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📜  Python Pandas-基本功能

📅  最后修改于: 2020-11-06 05:38:59             🧑  作者: Mango

到目前为止，我们了解了三个Pandas DataStructures以及如何创建它们。由于它在实时数据处理中的重要性，因此我们将主要关注DataFrame对象，并讨论其他一些DataStructures。

系列基本功能

Sr.No. Attribute or
Method & Description
1

axes

Returns a list of the row axis labels
2

dtype

Returns the dtype of the object.
3

empty

Returns True if series is empty.
4

ndim

Returns the number of dimensions of the underlying data, by
definition 1.
5

size

Returns the number of elements in the underlying data.
6

values

Returns the Series as ndarray.
7

head()

Returns the first n rows.
8

tail()

Returns the last n rows.

现在让我们创建一个Series并查看以上所有列表化的属性操作。

例

现场演示
import pandas as pd
import numpy as np

#Create a series with 100 random numbers
s = pd.Series(np.random.randn(4))
print s

其输出如下-

0   0.967853
1  -0.148368
2  -1.395906
3  -1.758394
dtype: float64

轴数

返回系列标签的列表。

现场演示
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

其输出如下-

The axes are:
[RangeIndex(start=0, stop=4, step=1)]

以上结果是0到5(即[0,1,2,3,4])值列表的紧凑格式。

空的

返回布尔值，说明对象是否为空。 True表示对象为空。

现场演示
import pandas as pd
import numpy as np

#Create a series with 100 random numbers
s = pd.Series(np.random.randn(4))
print ("Is the Object empty?")
print s.empty

其输出如下-

Is the Object empty?
False

恩迪姆

返回对象的尺寸数。根据定义，系列是一维数据结构，因此它返回

现场演示
import pandas as pd
import numpy as np

#Create a series with 4 random numbers
s = pd.Series(np.random.randn(4))
print s

print ("The dimensions of the object:")
print s.ndim

其输出如下-

0   0.175898
1   0.166197
2  -0.609712
3  -1.377000
dtype: float64

The dimensions of the object:
1

尺寸

返回系列的大小(长度)。

现场演示
import pandas as pd
import numpy as np

#Create a series with 4 random numbers
s = pd.Series(np.random.randn(2))
print s
print ("The size of the object:")
print s.size

其输出如下-

0   3.078058
1  -1.207803
dtype: float64

The size of the object:
2

价值观

以数组形式返回系列中的实际数据。

现场演示
import pandas as pd
import numpy as np

#Create a series with 4 random numbers
s = pd.Series(np.random.randn(4))
print s

print ("The actual data series is:")
print s.values

其输出如下-

0   1.787373
1  -0.605159
2   0.180477
3  -0.140922
dtype: float64

The actual data series is:
[ 1.78737302 -0.60515881 0.18047664 -0.1409218 ]

头尾

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)。默认显示的元素数是5，但是您可以传递自定义数字。

现场演示
import pandas as pd
import numpy as np

#Create a series with 4 random numbers
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)

其输出如下-

The original series is:
0   0.720876
1  -0.765898
2   0.479221
3  -0.139547
dtype: float64

The first two rows of the data series:
0   0.720876
1  -0.765898
dtype: float64

tail()返回最后n行(观察索引值)。默认显示的元素数是5，但是您可以传递自定义数字。

现场演示
import pandas as pd
import numpy as np

#Create a series with 4 random numbers
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)

其输出如下-

The original series is:
0 -0.655091
1 -0.881407
2 -0.608592
3 -2.341413
dtype: float64

The last two rows of the data series:
2 -0.608592
3 -2.341413
dtype: float64

DataFrame基本功能

现在让我们了解什么是DataFrame基本功能。下表列出了有助于DataFrame基本功能的重要属性或方法。

Sr.No. Attribute or
Method & Description
1

T

Transposes rows and columns.
2

axes

Returns a list with the row axis labels and column axis labels as the only members.
3

dtypes

Returns the dtypes in this object.
4

empty

True if NDFrame is entirely empty [no items]; if any of the axes are of length 0.
5

ndim

Number of axes / array dimensions.
6

shape

Returns a tuple representing the dimensionality of the DataFrame.
7

size

Number of elements in the NDFrame.
8

values

Numpy representation of NDFrame.
9

head()

Returns the first n rows.
10

tail()

Returns last n rows.

