第01章 Pandas基礎(chǔ)
第02章 DataFrame基礎(chǔ)運(yùn)算
第03章 創(chuàng)建和持久化DataFrame
第04章 開始數(shù)據(jù)分析
第05章 探索性數(shù)據(jù)分析
第06章 選取數(shù)據(jù)子集
第07章 過濾行
第08章 索引對(duì)齊
7.1 計(jì)算布爾統(tǒng)計(jì)信息
讀取電影數(shù)據(jù)集，檢查前幾行：
>>> import pandas as pd
>>> import numpy as np
>>> movie = pd.read_csv(
... "data/movie.csv", index_col="movie_title"
... )
>>> movie[["duration"]].head()
Duration
movie_title
Avatar 178.0
Pirates of the Caribbean: At World's End 169.0
Spectre 148.0
The Dark Knight Rises 164.0
Star Wars: Episode VII - The Force Awakens NaN
判斷電影時(shí)長(zhǎng)是否超過兩小時(shí)：
>>> movie_2_hours = movie["duration"] > 120
>>> movie_2_hours.head(10)
movie_title
Avatar True
Pirates of the Caribbean: At World's End True
Spectre True
The Dark Knight Rises True
Star Wars: Episode VII - The Force Awakens False
John Carter True
Spider-Man 3 True
Tangled False
Avengers: Age of Ultron True
Harry Potter and the Half-Blood Prince True
Name: duration, dtype: bool
使用這個(gè)Series判斷時(shí)長(zhǎng)超過兩小時(shí)的電影總和：
>>> movie_2_hours.sum()
1039
時(shí)長(zhǎng)超過兩小時(shí)的電影所占的比例：
>>> movie_2_hours.mean() * 100
21.13506916192026
前面的步驟沒有刪除缺失值，其實(shí)有誤導(dǎo)性：
>>> movie["duration"].dropna().gt(120).mean() * 100
21.199755152009794
使用.describe方法輸出概括統(tǒng)計(jì)性信息：
>>> movie_2_hours.describe()
count 4916
unique 2
top False
freq 3877
Name: duration, dtype: object
原理
使用.value_counts方法統(tǒng)計(jì)False和True所占的比例：
>>> movie_2_hours.value_counts(normalize=True)
False 0.788649
True 0.211351
Name: duration, dtype: float64
更多
可以使用DataFrame中的兩列，創(chuàng)建布爾Series：
>>> actors = movie[
... ["actor_1_facebook_likes", "actor_2_facebook_likes"]
... ].dropna()
>>> (
... actors["actor_1_facebook_likes"]
... > actors["actor_2_facebook_likes"]
... ).mean()
0.9777687130328371
7.2 構(gòu)造布爾條件
讀取數(shù)據(jù)：
>>> movie = pd.read_csv(
... "data/movie.csv", index_col="movie_title"
... )
創(chuàng)建變量用于存儲(chǔ)布爾數(shù)組：
>>> criteria1 = movie.imdb_score > 8
>>> criteria2 = movie.content_rating == "PG-13"
>>> criteria3 = (movie.title_year < 2000) | (
... movie.title_year > 2009
... )
將所有過濾器組成一個(gè)布爾數(shù)組：
>>> criteria_final = criteria1 & criteria2 & criteria3
>>> criteria_final.head()
movie_title
Avatar False
Pirates of the Caribbean: At World's End False
Spectre False
The Dark Knight Rises True
Star Wars: Episode VII - The Force Awakens False
dtype: bool
更多
比較運(yùn)算符是有順序的：
>>> 5 < 10 and 3 > 4
False
>>> 5 < 10 and 3 > 4
False
>>> True and 3 > 4
False
>>> True and False
False
>>> False
False
7.3 使用布爾數(shù)組進(jìn)行過濾
讀取數(shù)據(jù)，設(shè)置過濾條件：
>>> movie = pd.read_csv(
... "data/movie.csv", index_col="movie_title"
... )
>>> crit_a1 = movie.imdb_score > 8
>>> crit_a2 = movie.content_rating == "PG-13"
>>> crit_a3 = (movie.title_year < 2000) | (
... movie.title_year > 2009
... )
>>> final_crit_a = crit_a1 & crit_a2 & crit_a3
再創(chuàng)建一組條件：
>>> crit_b1 = movie.imdb_score < 5
>>> crit_b2 = movie.content_rating == "R"
>>> crit_b3 = (movie.title_year >= 2000) & (
... movie.title_year <= 2010
... )
>>> final_crit_b = crit_b1 & crit_b2 & crit_b3
將這兩個(gè)條件組成最后的條件：
>>> final_crit_all = final_crit_a | final_crit_b
>>> final_crit_all.head()
movie_title
Avatar False
Pirates of the Caribbean: At World's End False
Spectre False
The Dark Knight Rises True
Star Wars: Episode VII - The Force Awakens False
dtype: bool
用最后的條件過濾數(shù)據(jù)：
>>> movie[final_crit_all].head()
color ... movie/likes
movie_title ...
