pandas2

Posted 2021-03-26 guofen3399

一    、

1、排序，默认从小到大

food_info.sort_values("Sodium_(mg)",inplace=True)
print(food_info["Sodium_(mg)"])

inplace是不返回新的frame，也就是在原来的基础上


从大到小排列：

food_info.sort_values("Sodium_(mg)",inplace=True,ascending=False)
print(food_info["Sodium_(mg)"])

2、读取titanic_train.csv的数据，并显示默认的前5行

import pandas as pd         #造pandas的别名为pd
import numpy as np          #造numpy的别名为np

titanic_survival = pd.read_csv("titanic_train.csv")
titanic_survival.head()     #head()无参数，默认返回数据的前5行

 

 

3、

 

age_is_null = pd.isnull(age)
#print(age_is_null)
#null= titanic_survival["Age"][age_is_null==False]
#null
null= titanic_survival["Age"][age_is_null] ~ null= titanic_survival["Age"][age_is_null==True]
print(null)
#good_ages = titanic_survival["Age"][age_is_null == False] 
                  #[age_is_null == False]若没有缺失值,则保留。（由此滤去缺失值）
#print(good_ages)

输出结果：

5     NaN
17    NaN
19    NaN
26    NaN
28    NaN
29    NaN
31    NaN
32    NaN
36    NaN
42    NaN
45    NaN
46    NaN
47    NaN
48    NaN
55    NaN
64    NaN
65    NaN
76    NaN
77    NaN
82    NaN
87    NaN
95    NaN
101   NaN
107   NaN
109   NaN
121   NaN
126   NaN
128   NaN
140   NaN
154   NaN
       ..
718   NaN
727   NaN
732   NaN
738   NaN
739   NaN
740   NaN
760   NaN
766   NaN
768   NaN
773   NaN
776   NaN
778   NaN
783   NaN
790   NaN
792   NaN
793   NaN
815   NaN
825   NaN
826   NaN
828   NaN
832   NaN
837   NaN
839   NaN
846   NaN
849   NaN
859   NaN
863   NaN
868   NaN
878   NaN
888   NaN
Name: Age, Length: 177, dtype: float64

 

 4、

#将船舱等级进行一个转化，1——First Class....
def which_class(row):
    pclass = row["Pclass"]
    if pd.isnull(pclass):
        return "UnKnown"
    elif pclass == 1:
        return "First Class"
    elif pclass == 2:
        return "Second Class"
    elif pclass == 3:
        return "Third Class"
    
classes = titanic_survival.apply(which_class,axis = 1) #axis=1的时候是横着看
print(classes)

输出结果：
0       Third Class
1       First Class
2       Third Class
3       First Class
4       Third Class
5       Third Class
6       First Class
7       Third Class
8       Third Class
9      Second Class
10      Third Class
11      First Class
12      Third Class
13      Third Class
14      Third Class
15     Second Class
16      Third Class
17     Second Class
18      Third Class
19      Third Class
20     Second Class
21     Second Class
22      Third Class
23      First Class
24      Third Class
25      Third Class
26      Third Class
27      First Class
28      Third Class
29      Third Class
           ...     
861    Second Class
862     First Class
863     Third Class
864    Second Class
865    Second Class
866    Second Class
867     First Class
868     Third Class
869     Third Class
870     Third Class
871     First Class
872     First Class
873     Third Class
874    Second Class
875     Third Class
876     Third Class
877     Third Class
878     Third Class
879     First Class
880    Second Class
881     Third Class
882     Third Class
883    Second Class
884     Third Class
885     Third Class
886    Second Class
887     First Class
888     Third Class
889     First Class
890     Third Class
Length: 891, dtype: object

 

 

 

二 、对数据进行处理

1. 用 .isnull()来处理数据的缺失值

　　其实数据都有缺失值，在进行数据处理的时候首先对缺失值要有一个详细的了解。

　 下边将通过对列“age”列的处理来看一下缺失值的情况的。

　　用.isnull()可以返回缺失值的情况。若当前值缺失，返回true，否则返回false。