我的data1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"}
]
我的意图是通过根据数字是州还是区,在数字后附加一个字符“ S”或“ D”来重命名值标签。
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,但是有一种方法可以做到这一点
最佳答案
将str
与索引一起使用以获取cut_name
的第一个值,并在必要时通过Series.isin
对其进行过滤:
mask = data['cut_name'].isin(['State','District'])
data.loc[mask, 'value_label'] = data['cut_name'].str[0] + data['value_label'].astype(str)
如果仅
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(正常工作是不丢失值):
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']]
关于python - 如何将迭代下的功能转换为 Pandas 中的一行,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/55449388/