15分钟带你入门Pandas
2024-04-10 12:21:15
文章目录
- Series
- DataFrame
- CSV相关的读取与操作
- JSON的读取与操作
- 数据清洗
- 清洗格式错误数据
Series
Pandas的Series 类似表格中的一个列（column），类似于一维数组，可以保存任何数据类型。
Series 由索引（index）和列组成，函数如下：
pandas.Series( data, index, dtype, name, copy)
import pandas as pda = ["Google", "Runoob", "Wiki"]myvar = pd.Series(a, index = ["x", "y", "z"])print(myvar["y"])
Runoob
import pandas as pdsites = {1: "Google", 2: "Runoob", 3: "Wiki"}myvar = pd.Series(sites, index = [1, 2], name="RUNOOB-Series-TEST" )print(myvar)
1 Google
2 Runoob
Name: RUNOOB-Series-TEST, dtype: object
DataFrame
DataFrame 是一个表格型的数据结构，它含有一组有序的列，每列可以是不同的值类型（数值、字符串、布尔型值）。DataFrame 既有行索引也有列索引，它可以被看做由 Series 组成的字典（共同用一个索引）。
DataFrame 构造方法如下：
pandas.DataFrame( data, index, columns, dtype, copy)
import pandas as pddata = [['Google',10],['Runoob',12],['Wiki',13]]df = pd.DataFrame(data,columns=['Site','Age'],dtype=float)print(df)
Site Age
0 Google 10.0
1 Runoob 12.0
2 Wiki 13.0
import pandas as pddata = {'Site':['Google', 'Runoob', 'Wiki'], 'Age':[10, 12, 13]}df = pd.DataFrame(data)print (df)
Site Age
0 Google 10
1 Runoob 12
2 Wiki 13
import pandas as pddata = {"calories": [420, 380, 390],"duration": [50, 40, 45]
}# 数据载入到 DataFrame 对象
df = pd.DataFrame(data)# 返回第一行
print(df.loc[0])
# 返回第二行
print(df.loc[1])
calories 420
duration 50
Name: 0, dtype: int64
calories 380
duration 40
Name: 1, dtype: int64
import pandas as pddata = {"calories": [420, 380, 390],"duration": [50, 40, 45]
}df = pd.DataFrame(data, index = ["day1", "day2", "day3"])print(df)
calories duration
day1 420 50
day2 380 40
day3 390 45
import pandas as pddata = {"calories": [420, 380, 390],"duration": [50, 40, 45]
}df = pd.DataFrame(data, index = ["day1", "day2", "day3"])# 指定索引
print(df.loc["day2"])
print(df.iloc[0,0])
print(df.iloc[0])
calories 380
duration 40
Name: day2, dtype: int64
420
calories 420
duration 50
Name: day1, dtype: int64
CSV相关的读取与操作
import pandas as pddf = pd.read_csv('nba.csv')print(df.to_string())
# to_string() 用于返回 DataFrame 类型的数据，如果不使用该函数，则输出结果为数据的前面 5 行和末尾 5 行，中间部分以 ... 代替。
Name Team Number Position Age Height Weight College Salary
0 Avery Bradley Boston Celtics 0.0 PG 25.0 6-2 180.0 Texas 7730337.0
1 Jae Crowder Boston Celtics 99.0 SF 25.0 6-6 235.0 Marquette 6796117.0
2 John Holland Boston Celtics 30.0 SG 27.0 6-5 205.0 Boston University NaN
3 R.J. Hunter Boston Celtics 28.0 SG 22.0 6-5 185.0 Georgia State 1148640.0
4 Jonas Jerebko Boston Celtics 8.0 PF 29.0 6-10 231.0 NaN 5000000.0
5 Amir Johnson Boston Celtics 90.0 PF 29.0 6-9 240.0 NaN 12000000.0
6 Jordan Mickey Boston Celtics 55.0 PF 21.0 6-8 235.0 LSU 1170960.0
7 Kelly Olynyk Boston Celtics 41.0 C 25.0 7-0 238.0 Gonzaga 2165160.0
8 Terry Rozier Boston Celtics 12.0 PG 22.0 6-2 190.0 Louisville 1824360.0
9 Marcus Smart Boston Celtics 36.0 PG 22.0 6-4 220.0 Oklahoma State 3431040.0
10 Jared Sullinger Boston Celtics 7.0 C 24.0 6-9 260.0 Ohio State 2569260.0
11 Isaiah Thomas Boston Celtics 4.0 PG 27.0 5-9 185.0 Washington 6912869.0
12 Evan Turner Boston Celtics 11.0 SG 27.0 6-7 220.0 Ohio State 3425510.0
13 James Young Boston Celtics 13.0 SG 20.0 6-6 215.0 Kentucky 1749840.0
14 Tyler Zeller Boston Celtics 44.0 C 26.0 7-0 253.0 North Carolina 2616975.0
15 Bojan Bogdanovic Brooklyn Nets 44.0 SG 27.0 6-8 216.0 NaN 3425510.0
16 Markel Brown Brooklyn Nets 22.0 SG 24.0 6-3 190.0 Oklahoma State 845059.0
17 Wayne Ellington Brooklyn Nets 21.0 SG 28.0 6-4 200.0 North Carolina 1500000.0
18 Rondae Hollis-Jefferson Brooklyn Nets 24.0 SG 21.0 6-7 220.0 Arizona 1335480.0
19 Jarrett Jack Brooklyn Nets 2.0 PG 32.0 6-3 200.0 Georgia Tech 6300000.0
20 Sergey Karasev Brooklyn Nets 10.0 SG 22.0 6-7 208.0 NaN 1599840.0
21 Sean Kilpatrick Brooklyn Nets 6.0 SG 26.0 6-4 219.0 Cincinnati 134215.0
22 Shane Larkin Brooklyn Nets 0.0 PG 23.0 5-11 175.0 Miami (FL) 1500000.0
23 Brook Lopez Brooklyn Nets 11.0 C 28.0 7-0 275.0 Stanford 19689000.0
24 Chris McCullough Brooklyn Nets 1.0 PF 21.0 6-11 200.0 Syracuse 1140240.0
25 Willie Reed Brooklyn Nets 33.0 PF 26.0 6-10 220.0 Saint Louis 947276.0
26 Thomas Robinson Brooklyn Nets 41.0 PF 25.0 6-10 237.0 Kansas 981348.0
27 Henry Sims Brooklyn Nets 14.0 C 26.0 6-10 248.0 Georgetown 947276.0
28 Donald Sloan Brooklyn Nets 15.0 PG 28.0 6-3 205.0 Texas A&M 947276.0
29 Thaddeus Young Brooklyn Nets 30.0 PF 27.0 6-8 221.0 Georgia Tech 11235955.0
30 Arron Afflalo New York Knicks 4.0 SG 30.0 6-5 210.0 UCLA 8000000.0
31 Lou Amundson New York Knicks 17.0 PF 33.0 6-9 220.0 UNLV 1635476.0
32 Thanasis Antetokounmpo New York Knicks 43.0 SF 23.0 6-7 205.0 NaN 30888.0
33 Carmelo Anthony New York Knicks 7.0 SF 32.0 6-8 240.0 Syracuse 22875000.0