现在，让我们创建一个DataFrame并查看上述属性的所有操作方式。

例

现场演示
import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','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 series is:")
print df

其输出如下-

Our data series is:
    Age   Name    Rating
0   25    Tom     4.23
1   26    James   3.24
2   25    Ricky   3.98
3   23    Vin     2.56
4   30    Steve   3.20
5   29    Smith   4.60
6   23    Jack    3.80

T(移调)

返回DataFrame的转置。行和列将互换。

现场演示
import pandas as pd
import numpy as np
 
# Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','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

其输出如下-

The transpose of the data series is:
         0     1       2      3      4      5       6
Age      25    26      25     23     30     29      23
Name     Tom   James   Ricky  Vin    Steve  Smith   Jack
Rating   4.23  3.24    3.98   2.56   3.2    4.6     3.8

轴数

返回行轴标签和列轴标签的列表。

现场演示
import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','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

其输出如下-

Row axis labels and column axis labels are:

[RangeIndex(start=0, stop=7, step=1), Index([u'Age', u'Name', u'Rating'],
dtype='object')]

dtypes

返回每一列的数据类型。

现场演示
import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','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

其输出如下-

The data types of each column are:
Age     int64
Name    object
Rating  float64
dtype: object

空的

返回布尔值，说明对象是否为空； True表示对象为空。

现场演示
import pandas as pd
import numpy as np
 
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','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

其输出如下-

Is the object empty?
False

恩迪姆

返回对象的尺寸数。根据定义，DataFrame是2D对象。

现场演示
import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','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

其输出如下-

Our object is:
      Age    Name     Rating
0     25     Tom      4.23
1     26     James    3.24
2     25     Ricky    3.98
3     23     Vin      2.56
4     30     Steve    3.20
5     29     Smith    4.60
6     23     Jack     3.80

The dimension of the object is:
2

形状

返回表示DataFrame维数的元组。元组(a，b)，其中a表示行数， b表示列数。

现场演示
import pandas as pd
import numpy as np
 
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','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

其输出如下-

Our object is:
   Age   Name    Rating
0  25    Tom     4.23
1  26    James   3.24
2  25    Ricky   3.98
3  23    Vin     2.56
4  30    Steve   3.20
5  29    Smith   4.60
6  23    Jack    3.80

The shape of the object is:
(7, 3)

尺寸

返回DataFrame中的元素数。

现场演示
import pandas as pd
import numpy as np
 
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','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

其输出如下-

Our object is:
    Age   Name    Rating
0   25    Tom     4.23
1   26    James   3.24
2   25    Ricky   3.98
3   23    Vin     2.56
4   30    Steve   3.20
5   29    Smith   4.60
6   23    Jack    3.80

The total number of elements in our object is:
21

价值观

以NDarray的形式返回DataFrame中的实际数据。

现场演示
import pandas as pd
import numpy as np
 
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','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

其输出如下-

Our object is:
    Age   Name    Rating
0   25    Tom     4.23
1   26    James   3.24
2   25    Ricky   3.98
3   23    Vin     2.56
4   30    Steve   3.20
5   29    Smith   4.60
6   23    Jack    3.80
The actual data in our data frame is:
[[25 'Tom' 4.23]
[26 'James' 3.24]
[25 'Ricky' 3.98]
[23 'Vin' 2.56]
[30 'Steve' 3.2]
[29 'Smith' 4.6]
[23 'Jack' 3.8]]

头尾

要查看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)。默认显示的元素数是5，但是您可以传递自定义数字。

现场演示
import pandas as pd
import numpy as np
 
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','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)

其输出如下-

Our data frame is:
    Age   Name    Rating
0   25    Tom     4.23
1   26    James   3.24
2   25    Ricky   3.98
3   23    Vin     2.56
4   30    Steve   3.20
5   29    Smith   4.60
6   23    Jack    3.80

The first two rows of the data frame is:
   Age   Name   Rating
0  25    Tom    4.23
1  26    James  3.24

tail()返回最后n行(观察索引值)。默认显示的元素数是5，但是您可以传递自定义数字。

现场演示
import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','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)

其输出如下-

Our data frame is:
    Age   Name    Rating
0   25    Tom     4.23
1   26    James   3.24
2   25    Ricky   3.98
3   23    Vin     2.56
4   30    Steve   3.20
5   29    Smith   4.60
6   23    Jack    3.80

The last two rows of the data frame is:
    Age   Name    Rating
5   29    Smith    4.6
6   23    Jack     3.8