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 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 Age SibSp Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C 2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S 4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S
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 Name column lowercase, select the Name column (see tutorial on selection of data), add the str accessor and apply the lower method. As such, each of the strings is converted element wise.
Name
str
lower
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.
dt
Create a new column Surname that contains the surname of the Passengers by extracting the part before the comma.
Surname
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 the part after the comma.
Series.str.split()
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 str accessor and apply Series.str.get() to extract the relevant part. Indeed, these string functions can be concatenated to combine multiple functions at once!
Series.str.get()
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 Countess 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 SibSp Parch Ticket Fare Cabin Embarked Surname 759 760 1 1 Rothes, the Countess. of (Lucy Noel Martha Dye... female 33.0 0 0 110152 86.5 B77 S Rothes
(Interested in her story? SeeWikipedia!)
The string method Series.str.contains() checks for each of the values in the column Name if the string contains the word Countess and returns for each of the values True (Countess is part of the name) of False (Countess is notpart 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 1 Countess on the Titanic, we get one row as a result.
Series.str.contains()
Countess
True
False
Note
More powerful extractions on strings is supported, as the Series.str.contains() and Series.str.extract() methods accepts regular expressions, but out of scope of this tutorial.
Series.str.extract()
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 lenghts of each of the names in the Name column. By using pandas string methods, the Series.str.len() function is applied to each of the names individually (element-wise).
Series.str.len()
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 no str is used.
idxmax`()
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 the loc operator, introduced in the tutorial on subsetting.
307
loc
In the ‘Sex’ columns, replace values of ‘male’ by ‘M’ and all ‘female’ values 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 a dictionary to define the mapping {from : to}.
replace()
dictionary
{from : to}
Warning
There is also a replace() methods 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…
String methods are available using the str accessor.
String methods work element wise and can be used for conditional indexing.
The replace method is a convenient method to convert values according to a given dictionary.
replace
A full overview is provided in the user guide pages on working with text data.