pandas 中 stack 的使用

有時候需要將特徵名稱轉化為變數，也就是將資料集由橫向改為縱向，或者為轉秩。使用場景如下：

# 資料集

in [5]: test
out[5]: 
tweet_id doggo floofer  pupper puppo
0675003128568291329
none
none
none
none
1786233965241827333
none
none
none
none
2683481228088049664
none
none  pupper  none
3675497103322386432
none
none
none
none
# 先設定index，再使用.stack()方法由橫向變縱向，對特徵進行命名
in [6]: s1 = test.set_index('tweet_id').stack().rename('stage')
in [7]: s1
out[7]: 
tweet_id                   
675003128568291329  doggo        none
floofer      none
pupper       none
puppo        none
786233965241827333  doggo        none
floofer      none
pupper       none
puppo        none
683481228088049664  doggo        none
floofer      none
pupper     pupper
puppo        none
675497103322386432  doggo        none
floofer      none
pupper       none
puppo        none
name: stage, dtype: object
# 將多重索引reset
in [8]: s2 = s1.reset_index()
in [9]: s2
out[9]: 
tweet_id  level_1   stage
0675003128568291329    doggo    none
1675003128568291329  floofer    none
2675003128568291329   pupper    none
3675003128568291329    puppo    none
4786233965241827333    doggo    none
5786233965241827333  floofer    none
6786233965241827333   pupper    none
7786233965241827333    puppo    none
8683481228088049664    doggo    none
9683481228088049664  floofer    none
10683481228088049664   pupper  pupper
11683481228088049664    puppo    none
12675497103322386432    doggo    none
13675497103322386432  floofer    none
14675497103322386432   pupper    none
15675497103322386432    puppo    none
# 將level_1列刪除，同時stage列只保留不為none的資料
in [10]: s2.drop(['level_1'], axis=1, inplace=true)
in [11]: s3 = s2[s2.stage != 'none']
in [12]: s3
out[12]: 
tweet_id   stage
10683481228088049664  pupper
# 跟原始資料集進行合併
in [14]: result = pd.merge(test, s3, how='left', on='tweet_id')
in [15]: result
out[15]: 
tweet_id doggo floofer  pupper puppo   stage
0675003128568291329
none
none
none
none     nan
1786233965241827333
none
none
none
none     nan
2683481228088049664
none
none  pupper  none  pupper
3675497103322386432
none
none
none
none     nan
# 刪除中間特徵，得到最終結果
in [16]: result.drop(['doggo','floofer','pupper','puppo'], axis=1)
out[16]: 
tweet_id   stage
0675003128568291329     nan
1786233965241827333     nan
2683481228088049664  pupper
3675497103322386432     nan
in [17]: test
out[17]: 
tweet_id doggo floofer  pupper puppo
0675003128568291329
none
none
none
none
1786233965241827333
none
none
none
none
2683481228088049664
none
none  pupper  none
3675497103322386432
none
none
none
none

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