到目前为止，我们了解了三种Pandas数据结构以及如何创建它们。接下来将主要关注数据帧(DataFrame)对象，因为它在实时数据处理中非常重要，并且还讨论其他数据结构。
| 编号 | 属性或方法 | 描述 |
|---|---|---|
| 1 | axes |
返回行轴标签列表。 |
| 2 | dtype |
返回对象的数据类型(dtype)。 |
| 3 | empty |
如果系列为空，则返回True。 |
| 4 | ndim |
返回底层数据的维数，默认定义：1。 |
| 5 | size |
返回基础数据中的元素数。 |
| 6 | values |
将系列作为ndarray返回。 |
| 7 | head() |
返回前n行。 |
| 8 | tail() |
返回最后n行。 |
现在创建一个系列并演示如何使用上面所有列出的属性操作。
In [3]:
import pandas as pd
import numpy as np
#Create a series with 100 random numbers
s = pd.Series(np.random.randn(4))
print(s)
0 -0.174390 1 -0.506319 2 -1.082130 3 0.101745 dtype: float64
返回系列的标签列表。参考以下示例代码 -
In [4]:
import pandas as pd
import numpy as np
#Create a series with 100 random numbers
s = pd.Series(np.random.randn(4))
print ("The axes are:")
print(s.axes)
The axes are: [RangeIndex(start=0, stop=4, step=1)]
上述结果是从0到5的值列表的紧凑格式，即：[0,1,2,3,4]。
In [5]:
s = pd.Series(np.random.randn(4))
print("Is the Object empty?", s.empty)
Is the Object empty? False
In [6]:
s = pd.Series(np.random.randn(4))
print(s)
print("The dimensions of the object:", s.ndim)
0 1.408732 1 1.070586 2 0.065844 3 0.471072 dtype: float64 The dimensions of the object: 1
In [7]:
s = pd.Series(np.random.randn(2))
print(s)
print("The size of the object:", s.size)
0 -1.851026 1 0.698335 dtype: float64 The size of the object: 2
In [8]:
s = pd.Series(np.random.randn(4))
print(s)
print("The actual data series is:", s.values)
0 0.507342 1 0.040763 2 0.159354 3 -1.610987 dtype: float64 The actual data series is: [ 0.50734157 0.04076283 0.1593538 -1.61098664]
要查看Series或DataFrame对象的小样本，请使用 head() 和 tail() 方法。
head() 返回前n行(观察索引值)。要显示的元素的默认数量为5，但可以传递自定义这个数字值。
Create a series with 4 random numbers
In [9]:
s = pd.Series(np.random.randn(4))
print("The original series is:")
print(s)
print("The first two rows of the data series:")
print(s.head(2))
The original series is: 0 -0.313015 1 -0.274052 2 0.739818 3 -0.723503 dtype: float64 The first two rows of the data series: 0 -0.313015 1 -0.274052 dtype: float64
tail() 返回最后n行(观察索引值)。 要显示的元素的默认数量为 5 ，但可以传递自定义数字值。参考以下示例代码 -
Create a series with 4 random numbers。
In [10]:
s = pd.Series(np.random.randn(4))
print("The original series is:")
print(s)
print("The last two rows of the data series:")
print(s.tail(2))
The original series is: 0 0.945928 1 0.413040 2 1.891930 3 0.399307 dtype: float64 The last two rows of the data series: 2 1.891930 3 0.399307 dtype: float64
执行上面示例代码，得到以下结果 -
| 编号 | 属性或方法 | 描述 |
|---|---|---|
| 1 | T |
转置行和列。 |
| 2 | axes |
返回一个列，行轴标签和列轴标签作为唯一的成员。 |
| 3 | dtypes |
返回此对象中的数据类型(dtypes)。 |
| 4 | empty |
如果NDFrame完全为空[无项目]，则返回为True; 如果任何轴的长度为0。 |
| 5 | ndim |
轴/数组维度大小。 |
| 6 | shape |
返回表示DataFrame的维度的元组。 |
| 7 | size |
NDFrame中的元素数。 |
| 8 | values |
NDFrame的Numpy表示。 |
| 9 | head() |
返回开头前n行。 |
| 10 | tail() |
返回最后n行。 |
下面来看看如何创建一个DataFrame并使用上述属性和方法。
In [11]:
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:
In [12]:
df = pd.DataFrame(d)
print("Our data series is:")
print(df)
Our data series is:
Name Age Rating
0 Tom 25 4.23
1 James 26 3.24
2 Ricky 25 3.98
3 Vin 23 2.56
4 Steve 30 3.20
5 Minsu 29 4.60
6 Jack 23 3.80
In [13]:
import pandas as pd
import numpy as np
# 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)
print ("The transpose of the data series is:")
print (df.T)
The transpose of the data series is:
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
In [14]:
import pandas as pd
import numpy as np
#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)
print ("Row axis labels and column axis labels are:")
print( df.axes)
Row axis labels and column axis labels are: [RangeIndex(start=0, stop=7, step=1), Index(['Name', 'Age', 'Rating'], dtype='object')]
In [15]:
import pandas as pd
import numpy as np
#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)
print("The data types of each column are:")
print(df.dtypes)
The data types of each column are: Name object Age int64 Rating float64 dtype: object
In [16]:
import pandas as pd
import numpy as np
#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)
print ("Is the object empty?")
