Python Pandas 简明教程

Python Pandas - Basic Functionality

到目前为止，我们了解了这三个 Pandas 数据结构以及如何创建它们。我们将主要关注 DataFrame 对象，因为它在实时数据处理中非常重要，还将讨论一些其他数据结构。

By now, we learnt about the three Pandas DataStructures and how to create them. We will majorly focus on the DataFrame objects because of its importance in the real time data processing and also discuss a few other DataStructures.

Series Basic Functionality

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.

我们现在创建一个 Series，然后查看所有以上表格中的属性操作。

Let us now create a Series and see all the above tabulated attributes operation.

Example

import pandas as pd
import numpy as np

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

它的 output 如下所示 −

Its output is as follows −

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

axes

返回系列标签列表。

Returns the list of the labels of the series.

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

它的 output 如下所示 −

Its output is as follows −

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

以上结果是以 0 到 5 的值列表的紧凑形式，即 [0,1,2,3,4]。

The above result is a compact format of a list of values from 0 to 5, i.e., [0,1,2,3,4].

empty

返回布尔值，表示对象是否为空。True 指示对象为空。

Returns the Boolean value saying whether the Object is empty or not. True indicates that the object is empty.

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

它的 output 如下所示 −

Its output is as follows −

Is the Object empty?
False

ndim

返回对象维数。根据定义，Series 是 1D 数据结构，因此它返回

Returns the number of dimensions of the object. By definition, a Series is a 1D data structure, so it returns

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

它的 output 如下所示 −

Its output is as follows −

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

The dimensions of the object:
1

size

返回系列大小（长度）。

Returns the size(length) of the series.

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

它的 output 如下所示 −

Its output is as follows −

0   3.078058
1  -1.207803
dtype: float64

The size of the object:
2

values

返回系列中的实际数据作为数组。

Returns the actual data in the series as an array.

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

它的 output 如下所示 −

Its output is as follows −

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 ]

Head & Tail

若要查看 Series 或 DataFrame 对象的小样本，请使用 head() 和 tail() 方法。

To view a small sample of a Series or the DataFrame object, use the head() and the tail() methods.

head() 返回前 n 行（观察索引值）。要显示的元素默认值为 5，但你可以传递自定义数字。

head() returns the first n rows(observe the index values). The default number of elements to display is five, but you may pass a custom number.

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)

它的 output 如下所示 −

Its output is as follows −

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() 返回后 n 行（观察索引值）。要显示的元素默认值为 5，但你可以传递自定义数字。

tail() returns the last n rows(observe the index values). The default number of elements to display is five, but you may pass a custom number.

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)

它的 output 如下所示 −

Its output is as follows −

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 Basic Functionality

我们现在了解什么是 DataFrame 基本功能。下表列出了有助于实现 DataFrame 基本功能的重要属性或方法。

Let us now understand what DataFrame Basic Functionality is. The following tables lists down the important attributes or methods that help in DataFrame Basic Functionality.

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.

让我们创建 DataFrame，观察上述属性如何运作。

Let us now create a DataFrame and see all how the above mentioned attributes operate.

Example

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

它的 output 如下所示 −

Its output is as follows −

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 (Transpose)

返回 DataFrame 的转置。行列将交换。

Returns the transpose of the DataFrame. The rows and columns will interchange.

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

它的 output 如下所示 −

Its output is as follows −

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

axes

返回行轴标签和列轴标签的列表。

Returns the list of row axis labels and column axis labels.

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

它的 output 如下所示 −

Its output is as follows −

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

返回每列的数据类型。

Returns the data type of each column.

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

它的 output 如下所示 −

Its output is as follows −

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

empty

返回布尔值，指示对象是否为空；True 指示对象为空。

Returns the Boolean value saying whether the Object is empty or not; True indicates that the object is empty.

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

它的 output 如下所示 −

Its output is as follows −

Is the object empty?
False

ndim

返回对象的维度数。根据定义，DataFrame 是 2D 对象。

Returns the number of dimensions of the object. By definition, DataFrame is a 2D object.

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

它的 output 如下所示 −

Its output is as follows −

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

shape

返回一个元组表示 DataFrame 的维度。元组 (a,b)，其中 a 表示行数， b 表示列数。

Returns a tuple representing the dimensionality of the DataFrame. Tuple (a,b), where a represents the number of rows and b represents the number of columns.

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

它的 output 如下所示 −

Its output is as follows −

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)

size

返回 DataFrame 中的元素数。

Returns the number of elements in the DataFrame.

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

它的 output 如下所示 −

Its output is as follows −

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

values

以 NDarray. 的形式返回 DataFrame 中的实际数据

Returns the actual data in the DataFrame as an NDarray.

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

它的 output 如下所示 −

Its output is as follows −

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]]

Head & Tail

要查看 DataFrame 对象的小样本，使用 head() 和 tail() 方法。 head() 返回前 n 行（观察索引值）。显示的元素默认数为 5，但可传递自定义数字。

To view a small sample of a DataFrame object, use the head() and tail() methods. head() returns the first n rows (observe the index values). The default number of elements to display is five, but you may pass a custom number.

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)

它的 output 如下所示 −

Its output is as follows −

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）。显示的元素默认数为 5，但可传递自定义数字。

tail() returns the last n rows (observe the index values). The default number of elements to display is five, but you may pass a custom number.

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)

它的 output 如下所示 −

Its output is as follows −

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