我正在做應(yīng)用數(shù)據(jù)科學(xué)的作業(yè)。問題： 將可再生能源百分比削減為 5 個類別。按大陸劃分的前 15 名組，以及這些新的可再生百分比垃圾箱。每個組中有多少個國家？此函數(shù)應(yīng)返回一個具有 Continent MultiIndex 的系列，然后是可再生百分比的 bin。請勿包含沒有國家/地區(qū)的組。這是我的代碼：def answer_twelve(): Top15 = answer_one() ContinentDict = {'China':'Asia', 'United States':'North America', 'Japan':'Asia', 'United Kingdom':'Europe', 'Russian Federation':'Europe', 'Canada':'North America', 'Germany':'Europe', 'India':'Asia', 'France':'Europe', 'South Korea':'Asia', 'Italy':'Europe', 'Spain':'Europe', 'Iran':'Asia', 'Australia':'Australia', 'Brazil':'South America'} Top15['Continent'] = Top15.index.to_series().map(ContinentDict) Top15['bins'] = pd.cut(Top15['% Renewable'],5) return pd.Series(Top15.groupby(by = ['Continent', 'bins']).size())#,apply(lambda x:s if x['Rank']==0 continue))answer_twelve()這是我對上述代碼的輸出Continent bins Asia (2.212, 15.753] 4 (15.753, 29.227] 1 (29.227, 42.701] 0 (42.701, 56.174] 0 (56.174, 69.648] 0Australia (2.212, 15.753] 1 (15.753, 29.227] 0 (29.227, 42.701] 0 (42.701, 56.174] 0 (56.174, 69.648] 0Europe (2.212, 15.753] 1 (15.753, 29.227] 3 (29.227, 42.701] 2 (42.701, 56.174] 0 (56.174, 69.648] 0North America (2.212, 15.753] 1 (15.753, 29.227] 0 (29.227, 42.701] 0 (42.701, 56.174] 0 (56.174, 69.648] 1South America (2.212, 15.753] 0 (15.753, 29.227] 0 (29.227, 42.701] 0 (42.701, 56.174] 0 (56.174, 69.648] 1dtype: int64
3 回答
ITMISS
TA貢獻1871條經(jīng)驗 獲得超8個贊
lambda x:s if x['Rank']==0 continue
這沒有任何意義，因為continue僅在循環(huán)內(nèi)有用。請注意，您需要打印一個值。相反，將其留空：
lambda x:"" if x['Rank']==0 else s
鳳凰求蠱
TA貢獻1825條經(jīng)驗 獲得超4個贊
您可以使用“for”循環(huán)迭代這些值，然后使用replace()NaN 替換 0，現(xiàn)在您可以使用dropna(). 我嘗試使用drop()或droplevel()代替替換它們，但它不起作用。這是我的代碼：
for k,i in series_df.items():
if i == 0:
pd_series.replace(to_replace=i, value=np.nan, inplace=True)
pd_series.dropna(axis=0, inplace=True)
print(pd_series)
您可能需要更改結(jié)果的數(shù)據(jù)類型。輸出為：
Continent bins
Asia (2.212, 15.753] 4
(15.753, 29.227] 1
Australia (2.212, 15.753] 1
Europe (2.212, 15.753] 1
(15.753, 29.227] 3
(29.227, 42.701] 2
North America (2.212, 15.753] 1
(56.174, 69.648] 1
South America (56.174, 69.648] 1
dtype: int64
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