diff --git a/ml2/3_3_Data_Munging_with_Pandas.ipynb b/ml2/3_3_Data_Munging_with_Pandas.ipynb
index 2b21bb1..61b8777 100644
--- a/ml2/3_3_Data_Munging_with_Pandas.ipynb
+++ b/ml2/3_3_Data_Munging_with_Pandas.ipynb
@@ -63,9 +63,9 @@
    "source": [
     "[**Data munging**](https://en.wikipedia.org/wiki/Data_wrangling) or data wrangling is loosely the process of manually converting or mapping data from one \"raw\" form (*datos en bruto*) into another format that allows for more convenient consumption of the data with the help of semi-automated tools.\n",
     "\n",
-    "*Scikit-learn* estimators which assume that all values are numerical. This is a common feature in many machine learning libraries. So, we need to preprocess our raw dataset. \n",
+    "*Scikit-learn* estimators which assume that all values are numerical. This is a common in many machine learning libraries. So, we need to preprocess our raw dataset. \n",
     "Some of the most common tasks are:\n",
-    "* Remove samples with missing values or replace the missing values with a value (median, mean, or interpolation)\n",
+    "* Remove samples with missing values or replace the missing values with a value (median, mean or interpolation)\n",
     "* Encode categorical variables as integers\n",
     "* Combine  datasets\n",
     "* Rename variables and convert types\n",
@@ -73,7 +73,7 @@
     "\n",
     "We are going to play again with the Titanic dataset to practice with Pandas Dataframes and introduce a number of preprocessing facilities of scikit-learn.\n",
     "\n",
-    "First, we load the dataset, and we get a dataframe."
+    "First we load the dataset and we get a dataframe."
    ]
   },
   {
@@ -120,7 +120,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see some features have a numerical type (int64 and float64), and others have a type *object*. The object type in Pandas is a String. We observe that most features are integers, except for Name, Sex, Ticket, Cabin, and Embarked."
+    "We see some features have a numerical type (int64 and float64), and others has a type *object*. The object type is a String in Pandas. We observe that most features are integers, except for Name, Sex, Ticket, Cabin and Embarked."
    ]
   },
   {
@@ -129,7 +129,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# We can list non-numerical properties, with a boolean indexing of the Series df.dtypes\n",
+    "# We can list non numerical properties, with a boolean indexing of the Series df.dtypes\n",
     "df.dtypes[df.dtypes == object]"
    ]
   },
@@ -164,7 +164,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Observe that some of the statistics do not make sense in some columns (PassengerId or Pclass); we could have selected only the interesting columns."
+    "Observe that some of the statistics do not make sense in some columns (PassengerId or Pclass), we could have selected only the interesting columns."
    ]
   },
   {
@@ -181,7 +181,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Selecting rows in a DataFrame."
+    "## Selecting rows in a DataFrame"
    ]
   },
   {
@@ -281,14 +281,13 @@
    "source": [
     "DataFrames provide a set of functions for selection that we will need later\n",
     "\n",
-    "\n",
-    "|Operation | Syntax | Result |\n",
-    "|-----------------------------|\n",
-    "|Select column                  | df[col]       | Series |\n",
-    "|Select row by label            | df.loc[label] | Series |\n",
-    "|Select row by integer location | df.iloc[loc]  | Series |\n",
-    "|Slice rows\t                    | df[5:10]\t    | DataFrame |\n",
-    "|Select rows by boolean vector  | df[bool_vec]  | DataFrame |"
+    "| Operation                     | Syntax        | Result    |\n",
+    "|--------------------------------|--------------|----------|\n",
+    "| Select column                  | `df[col]`    | Series   |\n",
+    "| Select row by label            | `df.loc[label]` | Series |\n",
+    "| Select row by integer location | `df.iloc[loc]` | Series |\n",
+    "| Slice rows                     | `df[5:10]`   | DataFrame |\n",
+    "| Select rows by boolean vector  | `df[bool_vec]` | DataFrame |"
    ]
   },
   {
@@ -423,7 +422,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Mean age, SibSp, Survived of passengers older than 25 who survived, grouped by Passenger Class and Sex \n",
+    "# Mean age, SibSp , Survived of passengers older than 25 which survived, grouped by Passenger Class and Sex \n",
     "df[(df.Age > 25 & (df.Survived == 1))].groupby(['Pclass', 'Sex'])[['Age','SibSp','Survived']].mean()"
    ]
   },
@@ -433,7 +432,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# We can also decide which function applies in each column\n",
+    "# We can also decide which function apply in each column\n",
     "\n",
     "#Show mean Age, mean SibSp, and number of passengers older than 25 that survived,  grouped by Passenger Class and Sex\n",
     "df[(df.Age > 25 & (df.Survived == 1))].groupby(['Pclass', 'Sex'])[['Age','SibSp','Survived']].agg({'Age': np.mean, \n",
@@ -470,7 +469,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we want to analyze multi-index, the percentage of survivors, given sex and age, and distributed by Pclass."