34 Jose Calderon New York Knicks 3.0 PG 34.0 6-3 200.0 NaN 7402812.0
35 Cleanthony Early New York Knicks 11.0 SF 25.0 6-8 210.0 Wichita State 845059.0
36 Langston Galloway New York Knicks 2.0 SG 24.0 6-2 200.0 Saint Joseph's 845059.0
37 Jerian Grant New York Knicks 13.0 PG 23.0 6-4 195.0 Notre Dame 1572360.0
38 Robin Lopez New York Knicks 8.0 C 28.0 7-0 255.0 Stanford 12650000.0
39 Kyle O'Quinn New York Knicks 9.0 PF 26.0 6-10 250.0 Norfolk State 3750000.0
40 Kristaps Porzingis New York Knicks 6.0 PF 20.0 7-3 240.0 NaN 4131720.0
41 Kevin Seraphin New York Knicks 1.0 C 26.0 6-10 278.0 NaN 2814000.0
42 Lance Thomas New York Knicks 42.0 SF 28.0 6-8 235.0 Duke 1636842.0
43 Sasha Vujacic New York Knicks 18.0 SG 32.0 6-7 195.0 NaN 947276.0
44 Derrick Williams New York Knicks 23.0 PF 25.0 6-8 240.0 Arizona 4000000.0
45 Tony Wroten New York Knicks 5.0 SG 23.0 6-6 205.0 Washington 167406.0
46 Elton Brand Philadelphia 76ers 42.0 PF 37.0 6-9 254.0 Duke NaN
47 Isaiah Canaan Philadelphia 76ers 0.0 PG 25.0 6-0 201.0 Murray State 947276.0
48 Robert Covington Philadelphia 76ers 33.0 SF 25.0 6-9 215.0 Tennessee State 1000000.0
49 Joel Embiid Philadelphia 76ers 21.0 C 22.0 7-0 250.0 Kansas 4626960.0
50 Jerami Grant Philadelphia 76ers 39.0 SF 22.0 6-8 210.0 Syracuse 845059.0
51 Richaun Holmes Philadelphia 76ers 22.0 PF 22.0 6-10 245.0 Bowling Green 1074169.0
52 Carl Landry Philadelphia 76ers 7.0 PF 32.0 6-9 248.0 Purdue 6500000.0
53 Kendall Marshall Philadelphia 76ers 5.0 PG 24.0 6-4 200.0 North Carolina 2144772.0
54 T.J. McConnell Philadelphia 76ers 12.0 PG 24.0 6-2 200.0 Arizona 525093.0
55 Nerlens Noel Philadelphia 76ers 4.0 PF 22.0 6-11 228.0 Kentucky 3457800.0
56 Jahlil Okafor Philadelphia 76ers 8.0 C 20.0 6-11 275.0 Duke 4582680.0
57 Ish Smith Philadelphia 76ers 1.0 PG 27.0 6-0 175.0 Wake Forest 947276.0
58 Nik Stauskas Philadelphia 76ers 11.0 SG 22.0 6-6 205.0 Michigan 2869440.0
59 Hollis Thompson Philadelphia 76ers 31.0 SG 25.0 6-8 206.0 Georgetown 947276.0
60 Christian Wood Philadelphia 76ers 35.0 PF 20.0 6-11 220.0 UNLV 525093.0
61 Bismack Biyombo Toronto Raptors 8.0 C 23.0 6-9 245.0 NaN 2814000.0
62 Bruno Caboclo Toronto Raptors 20.0 SF 20.0 6-9 205.0 NaN 1524000.0
63 DeMarre Carroll Toronto Raptors 5.0 SF 29.0 6-8 212.0 Missouri 13600000.0
64 DeMar DeRozan Toronto Raptors 10.0 SG 26.0 6-7 220.0 USC 10050000.0
65 James Johnson Toronto Raptors 3.0 PF 29.0 6-9 250.0 Wake Forest 2500000.0
66 Cory Joseph Toronto Raptors 6.0 PG 24.0 6-3 190.0 Texas 7000000.0
67 Kyle Lowry Toronto Raptors 7.0 PG 30.0 6-0 205.0 Villanova 12000000.0
68 Lucas Nogueira Toronto Raptors 92.0 C 23.0 7-0 220.0 NaN 1842000.0
69 Patrick Patterson Toronto Raptors 54.0 PF 27.0 6-9 235.0 Kentucky 6268675.0
70 Norman Powell Toronto Raptors 24.0 SG 23.0 6-4 215.0 UCLA 650000.0
71 Terrence Ross Toronto Raptors 31.0 SF 25.0 6-7 195.0 Washington 3553917.0
72 Luis Scola Toronto Raptors 4.0 PF 36.0 6-9 240.0 NaN 2900000.0
73 Jason Thompson Toronto Raptors 1.0 PF 29.0 6-11 250.0 Rider 245177.0
74 Jonas Valanciunas Toronto Raptors 17.0 C 24.0 7-0 255.0 NaN 4660482.0
75 Delon Wright Toronto Raptors 55.0 PG 24.0 6-5 190.0 Utah 1509360.0
76 Leandro Barbosa Golden State Warriors 19.0 SG 33.0 6-3 194.0 NaN 2500000.0
77 Harrison Barnes Golden State Warriors 40.0 SF 24.0 6-8 225.0 North Carolina 3873398.0
78 Andrew Bogut Golden State Warriors 12.0 C 31.0 7-0 260.0 Utah 13800000.0
79 Ian Clark Golden State Warriors 21.0 SG 25.0 6-3 175.0 Belmont 947276.0
80 Stephen Curry Golden State Warriors 30.0 PG 28.0 6-3 190.0 Davidson 11370786.0
81 Festus Ezeli Golden State Warriors 31.0 C 26.0 6-11 265.0 Vanderbilt 2008748.0
82 Draymond Green Golden State Warriors 23.0 PF 26.0 6-7 230.0 Michigan State 14260870.0
83 Andre Iguodala Golden State Warriors 9.0 SF 32.0 6-6 215.0 Arizona 11710456.0
84 Shaun Livingston Golden State Warriors 34.0 PG 30.0 6-7 192.0 NaN 5543725.0
85 Kevon Looney Golden State Warriors 36.0 SF 20.0 6-9 220.0 UCLA 1131960.0
86 James Michael McAdoo Golden State Warriors 20.0 SF 23.0 6-9 240.0 North Carolina 845059.0
87 Brandon Rush Golden State Warriors 4.0 SF 30.0 6-6 220.0 Kansas 1270964.0
88 Marreese Speights Golden State Warriors 5.0 C 28.0 6-10 255.0 Florida 3815000.0
89 Klay Thompson Golden State Warriors 11.0 SG 26.0 6-7 215.0 Washington State 15501000.0
90 Anderson Varejao Golden State Warriors 18.0 PF 33.0 6-11 273.0 NaN 289755.0
91 Cole Aldrich Los Angeles Clippers 45.0 C 27.0 6-11 250.0 Kansas 1100602.0
92 Jeff Ayres Los Angeles Clippers 19.0 PF 29.0 6-9 250.0 Arizona State 111444.0
