From 03888d936d690568ce855cb6c3d57365720b4b13 Mon Sep 17 00:00:00 2001 From: pryn-kb Date: Mon, 2 Sep 2024 12:23:13 +0000 Subject: [PATCH] deploy: 9996be848b57df49fa81b4f20769b48de0668609 --- _sources/docs/pandas/03_Pandas_subsets.ipynb | 262 ++++++++++++++----- _sources/docs/pandas/03_pandas_subsets.ipynb | 262 ++++++++++++++----- _to_be_deleted_later/notebooks.html | 2 +- docs/pandas/02_Pandas_tabular_data.html | 2 +- docs/pandas/03_Pandas_subsets.html | 231 ++++++++++------ docs/pandas/03_pandas_subsets.html | 229 ++++++++++------ docs/pandas/04-Variables.html | 18 +- docs/pandas/04_Pandas_loc_iloc.html | 2 +- searchindex.js | 2 +- 9 files changed, 724 insertions(+), 286 deletions(-) diff --git a/_sources/docs/pandas/03_Pandas_subsets.ipynb b/_sources/docs/pandas/03_Pandas_subsets.ipynb index 1349e294..159e37a7 100644 --- a/_sources/docs/pandas/03_Pandas_subsets.ipynb +++ b/_sources/docs/pandas/03_Pandas_subsets.ipynb @@ -820,7 +820,7 @@ "id": "975963cb-241a-4036-b90e-c3c1290511a4", "metadata": {}, "source": [ - "Now that you’ve learned how to select specific columns from a DataFrame, it’s time to explore more advanced and flexible ways to access and manipulate your data. As you start filtering rows based on conditions or need to select both rows and columns simultaneously, using `.loc[]` and `.iloc[]` will give you greater control and clarity in your code.\n", + "Now that you’ve learned how to select specific columns from a `DataFrame`, it’s time to explore more advanced and flexible ways to access and manipulate your data. As you start filtering rows based on conditions or need to select both rows and columns simultaneously, using `.loc[]` and `.iloc[]` will give you greater control and clarity in your code.\n", "\n", "These powerful tools allow you to:\n", "\n", @@ -857,9 +857,9 @@ "\n", "**Why is this a bad idea?**\n", "\n", - "- **Ambiguity**: It’s not always clear if you're modifying a copy of your data or the original DataFrame, which can lead to unintended side effects.\n", - "- **Potential Errors**: Using square brackets for complex selections might trigger warnings like `SettingWithCopyWarning`, indicating that your code might not be doing what you think it is.\n", - "- **Lack of Flexibility**: As your data operations become more complex, relying on square brackets can limit your ability to write clear and maintainable code.\n", + "- **Ambiguity**: It’s not always clear if you're modifying a copy of your data or the original `DataFrame`, which can lead to unintended side effects.\n", + "- **Potential errors**: Using square brackets for complex selections might trigger warnings like `SettingWithCopyWarning`, indicating that your code might not be doing what you think it is.\n", + "- **Lack of flexibility**: As your data operations become more complex, relying on square brackets can limit your ability to write clear and maintainable code.\n", "\n", "For these reasons, we recommend using `.loc[]` and `.iloc[]` as they provide a more reliable and explicit way to handle data selection and modification in Pandas.\n", "\n", @@ -886,7 +886,7 @@ "tags": [] }, "source": [ - "### Filtering rows based on conditional expressions\n", + "## Filtering rows based on conditional expressions\n", "\n", "![Filtering specific rows from a DataFrame](images/03_subset_rows.svg)" ] @@ -896,7 +896,7 @@ "id": "7ece850f-2344-49d6-8dfb-6fed8c58f18c", "metadata": {}, "source": [ - "With `.loc[]` and `.iloc[]`, you have more control and flexibility when filtering rows and selecting columns in a DataFrame.\n", + "With `.loc[]` and `.iloc[]`, you have more control and flexibility when filtering rows and selecting columns in a `DataFrame`.\n", "\n", "Let’s start by exploring how you can use these tools to filter rows based on conditions." ] @@ -906,7 +906,7 @@ "id": "903513f6-4ba8-4a19-850f-918b5dd97b21", "metadata": {}, "source": [ - "#### Filtering specific rows with `.loc[]`\n", + "### Filtering specific rows with `.loc[]`\n", "\n", "To filter rows based on a condition, you can place the condition inside the `.loc[]` selector.\n", "\n", @@ -1198,7 +1198,7 @@ "tags": [] }, "source": [ - "This command filters the rows where the condition `titanic['Age'] > 35` is `True`, and returns the entire DataFrame for those rows.\n", + "This command filters the rows where the condition `titanic['Age'] > 35` is `True`, and returns the entire `DataFrame` for those rows.\n", "\n", "If you only want to select specific columns (e.g., `Name` and `Pclass`) for those filtered rows, you can add the column names after the comma:" ] @@ -1338,9 +1338,10 @@ "source": [ "```{admonition} When using loc/iloc\n", ":class: tip\n", - "The part *before the comma* is the *rows* you want\n", "\n", - "The part *after the comma* is the *columns* you want to select\n", + "The part *before the comma* is the *rows* you want to select.\n", + "\n", + "The part *after the comma* is the *columns* you want to select.\n", "```" ] }, @@ -1349,7 +1350,7 @@ "id": "39b84234-a6f9-48c8-82a6-e9515d0afa7e", "metadata": {}, "source": [ - "#### Using `.iloc[]` for positional indexing\n", + "### Using `.iloc[]` for positional indexing\n", "\n", "The `.iloc[]` method works similarly to `.loc[]`, but it selects rows and columns based on their integer positions rather than labels.\n", "\n", @@ -1456,7 +1457,7 @@ "tags": [] }, "source": [ - "### Inclusive vs. Exclusive Slicing\n", + "## Inclusive vs. Exclusive Slicing\n", "\n", "When slicing with `.loc[]`, the slicing is **inclusive** of the start and end labels. For example:" ] @@ -1631,19 +1632,27 @@ "titanic.iloc[0:3, 0:2]" ] }, + { + "cell_type": "markdown", + "id": "e0be5523-1168-4a3d-be4c-0cea81a4a2cc", + "metadata": {}, + "source": [ + "## Using `.loc[]` and `.iloc[]` for complex conditions" + ] + }, { "cell_type": "markdown", "id": "b48d6163-81a7-4bea-9fa8-cbdab2288cb5", "metadata": {}, "source": [ - "### Using `.loc[]` for complex conditions\n", + "You can use both `.loc[]` and `.iloc[]` to filter rows based on more complex conditions, combining multiple criteria with logical operators.\n", "\n", - "You can also use `.loc[]` to filter rows based on more complex conditions, combining multiple criteria with logical operators. For instance, to filter rows where `Pclass` is either 2 or 3:" + "For instance, to filter rows where `Pclass` is either 2 or 3 using `.loc[]`:" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 27, "id": "0e5b553f-8987-4f9a-90af-30a3ac194a88", "metadata": { "tags": [ @@ -1887,7 +1896,7 @@ "[675 rows x 12 columns]" ] }, - "execution_count": 19, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1898,17 +1907,25 @@ "titanic.loc[titanic['Pclass'].isin([2, 3])]" ] }, + { + "cell_type": "markdown", + "id": "3db35580-9be7-4c17-9d1d-975da8faab2f", + "metadata": {}, + "source": [ + "Similarly, if you want to perform position-based filtering using `.iloc[]`, you would first need to identify the positions of the relevant rows and columns. However, `.iloc[]` is typically less useful for condition-based filtering because it requires knowledge of the exact row and column positions rather than labels." + ] + }, { "cell_type": "markdown", "id": "5b2120f5-0c03-41a4-8b98-4fbfb43ba993", "metadata": {}, "source": [ - "You can combine multiple conditions using logical operators like `&` (AND) and `|` (OR):" + "You can also combine multiple conditions using logical operators like `&` (AND) and `|` (OR):" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 26, "id": "a6ec973c-37e0-4aa7-bd61-ad4ef508206b", "metadata": { "tags": [ @@ -1943,11 +1960,6 @@ " \n", " \n", " \n", - " 9\n", - " Nasser, Mrs. Nicholas (Adele Achem)\n", - " 2\n", - " \n", - " \n", " 13\n", " Andersson, Mr. Anders Johan\n", " 3\n", @@ -1958,76 +1970,90 @@ " 2\n", " \n", " \n", - " 17\n", - " Williams, Mr. Charles Eugene\n", - " 2\n", + " 25\n", + " Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...\n", + " 3\n", " \n", " \n", - " 20\n", - " Fynney, Mr. Joseph J\n", + " 33\n", + " Wheadon, Mr. Edward H\n", " 2\n", " \n", " \n", + " 40\n", + " Ahlin, Mrs. Johan (Johanna Persdotter Larsson)\n", + " 3\n", + " \n", + " \n", " ...\n", " ...\n", " ...\n", " \n", " \n", - " 874\n", - " Abelson, Mrs. Samuel (Hannah Wizosky)\n", + " 854\n", + " Carter, Mrs. Ernest Courtenay (Lilian Hughes)\n", " 2\n", " \n", " \n", - " 880\n", - " Shelley, Mrs. William (Imanita Parrish Hall)\n", - " 2\n", + " 860\n", + " Hansen, Mr. Claus Peter\n", + " 3\n", " \n", " \n", - " 883\n", - " Banfield, Mr. Frederick James\n", + " 865\n", + " Bystrom, Mrs. (Karolina)\n", " 2\n", " \n", " \n", - " 885\n", - " Rice, Mrs. William (Margaret Norton)\n", + " 873\n", + " Vander Cruyssen, Mr. Victor\n", " 3\n", " \n", " \n", - " 886\n", - " Montvila, Rev. Juozas\n", - " 2\n", + " 885\n", + " Rice, Mrs. William (Margaret Norton)\n", + " 3\n", " \n", " \n", "\n", - "