The Dark Knight Rises Color ... 164000
The Avengers Color ... 123000
Captain America: Civil War Color ... 72000
Guardians of the Galaxy Color ... 96000
Interstellar Color ... 349000
.loc也可以使用這個(gè)過濾條件：
>>> movie.loc[final_crit_all].head()
color ... movie/likes
movie_title ...
The Dark Knight Rises Color ... 164000
The Avengers Color ... 123000
Captain America: Civil War Color ... 72000
Guardians of the Galaxy Color ... 96000
Interstellar Color ... 349000
另外也可以在.loc中指定列：
>>> cols = ["imdb_score", "content_rating", "title_year"]
>>> movie_filtered = movie.loc[final_crit_all, cols]
>>> movie_filtered.head(10)
imdb_score content_rating title_year
movie_title
The Dark ... 8.5 PG-13 2012.0
The Avengers 8.1 PG-13 2012.0
Captain A... 8.2 PG-13 2016.0
Guardians... 8.1 PG-13 2014.0
Interstellar 8.6 PG-13 2014.0
Inception 8.8 PG-13 2010.0
The Martian 8.1 PG-13 2015.0
Town & Co... 4.4 R 2001.0
Sex and t... 4.3 R 2010.0
Rollerball 3.0 R 2002.0
.iloc不支持布爾數(shù)組，但支持NumPy數(shù)組：
>>> movie.iloc[final_crit_all]
Traceback (most recent call last):
...
ValueError: iLocation based boolean indexing cannot use an indexable as a mask
>>> movie.iloc[final_crit_all.to_numpy()]
color ... movie/likes
movie_title ...
The Dark Knight Rises Color ... 164000
The Avengers Color ... 123000
Captain America: Civil War Color ... 72000
Guardians of the Galaxy Color ... 96000
Interstellar Color ... 349000
... ... ... ...
