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{{ header }}

.. ipython:: python

    import pandas as pd
Data used for this tutorial: 
  • .. ipython:: python

    titanic = pd.read_csv("data/titanic.csv")
    titanic.head()

How do I select a subset of a DataFrame?

How do I select specific columns from a DataFrame?

  • I’m interested in the age of the Titanic passengers.

    .. ipython:: python

    ages = titanic["Age"]
    ages.head()

    To select a single column, use square brackets [] with the column name of the column of interest.

Each column in a :class:`DataFrame` is a :class:`Series`. As a single column is selected, the returned object is a pandas :class:`Series`. We can verify this by checking the type of the output:

.. ipython:: python

    type(titanic["Age"])

And have a look at the shape of the output:

.. ipython:: python

    titanic["Age"].shape

:attr:`DataFrame.shape` is an attribute (remember :ref:`tutorial on reading and writing <10min_tut_02_read_write>`, do not use parentheses 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.

  • I’m interested in the age and sex of the Titanic passengers.

    .. ipython:: python

    age_sex = titanic[["Age", "Sex"]]
    age_sex.head()

    To select multiple columns, use a list of column names within the selection brackets [].

Note

The inner square brackets define a :ref:`Python list <python:tut-morelists>` with column names, whereas the outer brackets are used to select the data from a pandas DataFrame as seen in the previous example.

The returned data type is a pandas DataFrame:

.. ipython:: python

    type(titanic[["Age", "Sex"]])


.. ipython:: python

    titanic[["Age", "Sex"]].shape

The selection returned a DataFrame with 891 rows and 2 columns. Remember, a DataFrame is 2-dimensional with both a row and column dimension.

To user guide

For basic information on indexing, see the user guide section on :ref:`indexing and selecting data <indexing.basics>`.

How do I filter specific rows from a DataFrame?

  • I’m interested in the passengers older than 35 years.

    .. ipython:: python

    above_35 = titanic[titanic["Age"] > 35]
    above_35.head()

    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:

.. ipython:: python

    titanic["Age"] > 35

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.

We know from before that the original Titanic DataFrame consists of 891 rows. Let’s have a look at the number of rows which satisfy the condition by checking the shape attribute of the resulting DataFrame above_35:

.. ipython:: python

    above_35.shape

  • I’m interested in the Titanic passengers from cabin class 2 and 3.

    .. ipython:: python

    class_23 = titanic[titanic["Pclass"].isin([2, 3])]
    class_23.head()

    Similar to the conditional expression, the :func:`~Series.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.

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:

.. ipython:: python

    class_23 = titanic[(titanic["Pclass"] == 2) | (titanic["Pclass"] == 3)]
    class_23.head()

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 &.

To user guide

See the dedicated section in the user guide about :ref:`boolean indexing <indexing.boolean>` or about the :ref:`isin function <indexing.basics.indexing_isin>`.

  • I want to work with passenger data for which the age is known.

    .. ipython:: python

    age_no_na = titanic[titanic["Age"].notna()]
    age_no_na.head()

    The :meth:`~Series.notna` conditional function returns a True for each row the values are not a Null value. As such, this can be combined with the selection brackets [] to filter the data table.

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:

.. ipython:: python

    age_no_na.shape
To user guide

For more dedicated functions on missing values, see the user guide section about :ref:`handling missing data <missing_data>`.

How do I select specific rows and columns from a DataFrame?

  • I’m interested in the names of the passengers older than 35 years.

    .. ipython:: python

    adult_names = titanic.loc[titanic["Age"] > 35, "Name"]
    adult_names.head()

    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.

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 specifies you want to select all rows or columns.

  • I’m interested in rows 10 till 25 and columns 3 to 5.

    .. ipython:: python

    titanic.iloc[9:25, 2:5]

    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 [].

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:

.. ipython:: python

    titanic.iloc[0:3, 3] = "anonymous"
    titanic.head()
To user guide

See the user guide section on :ref:`different choices for indexing <indexing.choice>` to get more insight in the usage of loc and iloc.

REMEMBER

  • When selecting subsets of data, square brackets [] are used.
  • 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.
  • Select specific rows and/or columns using loc when using the row and column names.
  • Select specific rows and/or columns using iloc when using the positions in the table.
  • You can assign new values to a selection based on loc/iloc.
To user guide

A full overview of indexing is provided in the user guide pages on :ref:`indexing and selecting data <indexing>`.