DataFrame概念(可以通俗理解为excel中一片数据)

表格型数据结构，带有标签的二维数组，有行标签(index)和列标签(columns)，其值可以是数值、字符串、布尔值等。

1、.index 行标签

2、.columns 列标签

3、.values 值

.4、dtypes     return the dtypes in the DataFrame.

# -*- coding: utf-8 -*-

import pandas as pd

d = {'col1': [1, 2], 'col2': [3, 4]}

df = pd.DataFrame(data=d)

print(df)

print(df.index)

print(df.columns)

print(df.values)

print(df.dtypes)

D:python3installpython.exe D:/python/py3script/test.py

col1 col2

0 1 3

1 2 4

RangeIndex(start=0, stop=2, step=1)

Index(['col1', 'col2'], dtype='object')

[[1 3]

[2 4]]

col1 int64

col2 int64

dtype: object

Process finished with exit code 0

一：数组/list组成的字典创建DataFrame

1、由数组/list组成的字典(字典的值长度必须保持一致)来创建DataFrame，columns为字典的key，index默认为数字标签

2、columns参数即为列的顺序，格式为list，如果现有数据没有该列(比如列'www.python66.com')则显示控制NaN，如果指定的列少于现有数据，则只显示指定列那部分的数据

3、index参数指定行标签，格式为list

# -*- coding: utf-8 -*-

import numpy as np

import pandas as pd

# dictionary创建

d = {'col1': [1, 2], 'col2': [3, 4]}

df = pd.DataFrame(data=d)

print(df)

print('----------------')

# 强制更改dtype

df = pd.DataFrame(data=d, dtype=np.int8)

print(df.dtypes)

print('----------------')

# numpy的ndarray创建

df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),columns=['a', 'b', 'c'])

print(df2)

print('----------------')

# 更改columns和index

d = {'col1': [1, 2], 'col2': [3, 4]}

df = pd.DataFrame(data=d,columns=['python66','col2'],index=['python66',2])

print(df)

D:python3installpython.exe D:/python/py3script/test.py

col1 col2

0 1 3

1 2 4

----------------

col1 int8

col2 int8

dtype: object

----------------

a b c

0 1 2 3

1 4 5 6

2 7 8 9

----------------

python66 col2

python66 NaN 3

2 NaN 4

Process finished with exit code 0

二：Series组成的字典创建DataFrame

1、由Series组成的字典创建DataFrame，其columns为字典的key，index默认为Series的标签(默认是数字标签)

2、两个Series长度可以不一致(对比第一种方法字典的值长度必须一致)，会产生NaN值。

3、Series的index参数中的元素应避免出现相同的。

# -*- coding: utf-8 -*-

import numpy as np

import pandas as pd

# Series字典构造创建

series1 = pd.Series(np.random.rand(3))

df1 = pd.DataFrame({'col1':series1,'col2':series1})

print(df1)

print('---------------------')

series1 = pd.Series(['a','b','c'],index=['python66','py7','py8'])

series2 = pd.Series(['a','b','c','d'],index=['python66','py7','py8','py9'])

df1 = pd.DataFrame({'col1':series1,'col2':series2})

print(df1)

D:python3installpython.exe D:/python/py3script/test.py

col1 col2

0 0.954448 0.954448

1 0.465568 0.465568

2 0.286233 0.286233

---------------------

col1 col2

py7 b b

py8 c c

py9 NaN d

python66 a a

Process finished with exit code 0

三：通过二维数组直接创建

1、通过二维数组创建DataFrame得到形状一样的二位数据，不指定index和columns参数，则默认是数字标签。

2、index和columns和原数组长度保持一致。

# -*- coding: utf-8 -*-

import numpy as np

import pandas as pd

arr = np.random.rand(12).reshape(3,4)

df1 = pd.DataFrame(arr)

print(df1)

print('---------------')

arr = np.random.rand(12).reshape(3,4)

df2 = pd.DataFrame(arr,index=['a','b','c'],columns=['col1','col2','col3','col4'])

print(df2)

D:python3installpython.exe D:/python/py3script/test.py

0 1 2 3

0 0.374332 0.139147 0.413843 0.866404

1 0.223835 0.454294 0.741501 0.583207

2 0.373477 0.602602 0.322788 0.016215

---------------

col1 col2 col3 col4

a 0.409282 0.808191 0.833259 0.972950

b 0.347496 0.673279 0.502993 0.889290

c 0.427744 0.129409 0.894754 0.373354

Process finished with exit code 0

四：由字典组成的列表创建

# -*- coding: utf-8 -*-

import pandas as pd

lis = [{'course','python'},{'domain':'www.python66.com'}]

df = pd.DataFrame(lis)

print(df)

print('----------------')

df = pd.DataFrame(lis,index=['a','b'])

print(df)

D:python3installpython.exe D:/python/py3script/test.py

0 1

0 python course

1 domain None

----------------

0 1

a python course

b domain None

Process finished with exit code 0

五：由字典组成的字典(嵌套字典)创建

1、最外层的key是列标签columns，里面的字典的key为index。

2、可以通过columns参数改变列标签，也可以通过index参数改变行标签，但是原有的index不会变，相当于新增加了一些Index，值为空值。

# -*- coding: utf-8 -*-

import pandas as pd

dic = {'col1':{'index1':'python','index2':'shell'},'col2':{'index1':'python66','index2':'php'}}

df = pd.DataFrame(dic)

print(df)

print('----------------')

df = pd.DataFrame(dic,index=['a','b'])

print(df)

D:python3installpython.exe D:/python/py3script/test.py

col1 col2

index1 python python66

index2 shell php

----------------

col1 col2

a NaN NaN

b NaN NaN

Process finished with exit code 0