The Young Unknowns Color ... 4
Bled Color ... 128
Hoop Dreams Color ... 0
Death Calls Color ... 16
The Legend of God's Gun Color ... 13
更多
可以將所有條件放入一行：
>>> final_crit_a2 = (
... (movie.imdb_score > 8)
... & (movie.content_rating == "PG-13")
... & (
... (movie.title_year < 2000)
... | (movie.title_year > 2009)
... )
... )
>>> final_crit_a2.equals(final_crit_a)
True
7.4 對(duì)比行過濾和索引過濾
讀取數(shù)據(jù)，并進(jìn)行篩選：
>>> college = pd.read_csv("data/college.csv")
>>> college[college["STABBR"] == "TX"].head()
INSTNM ... GRAD_/_SUPP
3610 Abilene Christian University ... 25985
3611 Alvin Community College ... 6750
3612 Amarillo College ... 10950
3613 Angelina College ... PrivacySuppressed
3614 Angelo State University ... 21319.5
重復(fù)上面的步驟，使用STABBR列作為行索引，然后使用基于標(biāo)簽的進(jìn)行提取：
>>> college2 = college.set_index("STABBR")
>>> college2.loc["TX"].head()
INSTNM ... GRAD_/_SUPP
3610 Abilene Christian University ... 25985
3611 Alvin Community College ... 6750
3612 Amarillo College ... 10950
3613 Angelina College ... PrivacySuppressed
3614 Angelo State University ... 21319.5
比較兩種方法的速度：
>>> %timeit college[college['STABBR'] == 'TX']
1.75 ms ± 187 μs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
>>> %timeit college2.loc['TX']
882 μs ± 69.3 μs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
雖然用行索引快，但是創(chuàng)建行索引也需要時(shí)間：
>>> %timeit college2 = college.set_index('STABBR')
2.01 ms ± 107 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)
更多
使用布爾條件選取多列：
>>> states = ["TX", "CA", "NY"]
>>> college[college["STABBR"].isin(states)]
INSTNM CITY ... MD_EARN_WNE_P10 GRAD_DEBT_MDN_SUPP
192 Academy ... San Fran... ... 36000 35093
193 ITT Tech... Rancho C... ... 38800 25827.5
194 Academy ... Oakland ... NaN PrivacyS...
195 The Acad... Huntingt... ... 28400 9500
196 Avalon S... Alameda ... 21600 9860
... ... ... ... ... ...
7528 WestMed ... Merced ... NaN 15623.5
7529 Vantage ... El Paso ... NaN 9500
7530 SAE Inst... Emeryville ... NaN 9500
7533 Bay Area... San Jose ... NaN PrivacyS...
7534 Excel Le... San Antonio ... NaN 12125
>>> college2.loc[states]
INSTNM CITY ... MD_EARN_WNE_P10 GRAD_DEBT_MDN_SUPP
STABBR ...
TX Abilene ... Abilene ... 40200 25985
TX Alvin Co... Alvin ... 34500 6750
TX Amarillo... Amarillo ... 31700 10950
TX Angelina... Lufkin ... 26900 PrivacyS...
TX Angelo S... San Angelo ... 37700 21319.5
... ... ... ... ... ...
NY Briarcli... Patchogue ... 38200 28720.5
NY Jamestow... Salamanca ... NaN 12050
NY Pratt Ma... New York ... 40900 26691
NY Saint Jo... Patchogue ... 52000 22143.5
NY Franklin... Brooklyn ... 20000 PrivacyS...
7.5 使用唯一和有序索引選取
讀取數(shù)據(jù)集，使用STABBR作為索引，判斷索引是否是單調(diào)的：
>>> college = pd.read_csv("data/college.csv")
>>> college2 = college.set_index("STABBR")
>>> college2.index.is_monotonic
False
對(duì)索引進(jìn)行排序，并判斷是否單調(diào)：
>>> college3 = college2.sort_index()
>>> college3.index.is_monotonic
True
查詢從這三個(gè)DataFrame選取TX的速度：
>>> %timeit college[college['STABBR'] == 'TX']
1.75 ms ± 187 μs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
>>> %timeit college2.loc['TX']
1.09 ms ± 232 μs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
>>> %timeit college3.loc['TX']
304 μs ± 17.8 μs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
有序索引大大提高了速度?，F(xiàn)在試試唯一索引：
>>> college_unique = college.set_index("INSTNM")
>>> college_unique.index.is_unique
True
使用布爾索引選取數(shù)據(jù)，返回的是個(gè)DataFrame：
>>> college[college["INSTNM"] == "Stanford University"]
INSTNM CITY ... MD_EARN_WNE_P10 GRAD_DEBT_MDN_SUPP
4217 Stanford... Stanford ... 86000 12782
使用行索引進(jìn)行選?。?/p>
>>> college_unique.loc["Stanford University"]
CITY Stanford
STABBR CA
HBCU 0
MENONLY 0
WOMENONLY 0
...
