>>>df.sort_values(...by="city08",...ascending=False...)city08 cylinders fuelType...mpgData trany year9234Regular...YAutomatic4-spd19932234Regular...YManual5-spd19857234Regular...YAutomatic3-spd19938234Regular...
random.randn(10,2),index=[1,4,6,2,3,5,9,8,0,7],columns = ['col2','col1']) # 按标签排序 sorted_df=unsorted_df.sort_index() # 排序顺序desc unsorted_df.sort_index(ascending=False) # 按列排列 unsorted_df.sort_index(axis=1) # 按值排序 unsorted_df.sort_values(by='col1') ...
一、sort_values()函数用途 pandas中的sort_values()函数原理类似于SQL中的order by,可以将数据集依照某个字段中的数据进行排序,该函数即可根据指定列数据也可根据指定行的数据排序。 二、sort_values()函数的具体参数 用法: DataFrame.sort_values(by=‘##’,axis=0,ascending=True,inplace=False,na_position=...
sort_values(key=lambda x: x.str.lower(),ascending=False) # 按索引列的字符串的小写降序排列 1.2 DataFrame.sort_values() by:str or list of str || Name or list of names to sort by. # by是区别于Series的部分 axis:{0 or ‘index’, 1 or ‘columns’}, default 0 ascending:bool or ...
df.sort_values(['column_name1', 'column_name2'], ascending=[True, False]) # 按照索引排序 df.sort_index()数据分组和聚合函数说明 df.groupby(column_name) 按照指定列进行分组; df.aggregate(function_name) 对分组后的数据进行聚合操作; df.pivot_table(values, index, columns, aggfunc) 生成透视表...
2 34.0 3 36.0 4 38.0 5NaN6NaN7NaN8NaN9NaN dtype: float64 DataFrame的对齐运算 1. DataFrame按行、列索引对齐 df1 = pd.DataFrame(np.ones((2, 2)), columns=['a','b']) df2= pd.DataFrame(np.ones((3, 3)), columns=['a','b','c'])print('df1:')print(df1)print('')print('df2...
df.rename(columns={'team':'class'}) 常用方法如下: df.rename(columns={"Q1":"a", "Q2": "b"}) # 对表头进行修改df.rename(index={0: "x", 1:"y", 2: "z"}) # 对索引进行修改df.rename(index=str) # 对类型进行修改df.rename(str.lower, axis=...
forcolindf1.columns: df1[col] = df1[col].str.extract(pat='\(([0-9]{1,}(?:\.[0-9]+)?)\)') # 剔除无用索引,重新排序 df1.index = df1.index.droplevel(level=0) df1.reset_index.sort_values(by='姓名',ascending=True).reset_index(drop=True) ...
import pandas as pd data = {'state':['Ohio','Ohio','Ohio','Nevada'], 'year':[2000,2001,2002,2003], 'pop':[1.5,1.7,3.6,2.4]} pd1 = pd.DataFrame(data,columns=['year','state','pop'],index=['one','two','three','four']) pd1.iloc[[0,2],[2,0,1]] # 选择某些行的某些...
After grouping data usinggroupby, you can sort values within each group to order data based on specified columns, facilitating analysis of highest or lowest values in each group. Sorting within groups can be achieved by chaining thesort_values()method to the grouped DataFrame, specifying the colum...