diff --git a/notebooks/3_subset_data.ipynb b/notebooks/3_subset_data.ipynb new file mode 100644 index 0000000..aaa1880 --- /dev/null +++ b/notebooks/3_subset_data.ipynb @@ -0,0 +1,1693 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "This tutorial uses the titanic data set, stored as CSV. The data consists of the following data columns:\n", + "\n", + "- PassengerId: Id of every passenger.\n", + "- Survived: This feature have value 0 and 1. 0 for not survived and 1 for survived.\n", + "- Pclass: There are 3 classes: Class 1, Class 2 and Class 3.\n", + "- Name: Name of passenger.\n", + "- Sex: Gender of passenger.\n", + "- Age: Age of passenger.\n", + "- SibSp: Indication that passenger have siblings and spouse.\n", + "- Parch: Whether a passenger is alone or have family.\n", + "- Ticket: Ticket number of passenger.\n", + "- Fare: Indicating the fare.\n", + "- Cabin: The cabin of passenger.\n", + "- Embarked: The embarked category.\n", + "\n", + "Reading in a data set is explained in the [tutorial on read/write operations](./2_read_write.ipynb).\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic = pd.read_csv(\"../data/titanic.csv\")\n", + "titanic.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How do I select a subset of data in a `DataFrame`? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How do I select specific columns from a `DataFrame`?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](../schemas/03_subset_columns.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " > I'm interested in the age of the titanic passengers." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 22.0\n", + "1 38.0\n", + "2 26.0\n", + "3 35.0\n", + "4 35.0\n", + "Name: Age, dtype: float64" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ages = titanic[\"Age\"]\n", + "ages.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To select a single column, use square brackets `[]` with the column name of the column of interest." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each column in a `DataFrame` is a `Series`. As a single column is selected, the returned object is a pandas `Series`. We can verify this by checking the type of the output:" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(titanic[\"Age\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And have a look at the `shape` of the output:" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(891,)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic[\"Age\"].shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`shape` is an attribute (remember [previous tutorial](./2_read_write.ipynb), no parantheses for attributes) of a pandas `Series` and `DataFrame` containing the number of rows and columns: _(nrows, ncolumns)_. A pandas Series is 1-dimensional and only the number of rows is returned." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " > I'm interested in the age and sex of the titanic passengers." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Age Sex\n", + "0 22.0 male\n", + "1 38.0 female\n", + "2 26.0 female\n", + "3 35.0 female\n", + "4 35.0 male" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "age_sex = titanic[[\"Age\", \"Sex\"]]\n", + "age_sex.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To select multiple columns, use a list of column names within the selection brackets `[]`. \n", + "\n", + "
\n", + " \n", + "__Note:__ The inner square brackets define a :ref:`Python list ` with column names, whereas the outer brackets are used to select the data from a pandas `DataFrame` as seen in the previous example.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The returned data type is a Pandas DataFrame:" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(titanic[[\"Age\", \"Sex\"]])" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(891, 2)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic[[\"Age\", \"Sex\"]].shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The selection returned a `DataFrame` with 891 rows and 2 columns. A `DataFrame` is 2-dimensional with both a row and column dimension." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__To user guide:__ For basic information on indexing, see :ref:`indexing.basics`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How do I filter specific rows from a `DataFrame`?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](../schemas/03_subset_rows.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> I'm interested in the passengers older than 35 years." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
111211Bonnell, Miss. Elizabethfemale58.00011378326.5500C103S
131403Andersson, Mr. Anders Johanmale39.01534708231.2750NaNS