93 Jamal Crawford Los Angeles Clippers 11.0 SG 36.0 6-5 195.0 Michigan 5675000.0
94 Branden Dawson Los Angeles Clippers 22.0 SF 23.0 6-6 225.0 Michigan State 525093.0
95 Jeff Green Los Angeles Clippers 8.0 SF 29.0 6-9 235.0 Georgetown 9650000.0
96 Blake Griffin Los Angeles Clippers 32.0 PF 27.0 6-10 251.0 Oklahoma 18907726.0
97 Wesley Johnson Los Angeles Clippers 33.0 SF 28.0 6-7 215.0 Syracuse 1100602.0
98 DeAndre Jordan Los Angeles Clippers 6.0 C 27.0 6-11 265.0 Texas A&M 19689000.0
99 Luc Richard Mbah a Moute Los Angeles Clippers 12.0 PF 29.0 6-8 230.0 UCLA 947276.0
100 Chris Paul Los Angeles Clippers 3.0 PG 31.0 6-0 175.0 Wake Forest 21468695.0
101 Paul Pierce Los Angeles Clippers 34.0 SF 38.0 6-7 235.0 Kansas 3376000.0
102 Pablo Prigioni Los Angeles Clippers 9.0 PG 39.0 6-3 185.0 NaN 947726.0
103 JJ Redick Los Angeles Clippers 4.0 SG 31.0 6-4 190.0 Duke 7085000.0
104 Austin Rivers Los Angeles Clippers 25.0 PG 23.0 6-4 200.0 Duke 3110796.0
105 C.J. Wilcox Los Angeles Clippers 30.0 SG 25.0 6-5 195.0 Washington 1159680.0
106 Brandon Bass Los Angeles Lakers 2.0 PF 31.0 6-8 250.0 LSU 3000000.0
107 Tarik Black Los Angeles Lakers 28.0 C 24.0 6-9 250.0 Kansas 845059.0
108 Anthony Brown Los Angeles Lakers 3.0 SF 23.0 6-7 210.0 Stanford 700000.0
109 Kobe Bryant Los Angeles Lakers 24.0 SF 37.0 6-6 212.0 NaN 25000000.0
110 Jordan Clarkson Los Angeles Lakers 6.0 PG 24.0 6-5 194.0 Missouri 845059.0
111 Roy Hibbert Los Angeles Lakers 17.0 C 29.0 7-2 270.0 Georgetown 15592217.0
112 Marcelo Huertas Los Angeles Lakers 9.0 PG 33.0 6-3 200.0 NaN 525093.0
113 Ryan Kelly Los Angeles Lakers 4.0 PF 25.0 6-11 230.0 Duke 1724250.0
114 Larry Nance Jr. Los Angeles Lakers 7.0 PF 23.0 6-9 230.0 Wyoming 1155600.0
115 Julius Randle Los Angeles Lakers 30.0 PF 21.0 6-9 250.0 Kentucky 3132240.0
116 D'Angelo Russell Los Angeles Lakers 1.0 PG 20.0 6-5 195.0 Ohio State 5103120.0
117 Robert Sacre Los Angeles Lakers 50.0 C 27.0 7-0 270.0 Gonzaga 981348.0
118 Louis Williams Los Angeles Lakers 23.0 SG 29.0 6-1 175.0 NaN 7000000.0
119 Metta World Peace Los Angeles Lakers 37.0 SF 36.0 6-7 260.0 St. John's 947276.0
120 Nick Young Los Angeles Lakers 0.0 SF 31.0 6-7 210.0 USC 5219169.0
121 Eric Bledsoe Phoenix Suns 2.0 PG 26.0 6-1 190.0 Kentucky 13500000.0
122 Devin Booker Phoenix Suns 1.0 SG 19.0 6-6 206.0 Kentucky 2127840.0
123 Chase Budinger Phoenix Suns 10.0 SF 28.0 6-7 209.0 Arizona 206192.0
124 Tyson Chandler Phoenix Suns 4.0 C 33.0 7-1 240.0 NaN 13000000.0
125 Archie Goodwin Phoenix Suns 20.0 SG 21.0 6-5 200.0 Kentucky 1160160.0
126 John Jenkins Phoenix Suns 23.0 SG 25.0 6-4 215.0 Vanderbilt 981348.0
127 Brandon Knight Phoenix Suns 3.0 PG 24.0 6-3 189.0 Kentucky 13500000.0
128 Alex Len Phoenix Suns 21.0 C 22.0 7-1 260.0 Maryland 3807120.0
129 Jon Leuer Phoenix Suns 30.0 PF 27.0 6-10 228.0 Wisconsin 1035000.0
130 Phil Pressey Phoenix Suns 25.0 PG 25.0 5-11 175.0 Missouri 55722.0
131 Ronnie Price Phoenix Suns 14.0 PG 32.0 6-2 190.0 Utah Valley 947276.0
132 Mirza Teletovic Phoenix Suns 35.0 PF 30.0 6-9 242.0 NaN 5500000.0
133 P.J. Tucker Phoenix Suns 17.0 SF 31.0 6-6 245.0 Texas 5500000.0
134 T.J. Warren Phoenix Suns 12.0 SF 22.0 6-8 230.0 North Carolina State 2041080.0
135 Alan Williams Phoenix Suns 15.0 C 23.0 6-8 260.0 UC Santa Barbara 83397.0
136 Quincy Acy Sacramento Kings 13.0 SF 25.0 6-7 240.0 Baylor 981348.0
137 James Anderson Sacramento Kings 5.0 SG 27.0 6-6 213.0 Oklahoma State 1015421.0
138 Marco Belinelli Sacramento Kings 3.0 SG 30.0 6-5 210.0 NaN 6060606.0
139 Caron Butler Sacramento Kings 31.0 SF 36.0 6-7 228.0 Connecticut 1449187.0
140 Omri Casspi Sacramento Kings 18.0 SF 27.0 6-9 225.0 NaN 2836186.0
141 Willie Cauley-Stein Sacramento Kings 0.0 C 22.0 7-0 240.0 Kentucky 3398280.0
142 Darren Collison Sacramento Kings 7.0 PG 28.0 6-0 175.0 UCLA 5013559.0
143 DeMarcus Cousins Sacramento Kings 15.0 C 25.0 6-11 270.0 Kentucky 15851950.0
144 Seth Curry Sacramento Kings 30.0 SG 25.0 6-2 185.0 Duke 947276.0
145 Duje Dukan Sacramento Kings 26.0 PF 24.0 6-9 220.0 Wisconsin 525093.0
146 Rudy Gay Sacramento Kings 8.0 SF 29.0 6-8 230.0 Connecticut 12403101.0
147 Kosta Koufos Sacramento Kings 41.0 C 27.0 7-0 265.0 Ohio State 7700000.0
148 Ben McLemore Sacramento Kings 23.0 SG 23.0 6-5 195.0 Kansas 3156600.0
149 Eric Moreland Sacramento Kings 25.0 PF 24.0 6-10 238.0 Oregon State 845059.0
150 Rajon Rondo Sacramento Kings 9.0 PG 30.0 6-1 186.0 Kentucky 9500000.0
151 Cameron Bairstow Chicago Bulls 41.0 PF 25.0 6-9 250.0 New Mexico 845059.0
152 Aaron Brooks Chicago Bulls 0.0 PG 31.0 6-0 161.0 Oregon 2250000.0
