以如下Series作为例子
import pandas as pd
import numpy as np
s = pd.Series(np.random.randn(4))
axes
:返回行轴标签列表print(s.axes)
# [RangeIndex(start=0, stop=4, step=1)]
# 这是[0, 5)的紧凑格式即[0,1,2,3,4]
empty
:返回对象是否为空print(s.empty)
# False
dtype
:返回对象数据类型print(s.dtype)
# float64
ndim
:返回底层数据的维数,默认定义:1print(s.ndim)
# 1
size
:返回元素数据数print(s.size)
# 4
values
:将Series作为ndarray返回s.values
# array([ 0.86667361, -1.05757677, -0.26174514, -0.49398881])
head()
:返回前n行print(s.head(2))
# 0 0.866674
# 1 -1.057577
# dtype: float64
tail()
:返回最后n行print(s.tail(2))
# 2 -0.261745
# 3 -0.493989
# dtype: float64
以以下代码作为例子
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Minsu','Jack']),
'Age':pd.Series([25,26,25,23,30,29,23]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8])}
#Create a DataFrame
df = pd.DataFrame(d)
T
:转置print(df.T)
# 0 1 2 3 4 5 6
# Name Tom James Ricky Vin Steve Minsu Jack
# Age 25 26 25 23 30 29 23
# Rating 4.23 3.24 3.98 2.56 3.2 4.6 3.8
axes
:返回一列,行轴标签和列轴标签作为唯一的成员print(df.axes)
# [RangeIndex(start=0, stop=7, step=1), Index(['Name', 'Age', 'Rating'], dtype='object')]
dtypes
:返回对象中的数据类型print(df.dtypes)
# Name object
# Age int64
# Rating float64
# dtype: object
empty
:返回对象是否为空print(df.empty)
# False
ndim
:轴/数组维度大小print(df.ndim)
# 2
shape
:返回标识DataFrame的维度的元组print(df.shape)
# (7, 3)
size
:元素数print(df.size)
# 21
values
:对象的Numpy表示print(df.values)
'''
[['Tom' 25 4.23]
['James' 26 3.24]
['Ricky' 25 3.98]
['Vin' 23 2.56]
['Steve' 30 3.2]
['Minsu' 29 4.6]
['Jack' 23 3.8]]
'''
head()
:开头前n行print(df.head(2))
'''
Name Age Rating
0 Tom 25 4.23
1 James 26 3.24
'''
tail()
:最后前n行print(df.tail(2))
'''
Name Age Rating
5 Minsu 29 4.6
6 Jack 23 3.8
'''