247 rows × 2 columns

\n", + "

113 rows × 2 columns

\n", "" ], "text/plain": [ - " Name Pclass\n", - "9 Nasser, Mrs. Nicholas (Adele Achem) 2\n", - "13 Andersson, Mr. Anders Johan 3\n", - "15 Hewlett, Mrs. (Mary D Kingcome) 2\n", - "17 Williams, Mr. Charles Eugene 2\n", - "20 Fynney, Mr. Joseph J 2\n", - ".. ... ...\n", - "874 Abelson, Mrs. Samuel (Hannah Wizosky) 2\n", - "880 Shelley, Mrs. William (Imanita Parrish Hall) 2\n", - "883 Banfield, Mr. Frederick James 2\n", - "885 Rice, Mrs. William (Margaret Norton) 3\n", - "886 Montvila, Rev. Juozas 2\n", + " Name Pclass\n", + "13 Andersson, Mr. Anders Johan 3\n", + "15 Hewlett, Mrs. (Mary D Kingcome) 2\n", + "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... 3\n", + "33 Wheadon, Mr. Edward H 2\n", + "40 Ahlin, Mrs. Johan (Johanna Persdotter Larsson) 3\n", + ".. ... ...\n", + "854 Carter, Mrs. Ernest Courtenay (Lilian Hughes) 2\n", + "860 Hansen, Mr. Claus Peter 3\n", + "865 Bystrom, Mrs. (Karolina) 2\n", + "873 Vander Cruyssen, Mr. Victor 3\n", + "885 Rice, Mrs. William (Margaret Norton) 3\n", "\n", - "[247 rows x 2 columns]" + "[113 rows x 2 columns]" ] }, - "execution_count": 20, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Filter rows where 'Pclass' is 2 or 3 and 'Age' is greater than 35, and select 'Name' and 'Pclass'.\n", + "# Filter rows where ('Pclass' is either 2 or 3) AND ('Age' is greater than 35),\n", + "# and then select the 'Name' and 'Pclass' columns.\n", "\n", - "titanic.loc[(titanic['Pclass'] == 2) | (titanic['Pclass'] == 3) & (titanic['Age'] > 35), ['Name', 'Pclass']]" + "titanic.loc[((titanic['Pclass'] == 2) | (titanic['Pclass'] == 3)) & (titanic['Age'] > 35), ['Name', 'Pclass']]" + ] + }, + { + "cell_type": "markdown", + "id": "da9cb04b-4246-4c5f-be72-bf8f85fa0a5b", + "metadata": {}, + "source": [ + "## Handling missing values with `.loc[]` and `.iloc[]`" ] }, { @@ -2035,9 +2061,10 @@ "id": "406599e3-1a91-4e45-b609-cf6f3e1f7cc0", "metadata": {}, "source": [ - "### Handling missing values with `.loc[]`\n", + "Both `.loc[]` and `.iloc[]` allow you to filter rows based on the presence or absence of missing values.\\\n", + "(However, `.loc[]` is generally more convenient when working with labeled data, while `.iloc[]` requires knowing the exact positions.)\n", "\n", - "The `.loc[]` method also allows you to filter rows based on the presence or absence of missing values. For example, to filter rows where the `Embarked` column is not null:" + "For example, to filter rows where the `Embarked` column is not null using `.loc[]`:" ] }, { @@ -2360,6 +2387,115 @@ "titanic.info()" ] }, + { + "cell_type": "markdown", + "id": "47535c74-e158-4a89-9bc9-a1cea1aa70df", + "metadata": {}, + "source": [ + "Comparing the two number of rows (889 and 891) tells us, that two rows are missing embarkation data.\n", + "\n", + "As we saw in the [exercise](02_pandas_tabular_data.md#exercise) on the previous page on reading and writing tabular data, the two rows missing embarkation data can be found like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "e4a29062-fc16-4519-bde3-ad8f802a0b14", + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Name \\\n", + "61 62 1 1 Icard, Miss. Amelie \n", + "829 830 1 1 Stone, Mrs. George Nelson (Martha Evelyn) \n", + "\n", + " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", + "61 female 38.0 0 0 113572 80.0 B28 NaN \n", + "829 female 62.0 0 0 113572 80.0 B28 NaN " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic[titanic['Embarked'].isna()]" + ] + }, { "cell_type": "markdown", "id": "c50b13ad-3a6a-4162-ac04-fc0aa86da086", @@ -2385,7 +2521,7 @@ "tags": [] }, "source": [ - "### Selecting rows and columns\n", + "## Selecting rows and columns\n", "\n", "![Selecting specific rows and columns from a DataFrame](images/03_subset_columns_rows.svg)" ] @@ -2537,7 +2673,7 @@ "id": "0ee5aef8-30b9-4e07-ad18-d938822d9ad2", "metadata": {}, "source": [ - "### Combining `loc` and `iloc`" + "## Combining `loc` and `iloc`" ] }, { @@ -2645,7 +2781,7 @@ "tags": [] }, "source": [ - "### Summary\n", + "## Summary\n", "\n", "Using `.loc[]` and `.iloc[]` allows you to write clearer, more precise code for selecting and manipulating data in Pandas. Whether you are filtering rows based on complex conditions or selecting specific rows and columns simultaneously, these tools offer the flexibility and reliability you need for more advanced data manipulation tasks." ] @@ -2687,7 +2823,7 @@ " * Slicing with `.loc[]` is inclusive, while slicing with `.iloc[]` is exclusive.\n", "\n", "* **Filtering data**:\n", - " * Filter rows based on conditions using `.loc[]`, and apply additional conditions or select specific columns within the same operation." + " * Filter rows based on conditions using `.loc[]` and `.iloc[]`, and apply additional conditions or select specific columns within the same operation." ] }, { diff --git a/_sources/docs/pandas/03_pandas_subsets.ipynb b/_sources/docs/pandas/03_pandas_subsets.ipynb index 1349e294..159e37a7 100644 --- a/_sources/docs/pandas/03_pandas_subsets.ipynb +++ b/_sources/docs/pandas/03_pandas_subsets.ipynb @@ -820,7 +820,7 @@ "id": "975963cb-241a-4036-b90e-c3c1290511a4", "metadata": {}, "source": [ - "Now that you’ve learned how to select specific columns from a DataFrame, it’s time to explore more advanced and flexible ways to access and manipulate your data. As you start filtering rows based on conditions or need to select both rows and columns simultaneously, using `.loc[]` and `.iloc[]` will give you greater control and clarity in your code.\n", + "Now that you’ve learned how to select specific columns from a `DataFrame`, it’s time to explore more advanced and flexible ways to access and manipulate your data. As you start filtering rows based on conditions or need to select both rows and columns simultaneously, using `.loc[]` and `.iloc[]` will give you greater control and clarity in your code.\n", "\n", "These powerful tools allow you to:\n", "\n", @@ -857,9 +857,9 @@ "\n", "**Why is this a bad idea?**\n", "\n", - "- **Ambiguity**: It’s not always clear if you're modifying a copy of your data or the original DataFrame, which can lead to unintended side effects.\n", - "- **Potential Errors**: Using square brackets for complex selections might trigger warnings like `SettingWithCopyWarning`, indicating that your code might not be doing what you think it is.\n", - "- **Lack of Flexibility**: As your data operations become more complex, relying on square brackets can limit your ability to write clear and maintainable code.