+    "Now we want to analyze multi-index, the percentage of survivoers, given sex and age, and distributed by Pclass."
    ]
   },
   {
@@ -581,7 +580,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this case, there are no duplicates. In case we would need, we could have removed them with [*df.drop_duplicates()*](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop_duplicates.html), which can receive a list of columns to be considered for identifying duplicates (otherwise, it uses all the columns)."
+    "In this case there not duplicates. In case we would needed, we could have removed them with [*df.drop_duplicates()*](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop_duplicates.html), which can receive a list of columns to be considered for identifying duplicates (otherwise, it uses all the columns)."
    ]
   },
   {
@@ -597,7 +596,7 @@
    "source": [
     "Here we check how many null values there are.\n",
     "\n",
-    "We use sum() instead of count(), or we would get the total number of records. Notice how we do not use size() now, either. You can print 'df.isnull()' and will see a DataFrame with boolean values."
+    "We use sum() instead of count() or we would get the total number of records). Notice how we do not use size() now, either. You can print 'df.isnull()' and will see a DataFrame with boolean values."
    ]
   },
   {
@@ -626,7 +625,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Most of the samples have been deleted. We could have used [*dropna*](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.dropna.html) with the argument *how=all* that deletes a sample if all the values are missing, instead of the default *how=any*."
+    "Most of samples have been deleted. We could have used [*dropna*](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.dropna.html) with the argument *how=all* that deletes a sample if all the values are missing, instead of the default *how=any*."
    ]
   },
   {
@@ -654,13 +653,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Observe that the Passenger with 889 now has an Agent of 28 (median) instead of NaN. \n",
+    "Observe that the Passenger with 889 has now an Agent of 28 (median) instead of NaN. \n",
     "\n",
     "Regarding the column *cabins*, there are still NaN values, since the *Cabin* column is not numeric. We will see later how to change it.\n",
     "\n",
-    "In addition, we could drop rows with any or all null values (method *dropna()*).\n",
-    "\n",
-    "If we want to modify the* df* object directly, we should add the parameter *inplace* with value *True*."
+    "In addition, we could drop rows with any or all null values (method *dropna()*)."
    ]
   },
   {
@@ -669,7 +666,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df['Age'].fillna(df['Age'].mean(), inplace=True)\n",
+    "df['Age'] = df['Age'].fillna(df['Age'].mean())\n",
     "df[-5:]"
    ]
   },
@@ -700,7 +697,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# There are no labels for rows, so we use the numeric index\n",
+    "# There are not labels for rows, so we use the numeric index\n",
     "df.iloc[889]"
    ]
   },
@@ -762,7 +759,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df['Sex'].fillna('male', inplace=True)\n",
+    "df['Sex'] = df['Sex'].fillna('male')\n",
     "df[-5:]"
    ]
   },
@@ -780,27 +777,27 @@
    "metadata": {},
    "source": [
     "\n",
-    "**Scikit-learn** also provides a preprocessing facility for managing null values in the [**Imputer**](http://scikit-learn.org/stable/modules/preprocessing.html) class. We can include *Imputer* as a step in the *Pipeline*."
