Pandas系列教程和实例

系列结构，也被称为系列序列，是pandas中常用的数据结构之一。它是一种类似于一维数组的结构，由一组数据值和一组标签组成，其中标签和数据值是一对一的对应关系。

系列可以保存任何数据类型，如整数、字符串、浮点数、python对象等，其标签默认为整数，从0开始。 通过索引标签，我们可以更直观地查看数据的索引位置。

1.如何创建系列对象

1. Pandas使用series()函数来创建一个系列对象。

2. 通过这个系列对象，你可以调用相应的方法和属性来处理数据。

   import pandas as pd
   
   series = pd.Series( data, index, dtype, copy)
   
   data: The input data, can be lists, constants, ndarray arrays, etc.
   
   index: The index value must be unique. If no index is passed, it defaults to np.arrange(n).
   
   dtype: dtype indicates the data type. If it is not provided, it will be determined automatically.
   
   copy: Indicates copying data. The default value is false.
   

3. 我们也可以使用数组、字典、标量值或Python对象创建系列对象。下面是创建系列对象的例子。

1.1 创建一个空的系列对象

1. 创建一个空的系列对象，这已经被废弃了。

   >>> import pandas as pd
   >>> # create an empty Series object, this is deprecated.
   ... s = pd.Series()
   __main__:2: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
   >>> print(s)
   Series([], dtype: float64)
   

1.2 从NumPy ndarray对象创建系列对象

1. Ndarray是NumPy的一个数组类型。当数据类型为Ndarray时，传递的索引必须与数组的长度相同。

2. 如果没有传入索引参数，默认情况下，索引值将使用range(n)函数生成，其中n代表数组的长度。

3. 下面的例子将使用默认的索引创建一个系列序列对象。因为没有传递索引，所以默认从0开始分配索引，索引范围是0到len(data)-1，也就是0到3。这种设置方法被称为 "隐式索引"。

   >>> # import the pandas module.
   ... import pandas as pd
   >>> 
   >>> # import the numpy module.
   ... import numpy as np
   >>> 
   >>> # create a ndarray object.
   ... data = np.array(['python','javascript','java','php'])
   >>> 
   >>> # create a Series object with the above ndarray object.
   ... s = pd.Series(data)
   >>> 
   >>> # print out the Series object.
   ... print (s)
   0        python
   1    javascript
   2          java
   3           php
   dtype: object
   

4. 你也可以使用 "显式索引"来定义索引标签，就像下面的例子。

   >>> # import pandas, numpy python library.
   ... import pandas as pd
   >>> import numpy as np
   >>> 
   >>> # create a numpy ndarray object. 
   ... data = np.array(['python','javascript','java'])
   >>> 
   >>> # create an list object.
   ... idx = ['language-1', 2, 'language-3'] 
   >>> 
   >>> # set the Series object's index explicitly. 
   >>> s = pd.Series(data=data,index=idx)
   >>> 
   >>> print(s)
   language-1        python
   2             javascript
   language-3          java
   dtype: object
   
   

1.3 从一个Python字典对象创建Pandas系列对象

1. 你可以使用一个Python字典对象作为输入数据来创建系列对象。

2. 如果没有传入索引，索引将根据字典对象的键来构建。

   >>> # import the python pandas library
   >>> import pandas as pd
   >>>
   >>> # create the python dictionary object.
   >>> dict_data = {'PL' : 'Python', 'OS' : 'Windows', 'DB' : 'MySQL'}
   >>>
   >>> # create the pandas Series object based on the above python dictionary object, do not pass in the index parameter.
   >>> series_obj = pd.Series(dict_data)
   >>>
   >>> # print out the pandas Series object，the keys value is the python dictionary object keys value.
   >>> print(series_obj)
   PL     Python
   OS    Windows
   DB      MySQL
   dtype: object
   
   

3. 相反，当传入索引参数时，索引标签需要与字典中的值逐一对应。

   >>> # import the python pandas library
   >>> import pandas as pd
   >>>
   >>> # create the python dictionary object.
   >>> dict_data = {'PL' : 'Python', 'OS' : 'Windows', 'DB' : 'MySQL'}
   >>> 
   >>> # define the Series object's index list, the index list element value should match the above dictionary object keys' value.  
   >>> index_data= ['DB', 'OS', 'PL', 'ISO']
   >>>
   >>> # create the pandas Series object based on the above python dictionary object, pass in the index parameter.
   >>> series_obj = pd.Series(dict_data, index=index_data)
   >>>
   >>> # print out the pandas Series object，the keys value is the python dictionary object keys value.
   >>> print(series_obj)
   DB      MySQL
   OS    Windows
   PL     Python
   ISO       NaN # Because the dictionary object's keys value does not contain 'ISO', then this element's value is NaN.
   dtype: object
   