PCTPELL 0.1556
PCTFLOAN 0.1256
UG25ABV 0.0401
MD_EARN_WNE_P10 86000
GRAD_DEBT_MDN_SUPP 12782
Name: Stanford University, Length: 26, dtype: object
更多
使用城市名和州縮寫作為行索引：
>>> college.index = (
... college["CITY"] + ", " + college["STABBR"]
... )
>>> college = college.sort_index()
>>> college.head()
INSTNM CITY ... MD_EARN_WNE_P10 GRAD_DEBT_MDN_SUPP
ARTESIA, CA Angeles ... ARTESIA ... NaN 16850
Aberdeen, SD Presenta... Aberdeen ... 35900 25000
Aberdeen, SD Northern... Aberdeen ... 33600 24847
Aberdeen, WA Grays Ha... Aberdeen ... 27000 11490
Abilene, TX Hardin-S... Abilene ... 38700 25864
選取所有來自Miami, FL的學(xué)校：
>>> college.loc["Miami, FL"].head()
INSTNM CITY ... MD_EARN_WNE_P10 GRAD_DEBT_MDN_SUPP
Miami, FL New Prof... Miami ... 18700 8682
Miami, FL Manageme... Miami ... PrivacyS... 12182
Miami, FL Strayer ... Miami ... 49200 36173.5
Miami, FL Keiser U... Miami ... 29700 26063
Miami, FL George T... Miami ... 38600 PrivacyS...
比較二者的速度：
>>> %%timeit
>>> crit1 = college["CITY"] == "Miami"
>>> crit2 = college["STABBR"] == "FL"
>>> college[crit1 & crit2]
3.05 ms ± 66.4 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)
>>> %timeit college.loc['Miami, FL']
369 μs ± 130 μs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
7.6 翻譯SQL的WHERE子句
SQL語句如下：
SELECT
UNIQUE_ID,
DEPARTMENT,
GENDER,
BASE_SALARY
FROM
EMPLOYEE
WHERE
DEPARTMENT IN ('Houston Police Department-HPD',
'Houston Fire Department (HFD)') AND
GENDER = 'Female' AND
BASE_SALARY BETWEEN 80000 AND 120000;
使用Pandas實(shí)現(xiàn)上面SQL語句同樣的目的：
>>> employee = pd.read_csv("data/employee.csv")
查看數(shù)據(jù)集的信息：
>>> employee.dtypes
UNIQUE_ID int64
POSITION_TITLE object
DEPARTMENT object
BASE_SALARY float64
RACE object
EMPLOYMENT_TYPE object
GENDER object
EMPLOYMENT_STATUS object
HIRE_DATE object
JOB_DATE object
dtype: object
>>> employee.DEPARTMENT.value_counts().head()
Houston Police Department-HPD 638
Houston Fire Department (HFD) 384
Public Works & Engineering-PWE 343
Health & Human Services 110
Houston Airport System (HAS) 106
Name: DEPARTMENT, dtype: int64
>>> employee.GENDER.value_counts()
Male 1397
Female 603
Name: GENDER, dtype: int64
>>> employee.BASE_SALARY.describe()
count 1886.000000
mean 55767.931601
std 21693.706679
min 24960.000000
25% 40170.000000
50% 54461.000000
75% 66614.000000
max 275000.000000
Name: BASE_SALARY, dtype: float64
創(chuàng)建過濾條件：
>>> depts = [
... "Houston Police Department-HPD",
... "Houston Fire Department (HFD)",
... ]
>>> criteria_dept = employee.DEPARTMENT.isin(depts)
>>> criteria_gender = employee.GENDER == "Female"
>>> criteria_sal = (employee.BASE_SALARY >= 80000) & (
... employee.BASE_SALARY <= 120000
