CODEWITHSUNDEEP
pandasindexingtransformation
Christopher
Christopher

Reputation: 2232

Parsing unstructured data to pandas data frame

I currently have following data structure in a pandas dataframe, after importing a *.txt file via read_csv:

    label   text
0   ###24293578 NaN
1   INTRO   Some text...
2   METHODS Some text...
3   METHODS Some text...
4   METHODS Some text...
5   RESULTS Some text...
6   ###24854809 NaN
7   BACKGROUND  Some text...
8   INTRO   Some text...
9   METHODS Some text...
10  METHODS Some text...
11  RESULTS Some text...
12  ###25165090 NaN
13  BACKGROUND  Some text...
14  METHODS Some text...
...

What I like to achieve is a running index for each row, retrieved from the id marked with "###":

id        label       text
24293578  INTRO       Some text...
24293578  METHODS     Some text...
24293578  ...         ...
24854809  BACKGROUND  Some text...
24854809  ...         ...
25165090  BACKGROUND  Some text...
25165090  ...         ...

I currently use following code to transform the data:

m = df['label'].str.contains("###", na=False) 
df['new'] = df['label'].where(m).ffill()
df = df[df['label'] != df['new']].copy()
df['label'] = df.pop('new').str.lstrip('#') + ' ' + df['label']
df[['id','area']] = df['label'].str.split(' ',expand=True)
df = df.drop(columns=['label'])
df

Out:

    text            id          area
1   Some text...    24293578    OBJECTIVE
...
6   Some text...    24854809    BACKGROUND
...

It does the job but I feel this isn't the best approach. Is there a way to write the code cleaner, or make it more efficient? I'm also curious, whether the a function could be directly embedded into the read_csv step.

Thank you!

Upvotes: 1

Views: 1640

Answers (1)

sacuL
sacuL

Reputation: 51395

Here you can do it in 3 steps:

# put in the label column into id where text is null, and strip out the #. 
# The rest will be NaN
df['id'] = df.loc[df['text'].isnull(),'label'].str.strip('#')

# forward fill in ID
df['id'].ffill(inplace=True)

# Remove the columns where text is null
df.dropna(subset=['text'], inplace=True)

>>> df
         label          text        id
1        INTRO  Some text...  24293578
2      METHODS  Some text...  24293578
3      METHODS  Some text...  24293578
4      METHODS  Some text...  24293578
5      RESULTS  Some text...  24293578
7   BACKGROUND  Some text...  24854809
8        INTRO  Some text...  24854809
9      METHODS  Some text...  24854809
10     METHODS  Some text...  24854809
11     RESULTS  Some text...  24854809
13  BACKGROUND  Some text...  25165090
14     METHODS  Some text...  25165090

Upvotes: 4

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