1.4 从标量值创建Pandas系列对象

1. 你可以从一个标量值创建pandas系列对象，在这种情况下，必须提供一个索引，下面是例子。

   >>> # import python pandas module with the pd name alias.
   >>> import pandas as pd
   >>>
   >>> # create a list object.
   >>> index_list=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
   >>>
   >>> # create a pandas Series object from a scalar value 10, you must pass in the index array, the scalar values are repeated according to the number of indexes and correspond to them one by one.
   >>> s = pd.Series(10, index = index_list)
   >>>
   >>> # print out the pandas Series object
   >>> print(s)
   1     10
   2     10
   3     10
   4     10
   5     10
   6     10
   7     10
   8     10
   9     10
   10    10
   dtype: int64
   

2.如何读取系列对象

1. 上面一节解释了创建pandas系列对象的各种方法，但是你应该如何读取系列对象中的元素呢？

2. 我们可以通过系列号索引、标签索引或索引片来读取系列对象的元素。

3. 下面是一些例子：

   >>> # import the python pandas library 
   >>> import pandas as pd 
   >>> 
   >>> # create the python dictionary object. 
   >>> dict_data = {'PL' : 'Python', 'OS' : 'Windows', 'DB' : 'MySQL'} 
   >>> 
   >>> # define the Series object's index list, the index list element value should match the above dictionary object keys' value. 
   >>> index_data= ['DB', 'OS', 'PL', 'ISO']
   >>>
   >>> # create the pandas Series object based on the above python dictionary object, pass in the index parameter. 
   >>> series_obj = pd.Series(dict_data, index=index_data)
   >>> 
   >>> print(series_obj)
   DB       MySQL
   OS     Windows
   PL      Python
   ISO        NaN
   dtype: object
   >>> print(series_obj[0]) # get series element by number index.
   MySQL
   >>>
   >>> print(series_obj['OS']) # get series element by tag index.
   Windows
   >>>
   >>> print(series_obj[:3]) # get series elements by number index slice.
   DB      MySQL
   OS    Windows
   PL     Python
   dtype: object
   >>>
   >>> print(series_obj[-3:])# get series elements by number index slice.
   OS     Windows
   PL      Python
   ISO        NaN
   dtype: object
   >>>
   >>> print(series_obj[['OS','DB']])# get series elements by tag index slice.
   OS    Windows
   DB      MySQL
   dtype: object
   >>>
   >>> print(series_obj[['OS','DB','BD']]) # when the tag index dose not exist then it will throw KeyError.
   Traceback (most recent call last):
     File "", line 1, in 
     File "C:\Users\zhaosong\anaconda3\envs\MyPythonEnv\lib\site-packages\pandas\core\series.py", line 966, in __getitem__
       return self._get_with(key)
     File "C:\Users\zhaosong\anaconda3\envs\MyPythonEnv\lib\site-packages\pandas\core\series.py", line 1006, in _get_with
       return self.loc[key]
     File "C:\Users\zhaosong\anaconda3\envs\MyPythonEnv\lib\site-packages\pandas\core\indexing.py", line 931, in __getitem__
       return self._getitem_axis(maybe_callable, axis=axis)
     File "C:\Users\zhaosong\anaconda3\envs\MyPythonEnv\lib\site-packages\pandas\core\indexing.py", line 1153, in _getitem_axis
       return self._getitem_iterable(key, axis=axis)
     File "C:\Users\zhaosong\anaconda3\envs\MyPythonEnv\lib\site-packages\pandas\core\indexing.py", line 1093, in _getitem_iterable
       keyarr, indexer = self._get_listlike_indexer(key, axis)
     File "C:\Users\zhaosong\anaconda3\envs\MyPythonEnv\lib\site-packages\pandas\core\indexing.py", line 1314, in _get_listlike_indexer
       self._validate_read_indexer(keyarr, indexer, axis)
     File "C:\Users\zhaosong\anaconda3\envs\MyPythonEnv\lib\site-packages\pandas\core\indexing.py", line 1377, in _validate_read_indexer
       raise KeyError(f"{not_found} not in index")
   KeyError: "['BD'] not in index"
   
   

3.如何删除系列对象的数据

1. 调用系列对象的drop()方法来删除其数据。

   >>> import pandas as pd
   >>>
   >>> dict_data = {'PL' : 'Python', 'OS' : 'Windows', 'DB' : 'MySQL'}
   >>>
   >>> index_data= ['DB', 'OS', 'PL', 'ISO']
   >>>
   >>> series_obj = pd.Series(dict_data, index=index_data)
   >>>
   >>> print(series_obj)
   DB       MySQL
   OS     Windows
   PL      Python
   ISO        NaN
   dtype: object
   >>>
   >>> series_obj.drop(['DB'])
   OS     Windows
   PL      Python
   ISO        NaN
   dtype: object
   

