我有這個帶有大地水準面的數(shù)據(jù)框，看起來像這樣我想要做的是將每個 msaid 的每個大地水準面編號放入列表中。理想情況下，我希望有一個看起來像這樣的數(shù)據(jù)框我希望這是有道理的。任何幫助，將不勝感激。這里有兩個例子：159 [{"geoid":"02020000101"},{"geoid":"02020000204"},{"geoid":"02020000300"},{"geoid":"02020000400"},{"geoid":"02020000500"},{"geoid":"02020000600"},{"geoid":"02020000802"},{"geoid":"02020000901"},{"geoid":"02020000902"},{"geoid":"02020001000"},{"geoid":"02020001500"},{"geoid":"02020001601"},{"geoid":"02020001602"},{"geoid":"02020001701"},{"geoid":"02020001802"},{"geoid":"02020001900"},{"geoid":"02020002000"},{"geoid":"02020002100"},{"geoid":"02020002201"},{"geoid":"02020002400"},{"geoid":"02020002501"},{"geoid":"02020002502"},{"geoid":"02020002601"},{"geoid":"02020002712"},{"geoid":"02020002811"},{"geoid":"02020002812"},{"geoid":"02020002813"},{"geoid":"02122000100"},{"geoid":"02122000300"},{"geoid":"02170001300"},{"geoid":"02170000300"},{"geoid":"02170001100"},{"geoid":"02170000800"},{"geoid":"02261000300"},{"geoid":"02290000400"},{"geoid":"02240000400"},{"geoid":"02170000102"},{"geoid":"02170000402"},{"geoid":"02170000101"},{"geoid":"02170001201"},{"geoid":"02170001001"},{"geoid":"02170000706"},{"geoid":"02170001202"},{"geoid":"02170001004"},{"geoid":"02170000705"},{"geoid":"02170000603"},{"geoid":"02020000102"},{"geoid":"02020000201"},{"geoid":"02020000202"},{"geoid":"02020000203"},{"geoid":"02020000701"},{"geoid":"02020000702"},{"geoid":"02020000703"},{"geoid":"02020000801"},{"geoid":"02020001100"},{"geoid":"02020001200"},
2 回答
翻過高山走不出你
TA貢獻1875條經(jīng)驗 獲得超3個贊
我下載了該文件并將其作為 csv 文件保存在我的計算機中。然后我運行了以下代碼。
import pandas as pd
df = pd.read_csv('parse_this.csv')
#remove characters and convert to list
df.tracts = df.tracts.apply(lambda x: x.strip('][').split(','))
#explode tracts series
df = df.explode('tracts')
#resetting index and renaming columns
df.reset_index(drop = True, inplace = True)
df.rename(columns={"tracts": "geoid"} , inplace = True)
#removing extra characters to keep only the geoid number
df.geoid = df.geoid.apply(lambda x: x.strip('geoid{}:""'))
df
江戶川亂折騰
TA貢獻1851條經(jīng)驗 獲得超5個贊
我希望這個例子有幫助：
#creating a dataframe for example:
d = [{'A':3,'B':[{'id':'001'},{'id':'002'}]},
{'A':4,'B':[{'id':'003'},{'id':'004'}]},
{'A':5,'B':[{'id':'005'},{'id':'006'}]},
{'A':6,'B':[{'id':'007'},{'id':'008'}]}]
df = pd.DataFrame(d)
df
A B
0 3 [{'id': '001'}, {'id': '002'}]
1 4 [{'id': '003'}, {'id': '004'}]
2 5 [{'id': '005'}, {'id': '006'}]
3 6 [{'id': '007'}, {'id': '008'}]
#apply an explode to the column B and reset index
df = df.explode('B')
df.reset_index(drop = True, inplace = True)
df
# now it looks like this
A B
0 3 {'id': '001'}
1 3 {'id': '002'}
2 4 {'id': '003'}
3 4 {'id': '004'}
4 5 {'id': '005'}
5 5 {'id': '006'}
6 6 {'id': '007'}
7 6 {'id': '008'}
# now we need to remove the extra text and rename the column from B to id
df.B = df.B.apply(lambda x: x['id'])
df.rename(columns={"B": "id"} , inplace = True)
# this is the final product:
df
A id
0 3 001
1 3 002
2 4 003
3 4 004
4 5 005
5 5 006
6 6 007
7 6 008
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