\n", + "- **Ambiguity**: It’s not always clear if you're modifying a copy of your data or the original `DataFrame`, which can lead to unintended side effects.\n", + "- **Potential errors**: Using square brackets for complex selections might trigger warnings like `SettingWithCopyWarning`, indicating that your code might not be doing what you think it is.\n", + "- **Lack of flexibility**: As your data operations become more complex, relying on square brackets can limit your ability to write clear and maintainable code.\n", "\n", "For these reasons, we recommend using `.loc[]` and `.iloc[]` as they provide a more reliable and explicit way to handle data selection and modification in Pandas.\n", "\n", @@ -886,7 +886,7 @@ "tags": [] }, "source": [ - "### Filtering rows based on conditional expressions\n", + "## Filtering rows based on conditional expressions\n", "\n", "![Filtering specific rows from a DataFrame](images/03_subset_rows.svg)" ] @@ -896,7 +896,7 @@ "id": "7ece850f-2344-49d6-8dfb-6fed8c58f18c", "metadata": {}, "source": [ - "With `.loc[]` and `.iloc[]`, you have more control and flexibility when filtering rows and selecting columns in a DataFrame.\n", + "With `.loc[]` and `.iloc[]`, you have more control and flexibility when filtering rows and selecting columns in a `DataFrame`.\n", "\n", "Let’s start by exploring how you can use these tools to filter rows based on conditions." ] @@ -906,7 +906,7 @@ "id": "903513f6-4ba8-4a19-850f-918b5dd97b21", "metadata": {}, "source": [ - "#### Filtering specific rows with `.loc[]`\n", + "### Filtering specific rows with `.loc[]`\n", "\n", "To filter rows based on a condition, you can place the condition inside the `.loc[]` selector.\n", "\n", @@ -1198,7 +1198,7 @@ "tags": [] }, "source": [ - "This command filters the rows where the condition `titanic['Age'] > 35` is `True`, and returns the entire DataFrame for those rows.\n", + "This command filters the rows where the condition `titanic['Age'] > 35` is `True`, and returns the entire `DataFrame` for those rows.\n", "\n", "If you only want to select specific columns (e.g., `Name` and `Pclass`) for those filtered rows, you can add the column names after the comma:" ] @@ -1338,9 +1338,10 @@ "source": [ "```{admonition} When using loc/iloc\n", ":class: tip\n", - "The part *before the comma* is the *rows* you want\n", "\n", - "The part *after the comma* is the *columns* you want to select\n", + "The part *before the comma* is the *rows* you want to select.\n", + "\n", + "The part *after the comma* is the *columns* you want to select.\n", "```" ] }, @@ -1349,7 +1350,7 @@ "id": "39b84234-a6f9-48c8-82a6-e9515d0afa7e", "metadata": {}, "source": [ - "#### Using `.iloc[]` for positional indexing\n", + "### Using `.iloc[]` for positional indexing\n", "\n", "The `.iloc[]` method works similarly to `.loc[]`, but it selects rows and columns based on their integer positions rather than labels.\n", "\n", @@ -1456,7 +1457,7 @@ "tags": [] }, "source": [ - "### Inclusive vs. Exclusive Slicing\n", + "## Inclusive vs. Exclusive Slicing\n", "\n", "When slicing with `.loc[]`, the slicing is **inclusive** of the start and end labels. For example:" ] @@ -1631,19 +1632,27 @@ "titanic.iloc[0:3, 0:2]" ] }, + { + "cell_type": "markdown", + "id": "e0be5523-1168-4a3d-be4c-0cea81a4a2cc", + "metadata": {}, + "source": [ + "## Using `.loc[]` and `.iloc[]` for complex conditions" + ] + }, { "cell_type": "markdown", "id": "b48d6163-81a7-4bea-9fa8-cbdab2288cb5", "metadata": {}, "source": [ - "### Using `.loc[]` for complex conditions\n", + "You can use both `.loc[]` and `.iloc[]` to filter rows based on more complex conditions, combining multiple criteria with logical operators.\n", "\n", - "You can also use `.loc[]` to filter rows based on more complex conditions, combining multiple criteria with logical operators. For instance, to filter rows where `Pclass` is either 2 or 3:" + "For instance, to filter rows where `Pclass` is either 2 or 3 using `.loc[]`:" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 27, "id": "0e5b553f-8987-4f9a-90af-30a3ac194a88", "metadata": { "tags": [ @@ -1887,7 +1896,7 @@ "[675 rows x 12 columns]" ] }, - "execution_count": 19, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1898,17 +1907,25 @@ "titanic.loc[titanic['Pclass'].isin([2, 3])]" ] }, + { + "cell_type": "markdown", + "id": "3db35580-9be7-4c17-9d1d-975da8faab2f", + "metadata": {}, + "source": [ + "Similarly, if you want to perform position-based filtering using `.iloc[]`, you would first need to identify the positions of the relevant rows and columns. However, `.iloc[]` is typically less useful for condition-based filtering because it requires knowledge of the exact row and column positions rather than labels." + ] + }, { "cell_type": "markdown", "id": "5b2120f5-0c03-41a4-8b98-4fbfb43ba993", "metadata": {}, "source": [ - "You can combine multiple conditions using logical operators like `&` (AND) and `|` (OR):" + "You can also combine multiple conditions using logical operators like `&` (AND) and `|` (OR):" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 26, "id": "a6ec973c-37e0-4aa7-bd61-ad4ef508206b", "metadata": { "tags": [ @@ -1943,11 +1960,6 @@ " \n", " \n", " \n", - " 9\n", - " Nasser, Mrs. Nicholas (Adele Achem)\n", - " 2\n", - " \n", - " \n", " 13\n", " Andersson, Mr. Anders Johan\n", " 3\n", @@ -1958,76 +1970,90 @@ " 2\n", " \n", " \n", - " 17\n", - " Williams, Mr. Charles Eugene\n", - " 2\n", + " 25\n", + " Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...\n", + " 3\n", " \n", " \n", - " 20\n", - " Fynney, Mr. Joseph J\n", + " 33\n", + " Wheadon, Mr. Edward H\n", " 2\n", " \n", " \n", + " 40\n", + " Ahlin, Mrs. Johan (Johanna Persdotter Larsson)\n", + " 3\n", + " \n", + " \n", " ...\n", " ...\n", " ...\n", " \n", " \n", - " 874\n", - " Abelson, Mrs. Samuel (Hannah Wizosky)\n", + " 854\n", + " Carter, Mrs. Ernest Courtenay (Lilian Hughes)\n", " 2\n", " \n", " \n", - " 880\n", - " Shelley, Mrs. William (Imanita Parrish Hall)\n", - " 2\n", + " 860\n", + " Hansen, Mr. Claus Peter\n", + " 3\n", " \n", " \n", - " 883\n", - " Banfield, Mr. Frederick James\n", + " 865\n", + " Bystrom, Mrs. (Karolina)\n", " 2\n", " \n", " \n", - " 885\n", - " Rice, Mrs. William (Margaret Norton)\n", + " 873\n", + " Vander Cruyssen, Mr. Victor\n", " 3\n", " \n", " \n", - " 886\n", - " Montvila, Rev. Juozas\n", - " 2\n", + " 885\n", + " Rice, Mrs. William (Margaret Norton)\n", + " 3\n", " \n", " \n", "\n", - "

