[英]Pandas pivot table using custom conditions on the dataframe
我想根據數據框中的自定義條件制作數據透視表:
數據框如下所示:
>>> df = pd.DataFrame({"Area": ["A", "A", "B", "A", "C", "A", "D", "A"],
"City" : ["X", "Y", "Z", "P", "Q", "R", "S", "X"],
"Condition" : ["Good", "Bad", "Good", "Good", "Good", "Bad", "Good", "Good"],
"Population" : [100,150,50,200,170,390,80,100]
"Pincode" : ["X1", "Y1", "Z1", "P1", "Q1", "R1", "S1", "X2"] })
>>> df
Area City Condition Population Pincode
0 A X Good 100 X1
1 A Y Bad 150 Y1
2 B Z Good 50 Z1
3 A P Good 200 P1
4 C Q Good 170 Q1
5 A R Bad 390 R1
6 D S Good 80 S1
7 A X Good 100 X2
現在我想以一種方式來轉動數據框df
,這樣我就可以看到針對每個區域的城市的唯一計數以及相應的“好”城市數量以及該區域的人口數量。
我期待這樣的輸出:
Area city_count good_city_count Population
A 4 2 940
B 1 1 50
C 1 1 170
D 1 1 80
All 7 5 1240
我可以給aggfunc
參數一個字典,但這並沒有給我好城市之間的城市數量。
>>> city_count = df.pivot_table(index=["Area"],
values=["City", "Population"],
aggfunc={"City": lambda x: len(x.unique()),
"Population": "sum"},
margins=True)
Area City Population
0 A 4 940
1 B 1 50
2 C 1 170
3 D 1 80
4 All 7 1240
我可以合並兩個不同的數據透視表 - 一個具有城市數量,另一個具有總體數量但是對於具有大型aggfunc
字典的大型數據集而言,這是不可擴展的。
使用fill_value
添加新參數columns
,也可以使用nunique
作為聚合函數:
city_count = df.pivot_table(index = "Area",
values = "City",
columns='Condition',
aggfunc = lambda x : x.nunique(),
margins = True,
fill_value=0)
print (city_count)
Condition Bad Good All
Area
A 2 2 4
B 0 1 1
C 0 1 1
D 0 1 1
All 2 5 7
最后如果需要將索引轉換為列並更改列名稱:
city_count = city_count.add_suffix('_count').reset_index().rename_axis(None, 1)
print (city_count)
Area Bad_count Good_count All_count
0 A 2 2 4
1 B 0 1 1
2 C 0 1 1
3 D 0 1 1
4 All 2 5 7
編輯:
d = {'City':'nunique','Population':'sum', 'good_city_count':'nunique'}
d1 = {'City':'city_count','Condition':'good_city_count'}
mask = df["Condition"] == 'Good'
df = (df.assign(good_city_count = lambda x: np.where(mask, x['City'], np.nan))
.groupby('Area')
.agg(d)
.rename(columns=d1))
df = df.append(df.sum().rename('All')).reset_index()
print (df)
Area city_count Population good_city_count
0 A 4 940 2
1 B 1 50 1
2 C 1 170 1
3 D 1 80 1
4 All 7 1240 5
不使用pivot_table
另一種方法。 使用帶有groupby
+ agg
np.where
:
df['Condition'] = np.where(df['Condition']=='Good', df['City'], np.nan)
df = df.groupby('Area').agg({'City':'nunique', 'Condition':'nunique', 'Population':'sum'})\
.rename(columns={'City':'city_count', 'Condition':'good_city_count'})
df.loc['All',:] = df.sum()
df = df.astype(int).reset_index()
print(df)
Area city_count good_city_count Population
0 A 4 2 940
1 B 1 1 50
2 C 1 1 170
3 D 1 1 80
4 All 7 5 1240
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