In [1]: import pandas as pd
This tutorial uses the Titanic data set, stored as CSV. The data consists of the following data columns:
PassengerId: Id of every passenger.
Survived: This feature has value 0 and 1. 0 for not survived and 1 for survived.
Pclass: There are 3 classes: Class 1, Class 2 and Class 3.
Name: Name of passenger.
Sex: Gender of passenger.
Age: Age of passenger.
SibSp: Indication that passengers have siblings and spouses.
Parch: Whether a passenger is alone or has a family.
Ticket: Ticket number of passenger.
Fare: Indicating the fare.
Cabin: The cabin of passenger.
Embarked: The embarked category.
In [2]: titanic = pd.read_csv("data/titanic.csv") In [3]: titanic.head() Out[3]: PassengerId Survived Pclass ... Fare Cabin Embarked 0 1 0 3 ... 7.2500 NaN S 1 2 1 1 ... 71.2833 C85 C 2 3 1 3 ... 7.9250 NaN S 3 4 1 1 ... 53.1000 C123 S 4 5 0 3 ... 8.0500 NaN S [5 rows x 12 columns]
DataFrame
I’m interested in the age of the Titanic passengers.
In [4]: ages = titanic["Age"] In [5]: ages.head() Out[5]: 0 22.0 1 38.0 2 26.0 3 35.0 4 35.0 Name: Age, dtype: float64
To select a single column, use square brackets [] with the column name of the column of interest.
[]
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:
Series
In [6]: type(titanic["Age"]) Out[6]: pandas.core.series.Series
And have a look at the shape of the output:
shape
In [7]: titanic["Age"].shape Out[7]: (891,)
DataFrame.shape is an attribute (remember tutorial on reading and writing, 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.
DataFrame.shape
I’m interested in the age and sex of the Titanic passengers.
In [8]: age_sex = titanic[["Age", "Sex"]] In [9]: age_sex.head() Out[9]: Age Sex 0 22.0 male 1 38.0 female 2 26.0 female 3 35.0 female 4 35.0 male
To select multiple columns, use a list of column names within the selection brackets [].
Note
The inner square brackets define a 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.
The returned data type is a pandas DataFrame:
In [10]: type(titanic[["Age", "Sex"]]) Out[10]: pandas.core.frame.DataFrame
In [11]: titanic[["Age", "Sex"]].shape Out[11]: (891, 2)
The selection returned a DataFrame with 891 rows and 2 columns. Remember, a DataFrame is 2-dimensional with both a row and column dimension.
For basic information on indexing, see the user guide section on indexing and selecting data.
I’m interested in the passengers older than 35 years.
In [12]: above_35 = titanic[titanic["Age"] > 35] In [13]: above_35.head() Out[13]: PassengerId Survived Pclass Name ... Ticket Fare Cabin Embarked 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... ... PC 17599 71.2833 C85 C 6 7 0 1 McCarthy, Mr. Timothy J ... 17463 51.8625 E46 S 11 12 1 1 Bonnell, Miss. Elizabeth ... 113783 26.5500 C103 S 13 14 0 3 Andersson, Mr. Anders Johan ... 347082 31.2750 NaN S 15 16 1 2 Hewlett, Mrs. (Mary D Kingcome) ... 248706 16.0000 NaN S [5 rows x 12 columns]
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:
titanic["Age"] > 35
Age
In [14]: titanic["Age"] > 35 Out[14]: 0 False 1 True 2 False 3 False 4 False ... 886 False 887 False 888 False 889 False 890 False Name: Age, Length: 891, dtype: bool
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.
>
==
!=
<
<=
True
False
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:
above_35
In [15]: above_35.shape Out[15]: (217, 12)
I’m interested in the Titanic passengers from cabin class 2 and 3.