247 rows × 2 columns

\n", + "

113 rows × 2 columns

\n", "" ], "text/plain": [ - " Name Pclass\n", - "9 Nasser, Mrs. Nicholas (Adele Achem) 2\n", - "13 Andersson, Mr. Anders Johan 3\n", - "15 Hewlett, Mrs. (Mary D Kingcome) 2\n", - "17 Williams, Mr. Charles Eugene 2\n", - "20 Fynney, Mr. Joseph J 2\n", - ".. ... ...\n", - "874 Abelson, Mrs. Samuel (Hannah Wizosky) 2\n", - "880 Shelley, Mrs. William (Imanita Parrish Hall) 2\n", - "883 Banfield, Mr. Frederick James 2\n", - "885 Rice, Mrs. William (Margaret Norton) 3\n", - "886 Montvila, Rev. Juozas 2\n", + " Name Pclass\n", + "13 Andersson, Mr. Anders Johan 3\n", + "15 Hewlett, Mrs. (Mary D Kingcome) 2\n", + "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... 3\n", + "33 Wheadon, Mr. Edward H 2\n", + "40 Ahlin, Mrs. Johan (Johanna Persdotter Larsson) 3\n", + ".. ... ...\n", + "854 Carter, Mrs. Ernest Courtenay (Lilian Hughes) 2\n", + "860 Hansen, Mr. Claus Peter 3\n", + "865 Bystrom, Mrs. (Karolina) 2\n", + "873 Vander Cruyssen, Mr. Victor 3\n", + "885 Rice, Mrs. William (Margaret Norton) 3\n", "\n", - "[247 rows x 2 columns]" + "[113 rows x 2 columns]" ] }, - "execution_count": 20, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Filter rows where 'Pclass' is 2 or 3 and 'Age' is greater than 35, and select 'Name' and 'Pclass'.\n", + "# Filter rows where ('Pclass' is either 2 or 3) AND ('Age' is greater than 35),\n", + "# and then select the 'Name' and 'Pclass' columns.\n", "\n", - "titanic.loc[(titanic['Pclass'] == 2) | (titanic['Pclass'] == 3) & (titanic['Age'] > 35), ['Name', 'Pclass']]" + "titanic.loc[((titanic['Pclass'] == 2) | (titanic['Pclass'] == 3)) & (titanic['Age'] > 35), ['Name', 'Pclass']]" + ] + }, + { + "cell_type": "markdown", + "id": "da9cb04b-4246-4c5f-be72-bf8f85fa0a5b", + "metadata": {}, + "source": [ + "## Handling missing values with `.loc[]` and `.iloc[]`" ] }, { @@ -2035,9 +2061,10 @@ "id": "406599e3-1a91-4e45-b609-cf6f3e1f7cc0", "metadata": {}, "source": [ - "### Handling missing values with `.loc[]`\n", + "Both `.loc[]` and `.iloc[]` allow you to filter rows based on the presence or absence of missing values.\\\n", + "(However, `.loc[]` is generally more convenient when working with labeled data, while `.iloc[]` requires knowing the exact positions.)\n", "\n", - "The `.loc[]` method also allows you to filter rows based on the presence or absence of missing values. For example, to filter rows where the `Embarked` column is not null:" + "For example, to filter rows where the `Embarked` column is not null using `.loc[]`:" ] }, { @@ -2360,6 +2387,115 @@ "titanic.info()" ] }, + { + "cell_type": "markdown", + "id": "47535c74-e158-4a89-9bc9-a1cea1aa70df", + "metadata": {}, + "source": [ + "Comparing the two number of rows (889 and 891) tells us, that two rows are missing embarkation data.\n", + "\n", + "As we saw in the [exercise](02_pandas_tabular_data.md#exercise) on the previous page on reading and writing tabular data, the two rows missing embarkation data can be found like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "e4a29062-fc16-4519-bde3-ad8f802a0b14", + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
616211Icard, Miss. Ameliefemale38.00011357280.0B28NaN
82983011Stone, Mrs. George Nelson (Martha Evelyn)female62.00011357280.0B28NaN
\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass Name \\\n", + "61 62 1 1 Icard, Miss. Amelie \n", + "829 830 1 1 Stone, Mrs. George Nelson (Martha Evelyn) \n", + "\n", + " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", + "61 female 38.0 0 0 113572 80.0 B28 NaN \n", + "829 female 62.0 0 0 113572 80.0 B28 NaN " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic[titanic['Embarked'].isna()]" + ] + }, { "cell_type": "markdown", "id": "c50b13ad-3a6a-4162-ac04-fc0aa86da086", @@ -2385,7 +2521,7 @@ "tags": [] }, "source": [ - "### Selecting rows and columns\n", + "## Selecting rows and columns\n", "\n", "![Selecting specific rows and columns from a DataFrame](images/03_subset_columns_rows.svg)" ] @@ -2537,7 +2673,7 @@ "id": "0ee5aef8-30b9-4e07-ad18-d938822d9ad2", "metadata": {}, "source": [ - "### Combining `loc` and `iloc`" + "## Combining `loc` and `iloc`" ] }, { @@ -2645,7 +2781,7 @@ "tags": [] }, "source": [ - "### Summary\n", + "## Summary\n", "\n", "Using `.loc[]` and `.iloc[]` allows you to write clearer, more precise code for selecting and manipulating data in Pandas. Whether you are filtering rows based on complex conditions or selecting specific rows and columns simultaneously, these tools offer the flexibility and reliability you need for more advanced data manipulation tasks." ] @@ -2687,7 +2823,7 @@ " * Slicing with `.loc[]` is inclusive, while slicing with `.iloc[]` is exclusive.\n", "\n", "* **Filtering data**:\n", - " * Filter rows based on conditions using `.loc[]`, and apply additional conditions or select specific columns within the same operation." + " * Filter rows based on conditions using `.loc[]` and `.iloc[]`, and apply additional conditions or select specific columns within the same operation." ] }, { diff --git a/_to_be_deleted_later/notebooks.html b/_to_be_deleted_later/notebooks.html index 37c27559..e9a983bf 100644 --- a/_to_be_deleted_later/notebooks.html +++ b/_to_be_deleted_later/notebooks.html @@ -478,7 +478,7 @@

