    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: 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.

  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]:
      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 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]:
      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 `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.