How To Read Text File's Key, Value Pair Using Pandas?
I want to parse one text file which contains following data. Input.txt- 1=88|11=1438|15=KKK|45=7.7|45=00|21=66|86=a 4=13|4=1388|49=DDD|8=157.73|67=00|45=08|84=b|45=k 6=84|41=18|56=
Solution 1:
You can first read_csv
with separator which is not in data e.g. ;
, then double split
with stack
:
import pandas as pd
import numpy as np
import io
temp=u"""1=88|11=1438|15=KKK|45=7.7|45=00|21=66|86=a
4=13|4=1388|49=DDD|8=157.73|67=00|45=08|84=b|45=k
6=84|41=18|56=TTT|67=1.2|4=21|45=78|07=d
"""#after testing replace io.StringIO(temp) to filename
df = pd.read_csv(io.StringIO(temp), sep=";", index_col=None, names=['text'])
print (df)
text
01=88|11=1438|15=KKK|45=7.7|45=00|21=66|86=a
14=13|4=1388|49=DDD|8=157.73|67=00|45=08|84=b|45=k
26=84|41=18|56=TTT|67=1.2|4=21|45=78|07=d
s = df.text.str.split('|', expand=True).stack().str.split('=', expand=True)
print (s)
01001881111438215 KKK
3457.74450052166686 a
10413141388249 DDD
38157.7346700545 08
684 b
745 k
2068414118256 TTT
3671.24421545786 07 d
dfs = [g.set_index(0).rename_axis(None) for i, g in s.groupby(level=0)]
print (dfs[0])
118811143815 KKK
457.74500216686 a
fori, gins.groupby(level=0):
print (g.set_index(0).rename_axis(None))
118811143815KKK457.74500216686a14134138849DDD8157.736700450884b45k1684411856TTT671.2421457807d
EDIT by comment:
If need write to file s
, use to_csv
:
s.to_csv('file.txt', header=None, index=None, sep='\t')
EDIT1 by comment:
You can set column name to empty string and remove index name by rename_axis
(new in pandas
0.18.0
), but more common is set column name to some text (e.g. s.columns = ['idx','a']
):
s = df.text.str.split('|', expand=True).stack().str.split('=', expand=True)
s.columns = ['idx','']
print (s)
idx
001881111438215 KKK
3457.74450052166686 a
10413141388249 DDD
38157.7346700545 08
684 b
745 k
2068414118256 TTT
3671.24421545786 07 d
dfs = [g.set_index('idx').rename_axis(None) for i, g in s.groupby(level=0)]
print (dfs[0])
18811143815 KKK
457.74500216686 a
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