Pandas中DataFrame表格型数据结构

1、DataFrame是什么

series是有一组数据与一组索引（行索引）组成的数据结构，而dataframe是由一组数据与一对索引（行索引和列索引）组成的表格型数据结构。之所以叫表格型数据结构，是因为dataframe的数据形式和Excel的数据存储形式很相近。

2、创建一个dataframe

创建一个dataframe使用的方法是pd.dataframe（），通过给dataframe（）方法传入不同的对象即可实现。

import pandas as pd
df1 = pd.DataFrame(["a","b","c","d"])
df2 = pd.DataFrame([["a","A"],["b","B"],["c","C"],["d","D"]])
df31 = pd.DataFrame([["a","A"],["b","B"],["c","C"],["d","D"]],columns = ["小写","大写"])
df32 = pd.DataFrame([["a","A"],["b","B"],["c","C"],["d","D"]],index = ["一","二","三","四"])
df33 = pd.DataFrame([["a","A"],["b","B"],["c","C"],["d","D"]],columns = ["小写","大写"],index = ["一","二","三","四"])print(df1)
print(df2)
print(df31)
print(df32)
print(df33)0
0  a
1  b
2  c
3  d0  1
0  a  A
1  b  B
2  c  C
3  d  D小写 大写
0  a  A
1  b  B
2  c  C
3  d  D0  1
一  a  A
二  b  B
三  c  C
四  d  D小写 大写
一  a  A
二  b  B
三  c  C
四  d  D

总结：
1.只传入一个单一列表时，该列表的值会显示成一列，且行和列都是从0列开始的默认索引。
2.当传入一个嵌套列表时，会根据嵌套列表数显示成多列数据，行、列索引同样是从0开始的默认索引。列表里面嵌套的列表也可以换成元组。也可以手动设置行列索引

import pandas as pddf41 = pd.DataFrame({"小写":["a","b","c","d"],"大写":["A","B","C","D"]})
df42 = pd.DataFrame({"小写":["a","b","c","d"],"大写":["A","B","C","D"]},index = ["一","二","三","四"])print(df41)
print(df42)小写 大写
0  a  A
1  b  B
2  c  C
3  d  D小写 大写
一  a  A
二  b  B
三  c  C
四  d  D

总结：直接以字典的形式传入dataframe时，字典的key值就相当于列索引，如果没有设置行索引，行索引还是使用从0开始的默认索引，同样可以使用index参数自定义行索引。

3、获取dataframe的行、列索引

import pandas as pddf41 = pd.DataFrame({"小写":["a","b","c","d"],"大写":["A","B","C","D"]})
df42 = pd.DataFrame({"小写":["a","b","c","d"],"大写":["A","B","C","D"]},index = ["一","二","三","四"])print(df41.columns)
print(df42.columns)print(df41.index)
print(df42.index)Index(['小写', '大写'], dtype='object')
Index(['小写', '大写'], dtype='object')
RangeIndex(start=0, stop=4, step=1)
Index(['一', '二', '三', '四'], dtype='object')

4、获取dataframe的值

通过列表的形式获取某一行或某一列或某几行或某几类。

import pandas as pd
import numpy as np
date =pd.date_range('20160101',periods=6)
# print(date)df = pd.DataFrame(np.arange(24).reshape((6,4)),index=date,columns=['A','B','C','D'])
print(df['A'],df.A)print(df[0:3])
print(df['2016-01-01':'2016-01-03'])2016-01-01     0
2016-01-02     4
2016-01-03     8
2016-01-04    12
2016-01-05    16
2016-01-06    20
Freq: D, Name: A, dtype: int32 2016-01-01     0
2016-01-02     4
2016-01-03     8
2016-01-04    12
2016-01-05    16
2016-01-06    20
Freq: D, Name: A, dtype: int32A  B   C   D
2016-01-01  0  1   2   3
2016-01-02  4  5   6   7
2016-01-03  8  9  10  11A  B   C   D
2016-01-01  0  1   2   3
2016-01-02  4  5   6   7
2016-01-03  8  9  10  11

通过select by label: loc

import pandas as pd
import numpy as npdate =pd.date_range('20160101',periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)),index=date,columns=['A','B','C','D'])print(df.loc['2016-01-02'])
print(df.loc[:,['A','B']])
print(df.loc['2016-01-02',['A','B']])A    4
B    5
C    6
D    7
Name: 2016-01-02 00:00:00, dtype: int32A   B
2016-01-01   0   1
2016-01-02   4   5
2016-01-03   8   9
2016-01-04  12  13
2016-01-05  16  17
2016-01-06  20  21
A    4
B    5
Name: 2016-01-02 00:00:00, dtype: int32

通过select by position: iloc

import pandas as pd
import numpy as npdate =pd.date_range('20160101',periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)),index=date,columns=['A','B','C','D'])print(df.iloc[3:5,1:3])
print(df.iloc[[1,3,5],1:3])B   C
2016-01-04  13  14
2016-01-05  17  18B   C
2016-01-02   5   6
2016-01-04  13  14
2016-01-06  21  22

通过boolean indexing

import pandas as pd
import numpy as npdate =pd.date_range('20160101',periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)),index=date,columns=['A','B','C','D'])df.iloc[2,2] =111
df.loc['2016-01-01','B'] =222
df.C[df.C>10] =0
df.B[df.A>16] =0
#df[df.D>6] =0df['E'] =pd.Series([1,2,3,4,5,6],index=pd.date_range('2016-01-01',periods=6))
df['F']=np.nanprint(df)