153 Jimmy Butler Chicago Bulls 21.0 SG 26.0 6-7 220.0 Marquette 16407500.0
154 Mike Dunleavy Chicago Bulls 34.0 SG 35.0 6-9 230.0 Duke 4500000.0
155 Cristiano Felicio Chicago Bulls 6.0 PF 23.0 6-10 275.0 NaN 525093.0
156 Pau Gasol Chicago Bulls 16.0 C 35.0 7-0 250.0 NaN 7448760.0
157 Taj Gibson Chicago Bulls 22.0 PF 30.0 6-9 225.0 USC 8500000.0
158 Justin Holiday Chicago Bulls 7.0 SG 27.0 6-6 185.0 Washington 947276.0
159 Doug McDermott Chicago Bulls 3.0 SF 24.0 6-8 225.0 Creighton 2380440.0
160 Nikola Mirotic Chicago Bulls 44.0 PF 25.0 6-10 220.0 NaN 5543725.0
161 E'Twaun Moore Chicago Bulls 55.0 SG 27.0 6-4 191.0 Purdue 1015421.0
162 Joakim Noah Chicago Bulls 13.0 C 31.0 6-11 232.0 Florida 13400000.0
163 Bobby Portis Chicago Bulls 5.0 PF 21.0 6-11 230.0 Arkansas 1391160.0
164 Derrick Rose Chicago Bulls 1.0 PG 27.0 6-3 190.0 Memphis 20093064.0
165 Tony Snell Chicago Bulls 20.0 SF 24.0 6-7 200.0 New Mexico 1535880.0
166 Matthew Dellavedova Cleveland Cavaliers 8.0 PG 25.0 6-4 198.0 Saint Mary's 1147276.0
167 Channing Frye Cleveland Cavaliers 9.0 PF 33.0 6-11 255.0 Arizona 8193029.0
168 Kyrie Irving Cleveland Cavaliers 2.0 PG 24.0 6-3 193.0 Duke 16407501.0
169 LeBron James Cleveland Cavaliers 23.0 SF 31.0 6-8 250.0 NaN 22970500.0
170 Richard Jefferson Cleveland Cavaliers 24.0 SF 35.0 6-7 233.0 Arizona 947276.0
171 Dahntay Jones Cleveland Cavaliers 30.0 SG 35.0 6-6 225.0 Duke NaN
172 James Jones Cleveland Cavaliers 1.0 SG 35.0 6-8 218.0 Miami (FL) 947276.0
173 Sasha Kaun Cleveland Cavaliers 14.0 C 31.0 6-11 260.0 Kansas 1276000.0
174 Kevin Love Cleveland Cavaliers 0.0 PF 27.0 6-10 251.0 UCLA 19689000.0
175 Jordan McRae Cleveland Cavaliers 12.0 SG 25.0 6-5 179.0 Tennessee 111196.0
176 Timofey Mozgov Cleveland Cavaliers 20.0 C 29.0 7-1 275.0 NaN 4950000.0
177 Iman Shumpert Cleveland Cavaliers 4.0 SG 25.0 6-5 220.0 Georgia Tech 8988765.0
178 J.R. Smith Cleveland Cavaliers 5.0 SG 30.0 6-6 225.0 NaN 5000000.0
179 Tristan Thompson Cleveland Cavaliers 13.0 C 25.0 6-9 238.0 Texas 14260870.0
180 Mo Williams Cleveland Cavaliers 52.0 PG 33.0 6-1 198.0 Alabama 2100000.0
181 Joel Anthony Detroit Pistons 50.0 C 33.0 6-9 245.0 UNLV 2500000.0
182 Aron Baynes Detroit Pistons 12.0 C 29.0 6-10 260.0 Washington State 6500000.0
183 Steve Blake Detroit Pistons 22.0 PG 36.0 6-3 172.0 Maryland 2170465.0
184 Lorenzo Brown Detroit Pistons 17.0 PG 25.0 6-5 189.0 North Carolina State 111444.0
185 Reggie Bullock Detroit Pistons 25.0 SF 25.0 6-7 205.0 North Carolina 1252440.0
186 Kentavious Caldwell-Pope Detroit Pistons 5.0 SG 23.0 6-5 205.0 Georgia 2891760.0
187 Spencer Dinwiddie Detroit Pistons 8.0 PG 23.0 6-6 200.0 Colorado 845059.0
188 Andre Drummond Detroit Pistons 0.0 C 22.0 6-11 279.0 Connecticut 3272091.0
189 Tobias Harris Detroit Pistons 34.0 SF 23.0 6-9 235.0 Tennessee 16000000.0
190 Darrun Hilliard Detroit Pistons 6.0 SF 23.0 6-6 205.0 Villanova 600000.0
191 Reggie Jackson Detroit Pistons 1.0 PG 26.0 6-3 208.0 Boston College 13913044.0
192 Stanley Johnson Detroit Pistons 3.0 SF 20.0 6-7 245.0 Arizona 2841960.0
193 Jodie Meeks Detroit Pistons 20.0 SG 28.0 6-4 210.0 Kentucky 6270000.0
194 Marcus Morris Detroit Pistons 13.0 PF 26.0 6-9 235.0 Kansas 5000000.0
195 Anthony Tolliver Detroit Pistons 43.0 PF 31.0 6-8 240.0 Creighton 3000000.0
196 Lavoy Allen Indiana Pacers 5.0 PF 27.0 6-9 255.0 Temple 4050000.0
197 Rakeem Christmas Indiana Pacers 25.0 PF 24.0 6-9 250.0 Syracuse 1007026.0
198 Monta Ellis Indiana Pacers 11.0 SG 30.0 6-3 185.0 NaN 10300000.0
199 Paul George Indiana Pacers 13.0 SF 26.0 6-9 220.0 Fresno State 17120106.0
200 George Hill Indiana Pacers 3.0 PG 30.0 6-3 188.0 IUPUI 8000000.0
201 Jordan Hill Indiana Pacers 27.0 C 28.0 6-10 235.0 Arizona 4000000.0
202 Solomon Hill Indiana Pacers 44.0 SF 25.0 6-7 225.0 Arizona 1358880.0
203 Ty Lawson Indiana Pacers 10.0 PG 28.0 5-11 195.0 North Carolina 211744.0
204 Ian Mahinmi Indiana Pacers 28.0 C 29.0 6-11 250.0 NaN 4000000.0
205 C.J. Miles Indiana Pacers 0.0 SF 29.0 6-6 231.0 NaN 4394225.0
206 Glenn Robinson III Indiana Pacers 40.0 SG 22.0 6-7 222.0 Michigan 1100000.0
207 Rodney Stuckey Indiana Pacers 2.0 PG 30.0 6-5 205.0 Eastern Washington 7000000.0
208 Myles Turner Indiana Pacers 33.0 PF 20.0 6-11 243.0 Texas 2357760.0
209 Shayne Whittington Indiana Pacers 42.0 PF 25.0 6-11 250.0 Western Michigan 845059.0
210 Joe Young Indiana Pacers 1.0 PG 23.0 6-2 180.0 Oregon 1007026.0
211 Giannis Antetokounmpo Milwaukee Bucks 34.0 SF 21.0 6-11 222.0 NaN 1953960.0
212 Jerryd Bayless Milwaukee Bucks 19.0 PG 27.0 6-3 200.0 Arizona 3000000.0
213 Michael Carter-Williams Milwaukee Bucks 5.0 PG 24.0 6-6 190.0 Syracuse 2399040.0
