{ "cells": [ { "cell_type": "markdown", "id": "tested-egyptian", "metadata": {}, "source": [ "#
Advanced Searching on Strings
" ] }, { "cell_type": "markdown", "id": "frequent-mobile", "metadata": {}, "source": [ "
Dr. W.J.B. Mattingly
\n", "\n", "
Smithsonian Data Science Lab and United States Holocaust Memorial Museum
\n", "\n", "
August 2021
" ] }, { "cell_type": "markdown", "id": "encouraging-shadow", "metadata": {}, "source": [ "## Covered in this Chapter" ] }, { "cell_type": "markdown", "id": "sitting-porcelain", "metadata": {}, "source": [ "1) How to find Strings with Specific Features
\n", "2) Finding Strings without Certain Features
\n", "3) Larger Pandas Queries with RegEx
" ] }, { "cell_type": "markdown", "id": "provincial-interference", "metadata": {}, "source": [ "## Finding Features within a String" ] }, { "cell_type": "code", "execution_count": 1, "id": "following-disney", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
\n", "

891 rows × 12 columns

\n", "
" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", ".. ... ... ... \n", "886 887 0 2 \n", "887 888 1 1 \n", "888 889 0 3 \n", "889 890 1 1 \n", "890 891 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", ".. ... ... ... ... \n", "886 Montvila, Rev. Juozas male 27.0 0 \n", "887 Graham, Miss. Margaret Edith female 19.0 0 \n", "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", "889 Behr, Mr. Karl Howell male 26.0 0 \n", "890 Dooley, Mr. Patrick male 32.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S \n", ".. ... ... ... ... ... \n", "886 0 211536 13.0000 NaN S \n", "887 0 112053 30.0000 B42 S \n", "888 2 W./C. 6607 23.4500 NaN S \n", "889 0 111369 30.0000 C148 C \n", "890 0 370376 7.7500 NaN Q \n", "\n", "[891 rows x 12 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "df = pd.read_csv(\"data/titanic.csv\")\n", "df" ] }, { "cell_type": "markdown", "id": "several-former", "metadata": {}, "source": [ "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:" ] }, { "cell_type": "code", "execution_count": 2, "id": "printable-bulgaria", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Byles, Rev. Thomas Roussel Davids', 'Bateman, Rev. Robert James', 'Carter, Rev. Ernest Courtenay', 'Kirkland, Rev. Charles Leonard', 'Harper, Rev. John', 'Montvila, Rev. Juozas']\n" ] } ], "source": [ "names = df.Name.tolist()\n", "revs = []\n", "for name in names:\n", " if \"Rev.\" in name:\n", " revs.append(name)\n", "print (revs)" ] }, { "cell_type": "markdown", "id": "floral-medline", "metadata": {}, "source": [ "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." ] }, { "cell_type": "code", "execution_count": 3, "id": "finished-danger", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
14915002Byles, Rev. Thomas Roussel Davidsmale42.00024431013.000NaNS
15015102Bateman, Rev. Robert Jamesmale51.000S.O.P. 116612.525NaNS
24925002Carter, Rev. Ernest Courtenaymale54.01024425226.000NaNS
62662702Kirkland, Rev. Charles Leonardmale57.00021953312.350NaNQ
84884902Harper, Rev. Johnmale28.00124872733.000NaNS
88688702Montvila, Rev. Juozasmale27.00021153613.000NaNS
\n", "
" ], "text/plain": [ " PassengerId Survived Pclass Name Sex \\\n", "149 150 0 2 Byles, Rev. Thomas Roussel Davids male \n", "150 151 0 2 Bateman, Rev. Robert James male \n", "249 250 0 2 Carter, Rev. Ernest Courtenay male \n", "626 627 0 2 Kirkland, Rev. Charles Leonard male \n", "848 849 0 2 Harper, Rev. John male \n", "886 887 0 2 Montvila, Rev. Juozas male \n", "\n", " Age SibSp Parch Ticket Fare Cabin Embarked \n", "149 42.0 0 0 244310 13.000 NaN S \n", "150 51.0 0 0 S.O.P. 1166 12.525 NaN S \n", "249 54.0 1 0 244252 26.000 NaN S \n", "626 57.0 0 0 219533 12.350 NaN Q \n", "848 28.0 0 1 248727 33.000 NaN S \n", "886 27.0 0 0 211536 13.000 NaN S " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[df[\"Name\"].str.contains(\"Rev.\")]" ] }, { "cell_type": "markdown", "id": "fatty-batch", "metadata": {}, "source": [ "We can, therefore, see not only the reverends, but also their corresponding data." ] }, { "cell_type": "markdown", "id": "hundred-response", "metadata": {}, "source": [ "## Finding Strings that Don't Contain Feature" ] }, { "cell_type": "markdown", "id": "italian-semiconductor", "metadata": {}, "source": [ "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." ] }, { "cell_type": "code", "execution_count": 4, "id": "adequate-health", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250NaNQ
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
\n", "

