Python Pandas - 根据元素频率按升序对 DataFrame 进行排序
要按升序或降序对数据进行排序，请使用sort_values()method。对于升序，使用以下sort_values()方法-
ascending=True
导入所需的库-
import pandas as pd
创建一个包含3列的DataFrame-
dataFrame = pd.DataFrame(
{
"Car": ['BMW', 'Lexus', 'BMW', 'Mustang', 'Mercedes', 'Lexus'],"Reg_Price": [7000, 1500, 5000, 8000, 9000, 2000],"Place": ['Pune', 'Delhi', 'Mumbai', 'Hyderabad', 'Bangalore', 'Chandigarh']
}
)要根据元素频率按升序对DataFrame进行排序，我们需要计算出现次数。因此，count()也与sort_values()set一起用于升序排序-
dataFrame.groupby(['Car'])['Reg_Price'].count().reset_index(name='Count').sort_values(['Count'], ascending=True)
示例
以下是代码-
import pandas as pd
#CreateDataFrame
dataFrame = pd.DataFrame(
{
"Car": ['BMW', 'Lexus', 'BMW', 'Mustang', 'Mercedes', 'Lexus'],"Reg_Price": [7000, 1500, 5000, 8000, 9000, 2000],"Place": ['Pune', 'Delhi', 'Mumbai', 'Hyderabad', 'Bangalore', 'Chandigarh']
}
)
print"DataFrame ...\n",dataFrame
#SortDataFrameinascendingorderaccordingtotheelementfrequency
dataFrame = dataFrame.groupby(['Car'])['Reg_Price'].count().reset_index(name='Count').sort_values(['Count'], ascending=True)
print"\nSorting DataFrame in ascending order ...\n",dataFrame输出结果这将产生以下输出-
DataFrame ...
Car Place Reg_Price
0 BMW Pune 7000
1 Lexus Delhi 1500
2 BMW Mumbai 5000
3 Mustang Hyderabad 8000
4 Mercedes Bangalore 9000
5 Lexus Chandigarh 2000
Sorting DataFrame in ascending order ...
Car Count
2 Mercedes 1
3 Mustang 1
0 BMW 2
1 Lexus 2