Code blocks and outputs
-
<contextlib.ExitStack at 0x7f8a643c42c0>
+
<contextlib.ExitStack at 0x7f00d939d040>
diff --git a/docs/pandas/02_Pandas_tabular_data.html b/docs/pandas/02_Pandas_tabular_data.html index d5c77f1c..9312e3b9 100644 --- a/docs/pandas/02_Pandas_tabular_data.html +++ b/docs/pandas/02_Pandas_tabular_data.html @@ -32,7 +32,7 @@ - + diff --git a/docs/pandas/03_Pandas_subsets.html b/docs/pandas/03_Pandas_subsets.html index e8c65aa4..5e87beb8 100644 --- a/docs/pandas/03_Pandas_subsets.html +++ b/docs/pandas/03_Pandas_subsets.html @@ -32,7 +32,7 @@ - + @@ -421,20 +421,18 @@

Contents

  • Creating a DataFrame from a CSV file
  • Selecting specific columns
  • Calling multiple Series
    • -
  • Introducing .loc[] and .iloc[] @@ -920,7 +918,7 @@

    Calling multiple

    Introducing .loc[] and .iloc[]#

    -

    Now that you’ve learned how to select specific columns from a DataFrame, it’s time to explore more advanced and flexible ways to access and manipulate your data. As you start filtering rows based on conditions or need to select both rows and columns simultaneously, using .loc[] and .iloc[] will give you greater control and clarity in your code.

    +

    Now that you’ve learned how to select specific columns from a DataFrame, it’s time to explore more advanced and flexible ways to access and manipulate your data. As you start filtering rows based on conditions or need to select both rows and columns simultaneously, using .loc[] and .iloc[] will give you greater control and clarity in your code.

    These powerful tools allow you to:

    • Safely and explicitly select rows and columns by labels with .loc[].

      • @@ -938,21 +936,22 @@

      Introducing .lo

      Some of the operations we’ll be covering can technically be performed without using .loc[] or .iloc[]. For instance, you might see data selections or modifications done with simple square brackets like df[df['column'] > value]. While this can work for basic tasks, it can lead to issues in more complex scenarios, such as unexpected behavior, ambiguity, or performance inefficiencies.

      Why is this a bad idea?

        -

      • Ambiguity: It’s not always clear if you’re modifying a copy of your data or the original DataFrame, which can lead to unintended side effects.

        • -

      • Potential Errors: Using square brackets for complex selections might trigger warnings like SettingWithCopyWarning, indicating that your code might not be doing what you think it is.

        • -

      • Lack of Flexibility: As your data operations become more complex, relying on square brackets can limit your ability to write clear and maintainable code.

        • +

      • Ambiguity: It’s not always clear if you’re modifying a copy of your data or the original DataFrame, which can lead to unintended side effects.

        • +

      • Potential errors: Using square brackets for complex selections might trigger warnings like SettingWithCopyWarning, indicating that your code might not be doing what you think it is.

        • +

      • Lack of flexibility: As your data operations become more complex, relying on square brackets can limit your ability to write clear and maintainable code.

      For these reasons, we recommend using .loc[] and .iloc[] as they provide a more reliable and explicit way to handle data selection and modification in Pandas.

      If you’re interested in learning more about the potential pitfalls of using square brackets and the benefits of .loc[] and .iloc[], you can read more in the Pandas documentation on indexing and selecting data.

    • +
    -

    Filtering rows based on conditional expressions#

    +

    Filtering rows based on conditional expressions#

    Filtering specific rows from a DataFrame

    -

    With .loc[] and .iloc[], you have more control and flexibility when filtering rows and selecting columns in a DataFrame.

    +

    With .loc[] and .iloc[], you have more control and flexibility when filtering rows and selecting columns in a DataFrame.

    Let’s start by exploring how you can use these tools to filter rows based on conditions.

    -

    Filtering specific rows with .loc[]#

    +

    Filtering specific rows with .loc[]#

    To filter rows based on a condition, you can place the condition inside the .loc[] selector.

