzene\"])\n",
     "        - m.fs.state_block.mole_frac_phase_comp[\"Liq\", \"benzene\"]\n",
     "    ) ** 2\n",
     "    return expr * 1e4"
@@ -587,7 +597,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.8.19"
   }
  },
  "nbformat": 4,
diff --git a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model.ipynb b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model.ipynb
index d1648edd..9397f6cc 100644
--- a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model.ipynb
+++ b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model.ipynb
@@ -200,7 +200,12 @@
     "    m.fs.flash.initialize(outlvl=idaeslog.INFO_LOW)\n",
     "\n",
     "    # Fix at actual temperature\n",
-    "    m.fs.flash.inlet.temperature.fix(float(data[\"temperature\"]))\n",
+    "    if isinstance(data, dict) or isinstance(data, pd.Series):\n",
+    "        m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n",
+    "    elif isinstance(data, pd.DataFrame):\n",
+    "        m.fs.state_block.temperature.fix(float(data.iloc[0][\"temperature\"]))\n",
+    "    else:\n",
+    "        raise ValueError(\"Unrecognized data type.\") \n",
     "\n",
     "    # Set bounds on variables to be estimated\n",
     "    m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n",
@@ -267,7 +272,12 @@
     "    m.fs.flash.initialize(outlvl=idaeslog.INFO_LOW)\n",
     "\n",
     "    # Fix at actual temperature\n",
-    "    m.fs.flash.inlet.temperature.fix(float(data[\"temperature\"]))\n",
+    "    if isinstance(data, dict) or isinstance(data, pd.Series):\n",
+    "        m.fs.flash.inlet.temperature.fix(float(data[\"temperature\"]))\n",
+    "    elif isinstance(data, pd.DataFrame):\n",
+    "        m.fs.flash.inlet.temperature.fix(float(data.iloc[0][\"temperature\"]))\n",
+    "    else:\n",
+    "        raise ValueError(\"Unrecognized data type.\") \n",
     "\n",
     "    # Set bounds on variables to be estimated\n",
     "    m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n",
@@ -434,7 +444,7 @@
     "def SSE(m, data):\n",
     "    # Todo: Add expression for computing the sum of squared errors in mole fraction of benzene in the liquid\n",
     "    # and vapor phase. For example, the squared error for the vapor phase is:\n",
-    "    # (float(data[\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"])**2\n",
+    "    # (float(data.iloc[0][\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"])**2\n",
     "\n",
     "    return expr * 1e4"
    ]
@@ -455,9 +465,9 @@
     "    # and vapor phase. For example, the squared error for the vapor phase is:\n",
     "    # (float(data[\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"])**2\n",
     "    expr = (\n",
-    "        float(data[\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]\n",
+    "        float(data.iloc[0][\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]\n",
     "    ) ** 2 + (\n",
-    "        float(data[\"liq_benzene\"]) - m.fs.flash.liq_outlet.mole_frac_comp[0, \"benzene\"]\n",
+    "        float(data.iloc[0][\"liq_benzene\"]) - m.fs.flash.liq_outlet.mole_frac_comp[0, \"benzene\"]\n",
     "    ) ** 2\n",
     "    return expr * 1e4"
    ]
@@ -587,9 +597,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.8.19"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/idaes_examples/notebooks/docs/properties/parameter_estimation_pr.ipynb b/idaes_examples/notebooks/docs/properties/parameter_estimation_pr.ipynb
index 1546704a..8d91484d 100644
--- a/idaes_examples/notebooks/docs/properties/parameter_estimation_pr.ipynb
+++ b/idaes_examples/notebooks/docs/properties/parameter_estimation_pr.ipynb
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -155,15 +155,22 @@
     "    m = ConcreteModel()\n",
     "\n",
     "    m.fs = FlowsheetBlock(dynamic=False)\n",
-    "\n",
+    "  \n",
     "    m.fs.properties = GenericParameterBlock(**configuration)\n",
     "\n",
     "    m.fs.state_block = m.fs.properties.build_state_block([1], defined_state=True)\n",
-    "\n",
+    " \n",
