创建pandas对象

import pandas as pd
import numpy as np

Series

指定values和index来创建Series

data = pd.Series([0.5, 1.0, 2.0], index=['a', 'b', 'c'])
data

a    0.5
b    1.0
c    2.0
dtype: float64

# 查看values
data.values

array([0.5, 1. , 2. ])

# 查看index，它是一个pd.Index对象
data.index

Index(['a', 'b', 'c'], dtype='object')

字典转化为Series

population_dict = {"California": 3833, 
                   "Texas":2644, 
                   "New York": 1965}

# 由字典转化
population = pd.Series(population_dict)
population

California    3833
Texas         2644
New York      1965
dtype: int64

DataFrame

指定values,index和columns来创建DataFrame

# 指定 data, columns, index
df = pd.DataFrame(np.random.rand(3, 2), columns=['first', 'second'], index=['a', 'b', 'c'])
df

	first	second
a	0.748481	0.989163
b	0.033341	0.370059
c	0.514828	0.872531

# 查看values
df.values

array([[0.74848094, 0.98916317],
       [0.03334097, 0.370059  ],
       [0.51482786, 0.8725308 ]])

# index和columns都是pd.Index对象
df.index, df.columns

(Index(['a', 'b', 'c'], dtype='object'),
 Index(['first', 'second'], dtype='object'))

双重字典转化为DataFrame

area_dict = {'California': 423967, 'Texas': 695662}
population_area_dict = {'population': population_dict, 'area': area_dict}

# 默认外层是columns，内层是index，无对应数据则为NaN
pd.DataFrame(population_area_dict)

	population	area
California	3833	423967.0
Texas	2644	695662.0
New York	1965	NaN

多个Series转化成DataFrame

area = pd.Series(area_dict)
area

California    423967
Texas         695662
dtype: int64

pd.DataFrame({'population': population, 'area': area})

	population	area
California	3833	423967.0
New York	1965	NaN
Texas	2644	695662.0