diff --git a/f22/meena_lec_notes/lec-35/lec_35_pandas3_data_transformation.ipynb b/f22/meena_lec_notes/lec-35/lec_35_pandas3_data_transformation.ipynb
index e45724e9b33b1a169b74b0a439fccca96ec51184..91ba588b7cbd4b6fc52046eae29aa7dd63b0afe4 100644
--- a/f22/meena_lec_notes/lec-35/lec_35_pandas3_data_transformation.ipynb
+++ b/f22/meena_lec_notes/lec-35/lec_35_pandas3_data_transformation.ipynb
@@ -25,21 +25,7 @@
    "source": [
     "# Lecture 35 Pandas 3: Data Transformation\n",
     "* Data transformation is the process of changing the format, structure, or values of data. \n",
-    "* Often needed during data cleaning and sometimes during data analysis\n",
-    "\n",
-    "Possible data transformation: \n",
-    "* Parsing/Extraction\n",
-    "  * Parse CSV to Pandas DataFrame\n",
-    "* Missing Value Manipulations\n",
-    "  * Dropping\n",
-    "  * Imputation: replace missing value with substitute values\n",
-    "* Typecasting, Formating, Renaming\n",
-    "  * Typecasting: covert one column from int to float \n",
-    "  * Formating: format the time column to datatime object \n",
-    "  * Renaming: rename column and index names \n",
-    "* Applying/Mapping \n",
-    "* Filtering, Aggregation, Grouping, and Summarization\n",
-    "  * Covered in Pandas 1 & 2 lectures"
+    "* Often needed during data cleaning and sometimes during data analysis"
    ]
   },
   {
@@ -50,14 +36,15 @@
    "source": [
     "# Today's Learning Objectives: \n",
     "\n",
-    "* Missing Value Manipulations\n",
-    "  * check, drop, and fill NaN using Pandas .isna, .dropna, and .fillna\n",
-    "* Applying/Mapping\n",
-    "  * Use .apply on Pandas Series and DataFrame rows/columns \n",
-    "  * Use .replace to replace all target values \n",
-    "* Filtering, Aggregation, Grouping, and Summarization\n",
-    "  * More .groupby examples \n",
-    "  * Convert .groupby examples to SQL "
+    "* Setting column as index for pandas `DataFrame`\n",
+    "* Identify, drop, or fill missing values (`np.NaN`) using Pandas `isna`, `dropna`, and `fillna`\n",
+    "* Applying transformations to `DataFrame`:\n",
+    "  * Use `apply` on pandas `Series` to apply a transformation function\n",
+    "  * Use `replace` to replace all target values in Pandas `Series` and `DataFrame` rows / columns\n",
+    "* Filter, aggregate, group, and summarize information in a `DataFrame` with `groupby`\n",
+    "* Convert .groupby examples to SQL\n",
+    "* Solving the same question using SQL and pandas `DataFrame` manipulations:\n",
+    "  * filtering, grouping, and aggregation / summarization"
    ]
   },
   {
@@ -200,7 +187,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### WARMUP 3: Use pandas lookup expression to identify the column names and the types: use .iloc"
+    "### WARMUP 3: Use pandas lookup expression to extract the \"sql\" column and display the full query using .iloc lookup"
    ]
   },
   {
@@ -2172,7 +2159,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### `lambda` recap\n",
+    "### `lambda`\n",
     "\n",
     "Let's write a `lambda` function instead of the `replace_mode` function"
    ]
diff --git a/f22/meena_lec_notes/lec-35/lec_35_pandas3_data_transformation_template.ipynb b/f22/meena_lec_notes/lec-35/lec_35_pandas3_data_transformation_template.ipynb
index ebd46ae0cf3c5711b469ead673842a244dd42ce8..40be2272f22cc70354345435a6440dfc3f389fe7 100644
--- a/f22/meena_lec_notes/lec-35/lec_35_pandas3_data_transformation_template.ipynb
+++ b/f22/meena_lec_notes/lec-35/lec_35_pandas3_data_transformation_template.ipynb
@@ -25,21 +25,7 @@
    "source": [
     "# Lecture 35 Pandas 3: Data Transformation\n",
     "* Data transformation is the process of changing the format, structure, or values of data. \n",
-    "* Often needed during data cleaning and sometimes during data analysis\n",
-    "\n",
-    "Possible data transformation: \n",
-    "* Parsing/Extraction\n",
-    "  * Parse CSV to Pandas DataFrame\n",
-    "* Missing Value Manipulations\n",
-    "  * Dropping\n",
-    "  * Imputation: replace missing value with substitute values\n",
-    "* Typecasting, Formating, Renaming\n",
-    "  * Typecasting: covert one column from int to float \n",
-    "  * Formating: format the time column to datatime object \n",
-    "  * Renaming: rename column and index names \n",
-    "* Applying/Mapping \n",
-    "* Filtering, Aggregation, Grouping, and Summarization\n",
-    "  * Covered in Pandas 1 & 2 lectures"
+    "* Often needed during data cleaning and sometimes during data analysis"
    ]
   },
   {
@@ -50,14 +36,15 @@
    "source": [
     "# Today's Learning Objectives: \n",
     "\n",
-    "* Missing Value Manipulations\n",
-    "  * check, drop, and fill NaN using Pandas .isna, .dropna, and .fillna\n",
-    "* Applying/Mapping\n",
-    "  * Use .apply on Pandas Series and DataFrame rows/columns \n",
-    "  * Use .replace to replace all target values \n",
-    "* Filtering, Aggregation, Grouping, and Summarization\n",
-    "  * More .groupby examples \n",
-    "  * Convert .groupby examples to SQL "
+    "* Setting column as index for pandas `DataFrame`\n",
+    "* Identify, drop, or fill missing values (`np.NaN`) using Pandas `isna`, `dropna`, and `fillna`\n",
+    "* Applying transformations to `DataFrame`:\n",
+    "  * Use `apply` on pandas `Series` to apply a transformation function\n",
+    "  * Use `replace` to replace all target values in Pandas `Series` and `DataFrame` rows / columns\n",
+    "* Filter, aggregate, group, and summarize information in a `DataFrame` with `groupby`\n",
+    "* Convert .groupby examples to SQL\n",
+    "* Solving the same question using SQL and pandas `DataFrame` manipulations:\n",
+    "  * filtering, grouping, and aggregation / summarization"
    ]
   },
   {
@@ -136,7 +123,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### WARMUP 3: Use pandas lookup expression to identify the column names and the types: use .iloc"
+    "### WARMUP 3: Use pandas lookup expression to extract the \"sql\" column and display the full query using .iloc lookup"
    ]
   },
   {
@@ -372,7 +359,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### `lambda` recap\n",
+    "### `lambda`\n",
     "\n",
     "Let's write a `lambda` function instead of the `replace_mode` function"
    ]