    Here’s how you can filter rows where the Age column is greater than 35:

    @@ -1173,7 +1172,7 @@

    Filtering specific rows with

    -

    This command filters the rows where the condition titanic['Age'] > 35 is True, and returns the entire DataFrame for those rows.

    +

    This command filters the rows where the condition titanic['Age'] > 35 is True, and returns the entire DataFrame for those rows.

    If you only want to select specific columns (e.g., Name and Pclass) for those filtered rows, you can add the column names after the comma:

    @@ -1275,12 +1274,12 @@

    Filtering specific rows with

    When using loc/iloc

    -

    The part before the comma is the rows you want

    -

    The part after the comma is the columns you want to select

    +

    The part before the comma is the rows you want to select.

    +

    The part after the comma is the columns you want to select.

    -

    Using .iloc[] for positional indexing#

    +

    Using .iloc[] for positional indexing#

    The .iloc[] method works similarly to .loc[], but it selects rows and columns based on their integer positions rather than labels.

    For example, to select the first 5 rows and the first 2 columns, you can use:

    @@ -1353,7 +1352,7 @@

    Using .iloc[]

    -

    Inclusive vs. Exclusive Slicing#

    +

    Inclusive vs. Exclusive Slicing#

    When slicing with .loc[], the slicing is inclusive of the start and end labels. For example:

    @@ -1477,9 +1476,10 @@

    Inclusive vs. Exclusive Slicing -

    Using .loc[] for complex conditions#

    -

    You can also use .loc[] to filter rows based on more complex conditions, combining multiple criteria with logical operators. For instance, to filter rows where Pclass is either 2 or 3:

    +
    +

    Using .loc[] and .iloc[] for complex conditions#

    +

    You can use both .loc[] and .iloc[] to filter rows based on more complex conditions, combining multiple criteria with logical operators.

    +

    For instance, to filter rows where Pclass is either 2 or 3 using .loc[]:

    # Filter rows where 'Pclass' is either 2 or 3 using .loc[].
@@ -1698,12 +1698,14 @@ 
    Using .loc[]
    -

    You can combine multiple conditions using logical operators like & (AND) and | (OR):

    +

    Similarly, if you want to perform position-based filtering using .iloc[], you would first need to identify the positions of the relevant rows and columns. However, .iloc[] is typically less useful for condition-based filtering because it requires knowledge of the exact row and column positions rather than labels.

    +

    You can also combine multiple conditions using logical operators like & (AND) and | (OR):

    -
    # Filter rows where 'Pclass' is 2 or 3 and 'Age' is greater than 35, and select 'Name' and 'Pclass'.
+
    # Filter rows where ('Pclass' is either 2 or 3) AND ('Age' is greater than 35),
+# and then select the 'Name' and 'Pclass' columns.
 
-titanic.loc[(titanic['Pclass'] == 2) | (titanic['Pclass'] == 3) & (titanic['Age'] > 35), ['Name', 'Pclass']]
+titanic.loc[((titanic['Pclass'] == 2) | (titanic['Pclass'] == 3)) & (titanic['Age'] > 35), ['Name', 'Pclass']]
    @@ -1736,11 +1738,6 @@

    Using .loc[] - - 9 - Nasser, Mrs. Nicholas (Adele Achem) - 2 - 13 Andersson, Mr. Anders Johan @@ -1752,55 +1749,62 @@

    Using .loc[]2 - 17 - Williams, Mr. Charles Eugene - 2 + 25 + Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... + 3 - 20 - Fynney, Mr. Joseph J + 33 + Wheadon, Mr. Edward H 2 + + 40 + Ahlin, Mrs. Johan (Johanna Persdotter Larsson) + 3 + ... ... ... - 874 - Abelson, Mrs. Samuel (Hannah Wizosky) + 854 + Carter, Mrs. Ernest Courtenay (Lilian Hughes) 2 - 880 - Shelley, Mrs. William (Imanita Parrish Hall) - 2 + 860 + Hansen, Mr. Claus Peter + 3 - 883 - Banfield, Mr. Frederick James + 865 + Bystrom, Mrs. (Karolina) 2 - 885 - Rice, Mrs. William (Margaret Norton) + 873 + Vander Cruyssen, Mr. Victor 3 - 886 - Montvila, Rev. Juozas - 2 + 885 + Rice, Mrs. William (Margaret Norton) + 3 -

    247 rows × 2 columns

    +

    113 rows × 2 columns

    -
    -

    Handling missing values with .loc[]#

    -

    The .loc[] method also allows you to filter rows based on the presence or absence of missing values. For example, to filter rows where the Embarked column is not null:

    +
    +

    Handling missing values with .loc[] and .iloc[]#

    +

    Both .loc[] and .iloc[] allow you to filter rows based on the presence or absence of missing values.
    +(However, .loc[] is generally more convenient when working with labeled data, while .iloc[] requires knowing the exact positions.)

    +

    For example, to filter rows where the Embarked column is not null using .loc[]:

    # Filter rows where 'Embarked' is not null using .loc[].
@@ -2058,10 +2062,92 @@ 
    Handling missing values with
    +

    Comparing the two number of rows (889 and 891) tells us, that two rows are missing embarkation data.

    +

    As we saw in the exercise on the previous page on reading and writing tabular data, the two rows missing embarkation data can be found like this:

    +
    +
    +
    titanic[titanic['Embarked'].isna()]
+
    +
    +
    +
    + + +Hide code cell output + +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
    616211Icard, Miss. Ameliefemale38.00011357280.0B28NaN
    82983011Stone, Mrs. George Nelson (Martha Evelyn)female62.00011357280.0B28NaN
    +
    +
    +
    -

    Selecting rows and columns#

    +

    Selecting rows and columns#

    Selecting specific rows and columns from a DataFrame

    The true power of .loc[] becomes evident when you want to filter rows and simultaneously select specific columns. For example:

    @@ -2164,7 +2250,7 @@

    Selecting rows and columns -

    Combining loc and iloc#

    +

    Combining loc and iloc#

    We can use iloc to select:

    • Rows from index position 9 to 25 (exclusive).

      • @@ -2239,10 +2325,9 @@

      Combining loc

    -

    Summary#

    +

    Summary#

    Using .loc[] and .iloc[] allows you to write clearer, more precise code for selecting and manipulating data in Pandas. Whether you are filtering rows based on complex conditions or selecting specific rows and columns simultaneously, these tools offer the flexibility and reliability you need for more advanced data manipulation tasks.

    -

    Key points#

    @@ -2264,7 +2349,7 @@

    Key points.loc[], and apply additional conditions or select specific columns within the same operation.

    +

  • Filter rows based on conditions using .loc[] and .iloc[], and apply additional conditions or select specific columns within the same operation.