... )
>>> criteria_final = (
... criteria_dept & criteria_gender & criteria_sal
... )
使用過濾條件篩選數(shù)據(jù)：
>>> select_columns = [
... "UNIQUE_ID",
... "DEPARTMENT",
... "GENDER",
... "BASE_SALARY",
... ]
>>> employee.loc[criteria_final, select_columns].head()
UNIQUE_ID DEPARTMENT GENDER BASE_SALARY
61 61 Houston ... Female 96668.0
136 136 Houston ... Female 81239.0
367 367 Houston ... Female 86534.0
474 474 Houston ... Female 91181.0
513 513 Houston ... Female 81239.0
更多
和SQL類似，Pandas也有between方法：
''' {.sourceCode .pycon}
>>> criteria_sal = employee.BASE_SALARY.between(
... 80_000, 120_000
... )
'''
7.7 用查詢方法提高布爾索引的可讀性
本節(jié)使用DataFrame的query方法。
讀取數(shù)據(jù)：
>>> employee = pd.read_csv("data/employee.csv")
>>> depts = [
... "Houston Police Department-HPD",
... "Houston Fire Department (HFD)",
... ]
>>> select_columns = [
... "UNIQUE_ID",
... "DEPARTMENT",
... "GENDER",
... "BASE_SALARY",
... ]
創(chuàng)建查詢字符串：
>>> qs = (
... "DEPARTMENT in @depts "
... " and GENDER == 'Female' "
... " and 80000 <= BASE_SALARY <= 120000"
... )
>>> emp_filtered = employee.query(qs)
>>> emp_filtered[select_columns].head()
UNIQUE_ID DEPARTMENT GENDER BASE_SALARY
61 61 Houston ... Female 96668.0
136 136 Houston ... Female 81239.0
367 367 Houston ... Female 86534.0
474 474 Houston ... Female 91181.0
513 513 Houston ... Female 81239.0
>>> top10_depts = (
... employee.DEPARTMENT.value_counts()
... .index[:10]
... .tolist()
... )
>>> qs = "DEPARTMENT not in @top10_depts and GENDER == 'Female'"
>>> employee_filtered2 = employee.query(qs)
>>> employee_filtered2.head()
UNIQUE_ID POSITION_TITLE ... HIRE_DATE JOB_DATE
0 0 ASSISTAN... ... 2006-06-12 2012-10-13
73 73 ADMINIST... ... 2011-12-19 2013-11-23
96 96 ASSISTAN... ... 2013-06-10 2013-06-10
117 117 SENIOR A... ... 1998-03-20 2012-07-21
146 146 SENIOR S... ... 2014-03-17 2014-03-17
7.8 用.where方法保留Series的大小
讀取數(shù)據(jù)，電影名作為索引，actor_1_facebook_likes列不為空：
>>> movie = pd.read_csv(
... "data/movie.csv", index_col="movie_title"
... )
>>> fb_likes = movie["actor_1_facebook_likes"].dropna()
>>> fb_likes.head()
movie_title
Avatar 1000.0
Pirates of the Caribbean: At World's End 40000.0
Spectre 11000.0
The Dark Knight Rises 27000.0
Star Wars: Episode VII - The Force Awakens 131.0
Name: actor_1_facebook_likes, dtype: float64
使用describe方法查看：
>>> fb_likes.describe()
count 4909.000000
mean 6494.488491
std 15106.986884
min 0.000000
25% 607.000000
50% 982.000000
75% 11000.000000
max 640000.000000
Name: actor_1_facebook_likes, dtype: float64
用柱狀圖查看分布：
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(figsize=(10, 8))
>>> fb_likes.hist(ax=ax)
