我想根据数据框中的自定义条件制作数据透视表:
数据框看起来像这样:
>>> 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
现在我想以某种方式旋转数据框 df，这样我就可以看到每个地区的城市的独特数量以及“好”城市的相应数量以及该地区的人口地区。
我期望这样的输出:
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
我可以为 aggfunc 参数提供一个字典，但这并没有给我在好城市之间划分的城市计数。
>>> 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
我可以合并两个不同的数据透视表 - 一个包含城市计数，另一个包含人口，但这对于具有大型 aggfunc 字典的大型数据集而言不可扩展。
最佳答案
使用fill_value 添加新参数columns，也可以使用nunique对于聚合函数:
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
最后如果需要将索引转换为列并更改列名:
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
编辑:
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
关于python - 在数据框上使用自定义条件的 Pandas 数据透视表，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/53239514/