3.1.
Advanced Searching on Strings

Dr. W.J.B. Mattingly
Smithsonian Data Science Lab and United States Holocaust Memorial Museum
August 2021

3.1.1. Covered in this Chapter

  1. How to find Strings with Specific Features

  2. Finding Strings without Certain Features

  3. Larger Pandas Queries with RegEx

3.1.2. Finding Features within a String

import pandas as pd
df = pd.read_csv("data/titanic.csv")
df
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
... ... ... ... ... ... ... ... ... ... ... ... ...
886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q

891 rows × 12 columns

When I am looking at the df, I notice that there is a “Rev.” in index 886. As a historian, I find this fascinating. Now, I start to wonder, how many reverends were there on the Titanic? Is this individual unique? If I wanted to ask this question outside of Pandas, I could do the following:

names = df.Name.tolist()
revs = []
for name in names:
    if "Rev." in name:
        revs.append(name)
print (revs)
['Byles, Rev. Thomas Roussel Davids', 'Bateman, Rev. Robert James', 'Carter, Rev. Ernest Courtenay', 'Kirkland, Rev. Charles Leonard', 'Harper, Rev. John', 'Montvila, Rev. Juozas']

Sure, that works, but I don’t have any of the other data associated with each of these reverends. I would have to then do some manual searching in the DataFrame to find their corresponding data, or save the data as a dictionary and then run look ups. But why do all of that, when we can do it in a single line of code using Pandas’ built-in function. We can use .str.contains() which takes an argument of what we want to return.

df.loc[df["Name"].str.contains("Rev.")]
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
149 150 0 2 Byles, Rev. Thomas Roussel Davids male 42.0 0 0 244310 13.000 NaN S
150 151 0 2 Bateman, Rev. Robert James male 51.0 0 0 S.O.P. 1166 12.525 NaN S
249 250 0 2 Carter, Rev. Ernest Courtenay male 54.0 1 0 244252 26.000 NaN S
626 627 0 2 Kirkland, Rev. Charles Leonard male 57.0 0 0 219533 12.350 NaN Q
848 849 0 2 Harper, Rev. John male 28.0 0 1 248727 33.000 NaN S
886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.000 NaN S

We can, therefore, see not only the reverends, but also their corresponding data.

3.1.3. Finding Strings that Don’t Contain Feature

What if we wanted to eliminate all names that do not contain “Rev.”? We can introduce “~” prior to df to specify that the Names column should not have whatever condition we express.

df.loc[~df["Name"].str.contains("Rev.")]
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
... ... ... ... ... ... ... ... ... ... ... ... ...
885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 NaN Q
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q

885 rows × 12 columns

3.1.4. Using RegEx with Pandas

Out of the box, Pandas supports RegEx. RegEx stands for Regular Expressions. It is a powerful way of performing complex string matching. If we were interested in finding any instance of “Rev.” or “Mr.”, we would have to write something like this without RegEx:

df.loc[(df["Name"].str.contains("Rev.")) | (df["Name"].str.contains("Mr."))]
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
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
5 6 0 3 Moran, Mr. James male NaN 0 0 330877 8.4583 NaN Q
... ... ... ... ... ... ... ... ... ... ... ... ...
884 885 0 3 Sutehall, Mr. Henry Jr male 25.0 0 0 SOTON/OQ 392076 7.0500 NaN S
885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 NaN Q
886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q

653 rows × 12 columns

While this works, imagine if we had 20 or 30 different conditions! That would be a very long piece of code to write and while it would work, it is always best practice to write shorter, tighter code. So, let’s do the same thing, but with RegEx. We can add the Or-condition into the str.contains() argument. This is a RegEx command. To ensure that RegEx is registered, it may be necessary to pass it as an argument.

df.loc[df["Name"].str.contains("Rev.|Mr.", regex=True)]
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
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
5 6 0 3 Moran, Mr. James male NaN 0 0 330877 8.4583 NaN Q
... ... ... ... ... ... ... ... ... ... ... ... ...
884 885 0 3 Sutehall, Mr. Henry Jr male 25.0 0 0 SOTON/OQ 392076 7.0500 NaN S
885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 NaN Q
886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q

653 rows × 12 columns

In some instances, we may have uncleaned data and the use of “Rev.” may be lowercase in one instance. To ensure that we grab both upper and lowercase forms of this sequence, let’s ignore the case by using the case keyword and setting it to False.

import re
df.loc[df["Name"].str.contains("Rev.|Mr.", case=False, regex=True)]
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
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
5 6 0 3 Moran, Mr. James male NaN 0 0 330877 8.4583 NaN Q
... ... ... ... ... ... ... ... ... ... ... ... ...
884 885 0 3 Sutehall, Mr. Henry Jr male 25.0 0 0 SOTON/OQ 392076 7.0500 NaN S
885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 NaN Q
886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q

654 rows × 12 columns