>>> fig.savefig(
... "c7-hist.png", dpi=300
... )
這張圖看不出數(shù)據(jù)分布，大部分都是小于20000的：
>>> criteria_high = fb_likes < 20_000
>>> criteria_high.mean().round(2)
0.91
數(shù)據(jù)中有缺失值：
>>> fb_likes.where(criteria_high).head()
movie_title
Avatar 1000.0
Pirates of the Caribbean: At World's End NaN
Spectre 11000.0
The Dark Knight Rises NaN
Star Wars: Episode VII - The Force Awakens 131.0
Name: actor_1_facebook_likes, dtype: float64
where中可以設(shè)置other參數(shù)可以用于控制替換值：
>>> fb_likes.where(criteria_high, other=20000).head()
movie_title
Avatar 1000.0
Pirates of the Caribbean: At World's End 20000.0
Spectre 11000.0
The Dark Knight Rises 20000.0
Star Wars: Episode VII - The Force Awakens 131.0
Name: actor_1_facebook_likes, dtype: float64
創(chuàng)建另一個(gè)where條件：
>>> criteria_low = fb_likes > 300
>>> fb_likes_cap = fb_likes.where(
... criteria_high, other=20_000
... ).where(criteria_low, 300)
>>> fb_likes_cap.head()
movie_title
Avatar 1000.0
Pirates of the Caribbean: At World's End 20000.0
Spectre 11000.0
The Dark Knight Rises 20000.0
Star Wars: Episode VII - The Force Awakens 300.0
Name: actor_1_facebook_likes, dtype: float64
前后兩個(gè)Series大小相同：
>>> len(fb_likes), len(fb_likes_cap)
(4909, 4909)
重新用柱狀圖查看分布：
>>> fig, ax = plt.subplots(figsize=(10, 8))
>>> fb_likes_cap.hist(ax=ax)
>>> fig.savefig(
... "c7-hist2.png", dpi=300
... )
更多
Pandas有.clip、.clip_lower、.clip_upper三個(gè)方法用于最低值和最高值：
>>> fb_likes_cap2 = fb_likes.clip(lower=300, upper=20000)
>>> fb_likes_cap2.equals(fb_likes_cap)
True
7.9 遮掩DataFrame的行
讀取數(shù)據(jù)，創(chuàng)建條件：
>>> movie = pd.read_csv(
... "data/movie.csv", index_col="movie_title"
... )
>>> c1 = movie["title_year"] >= 2010
>>> c2 = movie["title_year"].isna()
>>> criteria = c1 | c2
用mask方法遮擋上述條件的數(shù)據(jù)：
>>> movie.mask(criteria).head()
color ...
movie_title ...
Avatar Color ...
Pirates of the Caribbean: At World's End Color ...
Spectre NaN ...
The Dark Knight Rises NaN ...
Star Wars: Episode VII - The Force Awakens NaN ...
注意上面三四五是缺失值：
>>> movie_mask = movie.mask(criteria).dropna(how="all")
>>> movie_mask.head()
color ...
movie_title ...
Avatar Color ...
Pirates of the Caribbean: At World's End Color ...
Spider-Man 3 Color ...
Harry Potter and the Half-Blood Prince Color ...
Superman Returns Color ...
.equals方法檢查這兩個(gè)條件是不一樣的：
>>> movie_boolean = movie[movie["title_year"] < 2010]
>>> movie_mask.equals(movie_boolean)
False
但形狀是一樣的：
>>> movie_mask.shape == movie_boolean.shape
True
檢查兩個(gè)條件的數(shù)據(jù)類型：
>>> movie_mask.dtypes == movie_boolean.dtypes
color True
director_name True
num_critic_for_reviews True
duration True
director_facebook_likes True
...
