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DataFrame的基本用法

建立数据表

  1. import pandas as pd#加载pandas模块
  3. d={
  4.     'name':['Harry','Ron','Hermione'],
  5.     'sex':['M','M','F'],
  6.     'age':[11,12,10]
  7. }
  8. df=pd.DataFrame(d,index=list('abc'))
  9. df
  10.     name        sex    age
  11. a    Harry        M    11
  12. b    Ron            M    12
  13. c    Hermione    F    10

数据表的操作

  • 查看表的信息

    1. #查看表的信息，.info()
    2. df.info()
    3. <class 'pandas.core.frame.DataFrame'>
    4. Index: 3 entries, a to c
    5. Data columns (total 3 columns):
    6. name    3 non-null object
    7. sex     3 non-null object
    8. age     3 non-null int64
    9. dtypes: int64(1), object(2)
    10. memory usage: 176.0+ bytes

  • 索引切片

    • 基于列的切片 ```python

      基于列的切片

      df[‘age’] a 11 b 12 c 10 Name: age, dtype: int64

# df[[‘name’,’age’]] name age a Harry 11 b Ron 12 c Hermione 10 


   - 基于行的切片（[点击查看loc与iloc的更多具体用法](https://blog.csdn.net/W_weiying/article/details/81411257?utm_medium=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.compare&depth_1-utm_source=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.compare)）
```python
#基于行的切片,iloc按行数提取
df.iloc[2]
name    Hermione
sex            F
age           10
Name: c, dtype: object
##基于行的切片,loc按索引名提取
df.loc['c']
name    Hermione
sex            F
age           10
Name: c, dtype: object
  • 修改数值
    • 修改整列值 ```python df name sex age a Harry M 11 b Ron M 12 c Hermione F 10

方法1：表名.列名=修改后的值

df.age=20 df name sex age a Harry M 20 b Ron M 20 c Hermione F 20

方法2索引:表名[‘列名’]=修改后的值

df[‘age’]=21 df name sex age a Harry M 21 b Ron M 21 c Hermione F 21


   - 修改某个值
```python
#方法：多重索引定位后赋值
df['age'][2]=30
df
    name        sex    age
a    Harry        M    21
b    Ron            M    21
c    Hermione    F    30
  • 修改索引 ```python df.index Index([‘a’, ‘b’, ‘c’], dtype=’object’)

df.index=list(‘123’) df name sex age 1 Harry M 21 2 Ron M 21 3 Hermione F 30 ```