【数据分析可视化】Series和DataFrame的排序
import numpy as np
import pandas as pd
from pandas import Series, DataFrameSeries的排序
s1 = Series(np.random.rand(10))
s10 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: float64s1.valuesarray([0.32458288, 0.52882927, 0.92202246, 0.05026548, 0.06927059,
0.44717888, 0.59570299, 0.51855686, 0.69546586, 0.68573564])s1.indexRangeIndex(start=0, stop=10, step=1)# value排序 升降可调ascending默认升序
s2 = s1.sort_values()
s23 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# 索引排序
s2.sort_index()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: float64DataFrame的排序
df1 = DataFrame(np.random.randn(40).reshape(8,5),columns=['A','B','C','D','E'])
df1A | 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 |
# 列排序 没法显示全部
df1['A'].sort_values()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# 对指定列排序 显示全部
df2 = df1.sort_values('A')
df2A | 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 |
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
# 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
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 |
# 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')!ls02file.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!more imdb.csv,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腾讯云开发者