214 Jared Cunningham Milwaukee Bucks 9.0 SG 25.0 6-4 195.0 Oregon State 947276.0
215 Tyler Ennis Milwaukee Bucks 11.0 PG 21.0 6-3 194.0 Syracuse 1662360.0
216 John Henson Milwaukee Bucks 31.0 PF 25.0 6-11 229.0 North Carolina 2943221.0
217 Damien Inglis Milwaukee Bucks 17.0 SF 21.0 6-8 246.0 NaN 855000.0
218 O.J. Mayo Milwaukee Bucks 3.0 SG 28.0 6-5 210.0 USC 8000000.0
219 Khris Middleton Milwaukee Bucks 22.0 SG 24.0 6-8 234.0 Texas A&M 14700000.0
220 Greg Monroe Milwaukee Bucks 15.0 C 26.0 6-11 265.0 Georgetown 16407500.0
221 Steve Novak Milwaukee Bucks 6.0 SF 32.0 6-10 225.0 Marquette 295327.0
222 Johnny O'Bryant III Milwaukee Bucks 77.0 PF 23.0 6-9 257.0 LSU 845059.0
223 Jabari Parker Milwaukee Bucks 12.0 PF 21.0 6-8 250.0 Duke 5152440.0
224 Miles Plumlee Milwaukee Bucks 18.0 C 27.0 6-11 249.0 Duke 2109294.0
225 Greivis Vasquez Milwaukee Bucks 21.0 PG 29.0 6-6 217.0 Maryland 6600000.0
226 Rashad Vaughn Milwaukee Bucks 20.0 SG 19.0 6-6 202.0 UNLV 1733040.0
227 Justin Anderson Dallas Mavericks 1.0 SG 22.0 6-6 228.0 Virginia 1449000.0
228 J.J. Barea Dallas Mavericks 5.0 PG 31.0 6-0 185.0 Northeastern 4290000.0
229 Jeremy Evans Dallas Mavericks 21.0 SF 28.0 6-9 200.0 Western Kentucky 1100602.0
230 Raymond Felton Dallas Mavericks 2.0 PG 31.0 6-1 205.0 North Carolina 3950313.0
231 Devin Harris Dallas Mavericks 34.0 PG 33.0 6-3 185.0 Wisconsin 4053446.0
232 David Lee Dallas Mavericks 42.0 PF 33.0 6-9 245.0 Florida 2085671.0
233 Wesley Matthews Dallas Mavericks 23.0 SG 29.0 6-5 220.0 Marquette 16407500.0
234 JaVale McGee Dallas Mavericks 11.0 C 28.0 7-0 270.0 Nevada 1270964.0
235 Salah Mejri Dallas Mavericks 50.0 C 29.0 7-2 245.0 NaN 525093.0
236 Dirk Nowitzki Dallas Mavericks 41.0 PF 37.0 7-0 245.0 NaN 8333334.0
237 Zaza Pachulia Dallas Mavericks 27.0 C 32.0 6-11 275.0 NaN 5200000.0
238 Chandler Parsons Dallas Mavericks 25.0 SF 27.0 6-10 230.0 Florida 15361500.0
239 Dwight Powell Dallas Mavericks 7.0 PF 24.0 6-11 240.0 Stanford 845059.0
240 Charlie Villanueva Dallas Mavericks 3.0 PF 31.0 6-11 232.0 Connecticut 947276.0
241 Deron Williams Dallas Mavericks 8.0 PG 31.0 6-3 200.0 Illinois 5378974.0
242 Trevor Ariza Houston Rockets 1.0 SF 30.0 6-8 215.0 UCLA 8193030.0
243 Michael Beasley Houston Rockets 8.0 SF 27.0 6-10 235.0 Kansas State 306527.0
244 Patrick Beverley Houston Rockets 2.0 PG 27.0 6-1 185.0 Arkansas 6486486.0
245 Corey Brewer Houston Rockets 33.0 SG 30.0 6-9 186.0 Florida 8229375.0
246 Clint Capela Houston Rockets 15.0 PF 22.0 6-10 240.0 NaN 1242720.0
247 Sam Dekker Houston Rockets 7.0 SF 22.0 6-9 230.0 Wisconsin 1646400.0
248 Andrew Goudelock Houston Rockets 0.0 PG 27.0 6-3 200.0 Charleston 200600.0
249 James Harden Houston Rockets 13.0 SG 26.0 6-5 220.0 Arizona State 15756438.0
250 Montrezl Harrell Houston Rockets 35.0 PF 22.0 6-8 240.0 Louisville 1000000.0
251 Dwight Howard Houston Rockets 12.0 C 30.0 6-11 265.0 NaN 22359364.0
252 Terrence Jones Houston Rockets 6.0 PF 24.0 6-9 252.0 Kentucky 2489530.0
253 K.J. McDaniels Houston Rockets 32.0 SG 23.0 6-6 205.0 Clemson 3189794.0
254 Donatas Motiejunas Houston Rockets 20.0 PF 25.0 7-0 222.0 NaN 2288205.0
255 Josh Smith Houston Rockets 5.0 C 30.0 6-9 225.0 NaN 947276.0
256 Jason Terry Houston Rockets 31.0 SG 38.0 6-2 185.0 Arizona 947276.0
257 Jordan Adams Memphis Grizzlies 3.0 SG 21.0 6-5 209.0 UCLA 1404600.0
258 Tony Allen Memphis Grizzlies 9.0 SG 34.0 6-4 213.0 Oklahoma State 5158539.0
259 Chris Andersen Memphis Grizzlies 7.0 PF 37.0 6-10 245.0 Blinn College 5000000.0
260 Matt Barnes Memphis Grizzlies 22.0 SF 36.0 6-7 226.0 UCLA 3542500.0
261 Vince Carter Memphis Grizzlies 15.0 SG 39.0 6-6 220.0 North Carolina 4088019.0
262 Mike Conley Memphis Grizzlies 11.0 PG 28.0 6-1 175.0 Ohio State 9588426.0
263 Bryce Cotton Memphis Grizzlies 8.0 PG 23.0 6-1 165.0 Providence 700902.0
264 Jordan Farmar Memphis Grizzlies 4.0 PG 29.0 6-2 180.0 UCLA NaN
265 Marc Gasol Memphis Grizzlies 33.0 C 31.0 7-1 255.0 NaN 19688000.0
266 JaMychal Green Memphis Grizzlies 0.0 PF 25.0 6-9 227.0 Alabama 845059.0
267 P.J. Hairston Memphis Grizzlies 19.0 SF 23.0 6-6 230.0 North Carolina 1201440.0
268 Jarell Martin Memphis Grizzlies 10.0 PF 22.0 6-10 239.0 LSU 1230840.0
269 Ray McCallum Memphis Grizzlies 5.0 PG 24.0 6-3 190.0 Detroit NaN
270 Xavier Munford Memphis Grizzlies 14.0 PG 24.0 6-3 180.0 Rhode Island NaN
271 Zach Randolph Memphis Grizzlies 50.0 PF 34.0 6-9 260.0 Michigan State 9638555.0
272 Lance Stephenson Memphis Grizzlies 1.0 SF 25.0 6-5 230.0 Cincinnati 9000000.0
273 Alex Stepheson Memphis Grizzlies 35.0 PF 28.0 6-10 270.0 USC NaN