    • @@ -2339,20 +2424,18 @@

    Key pointsCreating a DataFrame from a CSV file
  • Selecting specific columns
  • Calling multiple Series
    • -
  • Introducing .loc[] and .iloc[]

    • diff --git a/docs/pandas/03_pandas_subsets.html b/docs/pandas/03_pandas_subsets.html index 78ada82d..4b62de18 100644 --- a/docs/pandas/03_pandas_subsets.html +++ b/docs/pandas/03_pandas_subsets.html @@ -423,20 +423,18 @@

    Contents

  • Creating a DataFrame from a CSV file
  • Selecting specific columns
  • Calling multiple Series
    • -
  • Introducing .loc[] and .iloc[] @@ -922,7 +920,7 @@

    Calling multiple

    Introducing .loc[] and .iloc[]#

    -

    Now that you’ve learned how to select specific columns from a DataFrame, it’s time to explore more advanced and flexible ways to access and manipulate your data. As you start filtering rows based on conditions or need to select both rows and columns simultaneously, using .loc[] and .iloc[] will give you greater control and clarity in your code.

    +

    Now that you’ve learned how to select specific columns from a DataFrame, it’s time to explore more advanced and flexible ways to access and manipulate your data. As you start filtering rows based on conditions or need to select both rows and columns simultaneously, using .loc[] and .iloc[] will give you greater control and clarity in your code.

    These powerful tools allow you to:

    • Safely and explicitly select rows and columns by labels with .loc[].

      • @@ -940,21 +938,22 @@

      Introducing .lo

      Some of the operations we’ll be covering can technically be performed without using .loc[] or .iloc[]. For instance, you might see data selections or modifications done with simple square brackets like df[df['column'] > value]. While this can work for basic tasks, it can lead to issues in more complex scenarios, such as unexpected behavior, ambiguity, or performance inefficiencies.

      Why is this a bad idea?

        -

      • Ambiguity: It’s not always clear if you’re modifying a copy of your data or the original DataFrame, which can lead to unintended side effects.

        • -

      • Potential Errors: Using square brackets for complex selections might trigger warnings like SettingWithCopyWarning, indicating that your code might not be doing what you think it is.

        • -

      • Lack of Flexibility: As your data operations become more complex, relying on square brackets can limit your ability to write clear and maintainable code.

        • +

      • Ambiguity: It’s not always clear if you’re modifying a copy of your data or the original DataFrame, which can lead to unintended side effects.

        • +

      • Potential errors: Using square brackets for complex selections might trigger warnings like SettingWithCopyWarning, indicating that your code might not be doing what you think it is.

        • +

      • Lack of flexibility: As your data operations become more complex, relying on square brackets can limit your ability to write clear and maintainable code.

      For these reasons, we recommend using .loc[] and .iloc[] as they provide a more reliable and explicit way to handle data selection and modification in Pandas.

      If you’re interested in learning more about the potential pitfalls of using square brackets and the benefits of .loc[] and .iloc[], you can read more in the Pandas documentation on indexing and selecting data.

    • +
    -

    Filtering rows based on conditional expressions#

    +

    Filtering rows based on conditional expressions#

    Filtering specific rows from a DataFrame

    -

    With .loc[] and .iloc[], you have more control and flexibility when filtering rows and selecting columns in a DataFrame.

    +

    With .loc[] and .iloc[], you have more control and flexibility when filtering rows and selecting columns in a DataFrame.

    Let’s start by exploring how you can use these tools to filter rows based on conditions.

    -

    Filtering specific rows with .loc[]#

    +

    Filtering specific rows with .loc[]#

    To filter rows based on a condition, you can place the condition inside the .loc[] selector.

    Here’s how you can filter rows where the Age column is greater than 35:

    @@ -1175,7 +1174,7 @@

    Filtering specific rows with

    -

    This command filters the rows where the condition titanic['Age'] > 35 is True, and returns the entire DataFrame for those rows.

    +

    This command filters the rows where the condition titanic['Age'] > 35 is True, and returns the entire DataFrame for those rows.

    If you only want to select specific columns (e.g., Name and Pclass) for those filtered rows, you can add the column names after the comma:

    @@ -1277,12 +1276,12 @@

    Filtering specific rows with

    When using loc/iloc

    -

    The part before the comma is the rows you want

    -

    The part after the comma is the columns you want to select

    +

    The part before the comma is the rows you want to select.

    +

    The part after the comma is the columns you want to select.

    -

    Using .iloc[] for positional indexing#

    +

    Using .iloc[] for positional indexing#

    The .iloc[] method works similarly to .loc[], but it selects rows and columns based on their integer positions rather than labels.

    For example, to select the first 5 rows and the first 2 columns, you can use:

    @@ -1355,7 +1354,7 @@

    Using .iloc[]

    -

    Inclusive vs. Exclusive Slicing#

    +

    Inclusive vs. Exclusive Slicing#

    When slicing with .loc[], the slicing is inclusive of the start and end labels. For example:

    @@ -1479,9 +1478,10 @@

    Inclusive vs. Exclusive Slicing -

    Using .loc[] for complex conditions#

    -

    You can also use .loc[] to filter rows based on more complex conditions, combining multiple criteria with logical operators. For instance, to filter rows where Pclass is either 2 or 3:

    +
    +

    Using .loc[] and .iloc[] for complex conditions#

    +

    You can use both .loc[] and .iloc[] to filter rows based on more complex conditions, combining multiple criteria with logical operators.

    +

    For instance, to filter rows where Pclass is either 2 or 3 using .loc[]:

    # Filter rows where 'Pclass' is either 2 or 3 using .loc[].
@@ -1700,12 +1700,14 @@ 
    Using .loc[]
    -

    You can combine multiple conditions using logical operators like & (AND) and | (OR):

    +

    Similarly, if you want to perform position-based filtering using .iloc[], you would first need to identify the positions of the relevant rows and columns. However, .iloc[] is typically less useful for condition-based filtering because it requires knowledge of the exact row and column positions rather than labels.

    +

    You can also combine multiple conditions using logical operators like & (AND) and | (OR):

    -
    # Filter rows where 'Pclass' is 2 or 3 and 'Age' is greater than 35, and select 'Name' and 'Pclass'.
+
    # Filter rows where ('Pclass' is either 2 or 3) AND ('Age' is greater than 35),
+# and then select the 'Name' and 'Pclass' columns.
 