print( df.empty)
Is the object empty? False
In [17]:
import pandas as pd
import numpy as np
#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)
print ("Our object is:")
print (df)
print ("The dimension of the object is:")
print (df.ndim)
Our object is:
Name Age Rating
0 Tom 25 4.23
1 James 26 3.24
2 Ricky 25 3.98
3 Vin 23 2.56
4 Steve 30 3.20
5 Minsu 29 4.60
6 Jack 23 3.80
The dimension of the object is:
2
In [18]:
import pandas as pd
import numpy as np
#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)
print ("Our object is:")
print (df)
print ("The shape of the object is:")
print (df.shape)
Our object is:
Name Age Rating
0 Tom 25 4.23
1 James 26 3.24
2 Ricky 25 3.98
3 Vin 23 2.56
4 Steve 30 3.20
5 Minsu 29 4.60
6 Jack 23 3.80
The shape of the object is:
(7, 3)
In [19]:
import pandas as pd
import numpy as np
#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)
print ("Our object is:")
print (df)
print ("The total number of elements in our object is:")
print (df.size)
Our object is:
Name Age Rating
0 Tom 25 4.23
1 James 26 3.24
2 Ricky 25 3.98
3 Vin 23 2.56
4 Steve 30 3.20
5 Minsu 29 4.60
6 Jack 23 3.80
The total number of elements in our object is:
21
In [20]:
import pandas as pd
import numpy as np
#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)
print ("Our object is:")
print (df)
print ("The actual data in our data frame is:")
print (df.values)
Our object is:
Name Age Rating
0 Tom 25 4.23
1 James 26 3.24
2 Ricky 25 3.98
3 Vin 23 2.56
4 Steve 30 3.20
5 Minsu 29 4.60
6 Jack 23 3.80
The actual data in our data frame is:
[['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]]
In [21]:
import pandas as pd
import numpy as np
#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)
print ("Our data frame is:")
print (df)
print ("The first two rows of the data frame is:")
print (df.head(2))
Our data frame is:
Name Age Rating
0 Tom 25 4.23
1 James 26 3.24
2 Ricky 25 3.98
3 Vin 23 2.56
4 Steve 30 3.20
5 Minsu 29 4.60
6 Jack 23 3.80
The first two rows of the data frame is:
Name Age Rating
0 Tom 25 4.23
1 James 26 3.24
tail()返回最后n行(观察索引值)。显示元素的默认数量为5，但可以传递自定义数字值。
In [22]:
import pandas as pd
import numpy as np
#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)
print ("Our data frame is:")
print (df)
print ("The last two rows of the data frame is:")
print (df.tail(2))
Our data frame is:
Name Age Rating
0 Tom 25 4.23
1 James 26 3.24
2 Ricky 25 3.98
3 Vin 23 2.56
4 Steve 30 3.20
5 Minsu 29 4.60
6 Jack 23 3.80
The last two rows of the data frame is:
Name Age Rating
5 Minsu 29 4.6
6 Jack 23 3.8