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How to Create a Pandas DataFrame from a Text File with Specific Patterns?
How to Create a Pandas DataFrame from a Text File with Specific Patterns?
Creating a Pandas DataFrame from a Text File with Specific Patterns
You need to construct a Pandas DataFrame from a text file with 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]
The rows with "[edit]" indicate states, while the rows with "[number]" indicate regions. The task is to split the file based on these patterns and repeat the state name for each region name.
Solution:
- Read the text file using Pandas' read_csv function, specifying the column name as "Region Name" due to no separator.
- Create a new column named "State" using String Extraction to capture the state names from the rows with "[edit]" and fill the values forward.
- Replace all characters from the opening parenthesis "(" to the end of the string in the "Region Name" column.
- Filter out the rows containing "[edit]" using boolean indexing based on a mask created using String Contains.
This process will result in the desired Pandas DataFrame with "State" and "Region Name" columns.
Example:
<code class="python">import pandas as pd
df = pd.read_csv("filename.txt", sep=";", names=['Region Name'])
df.insert(0, 'State', df['Region Name'].str.extract('(.*)\[edit\]', expand=False).ffill())
df['Region Name'] = df['Region Name'].str.replace(r' \(.+$', '')
df = df[~df['Region Name'].str.contains('\[edit\]')].reset_index(drop=True)
print(df)</code>Output:
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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