4.Pandas系列对象的一般方法

1. head(n):返回数据的前n行，如果没有提供参数n，它的默认值是5，那么返回数据的前5行。

2. tail(n):返回数据的最后n行，默认返回最后5行。

   >>> import pandas as pd
   >>>
   >>> dict_data = {'PL' : 'Python', 'OS' : 'Windows', 'DB' : 'MySQL'}
   >>>
   >>> index_data= ['DB', 'OS', 'PL', 'ISO']
   >>>
   >>> series_obj = pd.Series(dict_data, index=index_data)
   >>>
   >>> print(series_obj)
   DB       MySQL
   OS     Windows
   PL      Python
   ISO        NaN
   dtype: object
   >>>
   >>> print(series_obj.head(3)) # return the first 3 rows in the Series object.
   DB      MySQL
   OS    Windows
   PL     Python
   dtype: object
   >>>
   >>> print(series_obj.tail(3))
   OS     Windows
   PL      Python
   ISO        NaN
   dtype: object
   

3. isnull() / notnull()。检测系列对象的元素值是否为空。如果元素的值是空的，那么isnull()返回True，notnull()返回False。

   >>> import pandas as pd
   >>>
   >>> dict_data = {'PL' : 'Python', 'OS' : 'Windows', 'DB' : 'MySQL'}
   >>>
   >>> index_data= ['DB', 'OS', 'PL', 'ISO']
   >>>
   >>> series_obj = pd.Series(dict_data, index=index_data)
   >>>
   >>> print(series_obj)
   DB       MySQL
   OS     Windows
   PL      Python
   ISO        NaN
   dtype: object
   >>>
   >>> print(pd.isnull(series_obj))
   DB     False
   OS     False
   PL     False
   ISO     True
   dtype: bool
   >>>
   >>> print(pd.notnull(series_obj))
   DB      True
   OS      True
   PL      True
   ISO    False
   dtype: bool
   

5.Pandas系列对象的一般属性

1. axes:返回列表中的所有行索引标签。

   >>> import pandas as pd
   >>>
   >>> dict_data = {'PL' : 'Python', 'OS' : 'Windows', 'DB' : 'MySQL'}
   >>>
   >>> index_data= ['DB', 'OS', 'PL', 'ISO']
   >>>
   >>> series_obj = pd.Series(dict_data, index=index_data)
   >>>
   >>> print(series_obj)
   DB       MySQL
   OS     Windows
   PL      Python
   ISO        NaN
   dtype: object
   >>>
   >>>
   >>> print(series_obj.axes)
   [Index(['DB', 'OS', 'PL', 'ISO'], dtype='object')]
   

2. dtype：系列元素的数据类型。

   >>> import pandas as pd
   >>>
   >>> dict_data = {'PL' : 'Python', 'OS' : 'Windows', 'DB' : 'MySQL'}
   >>>
   >>> index_data= ['DB', 'OS', 'PL', 'ISO']
   >>>
   >>> series_obj = pd.Series(dict_data, index=index_data)
   >>>
   >>> print(series_obj)
   DB       MySQL
   OS     Windows
   PL      Python
   ISO        NaN
   dtype: object
   >>>
   >>> print(series_obj.dtype)
   object
   

3. empty: 空的。返回一个布尔值，用于确定系列 对象是否为空。

   >>> import pandas as pd
   >>>
   >>> dict_data = {'PL' : 'Python', 'OS' : 'Windows', 'DB' : 'MySQL'}
   >>>
   >>> index_data= ['DB', 'OS', 'PL', 'ISO']
   >>>
   >>> series_obj = pd.Series(dict_data, index=index_data)
   >>>
   >>> print(series_obj)
   DB       MySQL
   OS     Windows
   PL      Python
   ISO        NaN
   dtype: object
   >>>
   >>> print(series_obj.empty)
   False
   

4. index：返回一个RangeIndex对象，用于描述该对象是否是空的。返回一个RangeIndex对象，描述索引的值范围。

   >>> import pandas as pd
   >>>
   >>> dict_data = {'PL' : 'Python', 'OS' : 'Windows', 'DB' : 'MySQL'}
   >>>
   >>> index_data= ['DB', 'OS', 'PL', 'ISO']
   >>>
   >>> series_obj = pd.Series(dict_data, index=index_data)
   >>>
   >>> print(series_obj)
   DB       MySQL
   OS     Windows
   PL      Python
   ISO        NaN
   dtype: object
   >>>
   >>> print(series_obj.index)
   Index(['DB', 'OS', 'PL', 'ISO'], dtype='object')
   >>>
   >>> print(series_obj.index.array)
   <PandasArray>
   ['DB', 'OS', 'PL', 'ISO']
   Length: 4, dtype: object
   >>>
   >>> for key in series_obj.index.array:
   ...      print(key, ' = ', series_obj[key])
   ...
   DB  =  MySQL
   OS  =  Windows
   PL  =  Python
   ISO  =  nan
   

5. ndim：返回系列对象的尺寸。根据定义，Series对象是一个一维的数据结构，所以它总是返回1。

   >>> print(series_obj.ndim)
   1
   

6. size：返回系列对象的尺寸（长度）。返回系列对象的大小（长度）。

   >>> print(series_obj.size)
   4
   

7. values（值）：以数组形式返回系列对象中的数据。

   >>> print(series_obj.values)
   ['MySQL' 'Windows' 'Python' nan]