-titanic.loc[(titanic['Pclass'] == 2) | (titanic['Pclass'] == 3) & (titanic['Age'] > 35), ['Name', 'Pclass']]
+titanic.loc[((titanic['Pclass'] == 2) | (titanic['Pclass'] == 3)) & (titanic['Age'] > 35), ['Name', 'Pclass']]
    @@ -1738,11 +1740,6 @@

    Using .loc[] - - 9 - Nasser, Mrs. Nicholas (Adele Achem) - 2 - 13 Andersson, Mr. Anders Johan @@ -1754,55 +1751,62 @@

    Using .loc[]2 - 17 - Williams, Mr. Charles Eugene - 2 + 25 + Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... + 3 - 20 - Fynney, Mr. Joseph J + 33 + Wheadon, Mr. Edward H 2 + + 40 + Ahlin, Mrs. Johan (Johanna Persdotter Larsson) + 3 + ... ... ... - 874 - Abelson, Mrs. Samuel (Hannah Wizosky) + 854 + Carter, Mrs. Ernest Courtenay (Lilian Hughes) 2 - 880 - Shelley, Mrs. William (Imanita Parrish Hall) - 2 + 860 + Hansen, Mr. Claus Peter + 3 - 883 - Banfield, Mr. Frederick James + 865 + Bystrom, Mrs. (Karolina) 2 - 885 - Rice, Mrs. William (Margaret Norton) + 873 + Vander Cruyssen, Mr. Victor 3 - 886 - Montvila, Rev. Juozas - 2 + 885 + Rice, Mrs. William (Margaret Norton) + 3 -

    247 rows × 2 columns

    +

    113 rows × 2 columns

    -
    -

    Handling missing values with .loc[]#

    -

    The .loc[] method also allows you to filter rows based on the presence or absence of missing values. For example, to filter rows where the Embarked column is not null:

    +
    +

    Handling missing values with .loc[] and .iloc[]#

    +

    Both .loc[] and .iloc[] allow you to filter rows based on the presence or absence of missing values.
    +(However, .loc[] is generally more convenient when working with labeled data, while .iloc[] requires knowing the exact positions.)

    +

    For example, to filter rows where the Embarked column is not null using .loc[]:

    # Filter rows where 'Embarked' is not null using .loc[].
@@ -2060,10 +2064,92 @@ 
    Handling missing values with
    +

    Comparing the two number of rows (889 and 891) tells us, that two rows are missing embarkation data.

    +

    As we saw in the exercise on the previous page on reading and writing tabular data, the two rows missing embarkation data can be found like this:

    +
    +
    +
    titanic[titanic['Embarked'].isna()]
+
    +
    +
    +
    + + +Hide code cell output + +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
    616211Icard, Miss. Ameliefemale38.00011357280.0B28NaN
    82983011Stone, Mrs. George Nelson (Martha Evelyn)female62.00011357280.0B28NaN
    +
    +
    +
    -

    Selecting rows and columns#

    +

    Selecting rows and columns#

    Selecting specific rows and columns from a DataFrame

    The true power of .loc[] becomes evident when you want to filter rows and simultaneously select specific columns. For example:

    @@ -2166,7 +2252,7 @@

    Selecting rows and columns -

    Combining loc and iloc#

    +

    Combining loc and iloc#

    We can use iloc to select:

    • Rows from index position 9 to 25 (exclusive).

      • @@ -2241,10 +2327,9 @@

      Combining loc

    -

    Summary#

    +

    Summary#

    Using .loc[] and .iloc[] allows you to write clearer, more precise code for selecting and manipulating data in Pandas. Whether you are filtering rows based on complex conditions or selecting specific rows and columns simultaneously, these tools offer the flexibility and reliability you need for more advanced data manipulation tasks.

    -

    Key points#

    @@ -2266,7 +2351,7 @@

    Key points.loc[], and apply additional conditions or select specific columns within the same operation.

    +

  • Filter rows based on conditions using .loc[] and .iloc[], and apply additional conditions or select specific columns within the same operation.

    • @@ -2341,20 +2426,18 @@

    Key pointsCreating a DataFrame from a CSV file
  • Selecting specific columns
  • Calling multiple Series
    • -
  • Introducing .loc[] and .iloc[]

    • diff --git a/docs/pandas/04-Variables.html b/docs/pandas/04-Variables.html index afc145cb..26dd756d 100644 --- a/docs/pandas/04-Variables.html +++ b/docs/pandas/04-Variables.html @@ -564,9 +564,9 @@

    Assigning Variables 
    <>:10: SyntaxWarning: invalid escape sequence '\W'
 <>:10: SyntaxWarning: invalid escape sequence '\W'
-/tmp/ipykernel_2407/1664984018.py:10: SyntaxWarning: invalid escape sequence '\W'
+/tmp/ipykernel_2510/1664984018.py:10: SyntaxWarning: invalid escape sequence '\W'
   split_words = re.split("\W+", lowercase_text)
-/tmp/ipykernel_2407/1664984018.py:10: SyntaxWarning: invalid escape sequence '\W'
+/tmp/ipykernel_2510/1664984018.py:10: SyntaxWarning: invalid escape sequence '\W'
   split_words = re.split("\W+", lowercase_text)
    @@ -979,9 +979,9 @@

    Striving for Good Variable Names 
    <>:3: SyntaxWarning: invalid escape sequence '\W'
 <>:3: SyntaxWarning: invalid escape sequence '\W'
-/tmp/ipykernel_2407/3249180260.py:3: SyntaxWarning: invalid escape sequence '\W'
+/tmp/ipykernel_2510/3249180260.py:3: SyntaxWarning: invalid escape sequence '\W'
   sw = re.split("\W+", lt)
-/tmp/ipykernel_2407/3249180260.py:3: SyntaxWarning: invalid escape sequence '\W'
+/tmp/ipykernel_2510/3249180260.py:3: SyntaxWarning: invalid escape sequence '\W'
   sw = re.split("\W+", lt)
    @@ -1031,9 +1031,9 @@

    Striving for Good Variable Names 
    <>:3: SyntaxWarning: invalid escape sequence '\W'
 <>:3: SyntaxWarning: invalid escape sequence '\W'
-/tmp/ipykernel_2407/1868761352.py:3: SyntaxWarning: invalid escape sequence '\W'
+/tmp/ipykernel_2510/1868761352.py:3: SyntaxWarning: invalid escape sequence '\W'
   split_words = re.split("\W+", lowercase_text)
-/tmp/ipykernel_2407/1868761352.py:3: SyntaxWarning: invalid escape sequence '\W'
+/tmp/ipykernel_2510/1868761352.py:3: SyntaxWarning: invalid escape sequence '\W'
   split_words = re.split("\W+", lowercase_text)
    @@ -1145,9 +1145,9 @@

    Re-Assigning Variables 
    <>:6: SyntaxWarning: invalid escape sequence '\W'
 <>:6: SyntaxWarning: invalid escape sequence '\W'
-/tmp/ipykernel_2407/3981446915.py:6: SyntaxWarning: invalid escape sequence '\W'
+/tmp/ipykernel_2510/3981446915.py:6: SyntaxWarning: invalid escape sequence '\W'
   split_words = re.split("\W+", lowercase_text)
-/tmp/ipykernel_2407/3981446915.py:6: SyntaxWarning: invalid escape sequence '\W'
+/tmp/ipykernel_2510/3981446915.py:6: SyntaxWarning: invalid escape sequence '\W'
   split_words = re.split("\W+", lowercase_text)
    @@ -1262,7 +1262,7 @@

    Your Turn 
    <>:6: SyntaxWarning: invalid escape sequence '\W'
 <>:6: SyntaxWarning: invalid escape sequence '\W'
-/tmp/ipykernel_2407/387211622.py:6: SyntaxWarning: invalid escape sequence '\W'
+/tmp/ipykernel_2510/387211622.py:6: SyntaxWarning: invalid escape sequence '\W'
   split_words = re.split("\W+", lowercase_text)
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