Add article and requirements files; update README.

lapets · lapets · commit b65f59e49351 · 2019-11-30T14:53:17.000-05:00
diff --git a/README.md b/README.md
@@ -1,2 +1,2 @@
 # strings-regular-expressions-and-text-data-analysis
-While built-in string methods have limited flexibility and regular expressions have limited expressive power, both can still be leveraged in creative ways to implement scalable workflows that analyze and process text data. This article explores these tools and introduces a few useful peripheral techniques within the context of a use case involving a large text data corpus: the set of article abstracts found in the English-language edition of Wikipedia.
+While built-in string methods have limited flexibility and regular expressions have limited expressive power, both can still be leveraged in creative ways to implement scalable workflows that process and analyze text data. This article explores these tools and introduces a few useful peripheral techniques within the context of a use case involving a large text data corpus: the set of article abstracts found in the English-language edition of Wikipedia.
diff --git a/requirements.txt b/requirements.txt
@@ -0,0 +1,4 @@
+jupyterlab
+tqdm
+requests
+matplotlib
diff --git a/strings-regular-expressions-and-text-data-analysis.ipynb b/strings-regular-expressions-and-text-data-analysis.ipynb

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`1`	`1`	`# strings-regular-expressions-and-text-data-analysis`
`2`		`-While built-in string methods have limited flexibility and regular expressions have limited expressive power, both can still be leveraged in creative ways to implement scalable workflows that analyze and process text data. This article explores these tools and introduces a few useful peripheral techniques within the context of a use case involving a large text data corpus: the set of article abstracts found in the English-language edition of Wikipedia.`
	`2`	`+While built-in string methods have limited flexibility and regular expressions have limited expressive power, both can still be leveraged in creative ways to implement scalable workflows that process and analyze text data. This article explores these tools and introduces a few useful peripheral techniques within the context of a use case involving a large text data corpus: the set of article abstracts found in the English-language edition of Wikipedia.`