+    "**Scikit-learn** provides also a preprocessing facility for managing null values in the [**Imputer**](http://scikit-learn.org/stable/modules/preprocessing.html) class. We can include *Imputer* as a step in the *Pipeline*."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Analysing non-numerical columns"
+    "# Analysing non numerical columns"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we saw, we have several non-numerical columns: **Name**, **Sex**, **Ticket**, **Cabin**, and **Embarked**.\n",
+    "As we saw, we have several non numerical columns: **Name**, **Sex**, **Ticket**, **Cabin** and **Embarked**.\n",
     "\n",
     "**Name** and **Ticket** do not seem informative.\n",
     "\n",
     "Regarding **Cabin**, most values were missing, so we can ignore it. \n",
     "\n",
-    "**Sex** and **Embarked** are categorical features, so we will encode them as integers."
+    "**Sex** and **Embarked** are categorical features, so we will encode as integers."
    ]
   },
   {
@@ -811,7 +808,7 @@
    "source": [
     "# We remove Cabin and Ticket. We should specify the axis\n",
     "# Use axis 0 for dropping rows and axis 1 for dropping columns\n",
-    "df.drop(['Cabin', 'Ticket'], axis=1, inplace=True)\n",
+    "df = df.drop(['Cabin', 'Ticket'], axis=1)\n",
     "df[-5:]"
    ]
   },
@@ -826,7 +823,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "*Sex* has been codified as a categorical feature. It is better to encode features as continuous variables, since  scikit-learn estimators expect continuous input and would interpret the categories as ordered, which is not the case. "
+    "*Sex* has been codified as a categorical feature. It is better to encode features as continuous variables, since  scikit-learn estimators expect continuous input, and they would interpret the categories as being ordered, which is not the case. "
    ]
   },
   {
@@ -835,7 +832,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#First, we check if there are any null values. Observe the use of any()\n",
+    "#First we check if there is any null values. Observe the use of any()\n",
     "df['Sex'].isnull().any()"
    ]
   },
@@ -862,17 +859,40 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df[\"Sex\"] = df["\Sex\"].map({\"male\": 0, \"female\": 1})\n",
+    "df[\"Sex\"] = df[\"Sex\"].map({\"male\": 0, \"female\": 1})\n",
     "df[-5:]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#An alternative is to create a new column with the encoded values and define a mapping\n",
+    "df.loc[df[\"Sex\"] == \"male\", \"Sex\"] = 0\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#An alternative is to create a new column with the encoded valuesm and define a mapping\n",
     "df = df_original.copy()\n",
     "df['Gender'] = df['Sex'].map( {'male': 0, 'female': 1} ).astype(int)\n",
     "df.head()"
@@ -926,7 +946,7 @@
    "outputs": [],
    "source": [
     "#Replace nulls with the most common value\n",
-    "df['Embarked'].fillna('S', inplace=True)\n",
+    "df['Embarked'] = df['Embarked'].fillna('S')\n",
     "df['Embarked'].isnull().any()"
    ]
   },
@@ -936,13 +956,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Now we replace, as previously, the categories with integers\n",
-    "df.loc[df[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n",
-    "df.loc[df[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n",
-    "df.loc[df[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n",
+    "# Now we replace as previously the categories with integers\n",
+    "df[\"Embarked\"] = df[\"Embarked\"].map({\"S\": 0, \"C\": 1, \"Q\": 2})\n",
     "df[-5:]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -984,7 +1009,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
+    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
     "\n",
     "©  Carlos A. Iglesias, Universidad Politécnica de Madrid."
    ]
@@ -1006,7 +1031,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.12.2"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
@@ -1027,5 +1052,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }