Create Pandas DataFrame from txt file with specific pattern
You can first read_csv
with parameter name
for create DataFrame
with column Region Name
, separator is value which is NOT in values (like ;
):
df = pd.read_csv('filename.txt', sep=";", names=['Region Name'])
Then insert
new column State
with extract
rows where text [edit]
and replace
all values from (
to the end to column Region Name
.
df.insert(0, 'State', df['Region Name'].str.extract('(.*)\[edit\]', expand=False).ffill())
df['Region Name'] = df['Region Name'].str.replace(r' \(.+$', '')
Last remove rows where text [edit]
by boolean indexing
, mask is created by str.contains
:
df = df[~df['Region Name'].str.contains('\[edit\]')].reset_index(drop=True)
print (df)
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
If need all values solution is easier:
df = pd.read_csv('filename.txt', sep=";", names=['Region Name'])
df.insert(0, 'State', df['Region Name'].str.extract('(.*)\[edit\]', expand=False).ffill())
df = df[~df['Region Name'].str.contains('\[edit\]')].reset_index(drop=True)
print (df)
State Region Name
0 Alabama Auburn (Auburn University)[1]
1 Alabama Florence (University of North Alabama)
2 Alabama Jacksonville (Jacksonville State University)[2]
3 Alabama Livingston (University of West Alabama)[2]
4 Alabama Montevallo (University of Montevallo)[2]
5 Alabama Troy (Troy University)[2]
6 Alabama Tuscaloosa (University of Alabama, Stillman Co...
7 Alabama Tuskegee (Tuskegee University)[5]
8 Alaska Fairbanks (University of Alaska Fairbanks)[2]
9 Arizona Flagstaff (Northern Arizona University)[6]
10 Arizona Tempe (Arizona State University)
11 Arizona Tucson (University of Arizona)
You could parse the file into tuples first:
import pandas as pd
from collections import namedtuple
Item = namedtuple('Item', 'state area')
items = []
with open('unis.txt') as f:
for line in f:
l = line.rstrip('\n')
if l.endswith('[edit]'):
state = l.rstrip('[edit]')
else:
i = l.index(' (')
area = l[:i]
items.append(Item(state, area))
df = pd.DataFrame.from_records(items, columns=['State', 'Area'])
print df
output:
State Area
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