151612Hewlett, Mrs. (Mary D Kingcome)female55.00024870616.0000NaNS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "1 2 1 1 \n", + "6 7 0 1 \n", + "11 12 1 1 \n", + "13 14 0 3 \n", + "15 16 1 2 \n", + "\n", + " Name Sex Age SibSp \\\n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "6 McCarthy, Mr. Timothy J male 54.0 0 \n", + "11 Bonnell, Miss. Elizabeth female 58.0 0 \n", + "13 Andersson, Mr. Anders Johan male 39.0 1 \n", + "15 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "1 0 PC 17599 71.2833 C85 C \n", + "6 0 17463 51.8625 E46 S \n", + "11 0 113783 26.5500 C103 S \n", + "13 5 347082 31.2750 NaN S \n", + "15 0 248706 16.0000 NaN S " + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "above_35 = titanic[titanic[\"Age\"] > 35]\n", + "above_35.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To select rows based on a conditional expression, use a condition inside the selection brackets `[]`. The condition inside the selection brackets `titanic[\"Age\"] > 35` checks for which rows the `Age` column has a value larger than 35:" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 True\n", + "2 False\n", + "3 False\n", + "4 False\n", + " ... \n", + "886 False\n", + "887 False\n", + "888 False\n", + "889 False\n", + "890 False\n", + "Name: Age, Length: 891, dtype: bool" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic[\"Age\"] > 35" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output of the conditional expression (`>`, but also `==`, `!=`, `<`, `<=`,... would work) is actually a pandas `Series` of boolean values (either `True` or `False`) with the same number of rows as the original `DataFrame`. Such a `Series` of boolean values can be used to filter the `DataFrame` by putting it in between the selection brackets `[]`. Only rows for which the value is `True` will be selected." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now from before that the original titanic `DataFrame` consists of 891 rows. Let's have a look at the amount of rows which satisfy the condition by checking the `shape` attribute of the resulting `DataFrame` above_35:" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(217, 12)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "above_35.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> I'm interested in the titanic passengers from cabin class 2 and 3." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Name Sex \\\n", + "0 1 0 3 Braund, Mr. Owen Harris male \n", + "2 3 1 3 Heikkinen, Miss. Laina female \n", + "4 5 0 3 Allen, Mr. William Henry male \n", + "5 6 0 3 Moran, Mr. James male \n", + "7 8 0 3 Palsson, Master. Gosta Leonard male \n", + "\n", + " Age SibSp Parch Ticket Fare Cabin Embarked \n", + "0 22.0 1 0 A/5 21171 7.2500 NaN S \n", + "2 26.0 0 0 STON/O2. 3101282 7.9250 NaN S \n", + "4 35.0 0 0 373450 8.0500 NaN S \n", + "5 NaN 0 0 330877 8.4583 NaN Q \n", + "7 2.0 3 1 349909 21.0750 NaN S " + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class_23 = titanic[titanic[\"Pclass\"].isin([2, 3])]\n", + "class_23.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similar to the conditional expression, the `isin` conditional function returns a `True` for each row the values are in the provided list. To filter the rows based on such a function, use the conditional function inside the selection brackets `[]`. In this case, the condition inside the selection brackets `titanic[\"Pclass\"].isin([2, 3])` checks for which rows the `Pclass` column is either 2 or 3." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above is equivalent to filtering by rows for which the class is either 2 or 3 and combining the two statements with an `|` (or) operator:" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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7803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Name Sex \\\n", + "0 1 0 3 Braund, Mr. Owen Harris male \n", + "2 3 1 3 Heikkinen, Miss. Laina female \n", + "4 5 0 3 Allen, Mr. William Henry male \n", + "5 6 0 3 Moran, Mr. James male \n", + "7 8 0 3 Palsson, Master. Gosta Leonard male \n", + "\n", + " Age SibSp Parch Ticket Fare Cabin Embarked \n", + "0 22.0 1 0 A/5 21171 7.2500 NaN S \n", + "2 26.0 0 0 STON/O2. 3101282 7.9250 NaN S \n", + "4 35.0 0 0 373450 8.0500 NaN S \n", + "5 NaN 0 0 330877 8.4583 NaN Q \n", + "7 2.0 3 1 349909 21.0750 NaN S " + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class_23 = titanic[(titanic[\"Pclass\"] == 2) | (titanic[\"Pclass\"] == 3)]\n", + "class_23.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "__Note:__ When combining multiple conditional statements, each condition must be surrounded by parentheses `()`. Moreover, you can not use `or`/`and` but need to use the `or` operator `|` and the `and` operator `&`.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__To user guide:__ Conditional (boolean) indexing, see :ref:`indexing.boolean`. Specific information on `isin`, see :ref:`indexing.basics.indexing_isin`. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> I want to work with passenger data for which the age is known." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
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2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S " + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "age_no_na = titanic[titanic[\"Age\"].notna()]\n", + "age_no_na.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `notna` conditional function returns a `True` for each row the values are not an `Null` value. As such, this can be combined with the selection brackets `[]` to filter the data table." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might wonder what actually changed, as the first 5 lines are still the same values. One way to verify is to check if the shape has changed:" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(714, 12)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "age_no_na.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__To user guide:__ For more dedicated functions on missing values, see :ref:`missing-data`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How do I select specific rows and columns from a `DataFrame`? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](../schemas/03_subset_columns_rows.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> I'm interested in the names of the passengers older than 35 years." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 Cumings, Mrs. John Bradley (Florence Briggs Th...\n", + "6 McCarthy, Mr. Timothy J\n", + "11 Bonnell, Miss. Elizabeth\n", + "13 Andersson, Mr. Anders Johan\n", + "15 Hewlett, Mrs. (Mary D Kingcome) \n", + "Name: Name, dtype: object" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adult_names = titanic.loc[titanic[\"Age\"] > 35, \"Name\"]\n", + "adult_names.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case, a subset of both rows and columns is made in one go and just using selection brackets `[]` is not sufficient anymore. The `loc`/`iloc` operators are required in front of the selection brackets `[]`. When using `loc`/`iloc`, the part before the comma is the rows you want, and the part after the comma is the columns you want to select.\n", + "\n", + "When using the column names, row labels or a condition expression, use the `loc` operator in front of the selection brackets `[]`. For both the part before and after the comma, you can use a single label, a list of labels, a slice of labels, a conditional expression or a colon. using a colon specificies you want to select all rows or columns." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> I'm interested in rows 10 till 25 and columns 3 to 5." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PclassNameSex
92Nasser, Mrs. Nicholas (Adele Achem)female
103Sandstrom, Miss. Marguerite Rutfemale
111Bonnell, Miss. Elizabethfemale
123Saundercock, Mr. William Henrymale
133Andersson, Mr. Anders Johanmale
143Vestrom, Miss. Hulda Amanda Adolfinafemale
152Hewlett, Mrs. (Mary D Kingcome)female
163Rice, Master. Eugenemale
172Williams, Mr. Charles Eugenemale
183Vander Planke, Mrs. Julius (Emelia Maria Vande...female
193Masselmani, Mrs. Fatimafemale
202Fynney, Mr. Joseph Jmale
212Beesley, Mr. Lawrencemale
223McGowan, Miss. Anna \"Annie\"female
231Sloper, Mr. William Thompsonmale
243Palsson, Miss. Torborg Danirafemale
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" + ], + "text/plain": [ + " Pclass Name Sex\n", + "9 2 Nasser, Mrs. Nicholas (Adele Achem) female\n", + "10 3 Sandstrom, Miss. Marguerite Rut female\n", + "11 1 Bonnell, Miss. Elizabeth female\n", + "12 3 Saundercock, Mr. William Henry male\n", + "13 3 Andersson, Mr. Anders Johan male\n", + "14 3 Vestrom, Miss. Hulda Amanda Adolfina female\n", + "15 2 Hewlett, Mrs. (Mary D Kingcome) female\n", + "16 3 Rice, Master. Eugene male\n", + "17 2 Williams, Mr. Charles Eugene male\n", + "18 3 Vander Planke, Mrs. Julius (Emelia Maria Vande... female\n", + "19 3 Masselmani, Mrs. Fatima female\n", + "20 2 Fynney, Mr. Joseph J male\n", + "21 2 Beesley, Mr. Lawrence male\n", + "22 3 McGowan, Miss. Anna \"Annie\" female\n", + "23 1 Sloper, Mr. William Thompson male\n", + "24 3 Palsson, Miss. Torborg Danira female" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.iloc[9:25, 2:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, a subset of both rows and columns is made in one go and just using selection brackets `[]` is not sufficient anymore. When specifically interested in certain rows and/or columns based on their position in the table, use the `iloc` operator in front of the selection brackets `[]`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When selecting specific rows and/or columns with `loc` or `iloc`, new values can be assigned to the selected data. For example, to assign the name `anonymous` to the first 3 elements of the third column:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103anonymousmale22.010A/5 211717.2500NaNS
1211anonymousfemale38.010PC 1759971.2833C85C
2313anonymousfemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp Parch \\\n", + "0 anonymous male 22.0 1 0 \n", + "1 anonymous female 38.0 1 0 \n", + "2 anonymous female 26.0 0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 \n", + "4 Allen, Mr. William Henry male 35.0 0 0 \n", + "\n", + " Ticket Fare Cabin Embarked \n", + "0 A/5 21171 7.2500 NaN S \n", + "1 PC 17599 71.2833 C85 C \n", + "2 STON/O2. 3101282 7.9250 NaN S \n", + "3 113803 53.1000 C123 S \n", + "4 373450 8.0500 NaN S " + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.iloc[0:3, 3] = \"anonymous\"\n", + "titanic.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__To user guide:__ For more detailed description on selecting subsets of a data table, see :ref:`indexing.choice`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## REMEMBER\n", + "\n", + "- When selecting subsets of data, square brackets `[]` are used.\n", + "- Inside these brackets, you can use a single column/row label, a list of column/row labels, a slice of labels, a conditional expression or a colon.\n", + "- Select specific rows and/or columns using `loc` when using the row and column names\n", + "- Select specific rows and/or columns using `iloc` when using the positions in the table\n", + "- You can assign new values to a selection based on `loc`/`iloc`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__To user guide:__ Further details about indexing is provided in :ref:`indexing`" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}