     "    m.fs.state_block[1].flow_mol.fix(1)\n",
-    "    x = float(data[\"x_carbon_dioxide\"]) + 0.5\n",
-    "    m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n",
-    "    m.fs.state_block[1].pressure.fix(float(data[\"pressure\"]))\n",
+    "    if isinstance(data, dict) or isinstance(data, pd.Series):\n",
+    "        x = float(data[\"x_carbon_dioxide\"]) + 0.5  \n",
+    "        m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n",
+    "        m.fs.state_block[1].pressure.fix(float(data[\"pressure\"]))\n",
+    "    elif isinstance(data, pd.DataFrame):\n",
+    "        x = float(data.iloc[0][\"x_carbon_dioxide\"]) + 0.5  \n",
+    "        m.fs.state_block[1].temperature.fix(float(data.iloc[0][\"temperature\"]))\n",
+    "        m.fs.state_block[1].pressure.fix(float(data.iloc[0][\"pressure\"]))        \n",
+    "    else:\n",
+    "        raise ValueError(\"Unrecognized data type.\") \n",
     "    m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(1 - x)\n",
     "    m.fs.state_block[1].mole_frac_comp[\"carbon_dioxide\"].fix(x)\n",
     "\n",
@@ -177,15 +184,28 @@
     "    m.fs.state_block.initialize(outlvl=idaeslog.INFO)\n",
     "\n",
     "    # Fix the state variables on the state block\n",
-    "    m.fs.state_block[1].pressure.unfix()\n",
-    "    m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n",
-    "    m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"bmimPF6\"].fix(\n",
+    "    if isinstance(data, dict) or isinstance(data, pd.Series):\n",
+    "        m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n",
+    "        m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"bmimPF6\"].fix(\n",
     "        float(data[\"x_bmimPF6\"])\n",
-    "    )\n",
-    "    m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"carbon_dioxide\"].fix(\n",
+    "        )\n",
+    "        m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"carbon_dioxide\"].fix(\n",
     "        float(data[\"x_carbon_dioxide\"])\n",
-    "    )\n",
-    "    m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(float(data[\"x_bmimPF6\"]))\n",
+    "        )\n",
+    "        m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(float(data[\"x_bmimPF6\"]))        \n",
+    "    elif isinstance(data, pd.DataFrame):\n",
+    "        m.fs.state_block[1].temperature.fix(float(data.iloc[0][\"temperature\"]))\n",
+    "        m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"bmimPF6\"].fix(\n",
+    "        float(data.iloc[0][\"x_bmimPF6\"])\n",
+    "        )\n",
+    "        m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"carbon_dioxide\"].fix(\n",
+    "        float(data.iloc[0][\"x_carbon_dioxide\"])\n",
+    "        )\n",
+    "        m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(float(data.iloc[0][\"x_bmimPF6\"]))              \n",
+    "    else:\n",
+    "        raise ValueError(\"Unrecognized data type.\") \n",
+    "        \n",
+    "    m.fs.state_block[1].pressure.unfix()\n",
     "    m.fs.state_block[1].mole_frac_comp[\"carbon_dioxide\"].unfix()\n",
     "    # Set bounds on variables to be estimated\n",
     "    m.fs.properties.PR_kappa[\"bmimPF6\", \"carbon_dioxide\"].setlb(-5)\n",
@@ -207,10 +227,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from idaes.core.util.model_statistics import degrees_of_freedom\n",
@@ -242,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -263,12 +281,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "def SSE(m, data):\n",
-    "    expr = (float(data[\"pressure\"]) - m.fs.state_block[1].pressure) ** 2\n",
+    "    expr = (float(data.iloc[0][\"pressure\"]) - m.fs.state_block[1].pressure) ** 2\n",
     "    return expr * 1e-7"
    ]
   },
@@ -283,10 +301,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "pest = parmest.Estimator(PR_model, data, variable_name, SSE, tee=True)\n",
@@ -305,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -347,7 +363,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.8.19"
   }
  },
  "nbformat": 4,