【数据分析可视化】Series和DataFrame的排序

代码语言：javascript复制

import numpy as np
import pandas as pd
from pandas import Series, DataFrame

Series的排序

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s1 = Series(np.random.rand(10))
s1

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0    0.324583
1    0.528829
2    0.922022
3    0.050265
4    0.069271
5    0.447179
6    0.595703
7    0.518557
8    0.695466
9    0.685736
dtype: float64

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s1.values

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array([0.32458288, 0.52882927, 0.92202246, 0.05026548, 0.06927059,
       0.44717888, 0.59570299, 0.51855686, 0.69546586, 0.68573564])

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s1.index

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RangeIndex(start=0, stop=10, step=1)

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# value排序 升降可调ascending默认升序
s2 = s1.sort_values()
s2

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3    0.050265
4    0.069271
0    0.324583
5    0.447179
7    0.518557
1    0.528829
6    0.595703
9    0.685736
8    0.695466
2    0.922022
dtype: float64

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# 索引排序
s2.sort_index()

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0    0.324583
1    0.528829
2    0.922022
3    0.050265
4    0.069271
5    0.447179
6    0.595703
7    0.518557
8    0.695466
9    0.685736
dtype: float64

DataFrame的排序

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df1 = DataFrame(np.random.randn(40).reshape(8,5),columns=['A','B','C','D','E'])
df1

	A	B	C	D	E
0	1.069063	0.266594	-0.129437	-0.361949	-1.491594
1	1.520675	1.673761	0.310567	-1.535689	0.388416
2	1.828228	0.221382	-0.092250	-0.111522	-1.187931
3	-1.049244	-0.093515	0.175138	0.627553	-0.357136
4	0.572511	-0.871314	1.142248	-0.489059	0.677733
5	0.088234	-0.786141	-0.222611	0.087407	-0.221874
6	2.199338	0.191928	0.278917	-0.388502	0.611719
7	1.260192	-0.001860	0.144536	-0.312155	1.664181

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# 列排序 没法显示全部
df1['A'].sort_values()

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3   -1.049244
5    0.088234
4    0.572511
0    1.069063
7    1.260192
1    1.520675
2    1.828228
6    2.199338
Name: A, dtype: float64

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# 对指定列排序 显示全部
df2 = df1.sort_values('A')
df2

	A	B	C	D	E
3	-1.049244	-0.093515	0.175138	0.627553	-0.357136
5	0.088234	-0.786141	-0.222611	0.087407	-0.221874
4	0.572511	-0.871314	1.142248	-0.489059	0.677733
0	1.069063	0.266594	-0.129437	-0.361949	-1.491594
7	1.260192	-0.001860	0.144536	-0.312155	1.664181
1	1.520675	1.673761	0.310567	-1.535689	0.388416
2	1.828228	0.221382	-0.092250	-0.111522	-1.187931
6	2.199338	0.191928	0.278917	-0.388502	0.611719

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df2.sort_index()

	A	B	C	D	E
0	1.069063	0.266594	-0.129437	-0.361949	-1.491594
1	1.520675	1.673761	0.310567	-1.535689	0.388416
2	1.828228	0.221382	-0.092250	-0.111522	-1.187931
3	-1.049244	-0.093515	0.175138	0.627553	-0.357136
4	0.572511	-0.871314	1.142248	-0.489059	0.677733
5	0.088234	-0.786141	-0.222611	0.087407	-0.221874
6	2.199338	0.191928	0.278917	-0.388502	0.611719
7	1.260192	-0.001860	0.144536	-0.312155	1.664181

