英文:
How to make one-liner `pd.Series` with `pd.CategoricalIndex` property
问题
以下是您要翻译的内容：
"I have a one-liner liner code that is working, which from pd.DataFrame create pd.Series with an index pd.CategoricalIndex.
Since pd.DataFrame is an API based on pd.Series I would like to generate the same series but now with pd.Series only, this is a question of optimization and API panda skills.
The pd.DataFrame code is listed below
import pandas as pdpd_series_1 = pd.DataFrame(data=[("2018-01", 0.0),("2019-02", 1200.0),("2019-03", 600.0),],columns=['TIME_PERIOD', "OBS_VALUE"],).astype({"TIME_PERIOD": "category"}).set_index("TIME_PERIOD")["OBS_VALUE"]assert pd_series_1.index.name == "TIME_PERIOD"assert repr(pd_series_1.index) == "CategoricalIndex(['2018-01', '2019-02', '2019-03'], " \"categories=['2018-01', '2019-02', '2019-03'], " \"ordered=False, " \"dtype='category', " \"name='TIME_PERIOD')", repr(pd_series_1.index)assert repr(pd_series_1) == "TIME_PERIOD\n" \"2018-01 0.0\n" \"2019-02 1200.0\n" \"2019-03 600.0\n" \"Name: OBS_VALUE, dtype: float64", repr(pd_series_1)
As you can see, the final series pd_series_1 has: CategoricalIndex.name equal with 'TIME_PERIOD' and the name as 'OBS_VALUE'.
The same is desired to have by using only pd.Series API within constructor or plus additional chain methods alike .set_index as in pd_series_1.
The code which I used for pd.Series is listed below
pd_series_2 = pd.Series(dict([("2018-01", 0.0),("2019-02", 1200.0),("2019-03", 600.0),]),name='OBS_VALUE',)print(pd_series_2)# 2018-01 0.0# 2019-02 1200.0# 2019-03 600.0# Name: OBS_VALUE, dtype: float64pd_series_2.index = pd.CategoricalIndex(pd_series_2.index, name='TIME_PERIOD')print(pd_series_2)# TIME_PERIOD# 2018-01 0.0# 2019-02 1200.0# 2019-03 600.0# Name: OBS_VALUE, dtype: float64
As you can observe, I managed to get the result, but the code is not one-liner.
Please suggest one-liner syntax here,
thank you in advance"
英文:
I have a one-liner liner code that is working, which from pd.DataFrame create pd.Series with an index pd.CategoricalIndex.
Since pd.DataFrame is an API based on pd.Series I would like to generate the same series but now with pd.Series only, this is a question of optimization and API panda skills.
The pd.DataFrame code is listed below
import pandas as pdpd_series_1 = pd.DataFrame(data=[("2018-01", 0.0),("2019-02", 1200.0),("2019-03", 600.0),],columns=['TIME_PERIOD', "OBS_VALUE"],).astype({"TIME_PERIOD": "category"}).set_index("TIME_PERIOD")["OBS_VALUE"]assert pd_series_1.index.name == "TIME_PERIOD"assert repr(pd_series_1.index) == "CategoricalIndex(['2018-01', '2019-02', '2019-03'], " \"categories=['2018-01', '2019-02', '2019-03'], " \"ordered=False, " \"dtype='category', " \"name='TIME_PERIOD')", repr(pd_series_1.index)assert repr(pd_series_1) == "TIME_PERIOD\n" \"2018-01 0.0\n" \"2019-02 1200.0\n" \"2019-03 600.0\n" \"Name: OBS_VALUE, dtype: float64", repr(pd_series_1)
As you can see, the final series pd_series_1 has: CategoricalIndex.name equal with 'TIME_PERIOD' and the name as 'OBS_VALUE'.
The same is desired to have by using only pd.Series API within constructor or plus additional chain methods alike .set_index as in pd_series_1.
The code which I used for pd.Series is listed below
pd_series_2 = pd.Series(dict([("2018-01", 0.0),("2019-02", 1200.0),("2019-03", 600.0),]),name='OBS_VALUE',)print(pd_series_2)# 2018-01 0.0# 2019-02 1200.0# 2019-03 600.0# Name: OBS_VALUE, dtype: float64pd_series_2.index = pd.CategoricalIndex(pd_series_2.index, name='TIME_PERIOD')print(pd_series_2)# TIME_PERIOD# 2018-01 0.0# 2019-02 1200.0# 2019-03 600.0# Name: OBS_VALUE, dtype: float64
As you can observe, I managed to get the result, but the code is not one-liner.
Please suggest one-liner syntax here,
thank you in advance
答案1
得分: 2
使用 Series.pipe 与 Series.set_axis：
pd_series_2 = pd.Series(dict([("2018-01", 0.0),("2019-02", 1200.0),("2019-03", 600.0),]),name='OBS_VALUE',).pipe(lambda x:x.set_axis(pd.CategoricalIndex(x.index, name='TIME_PERIOD')))print(pd_series_2.index)CategoricalIndex(['2018-01', '2019-02', '2019-03'],categories=['2018-01', '2019-02', '2019-03'],ordered=False,dtype='category',name='TIME_PERIOD')
英文:
Use Series.pipe with Series.set_axis:
pd_series_2 = pd.Series(dict([("2018-01", 0.0),("2019-02", 1200.0),("2019-03", 600.0),]),name='OBS_VALUE',).pipe(lambda x:x.set_axis(pd.CategoricalIndex(x.index, name='TIME_PERIOD')))print(pd_series_2.index)CategoricalIndex(['2018-01', '2019-02', '2019-03'],categories=['2018-01', '2019-02', '2019-03'],ordered=False,dtype='category',name='TIME_PERIOD')
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