274 Brandan Wright Memphis Grizzlies 34.0 PF 28.0 6-10 210.0 North Carolina 5464000.0
275 Alexis Ajinca New Orleans Pelicans 42.0 C 28.0 7-2 248.0 NaN 4389607.0
276 Ryan Anderson New Orleans Pelicans 33.0 PF 28.0 6-10 240.0 California 8500000.0
277 Omer Asik New Orleans Pelicans 3.0 C 29.0 7-0 255.0 NaN 9213483.0
278 Luke Babbitt New Orleans Pelicans 8.0 SF 26.0 6-9 225.0 Nevada 1100602.0
279 Norris Cole New Orleans Pelicans 30.0 PG 27.0 6-2 175.0 Cleveland State 3036927.0
280 Dante Cunningham New Orleans Pelicans 44.0 PF 29.0 6-8 230.0 Villanova 2850000.0
281 Anthony Davis New Orleans Pelicans 23.0 PF 23.0 6-10 253.0 Kentucky 7070730.0
282 Bryce Dejean-Jones New Orleans Pelicans 31.0 SG 23.0 6-6 203.0 Iowa State 169883.0
283 Toney Douglas New Orleans Pelicans 16.0 PG 30.0 6-2 195.0 Florida State 1164858.0
284 James Ennis New Orleans Pelicans 4.0 SF 25.0 6-7 210.0 Long Beach State 845059.0
285 Tyreke Evans New Orleans Pelicans 1.0 SG 26.0 6-6 220.0 Memphis 10734586.0
286 Tim Frazier New Orleans Pelicans 2.0 PG 25.0 6-1 170.0 Penn State 845059.0
287 Alonzo Gee New Orleans Pelicans 15.0 SF 29.0 6-6 225.0 Alabama 1320000.0
288 Eric Gordon New Orleans Pelicans 10.0 SG 27.0 6-4 215.0 Indiana 15514031.0
289 Jordan Hamilton New Orleans Pelicans 25.0 SG 25.0 6-7 220.0 Texas 1015421.0
290 Jrue Holiday New Orleans Pelicans 11.0 PG 25.0 6-4 205.0 UCLA 10595507.0
291 Orlando Johnson New Orleans Pelicans 0.0 SG 27.0 6-5 220.0 UC Santa Barbara 55722.0
292 Kendrick Perkins New Orleans Pelicans 5.0 C 31.0 6-10 270.0 NaN 947276.0
293 Quincy Pondexter New Orleans Pelicans 20.0 SF 28.0 6-7 220.0 Washington 3382023.0
294 LaMarcus Aldridge San Antonio Spurs 12.0 PF 30.0 6-11 240.0 Texas 19689000.0
295 Kyle Anderson San Antonio Spurs 1.0 SF 22.0 6-9 230.0 UCLA 1142880.0
296 Matt Bonner San Antonio Spurs 15.0 C 36.0 6-10 235.0 Florida 947276.0
297 Boris Diaw San Antonio Spurs 33.0 C 34.0 6-8 250.0 NaN 7500000.0
298 Tim Duncan San Antonio Spurs 21.0 C 40.0 6-11 250.0 Wake Forest 5250000.0
299 Manu Ginobili San Antonio Spurs 20.0 SG 38.0 6-6 205.0 NaN 2814000.0
300 Danny Green San Antonio Spurs 14.0 SG 28.0 6-6 215.0 North Carolina 10000000.0
301 Kawhi Leonard San Antonio Spurs 2.0 SF 24.0 6-7 230.0 San Diego State 16407500.0
302 Boban Marjanovic San Antonio Spurs 40.0 C 27.0 7-3 290.0 NaN 1200000.0
303 Kevin Martin San Antonio Spurs 23.0 SG 33.0 6-7 199.0 Western Carolina 200600.0
304 Andre Miller San Antonio Spurs 24.0 PG 40.0 6-3 200.0 Utah 250750.0
305 Patty Mills San Antonio Spurs 8.0 PG 27.0 6-0 185.0 Saint Mary's 3578947.0
306 Tony Parker San Antonio Spurs 9.0 PG 34.0 6-2 185.0 NaN 13437500.0
307 Jonathon Simmons San Antonio Spurs 17.0 SG 26.0 6-6 195.0 Houston 525093.0
308 David West San Antonio Spurs 30.0 PF 35.0 6-9 250.0 Xavier 1499187.0
309 Kent Bazemore Atlanta Hawks 24.0 SF 26.0 6-5 201.0 Old Dominion 2000000.0
310 Tim Hardaway Jr. Atlanta Hawks 10.0 SG 24.0 6-6 205.0 Michigan 1304520.0
311 Kirk Hinrich Atlanta Hawks 12.0 SG 35.0 6-4 190.0 Kansas 2854940.0
312 Al Horford Atlanta Hawks 15.0 C 30.0 6-10 245.0 Florida 12000000.0
313 Kris Humphries Atlanta Hawks 43.0 PF 31.0 6-9 235.0 Minnesota 1000000.0
314 Kyle Korver Atlanta Hawks 26.0 SG 35.0 6-7 212.0 Creighton 5746479.0
315 Paul Millsap Atlanta Hawks 4.0 PF 31.0 6-8 246.0 Louisiana Tech 18671659.0
316 Mike Muscala Atlanta Hawks 31.0 PF 24.0 6-11 240.0 Bucknell 947276.0
317 Lamar Patterson Atlanta Hawks 13.0 SG 24.0 6-5 225.0 Pittsburgh 525093.0
318 Dennis Schroder Atlanta Hawks 17.0 PG 22.0 6-1 172.0 NaN 1763400.0
319 Mike Scott Atlanta Hawks 32.0 PF 27.0 6-8 237.0 Virginia 3333333.0
320 Thabo Sefolosha Atlanta Hawks 25.0 SF 32.0 6-7 220.0 NaN 4000000.0
321 Tiago Splitter Atlanta Hawks 11.0 C 31.0 6-11 245.0 NaN 9756250.0
322 Walter Tavares Atlanta Hawks 22.0 C 24.0 7-3 260.0 NaN 1000000.0
323 Jeff Teague Atlanta Hawks 0.0 PG 27.0 6-2 186.0 Wake Forest 8000000.0
324 Nicolas Batum Charlotte Hornets 5.0 SG 27.0 6-8 200.0 NaN 13125306.0
325 Troy Daniels Charlotte Hornets 30.0 SG 24.0 6-4 205.0 Virginia Commonwealth 947276.0
326 Jorge Gutierrez Charlotte Hornets 12.0 PG 27.0 6-3 189.0 California 189455.0
327 Tyler Hansbrough Charlotte Hornets 50.0 PF 30.0 6-9 250.0 North Carolina 947276.0
328 Aaron Harrison Charlotte Hornets 9.0 SG 21.0 6-6 210.0 Kentucky 525093.0
329 Spencer Hawes Charlotte Hornets 0.0 PF 28.0 7-1 245.0 Washington 6110034.0
330 Al Jefferson Charlotte Hornets 25.0 C 31.0 6-10 289.0 NaN 13500000.0
331 Frank Kaminsky III Charlotte Hornets 44.0 C 23.0 7-0 240.0 Wisconsin 2612520.0
332 Michael Kidd-Gilchrist Charlotte Hornets 14.0 SF 22.0 6-7 232.0 Kentucky 6331404.0
333 Jeremy Lamb Charlotte Hornets 3.0 SG 24.0 6-5 185.0 Connecticut 3034356.0
334 Courtney Lee Charlotte Hornets 1.0 SG 30.0 6-5 200.0 Western Kentucky 5675000.0
335 Jeremy Lin Charlotte Hornets 7.0 PG 27.0 6-3 200.0 Harvard 2139000.0
336 Kemba Walker Charlotte Hornets 15.0 PG 26.0 6-1 184.0 Connecticut 12000000.0
337 Marvin Williams Charlotte Hornets 2.0 PF 29.0 6-9 237.0 North Carolina 7000000.0
338 Cody Zeller Charlotte Hornets 40.0 C 23.0 7-0 240.0 Indiana 4204200.0
339 Chris Bosh Miami Heat 1.0 PF 32.0 6-11 235.0 Georgia Tech 22192730.0
340 Luol Deng Miami Heat 9.0 SF 31.0 6-9 220.0 Duke 10151612.0
341 Goran Dragic Miami Heat 7.0 PG 30.0 6-3 190.0 NaN 14783000.0
342 Gerald Green Miami Heat 14.0 SF 30.0 6-7 205.0 NaN 947276.0
343 Udonis Haslem Miami Heat 40.0 PF 36.0 6-8 235.0 Florida 2854940.0
344 Joe Johnson Miami Heat 2.0 SF 34.0 6-7 240.0 Arkansas 261894.0
345 Tyler Johnson Miami Heat 8.0 SG 24.0 6-4 186.0 Fresno State 845059.0
346 Josh McRoberts Miami Heat 4.0 PF 29.0 6-10 240.0 Duke 5543725.0
347 Josh Richardson Miami Heat 0.0 SG 22.0 6-6 200.0 Tennessee 525093.0
348 Amar'e Stoudemire Miami Heat 5.0 PF 33.0 6-10 245.0 NaN 947276.0
349 Dwyane Wade Miami Heat 3.0 SG 34.0 6-4 220.0 Marquette 20000000.0
350 Briante Weber Miami Heat 12.0 PG 23.0 6-2 165.0 Virginia Commonwealth NaN
351 Hassan Whiteside Miami Heat 21.0 C 26.0 7-0 265.0 Marshall 981348.0
352 Justise Winslow Miami Heat 20.0 SF 20.0 6-7 225.0 Duke 2481720.0
353 Dorell Wright Miami Heat 11.0 SF 30.0 6-9 205.0 NaN NaN
354 Dewayne Dedmon Orlando Magic 3.0 C 26.0 7-0 245.0 USC 947276.0
355 Evan Fournier Orlando Magic 10.0 SG 23.0 6-7 205.0 NaN 2288205.0
356 Aaron Gordon Orlando Magic 0.0 PF 20.0 6-9 220.0 Arizona 4171680.0
357 Mario Hezonja Orlando Magic 23.0 SG 21.0 6-8 218.0 NaN 3741480.0
358 Ersan Ilyasova Orlando Magic 7.0 PF 29.0 6-10 235.0 NaN 7900000.0
359 Brandon Jennings Orlando Magic 55.0 PG 26.0 6-1 169.0 NaN 8344497.0
360 Devyn Marble Orlando Magic 11.0 SF 23.0 6-6 200.0 Iowa 845059.0
361 Shabazz Napier Orlando Magic 13.0 PG 24.0 6-1 175.0 Connecticut 1294440.0
362 Andrew Nicholson Orlando Magic 44.0 PF 26.0 6-9 250.0 St. Bonaventure 2380593.0
363 Victor Oladipo Orlando Magic 5.0 SG 24.0 6-4 210.0 Indiana 5192520.0
364 Elfrid Payton Orlando Magic 4.0 PG 22.0 6-4 185.0 Louisiana-Lafayette 2505720.0
365 Jason Smith Orlando Magic 14.0 PF 30.0 7-0 240.0 Colorado State 4300000.0
366 Nikola Vucevic Orlando Magic 9.0 C 25.0 7-0 260.0 USC 11250000.0
367 C.J. Watson Orlando Magic 32.0 PG 32.0 6-2 175.0 Tennessee 5000000.0
368 Alan Anderson Washington Wizards 6.0 SG 33.0 6-6 220.0 Michigan State 4000000.0
369 Bradley Beal Washington Wizards 3.0 SG 22.0 6-5 207.0 Florida 5694674.0
370 Jared Dudley Washington Wizards 1.0 SF 30.0 6-7 225.0 Boston College 4375000.0
371 Jarell Eddie Washington Wizards 8.0 SG 24.0 6-7 218.0 Virginia Tech 561716.0
372 Drew Gooden Washington Wizards 90.0 PF 34.0 6-10 250.0 Kansas 3300000.0
373 Marcin Gortat Washington Wizards 13.0 C 32.0 6-11 240.0 NaN 11217391.0
374 JJ Hickson Washington Wizards 21.0 C 27.0 6-9 242.0 North Carolina State 273038.0
375 Nene Hilario Washington Wizards 42.0 C 33.0 6-11 250.0 NaN 13000000.0
376 Markieff Morris Washington Wizards 5.0 PF 26.0 6-10 245.0 Kansas 8000000.0
377 Kelly Oubre Jr. Washington Wizards 12.0 SF 20.0 6-7 205.0 Kansas 1920240.0
378 Otto Porter Jr. Washington Wizards 22.0 SF 23.0 6-8 198.0 Georgetown 4662960.0
379 Ramon Sessions Washington Wizards 7.0 PG 30.0 6-3 190.0 Nevada 2170465.0
380 Garrett Temple Washington Wizards 17.0 SG 30.0 6-6 195.0 LSU 1100602.0
381 Marcus Thornton Washington Wizards 15.0 SF 29.0 6-4 205.0 LSU 200600.0
382 John Wall Washington Wizards 2.0 PG 25.0 6-4 195.0 Kentucky 15851950.0
383 Darrell Arthur Denver Nuggets 0.0 PF 28.0 6-9 235.0 Kansas 2814000.0
384 D.J. Augustin Denver Nuggets 12.0 PG 28.0 6-0 183.0 Texas 3000000.0
385 Will Barton Denver Nuggets 5.0 SF 25.0 6-6 175.0 Memphis 3533333.0
386 Wilson Chandler Denver Nuggets 21.0 SF 29.0 6-8 225.0 DePaul 10449438.0
387 Kenneth Faried Denver Nuggets 35.0 PF 26.0 6-8 228.0 Morehead State 11235955.0
388 Danilo Gallinari Denver Nuggets 8.0 SF 27.0 6-10 225.0 NaN 14000000.0
389 Gary Harris Denver Nuggets 14.0 SG 21.0 6-4 210.0 Michigan State 1584480.0
390 Nikola Jokic Denver Nuggets 15.0 C 21.0 6-10 250.0 NaN 1300000.0
391 Joffrey Lauvergne Denver Nuggets 77.0 C 24.0 6-11 220.0 NaN 1709719.0
392 Mike Miller Denver Nuggets 3.0 SG 36.0 6-8 218.0 Florida 947276.0
393 Emmanuel Mudiay Denver Nuggets 0.0 PG 20.0 6-5 200.0 NaN 3102240.0