In [16]: class_23 = titanic[titanic["Pclass"].isin([2, 3])] In [17]: class_23.head() Out[17]: PassengerId Survived Pclass Name ... Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris ... A/5 21171 7.2500 NaN S 2 3 1 3 Heikkinen, Miss. Laina ... STON/O2. 3101282 7.9250 NaN S 4 5 0 3 Allen, Mr. William Henry ... 373450 8.0500 NaN S 5 6 0 3 Moran, Mr. James ... 330877 8.4583 NaN Q 7 8 0 3 Palsson, Master. Gosta Leonard ... 349909 21.0750 NaN S [5 rows x 12 columns]
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.
isin()
titanic["Pclass"].isin([2, 3])
Pclass
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:
|
In [18]: class_23 = titanic[(titanic["Pclass"] == 2) | (titanic["Pclass"] == 3)] In [19]: class_23.head() Out[19]: PassengerId Survived Pclass Name ... Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris ... A/5 21171 7.2500 NaN S 2 3 1 3 Heikkinen, Miss. Laina ... STON/O2. 3101282 7.9250 NaN S 4 5 0 3 Allen, Mr. William Henry ... 373450 8.0500 NaN S 5 6 0 3 Moran, Mr. James ... 330877 8.4583 NaN Q 7 8 0 3 Palsson, Master. Gosta Leonard ... 349909 21.0750 NaN S [5 rows x 12 columns]
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 &.
()
or
and
&
See the dedicated section in the user guide about boolean indexing or about the isin function.
I want to work with passenger data for which the age is known.
In [20]: age_no_na = titanic[titanic["Age"].notna()] In [21]: age_no_na.head() Out[21]: PassengerId Survived Pclass ... Fare Cabin Embarked 0 1 0 3 ... 7.2500 NaN S 1 2 1 1 ... 71.2833 C85 C 2 3 1 3 ... 7.9250 NaN S 3 4 1 1 ... 53.1000 C123 S 4 5 0 3 ... 8.0500 NaN S [5 rows x 12 columns]
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.
notna()
Null
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:
In [22]: age_no_na.shape Out[22]: (714, 12)
For more dedicated functions on missing values, see the user guide section about handling missing data.
I’m interested in the names of the passengers older than 35 years.
In [23]: adult_names = titanic.loc[titanic["Age"] > 35, "Name"] In [24]: adult_names.head() Out[24]: 1 Cumings, Mrs. John Bradley (Florence Briggs Th... 6 McCarthy, Mr. Timothy J 11 Bonnell, Miss. Elizabeth 13 Andersson, Mr. Anders Johan 15 Hewlett, Mrs. (Mary D Kingcome) Name: Name, dtype: object
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.
loc
iloc
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.
In [25]: titanic.iloc[9:25, 2:5] Out[25]: Pclass Name Sex 9 2 Nasser, Mrs. Nicholas (Adele Achem) female 10 3 Sandstrom, Miss. Marguerite Rut female 11 1 Bonnell, Miss. Elizabeth female 12 3 Saundercock, Mr. William Henry male 13 3 Andersson, Mr. Anders Johan male .. ... ... ... 20 2 Fynney, Mr. Joseph J male 21 2 Beesley, Mr. Lawrence male 22 3 McGowan, Miss. Anna "Annie" female 23 1 Sloper, Mr. William Thompson male 24 3 Palsson, Miss. Torborg Danira female [16 rows x 3 columns]
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:
anonymous
In [26]: titanic.iloc[0:3, 3] = "anonymous" In [27]: titanic.head() Out[27]: PassengerId Survived Pclass ... Fare Cabin Embarked 0 1 0 3 ... 7.2500 NaN S 1 2 1 1 ... 71.2833 C85 C 2 3 1 3 ... 7.9250 NaN S 3 4 1 1 ... 53.1000 C123 S 4 5 0 3 ... 8.0500 NaN S [5 rows x 12 columns]
See the user guide section on different choices for indexing to get more insight in the usage of loc and iloc.
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.
A full overview of indexing is provided in the user guide pages on indexing and selecting data.