885 rows × 12 columns

\n", "
" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", ".. ... ... ... \n", "885 886 0 3 \n", "887 888 1 1 \n", "888 889 0 3 \n", "889 890 1 1 \n", "890 891 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", ".. ... ... ... ... \n", "885 Rice, Mrs. William (Margaret Norton) female 39.0 0 \n", "887 Graham, Miss. Margaret Edith female 19.0 0 \n", "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", "889 Behr, Mr. Karl Howell male 26.0 0 \n", "890 Dooley, Mr. Patrick male 32.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S \n", ".. ... ... ... ... ... \n", "885 5 382652 29.1250 NaN Q \n", "887 0 112053 30.0000 B42 S \n", "888 2 W./C. 6607 23.4500 NaN S \n", "889 0 111369 30.0000 C148 C \n", "890 0 370376 7.7500 NaN Q \n", "\n", "[885 rows x 12 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[~df[\"Name\"].str.contains(\"Rev.\")]" ] }, { "cell_type": "markdown", "id": "adolescent-plumbing", "metadata": {}, "source": [ "## Using RegEx with Pandas" ] }, { "cell_type": "markdown", "id": "fossil-dance", "metadata": {}, "source": [ "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:" ] }, { "cell_type": "code", "execution_count": 5, "id": "other-queensland", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
.......................................
88488503Sutehall, Mr. Henry Jrmale25.000SOTON/OQ 3920767.0500NaNS
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250NaNQ
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
\n", "

653 rows × 12 columns

\n", "
" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "3 4 1 1 \n", "4 5 0 3 \n", "5 6 0 3 \n", ".. ... ... ... \n", "884 885 0 3 \n", "885 886 0 3 \n", "886 887 0 2 \n", "889 890 1 1 \n", "890 891 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "5 Moran, Mr. James male NaN 0 \n", ".. ... ... ... ... \n", "884 Sutehall, Mr. Henry Jr male 25.0 0 \n", "885 Rice, Mrs. William (Margaret Norton) female 39.0 0 \n", "886 Montvila, Rev. Juozas male 27.0 0 \n", "889 Behr, Mr. Karl Howell male 26.0 0 \n", "890 Dooley, Mr. Patrick male 32.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S \n", "5 0 330877 8.4583 NaN Q \n", ".. ... ... ... ... ... \n", "884 0 SOTON/OQ 392076 7.0500 NaN S \n", "885 5 382652 29.1250 NaN Q \n", "886 0 211536 13.0000 NaN S \n", "889 0 111369 30.0000 C148 C \n", "890 0 370376 7.7500 NaN Q \n", "\n", "[653 rows x 12 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[(df[\"Name\"].str.contains(\"Rev.\")) | (df[\"Name\"].str.contains(\"Mr.\"))]" ] }, { "cell_type": "markdown", "id": "structured-millennium", "metadata": {}, "source": [ "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." ] }, { "cell_type": "code", "execution_count": 6, "id": "narrative-traveler", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
.......................................
88488503Sutehall, Mr. Henry Jrmale25.000SOTON/OQ 3920767.0500NaNS
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250NaNQ
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
\n", "

653 rows × 12 columns

\n", "
" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "3 4 1 1 \n", "4 5 0 3 \n", "5 6 0 3 \n", ".. ... ... ... \n", "884 885 0 3 \n", "885 886 0 3 \n", "886 887 0 2 \n", "889 890 1 1 \n", "890 891 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "5 Moran, Mr. James male NaN 0 \n", ".. ... ... ... ... \n", "884 Sutehall, Mr. Henry Jr male 25.0 0 \n", "885 Rice, Mrs. William (Margaret Norton) female 39.0 0 \n", "886 Montvila, Rev. Juozas male 27.0 0 \n", "889 Behr, Mr. Karl Howell male 26.0 0 \n", "890 Dooley, Mr. Patrick male 32.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S \n", "5 0 330877 8.4583 NaN Q \n", ".. ... ... ... ... ... \n", "884 0 SOTON/OQ 392076 7.0500 NaN S \n", "885 5 382652 29.1250 NaN Q \n", "886 0 211536 13.0000 NaN S \n", "889 0 111369 30.0000 C148 C \n", "890 0 370376 7.7500 NaN Q \n", "\n", "[653 rows x 12 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[df[\"Name\"].str.contains(\"Rev.|Mr.\", regex=True)]" ] }, { "cell_type": "markdown", "id": "particular-afternoon", "metadata": {}, "source": [ "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." ] }, { "cell_type": "code", "execution_count": 8, "id": "optical-auckland", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
.......................................
88488503Sutehall, Mr. Henry Jrmale25.000SOTON/OQ 3920767.0500NaNS
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250NaNQ
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
\n", "

654 rows × 12 columns

\n", "
" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "3 4 1 1 \n", "4 5 0 3 \n", "5 6 0 3 \n", ".. ... ... ... \n", "884 885 0 3 \n", "885 886 0 3 \n", "886 887 0 2 \n", "889 890 1 1 \n", "890 891 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "5 Moran, Mr. James male NaN 0 \n", ".. ... ... ... ... \n", "884 Sutehall, Mr. Henry Jr male 25.0 0 \n", "885 Rice, Mrs. William (Margaret Norton) female 39.0 0 \n", "886 Montvila, Rev. Juozas male 27.0 0 \n", "889 Behr, Mr. Karl Howell male 26.0 0 \n", "890 Dooley, Mr. Patrick male 32.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S \n", "5 0 330877 8.4583 NaN Q \n", ".. ... ... ... ... ... \n", "884 0 SOTON/OQ 392076 7.0500 NaN S \n", "885 5 382652 29.1250 NaN Q \n", "886 0 211536 13.0000 NaN S \n", "889 0 111369 30.0000 C148 C \n", "890 0 370376 7.7500 NaN Q \n", "\n", "[654 rows x 12 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import re\n", "df.loc[df[\"Name\"].str.contains(\"Rev.|Mr.\", case=False, regex=True)]" ] }, { "cell_type": "code", "execution_count": null, "id": "lightweight-reconstruction", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 5 }