45 lines
1.1 KiB
Python
45 lines
1.1 KiB
Python
|
import pandas as pd
|
||
|
|
import datetime
|
||
|
|
|
||
|
|
from utils.documents_to_database import documents_to_database
|
||
|
|
|
||
|
|
# In[ ]:
|
||
|
|
linkedin_data_path = "data/LinkedIn/shares/Shares.csv"
|
||
|
|
raw_shares = pd.read_csv(linkedin_data_path)
|
||
|
|
|
||
|
|
# In[ ]:
|
||
|
|
raw_shares['index'] = "rs_linkedin_shares"
|
||
|
|
raw_shares['type'] = "posts"
|
||
|
|
raw_shares['network'] = "LinkedIn"
|
||
|
|
raw_shares['chemin'] = linkedin_data_path
|
||
|
|
|
||
|
|
# In[ ]:
|
||
|
|
raw_shares["datepublication"] = raw_shares["Date"].apply(
|
||
|
|
lambda x: str(datetime.datetime.fromisoformat(x).isoformat()))
|
||
|
|
del raw_shares["Date"]
|
||
|
|
|
||
|
|
# In[ ]:
|
||
|
|
raw_shares.rename(columns={"ShareLink": "uri", "ShareCommentary": "texte"}, inplace=True)
|
||
|
|
|
||
|
|
# In[ ]:
|
||
|
|
raw_shares["texte"] = raw_shares["texte"].apply(lambda x: str(x))
|
||
|
|
|
||
|
|
# In[ ]:
|
||
|
|
del raw_shares["SharedUrl"]
|
||
|
|
del raw_shares["MediaUrl"]
|
||
|
|
del raw_shares["Visibility"]
|
||
|
|
|
||
|
|
# In[ ]:
|
||
|
|
raw_shares.fillna(value="", inplace=True)
|
||
|
|
|
||
|
|
# In[ ]:
|
||
|
|
raw_shares.drop_duplicates(subset=['texte', 'datepublication'], inplace=True)
|
||
|
|
|
||
|
|
# In[ ]:
|
||
|
|
# Filter empty texte
|
||
|
|
raw_shares = raw_shares[~raw_shares['texte'].str.strip('\n').str.strip().eq('')]
|
||
|
|
|
||
|
|
# In[ ]:
|
||
|
|
documents_to_database(raw_shares)
|
||
|
|
|