pandas-06 Series和Dataframe的排序操作
对pandas中的Series和Dataframe进行排序,主要使用sort_values()和sort_index()。
DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, kind=‘quicksort’, na_position=‘last’)
by:列名,按照某列排序
axis:按照index排序还是按照column排序
ascending:是否升序排列
kind:选择 排序算法{‘quicksort’, ‘mergesort’, ‘heapsort’}, 默认是‘quicksort’,也就是快排
na_position:nan排列的位置,是前还是后{‘first’, ‘last’}, 默认是‘last’
sort_index() 的参数和上面差不多。
实例:
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
np.random.seed(666)
s1 = Series(np.random.randn(10))
print(s1)
'''
0 0.824188
1 0.479966
2 1.173468
3 0.909048
4 -0.571721
5 -0.109497
6 0.019028
7 -0.943761
8 0.640573
9 -0.786443
dtype: float64
'''
# 为series排序的两种方式,1 用 value 来排序, 2 用 index 来排序
s2 = s1.sort_values() # 按照 value 来排序
print(s2)
'''
7 -0.943761
9 -0.786443
4 -0.571721
5 -0.109497
6 0.019028
1 0.479966
8 0.640573
0 0.824188
3 0.909048
2 1.173468
dtype: float64
'''
# axis 设置轴 的方向, ascending 设置升降序
s2 = s1.sort_values(axis = 0, ascending=False)
print(s2)
'''
2 1.173468
3 0.909048
0 0.824188
8 0.640573
1 0.479966
6 0.019028
5 -0.109497
4 -0.571721
9 -0.786443
7 -0.943761
dtype: float64
'''
# 通过 对 index 进行排序
s2.sort_index()
print(s2)
'''
2 1.173468
3 0.909048
0 0.824188
8 0.640573
1 0.479966
6 0.019028
5 -0.109497
4 -0.571721
9 -0.786443
7 -0.943761
dtype: float64
'''
# 对于 dataframe 的排序
df1 = DataFrame(np.random.randn(40).reshape(8, 5), columns=['a', 'b', 'c', 'd', 'e'])
print(df1)
'''
a b c d e
0 0.608870 -0.931012 0.978222 -0.736918 -0.298733
1 -0.460587 -1.088793 -0.575771 -1.682901 0.229185
2 -1.756625 0.844633 0.277220 0.852902 0.194600
3 1.310638 1.543844 -0.529048 -0.656472 -0.201506
4 -0.700616 0.687138 -0.026076 -0.829758 0.296554
5 -0.312680 -0.611301 -0.821752 0.897123 0.136079
6 -0.258655 1.110766 -0.188424 -0.041489 -0.984792
7 -1.352282 0.194324 0.267239 -0.426474 1.447735
'''
# 按照 columns 进行排序, 这种做法 和 对 series的 操作 差不多
print(df1['a'].sort_values())
'''
2 -1.756625
7 -1.352282
4 -0.700616
1 -0.460587
5 -0.312680
6 -0.258655
0 0.608870
3 1.310638
Name: a, dtype: float64
'''
# 将 dataframe 按照 其中 某个列进行排序, 参数ascending来控制 升降序
print(df1.sort_values('a'))
'''
a b c d e
2 -1.756625 0.844633 0.277220 0.852902 0.194600
7 -1.352282 0.194324 0.267239 -0.426474 1.447735
4 -0.700616 0.687138 -0.026076 -0.829758 0.296554
1 -0.460587 -1.088793 -0.575771 -1.682901 0.229185
5 -0.312680 -0.611301 -0.821752 0.897123 0.136079
6 -0.258655 1.110766 -0.188424 -0.041489 -0.984792
0 0.608870 -0.931012 0.978222 -0.736918 -0.298733
3 1.310638 1.543844 -0.529048 -0.656472 -0.201506
'''
df2 = df1.sort_values('a')
print(df2)
'''
a b c d e
2 -1.756625 0.844633 0.277220 0.852902 0.194600
7 -1.352282 0.194324 0.267239 -0.426474 1.447735
4 -0.700616 0.687138 -0.026076 -0.829758 0.296554
1 -0.460587 -1.088793 -0.575771 -1.682901 0.229185
5 -0.312680 -0.611301 -0.821752 0.897123 0.136079
6 -0.258655 1.110766 -0.188424 -0.041489 -0.984792
0 0.608870 -0.931012 0.978222 -0.736918 -0.298733
3 1.310638 1.543844 -0.529048 -0.656472 -0.201506
'''
# 对 df2 的 索引 进行排序, 又回到之前的原本的 df2
print(df2.sort_index())
'''
a b c d e
0 0.608870 -0.931012 0.978222 -0.736918 -0.298733
1 -0.460587 -1.088793 -0.575771 -1.682901 0.229185
2 -1.756625 0.844633 0.277220 0.852902 0.194600
3 1.310638 1.543844 -0.529048 -0.656472 -0.201506
4 -0.700616 0.687138 -0.026076 -0.829758 0.296554
5 -0.312680 -0.611301 -0.821752 0.897123 0.136079
6 -0.258655 1.110766 -0.188424 -0.041489 -0.984792
7 -1.352282 0.194324 0.267239 -0.426474 1.447735
'''
原文地址:https://www.cnblogs.com/wenqiangit/p/11252731.html
时间: 2024-10-09 20:56:13