title_year True
actor_2_facebook_likes True
imdb_score True
aspect_ratio True
movie_facebook_likes False
Length: 27, dtype: bool
Pandas有一個(gè)assert_frame_equal方法，也可以判斷DataFrame是否相同：
>>> from pandas.testing import assert_frame_equal
>>> assert_frame_equal(
... movie_boolean, movie_mask, check_dtype=False
... )
更多
比較這兩個(gè)條件的速度：
>>> %timeit movie.mask(criteria).dropna(how='all')
11.2 ms ± 144 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)
>>> %timeit movie[movie['title_year'] < 2010]
1.07 ms ± 34.9 μs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
7.10 使用布爾值、整數(shù)位置和標(biāo)簽選取數(shù)據(jù)
讀取數(shù)據(jù)，創(chuàng)建條件：
>>> movie = pd.read_csv(
... "data/movie.csv", index_col="movie_title"
... )
>>> c1 = movie["content_rating"] == "G"
>>> c2 = movie["imdb_score"] < 4
>>> criteria = c1 & c2
使用.loc過濾行：
>>> movie_loc = movie.loc[criteria]
>>> movie_loc.head()
color ... movie/likes
movie_title ...
The True Story of Puss'N Boots Color ... 90
Doogal Color ... 346
Thomas and the Magic Railroad Color ... 663
Barney's Great Adventure Color ... 436
Justin Bieber: Never Say Never Color ... 62000
這兩個(gè)方法是等價(jià)的：
>>> movie_loc.equals(movie[criteria])
True
iloc需要將條件轉(zhuǎn)換為numpy數(shù)組：
>>> movie_iloc = movie.iloc[criteria.to_numpy()]
>>> movie_iloc.equals(movie_loc)
True
選取數(shù)據(jù)類型是int64的：
>>> criteria_col = movie.dtypes == np.int64
>>> criteria_col.head()
color False
director_name False
num_critic_for_reviews False
duration False
director_facebook_likes False
dtype: bool
>>> movie.loc[:, criteria_col].head()
num_voted_users cast_total_facebook_likes movie_facebook_likes
movie_title
Avatar 886204 4834 33000
Pirates o... 471220 48350 0
Spectre 275868 11700 85000
The Dark ... 1144337 106759 164000
Star Wars... 8 143 0
因?yàn)槭荢eries，criteria_col必須要轉(zhuǎn)化為numpy就可以用于iloc：
>>> movie.iloc[:, criteria_col.to_numpy()].head()
num_voted_users cast_total_facebook_likes movie_facebook_likes
movie_title
Avatar 886204 4834 33000
Pirates o... 471220 48350 0
Spectre 275868 11700 85000
The Dark ... 1144337 106759 164000
Star Wars... 8 143 0
loc中將條件和列合用：
>>> cols = [
... "content_rating",
... "imdb_score",
... "title_year",
... "gross",
... ]
>>> movie.loc[criteria, cols].sort_values("imdb_score")
content_rating imdb_score title_year gross
movie_title
Justin Bi... G 1.6 2011.0 73000942.0
Sunday Sc... G 2.5 2008.0 NaN
Doogal G 2.8 2006.0 7382993.0
Barney's ... G 2.8 1998.0 11144518.0
The True ... G 2.9 2009.0 NaN
Thomas an... G 3.6 2000.0 15911333.0
.iloc必須使用列的位置：
>>> col_index = [movie.columns.get_loc(col) for col in cols]
>>> col_index
[20, 24, 22, 8]
>>> movie.iloc[criteria.to_numpy(), col_index].sort_values(
... "imdb_score"
... )
content_rating imdb_score title_year gross
movie_title
Justin Bi... G 1.6 2011.0 73000942.0
Sunday Sc... G 2.5 2008.0 NaN
Doogal G 2.8 2006.0 7382993.0
Barney's ... G 2.8 1998.0 11144518.0
The True ... G 2.9 2009.0 NaN
Thomas an... G 3.6 2000.0 15911333.0
（這小節(jié)和之前的內(nèi)容重復(fù)不少）
第01章 Pandas基礎(chǔ)
第02章 DataFrame基礎(chǔ)運(yùn)算
第03章 創(chuàng)建和持久化DataFrame
第04章 開始數(shù)據(jù)分析
第05章 探索性數(shù)據(jù)分析
第06章 選取數(shù)據(jù)子集
第07章 過濾行
第08章 索引對(duì)齊
