In [1]: import pandas as pd

Data used for this tutorial:

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: Indication whether passenger survived. 0 for yes and 1 for no.
- Pclass: One out of the 3 ticket classes: Class 1, Class 2 and Class 3.
- Name: Name of passenger.
- Sex: Gender of passenger.
- Age: Age of passenger in years.
- SibSp: Number of siblings or spouses aboard.
- Parch: Number of parents or children aboard.
- Ticket: Ticket number of passenger.
- Fare: Indicating the fare.
- Cabin: Cabin number of passenger.
- Embarked: Port of embarkation.
To raw data
```
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]
```

How to manipulate textual data#

Make all name characters lowercase.

In [4]: titanic["Name"].str.lower()
Out[4]: 
                              braund, mr. owen harris
    cumings, mrs. john bradley (florence briggs th...
                               heikkinen, miss. laina
         futrelle, mrs. jacques heath (lily may peel)
                             allen, mr. william henry
                             ...                        
                              montvila, rev. juozas
                       graham, miss. margaret edith
           johnston, miss. catherine helen "carrie"
                              behr, mr. karl howell
                                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 the tutorial on selection of data), add the str accessor and apply the lower method. 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 Surname that contains the surname of the passengers by extracting the part before the comma.

In [5]: titanic["Name"].str.split(",")
Out[5]: 
                           [Braund,  Mr. Owen Harris]
    [Cumings,  Mrs. John Bradley (Florence Briggs ...
                            [Heikkinen,  Miss. Laina]
      [Futrelle,  Mrs. Jacques Heath (Lily May Peel)]
                          [Allen,  Mr. William Henry]
                             ...                        
                           [Montvila,  Rev. Juozas]
                    [Graham,  Miss. Margaret Edith]
        [Johnston,  Miss. Catherine Helen "Carrie"]
                           [Behr,  Mr. Karl Howell]
                             [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 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!

To user guide

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  ... Cabin Embarked  Surname
759          760         1       1  ...   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 column Name if the string contains the word Countess and returns for each of the values True (Countess is part of the name) or False (Countess is 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.

To user guide

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 Name column. By using pandas string methods, the Series.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 no str is 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 the loc operator, 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 a dictionary to 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 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.

To user guide

A full overview is provided in the user guide pages on working with text data.