加减法运算:
import pandas
salary_Data = pandas.read_csv("E:\Anaconda\Salary_Data.csv")
print (salary_Data.head(5))
print (salary_Data.head(5)+100)
![](https://img.haomeiwen.com/i16440922/616e88bf900cbc7f.png)
乘除法运算:
print (salary_Data.head(5)*10) #对每一个数据都乘以10
![](https://img.haomeiwen.com/i16440922/5fb901fbc48e54c0.png)
行列数一样时对应位置相乘。
对DataFrame进行排序:
import pandas
salary_Data = pandas.read_csv("E:\Anaconda\Salary_Data.csv")
#参数inplace表示是否替换原来的数据,默认False
#参数ascending表示升序,默认True
sort_minToMax = salary_Data.sort_values("Salary",inplace=False)
#print (sort_minToMax)
sort_maxToMin = salary_Data.sort_values("Salary",ascending=False)
print (sort_maxToMin)
![](https://img.haomeiwen.com/i16440922/a52a78a83c889d2d.png)
排序后对新的DataFrame进行重新编号:
age_after_sort = titanic.sort_values("Age")
age_after_sort.reset_index(drop=True) #参数drop为丢弃原来的序号否则会留来原来的数据中(相当于新DataFrame中多了以原来序号为值得列)
![](https://img.haomeiwen.com/i16440922/cdf9d49777b9958e.png)
![](https://img.haomeiwen.com/i16440922/aadd5838fabdcc67.png)
网友评论