我的数据1如下:
[
{"cut_id":1,"cut_label":"v024","cut_name":"State","value_label":"1","value":"andaman and nicobar islands"},
{"cut_id":3,"cut_label":"v024","cut_name":"State","value_label":"3","value":"arunachal pradesh"},
{"cut_id":635,"cut_label":"sdistri","cut_name":"District","value_label":"599","value":"pathanamthitta"},
{"cut_id":636,"cut_label":"sdistri","cut_name":"District","value_label":"600","value":"kollam"},
{"cut_id":637,"cut_label":"sdistri","cut_name":"District","value_label":"601","value":"thiruvananthapuram"}
]
我想要的输出如下:
[
{"value_label":"S1","value":"andaman and nicobar islands"},
{"value_label":"S3","value":"arunachal pradesh"},
{"value_label":"D599","value":"pathanamthitta"},
{"value_label":"D600","value":"kollam"},
{"value_label":"D601","value":"thiruvananthapuram"}
]
我的意图是通过在数字后面附加字符“S”或“D”来重命名值标签,具体取决于它是州还是地区。
这是我的代码:
for _, r in data[
(data['cut_name'] == 'State') | (data['cut_name'] == 'District')][
['cut_name', 'value', 'value_label']
].iterrows():
cuts_data[r.cut_name[0]+r.value_label] = r.value
我得到了预期的结果,但是有没有办法在一行中做到这一点
将 str
与索引一起使用以获得 cut_name
的第一个值,并在必要时通过 Series.isin
过滤它:
mask = data['cut_name'].isin(['State','District'])
data.loc[mask, 'value_label'] = data['cut_name'].str[0] + data['value_label'].astype(str)
如果只有 State
或 District
可能的值:
data['value_label'] = data['cut_name'].str[0] + data['value_label'].astype(str)
为了提高性能,可以使用列表理解(工作良好是非缺失值):
data['value_label'] = [c[0] + str(v) for c, v in zip(data['cut_name'], data['value_label'])]
如果需要带有过滤列的新 DataFrame:
new_df = data[['value','value_label']]
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