How do you create a Pandas DataFrame from a text file with specific patterns, where states are indicated by \"[edit]\" and regions by \"[number]\"?

Creating a Pandas DataFrame from a Text File with Specific Patterns

Problem Statement:

The goal is to create a Pandas DataFrame from a text file that has the following structure:

Alabama[edit]
Auburn (Auburn University)[1]
Florence (University of North Alabama)
Jacksonville (Jacksonville State University)[2]
Livingston (University of West Alabama)[2]
Montevallo (University of Montevallo)[2]
Troy (Troy University)[2]
Tuscaloosa (University of Alabama, Stillman College, Shelton State)[3][4]
Tuskegee (Tuskegee University)[5]
Alaska[edit]
Fairbanks (University of Alaska Fairbanks)[2]
Arizona[edit]
Flagstaff (Northern Arizona University)[6]
Tempe (Arizona State University)
Tucson (University of Arizona)
Arkansas[edit]

Where rows with "[edit]" indicate states and rows with "[number]" indicate regions. The DataFrame should split the data based on these patterns and repeat the state name for each region name.

Solution:

To achieve this, we can follow the below steps:

1. Use pandas to read the text file as a DataFrame, using a semicolon as a separator and creating a column named "Region Name":

df = pd.read_csv('filename.txt', sep=";", names=['Region Name'])

2. Insert a new column named "State" using the string extract method to extract the state name from rows containing "[edit]". We then fill the missing values using forward fill (ffill):

df.insert(0, 'State', df['Region Name'].str.extract('(.*)\[edit\]', expand=False).ffill())

3. Replace any text enclosed in parentheses with an empty string in the "Region Name" column to remove Region Name characteristics:

df['Region Name'] = df['Region Name'].str.replace(r' \(.+$', '')

4. Remove rows containing "[edit]" using boolean indexing and the str.contains function. The resulting DataFrame contains the desired data:

df = df[~df['Region Name'].str.contains('\[edit\]')].reset_index(drop=True)
print (df)

Example Output:

The output DataFrame will look as follows:

      State   Region Name
0   Alabama        Auburn
1   Alabama      Florence
2   Alabama  Jacksonville
3   Alabama    Livingston
4   Alabama    Montevallo
5   Alabama          Troy
6   Alabama    Tuscaloosa
7   Alabama      Tuskegee
8    Alaska     Fairbanks
9   Arizona     Flagstaff
10  Arizona         Tempe
11  Arizona        Tucson

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