如何根据列名或行索引对熊猫数据帧进行排序？
熊猫 **dataframe.sort_index()**方法按照给定轴上的标签对对象进行排序。基本上，排序算法应用于轴标签，而不是数据框中的实际数据，并基于此重新排列数据。我们可以自由选择我们想要应用的排序算法。有三种可能的排序算法，我们可以使用“快速排序”、“mergesort”和“heapsort”。
语法: DataFrame.sort_index(axis=0，level=None，升序=True，inplace=False，kind='quicksort '，na_position='last '，sort_remaining=True，by=None)
参数: 轴: p 为索引，1 为直接排序的列 级别:如果不是无，则对指定索引级别 中的值进行排序升序:排序升序对降序 在位:如果为真，就地执行操作，即更改实际数据帧，否则返回已排序的数据帧。 种类: {'quicksort '，' mergesort '，' heapsort'}，默认为' quicksort '。排序算法的选择。有关更多信息，请参见 ndarray.np.sort。mergesort 是唯一稳定的算法。对于数据框，此选项仅在对单个列或标签进行排序时应用。 na_position:【{ ' First '，' last'}，默认为' last '】First 将 NaNs 放在开头，last 将 NaNs 放在结尾。未对多索引实现。 排序剩余:如果为真，按级别和索引排序是多级的，则按指定级别排序后，也按其他级别排序(按顺序)
返回:已排序 _obj:数据帧
用列表字典创建一个简单的数据框，比如列名是:“姓名”、“年龄”、“地点”和“学院”。
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
('Ankita', 31, 'Delhi', 'Gehu'),
('Rahul', 16, 'Tokyo', 'Abes'),
('Simran', 41, 'Delhi', 'Gehu'),
('Shaurya', 33, 'Delhi', 'Geu'),
('Harshita', 35, 'Mumbai', 'Bhu' ),
('Swapnil', 35, 'Mp', 'Geu'),
('Priya', 35, 'Uk', 'Geu'),
('Jeet', 35, 'Guj', 'Gehu'),
('Ananya', 35, 'Up', 'Bhu')
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
'Place', 'College'],
index =[ 'b', 'c', 'a', 'e', 'f', 'g',
'i', 'j', 'k', 'd'])
# show the dataframe
details
输出:
示例 1: 根据行索引标签名称对数据框的行进行排序。
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
('Ankita', 31, 'Delhi', 'Gehu'),
('Rahul', 16, 'Tokyo', 'Abes'),
('Simran', 41, 'Delhi', 'Gehu'),
('Shaurya', 33, 'Delhi', 'Geu'),
('Harshita', 35, 'Mumbai', 'Bhu' ),
('Swapnil', 35, 'Mp', 'Geu'),
('Priya', 35, 'Uk', 'Geu'),
('Jeet', 35, 'Guj', 'Gehu'),
('Ananya', 35, 'Up', 'Bhu')
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
'Place', 'College'],
index =[ 'b', 'c', 'a', 'e', 'f',
'g', 'i', 'j', 'k', 'd'])
# sort the rows of dataframe
# based on row index
rslt_df = details.sort_index()
# show the resultant Dataframe
rslt_df
输出:
示例 2: 根据行索引标签以降序对数据框的行进行排序。
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
('Ankita', 31, 'Delhi', 'Gehu'),
('Rahul', 16, 'Tokyo', 'Abes'),
('Simran', 41, 'Delhi', 'Gehu'),
('Shaurya', 33, 'Delhi', 'Geu'),
('Harshita', 35, 'Mumbai', 'Bhu' ),
('Swapnil', 35, 'Mp', 'Geu'),
('Priya', 35, 'Uk', 'Geu'),
('Jeet', 35, 'Guj', 'Gehu'),
('Ananya', 35, 'Up', 'Bhu')
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
'Place', 'College'],
index =[ 'b', 'c', 'a', 'e', 'f',
'g', 'i', 'j', 'k', 'd'])
# sort the rows of dataframe in descending
# order based on row index
rslt_df = details.sort_index(ascending = False)
# show the resultant Dataframe
rslt_df
输出:
示例 3: 根据行索引标签对数据框的行进行就地排序。
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
('Ankita', 31, 'Delhi', 'Gehu'),
('Rahul', 16, 'Tokyo', 'Abes'),
('Simran', 41, 'Delhi', 'Gehu'),
('Shaurya', 33, 'Delhi', 'Geu'),
('Harshita', 35, 'Mumbai', 'Bhu' ),
('Swapnil', 35, 'Mp', 'Geu'),
('Priya', 35, 'Uk', 'Geu'),
('Jeet', 35, 'Guj', 'Gehu'),
('Ananya', 35, 'Up', 'Bhu')
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
'Place', 'College'],
index =[ 'b', 'c', 'a', 'e', 'f',
'g', 'i', 'j', 'k', 'd'])
# sort the rows of dataframe in Place
# based on row index
details.sort_index(inplace = True)
# show the resultant Dataframe
details
输出:
示例 4: 基于列名对数据框的列进行排序。
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
('Ankita', 31, 'Delhi', 'Gehu'),
('Rahul', 16, 'Tokyo', 'Abes'),
('Simran', 41, 'Delhi', 'Gehu'),
('Shaurya', 33, 'Delhi', 'Geu'),
('Harshita', 35, 'Mumbai', 'Bhu' ),
('Swapnil', 35, 'Mp', 'Geu'),
('Priya', 35, 'Uk', 'Geu'),
('Jeet', 35, 'Guj', 'Gehu'),
('Ananya', 35, 'Up', 'Bhu')
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
'Place', 'College'],
index =[ 'b', 'c', 'a', 'e', 'f',
'g', 'i', 'j', 'k', 'd'])
# sort a dataframe based on column names
rslt_df = details.sort_index(axis = 1)
# show the resultant Dataframe
rslt_df
输出:
示例 5: 根据列名以降序对数据帧进行排序。
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
('Ankita', 31, 'Delhi', 'Gehu'),
('Rahul', 16, 'Tokyo', 'Abes'),
('Simran', 41, 'Delhi', 'Gehu'),
('Shaurya', 33, 'Delhi', 'Geu'),
('Harshita', 35, 'Mumbai', 'Bhu' ),
('Swapnil', 35, 'Mp', 'Geu'),
('Priya', 35, 'Uk', 'Geu'),
('Jeet', 35, 'Guj', 'Gehu'),
('Ananya', 35, 'Up', 'Bhu')
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
'Place', 'College'],
index =[ 'b', 'c', 'a', 'e', 'f',
'g', 'i', 'j', 'k', 'd'])
# sort a dataframe in descending
# order based on column names
rslt_df = details.sort_index(ascending = False, axis = 1)
# show the resultant Dataframe
rslt_df
输出:
示例 6: 根据列名对数据帧进行适当排序。
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
('Ankita', 31, 'Delhi', 'Gehu'),
('Rahul', 16, 'Tokyo', 'Abes'),
('Simran', 41, 'Delhi', 'Gehu'),
('Shaurya', 33, 'Delhi', 'Geu'),
('Harshita', 35, 'Mumbai', 'Bhu' ),
('Swapnil', 35, 'Mp', 'Geu'),
('Priya', 35, 'Uk', 'Geu'),
('Jeet', 35, 'Guj', 'Gehu'),
('Ananya', 35, 'Up', 'Bhu')
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
'Place', 'College'],
index =[ 'b', 'c', 'a', 'e', 'f',
'g', 'i', 'j', 'k', 'd'])
# sort a dataframe in place
# based on column names
details.sort_index(inplace = True, axis = 1)
# show the resultant Dataframe
details
输出:
