from Main import get_metadata
import pandas as pd
# get eppiID data
eppiid = get_metadata("ItemId")
eppiid_df = pd.DataFrame(eppiid)
eppiid_df.columns = ["id"]
eppiid_df.fillna("NA", inplace=True)
# save to disk
eppiid_df.to_csv("eppiid.csv", index=False)
from Main import load_json, get_metadata
import pandas as pd
# load json file
load_json()
# get pubtype eppi data
pubtype_eppi = get_metadata("TypeName")
pubtype_eppi_df = pd.DataFrame(pubtype_eppi)
pubtype_eppi_df.columns = ["pub_eppi"]
# fill blanks with NA
pubtype_eppi_df.fillna("NA", inplace=True)
# save to risk
""" pubtype_eppi_df.to_csv("pubtype_eppi.csv", index=False) """
from Main import get_metadata
import pandas as pd
# get abstract data
abstract = get_metadata("Abstract")
abstract_df = pd.DataFrame(abstract)
abstract_df.columns = ["abstract"]
abstract_df.fillna("NA", inplace=True)
# save to disk
abstract_df.to_csv("abstract.csv", index=False)
from Main import get_metadata
import pandas as pd
# get author data
author = get_metadata("ShortTitle")
author_df = pd.DataFrame(author)
author_df.columns = ["pub_author"]
author_df.fillna("NA", inplace=True)
# save to disk
author_df.to_csv("author.csv", index=False)
if int(row["pub_year"]) >= 1960 and int(row["pub_year"]) <= 1969:
decade = "1960-1969"
elif int(row["pub_year"]) >= 1970 and int(row["pub_year"]) <= 1979:
decade = "1970-1979"
elif int(row["pub_year"]) >= 1980 and int(row["pub_year"]) <= 1989:
decade = "1980-1989"
elif int(row["pub_year"]) >= 1990 and int(row["pub_year"]) <= 1999:
decade = "1990-1999"
elif int(row["pub_year"]) >= 2000 and int(row["pub_year"]) <= 2009:
decade = "2000-2010"
elif int(row["pub_year"]) >= 2010 and int(row["pub_year"]) <= 2019:
decade = "2010-2019"
elif int(row["pub_year"]) >= 2019 and int(row["pub_year"]) <= 2029:
decade = "2020-2029"
return decade
# get year data
year = get_metadata("Year")
year_df = pd.DataFrame(year)
year_df.columns = ["pub_year"]
# add decade column
""" year_df["decade"] = year_df.apply(decade_row, axis=1) """
# fill blanks with NA
year_df.fillna("NA", inplace=True)
# save to disk
year_df.to_csv("year.csv", index=False)