读取csv文件，电影评分降序，输出新的csv

代码语言：javascript复制

# 读取数据
csv_input = '/Users/bennyrhys/Desktop/数据分析可视化-数据集/homework/movie_metadata.csv'
pd.read_csv(csv_input).head()

	color	director_name	num_critic_for_reviews	duration	director_facebook_likes	actor_3_facebook_likes	actor_2_name	actor_1_facebook_likes	gross	genres	...	num_user_for_reviews	language	country	content_rating	budget	title_year	actor_2_facebook_likes	imdb_score	aspect_ratio	movie_facebook_likes
0	Color	James Cameron	723.0	178.0	0.0	855.0	Joel David Moore	1000.0	760505847.0	Action\|Adventure\|Fantasy\|Sci-Fi	...	3054.0	English	USA	PG-13	237000000.0	2009.0	936.0	7.9	1.78	33000
1	Color	Gore Verbinski	302.0	169.0	563.0	1000.0	Orlando Bloom	40000.0	309404152.0	Action\|Adventure\|Fantasy	...	1238.0	English	USA	PG-13	300000000.0	2007.0	5000.0	7.1	2.35	0
2	Color	Sam Mendes	602.0	148.0	0.0	161.0	Rory Kinnear	11000.0	200074175.0	Action\|Adventure\|Thriller	...	994.0	English	UK	PG-13	245000000.0	2015.0	393.0	6.8	2.35	85000
3	Color	Christopher Nolan	813.0	164.0	22000.0	23000.0	Christian Bale	27000.0	448130642.0	Action\|Thriller	...	2701.0	English	USA	PG-13	250000000.0	2012.0	23000.0	8.5	2.35	164000
4	NaN	Doug Walker	NaN	NaN	131.0	NaN	Rob Walker	131.0	NaN	Documentary	...	NaN	NaN	NaN	NaN	NaN	NaN	12.0	7.1	NaN	0

5 rows × 28 columns

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pd.read_csv(csv_input)[['movie_title','imdb_score']].sort_values('imdb_score',ascending=False).head()

	movie_title	imdb_score
2765	Towering Inferno	9.5
1937	The Shawshank Redemption	9.3
3466	The Godfather	9.2
4409	Kickboxer: Vengeance	9.1
2824	Dekalog	9.1

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# 一行代码排序并输出新csv
pd.read_csv(csv_input)[['movie_title','imdb_score']].sort_values('imdb_score',ascending=False).to_csv('imdb.csv')

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!ls

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02file.ipynb
4-1 DataFrame的简单数学计算.ipynb
4-2 Series和DataFrame的排序.ipynb
4-3 重命名Dataframe的index.ipynb
7B4349AB-7282-428F-A780-CB538E0517A3.dmp
[34mApplications[m[m
[34mCreative Cloud Files[m[m
[34mDesktop[m[m
[34mDocuments[m[m
[34mDownloads[m[m
[34mHadoop_VM[m[m
Java.gitignore
[34mLibrary[m[m
[34mMovies[m[m
[34mMusic[m[m
NumPy-排序.ipynb
Numpy-3.4数组读写.ipynb
Numpy1.ipynb
Pandas.ipynb
[34mPictures[m[m
[34mPostman[m[m
[34mPromotionRes[m[m
[34mPublic[m[m
[34mUntitled Folder[m[m
[34mUntitled Folder 1[m[m
Untitled.ipynb
Untitled1.ipynb
[34mVirtual Machines.localized[m[m
[34mWeChatProjects[m[m
ap.plist
apps.plist
bt.plist
[34meclipse-workspace[m[m
history.plist
[34miCloud 云盘（归档）[m[m
imdb.csv
[34minstall[m[m
nadarray.ipynb
[34mopt[m[m
[34msell[m[m
[34mvue-demo01[m[m
[34mvue-sell-cube[m[m
[34mvue-selll[m[m
输出1.spv
数据分析-分组 聚合 可视化.ipynb
班级成绩.ipynb

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!more imdb.csv

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,movie_title,imdb_score
2765,Towering Inferno             ,9.5
1937,The Shawshank Redemption ,9.3
3466,The Godfather ,9.2
4409,Kickboxer: Vengeance ,9.1
2824,Dekalog             ,9.1
3207,Dekalog             ,9.1
66,The Dark Knight ,9.0
2837,The Godfather: Part II ,9.0
3481,Fargo             ,9.0
339,The Lord of the Rings: The Return of the King ,8.9
4822,12 Angry Men ,8.9
4498,"The Good, the Bad and the Ugly ",8.9
3355,Pulp Fiction ,8.9
1874,Schindler's List ,8.9
683,Fight Club ,8.8
836,Forrest Gump ,8.8
270,The Lord of the Rings: The Fellowship of the Ring ,8.8
2051,Star Wars: Episode V - The Empire Strikes Back ,8.8
97,Inception ,8.8
1842,It's Always Sunny in Philadelphia             ,8.8
459,Daredevil             ,8.8
1620,Friday Night Lights             ,8.7
[7mimdb.csv[m[K

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