In [1]: import pandas as pd
- Titanic data
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 have 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 passenger have siblings and spouse.
Parch: Whether a passenger is alone or have 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 Name Sex ... Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C 2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S 4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 NaN S [5 rows x 12 columns]
How to manipulate textual data?¶
Make all name characters lowercase.
In [4]: titanic["Name"].str.lower() Out[4]: 0 braund, mr. owen harris 1 cumings, mrs. john bradley (florence briggs th... 2 heikkinen, miss. laina 3 futrelle, mrs. jacques heath (lily may peel) 4 allen, mr. william henry ... 886 montvila, rev. juozas 887 graham, miss. margaret edith 888 johnston, miss. catherine helen "carrie" 889 behr, mr. karl howell 890 dooley, mr. patrick Name: Name, Length: 891, dtype: object
To make each of the strings in the
Namecolumn lowercase, select theNamecolumn (see the tutorial on selection of data), add thestraccessor and apply thelowermethod. As such, each of the strings is converted element-wise.
Similar to datetime objects in the time series tutorial
having a dt accessor, a number of
specialized string methods are available when using the str
accessor. These methods have in general matching names with the
equivalent built-in string methods for single elements, but are applied
element-wise (remember element-wise calculations?)
on each of the values of the columns.
Create a new column
Surnamethat contains the surname of the passengers by extracting the part before the comma.In [5]: titanic["Name"].str.split(",") Out[5]: 0 [Braund, Mr. Owen Harris] 1 [Cumings, Mrs. John Bradley (Florence Briggs ... 2 [Heikkinen, Miss. Laina] 3 [Futrelle, Mrs. Jacques Heath (Lily May Peel)] 4 [Allen, Mr. William Henry] ... 886 [Montvila, Rev. Juozas] 887 [Graham, Miss. Margaret Edith] 888 [Johnston, Miss. Catherine Helen "Carrie"] 889 [Behr, Mr. Karl Howell] 890 [Dooley, Mr. Patrick] Name: Name, Length: 891, dtype: object
Using the
Series.str.split()method, each of the values is returned as a list of 2 elements. The first element is the part before the comma and the second element is the part after the comma.In [6]: titanic["Surname"] = titanic["Name"].str.split(",").str.get(0) In [7]: titanic["Surname"] Out[7]: 0 Braund 1 Cumings 2 Heikkinen 3 Futrelle 4 Allen ... 886 Montvila 887 Graham 888 Johnston 889 Behr 890 Dooley Name: Surname, Length: 891, dtype: object
As we are only interested in the first part representing the surname (element 0), we can again use the
straccessor and applySeries.str.get()to extract the relevant part. Indeed, these string functions can be concatenated to combine multiple functions at once!
More information on extracting parts of strings is available in the user guide section on splitting and replacing strings.
Extract the passenger data about the countesses on board of the Titanic.
In [8]: titanic["Name"].str.contains("Countess") Out[8]: 0 False 1 False 2 False 3 False 4 False ... 886 False 887 False 888 False 889 False 890 False Name: Name, Length: 891, dtype: bool
In [9]: titanic[titanic["Name"].str.contains("Countess")] Out[9]: PassengerId Survived Pclass Name Sex Age ... Parch Ticket Fare Cabin Embarked Surname 759 760 1 1 Rothes, the Countess. of (Lucy Noel Martha Dye... female 33.0 ... 0 110152 86.5 B77 S Rothes [1 rows x 13 columns]
(Interested in her story? See Wikipedia!)
The string method
Series.str.contains()checks for each of the values in the columnNameif the string contains the wordCountessand returns for each of the valuesTrue(Countessis part of the name) orFalse(Countessis not part of the name). This output can be used to subselect the data using conditional (boolean) indexing introduced in the subsetting of data tutorial. As there was only one countess on the Titanic, we get one row as a result.
Note
More powerful extractions on strings are supported, as the
Series.str.contains() and Series.str.extract() methods accept regular
expressions, but out of
scope of this tutorial.
More information on extracting parts of strings is available in the user guide section on string matching and extracting.
Which passenger of the Titanic has the longest name?
In [10]: titanic["Name"].str.len() Out[10]: 0 23 1 51 2 22 3 44 4 24 .. 886 21 887 28 888 40 889 21 890 19 Name: Name, Length: 891, dtype: int64
To get the longest name we first have to get the lengths of each of the names in the
Namecolumn. By using pandas string methods, theSeries.str.len()function is applied to each of the names individually (element-wise).In [11]: titanic["Name"].str.len().idxmax() Out[11]: 307
Next, we need to get the corresponding location, preferably the index label, in the table for which the name length is the largest. The
idxmax()method does exactly that. It is not a string method and is applied to integers, so nostris used.In [12]: titanic.loc[titanic["Name"].str.len().idxmax(), "Name"] Out[12]: 'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)'
Based on the index name of the row (
307) and the column (Name), we can do a selection using thelocoperator, introduced in the tutorial on subsetting.
In the “Sex” column, replace values of “male” by “M” and values of “female” by “F”.
In [13]: titanic["Sex_short"] = titanic["Sex"].replace({"male": "M", "female": "F"}) In [14]: titanic["Sex_short"] Out[14]: 0 M 1 F 2 F 3 F 4 M .. 886 M 887 F 888 F 889 M 890 M Name: Sex_short, Length: 891, dtype: object
Whereas
replace()is not a string method, it provides a convenient way to use mappings or vocabularies to translate certain values. It requires adictionaryto define the mapping{from : to}.
Warning
There is also a replace() method available to replace a
specific set of characters. However, when having a mapping of multiple
values, this would become:
titanic["Sex_short"] = titanic["Sex"].str.replace("female", "F")
titanic["Sex_short"] = titanic["Sex_short"].str.replace("male", "M")
This would become cumbersome and easily lead to mistakes. Just think (or try out yourself) what would happen if those two statements are applied in the opposite order…
REMEMBER
String methods are available using the
straccessor.String methods work element-wise and can be used for conditional indexing.
The
replacemethod is a convenient method to convert values according to a given dictionary.
A full overview is provided in the user guide pages on working with text data.