394 Jameer Nelson Denver Nuggets 1.0 PG 34.0 6-0 190.0 Saint Joseph's 4345000.0
395 Jusuf Nurkic Denver Nuggets 23.0 C 21.0 7-0 280.0 NaN 1842000.0
396 JaKarr Sampson Denver Nuggets 9.0 SG 23.0 6-9 214.0 St. John's 258489.0
397 Axel Toupane Denver Nuggets 6.0 SG 23.0 6-7 210.0 NaN NaN
398 Nemanja Bjelica Minnesota Timberwolves 88.0 PF 28.0 6-10 240.0 NaN 3950001.0
399 Gorgui Dieng Minnesota Timberwolves 5.0 C 26.0 6-11 241.0 Louisville 1474440.0
400 Kevin Garnett Minnesota Timberwolves 21.0 PF 40.0 6-11 240.0 NaN 8500000.0
401 Tyus Jones Minnesota Timberwolves 1.0 PG 20.0 6-2 195.0 Duke 1282080.0
402 Zach LaVine Minnesota Timberwolves 8.0 PG 21.0 6-5 189.0 UCLA 2148360.0
403 Shabazz Muhammad Minnesota Timberwolves 15.0 SF 23.0 6-6 223.0 UCLA 2056920.0
404 Adreian Payne Minnesota Timberwolves 33.0 PF 25.0 6-10 237.0 Michigan State 1938840.0
405 Nikola Pekovic Minnesota Timberwolves 14.0 C 30.0 6-11 307.0 NaN 12100000.0
406 Tayshaun Prince Minnesota Timberwolves 12.0 SF 36.0 6-9 212.0 Kentucky 947276.0
407 Ricky Rubio Minnesota Timberwolves 9.0 PG 25.0 6-4 194.0 NaN 12700000.0
408 Damjan Rudez Minnesota Timberwolves 10.0 SF 29.0 6-9 230.0 NaN 1149500.0
409 Greg Smith Minnesota Timberwolves 4.0 PF 25.0 6-10 250.0 Fresno State NaN
410 Karl-Anthony Towns Minnesota Timberwolves 32.0 C 20.0 7-0 244.0 Kentucky 5703600.0
411 Andrew Wiggins Minnesota Timberwolves 22.0 SG 21.0 6-8 199.0 Kansas 5758680.0
412 Steven Adams Oklahoma City Thunder 12.0 C 22.0 7-0 255.0 Pittsburgh 2279040.0
413 Nick Collison Oklahoma City Thunder 4.0 PF 35.0 6-10 255.0 Kansas 3750000.0
414 Kevin Durant Oklahoma City Thunder 35.0 SF 27.0 6-9 240.0 Texas 20158622.0
415 Randy Foye Oklahoma City Thunder 6.0 SG 32.0 6-4 213.0 Villanova 3135000.0
416 Josh Huestis Oklahoma City Thunder 34.0 SF 24.0 6-7 230.0 Stanford 1140240.0
417 Serge Ibaka Oklahoma City Thunder 9.0 PF 26.0 6-10 245.0 NaN 12250000.0
418 Enes Kanter Oklahoma City Thunder 11.0 C 24.0 6-11 245.0 Kentucky 16407500.0
419 Mitch McGary Oklahoma City Thunder 33.0 PF 24.0 6-10 255.0 Michigan 1463040.0
420 Nazr Mohammed Oklahoma City Thunder 13.0 C 38.0 6-10 250.0 Kentucky 222888.0
421 Anthony Morrow Oklahoma City Thunder 2.0 SG 30.0 6-5 210.0 Georgia Tech 3344000.0
422 Cameron Payne Oklahoma City Thunder 22.0 PG 21.0 6-3 185.0 Murray State 2021520.0
423 Andre Roberson Oklahoma City Thunder 21.0 SG 24.0 6-7 210.0 Colorado 1210800.0
424 Kyle Singler Oklahoma City Thunder 5.0 SF 28.0 6-8 228.0 Duke 4500000.0
425 Dion Waiters Oklahoma City Thunder 3.0 SG 24.0 6-4 220.0 Syracuse 5138430.0
426 Russell Westbrook Oklahoma City Thunder 0.0 PG 27.0 6-3 200.0 UCLA 16744218.0
427 Cliff Alexander Portland Trail Blazers 34.0 PF 20.0 6-8 240.0 Kansas 525093.0
428 Al-Farouq Aminu Portland Trail Blazers 8.0 SF 25.0 6-9 215.0 Wake Forest 8042895.0
429 Pat Connaughton Portland Trail Blazers 5.0 SG 23.0 6-5 206.0 Notre Dame 625093.0
430 Allen Crabbe Portland Trail Blazers 23.0 SG 24.0 6-6 210.0 California 947276.0
431 Ed Davis Portland Trail Blazers 17.0 C 27.0 6-10 240.0 North Carolina 6980802.0
432 Maurice Harkless Portland Trail Blazers 4.0 SF 23.0 6-9 215.0 St. John's 2894059.0
433 Gerald Henderson Portland Trail Blazers 9.0 SG 28.0 6-5 215.0 Duke 6000000.0
434 Chris Kaman Portland Trail Blazers 35.0 C 34.0 7-0 265.0 Central Michigan 5016000.0
435 Meyers Leonard Portland Trail Blazers 11.0 PF 24.0 7-1 245.0 Illinois 3075880.0
436 Damian Lillard Portland Trail Blazers 0.0 PG 25.0 6-3 195.0 Weber State 4236287.0
437 C.J. McCollum Portland Trail Blazers 3.0 SG 24.0 6-4 200.0 Lehigh 2525160.0
438 Luis Montero Portland Trail Blazers 44.0 SG 23.0 6-7 185.0 Westchester CC 525093.0
439 Mason Plumlee Portland Trail Blazers 24.0 C 26.0 6-11 235.0 Duke 1415520.0
440 Brian Roberts Portland Trail Blazers 2.0 PG 30.0 6-1 173.0 Dayton 2854940.0
441 Noah Vonleh Portland Trail Blazers 21.0 PF 20.0 6-9 240.0 Indiana 2637720.0
442 Trevor Booker Utah Jazz 33.0 PF 28.0 6-8 228.0 Clemson 4775000.0
443 Trey Burke Utah Jazz 3.0 PG 23.0 6-1 191.0 Michigan 2658240.0
444 Alec Burks Utah Jazz 10.0 SG 24.0 6-6 214.0 Colorado 9463484.0
445 Dante Exum Utah Jazz 11.0 PG 20.0 6-6 190.0 NaN 3777720.0
446 Derrick Favors Utah Jazz 15.0 PF 24.0 6-10 265.0 Georgia Tech 12000000.0
447 Rudy Gobert Utah Jazz 27.0 C 23.0 7-1 245.0 NaN 1175880.0
448 Gordon Hayward Utah Jazz 20.0 SF 26.0 6-8 226.0 Butler 15409570.0
449 Rodney Hood Utah Jazz 5.0 SG 23.0 6-8 206.0 Duke 1348440.0
450 Joe Ingles Utah Jazz 2.0 SF 28.0 6-8 226.0 NaN 2050000.0
451 Chris Johnson Utah Jazz 23.0 SF 26.0 6-6 206.0 Dayton 981348.0
452 Trey Lyles Utah Jazz 41.0 PF 20.0 6-10 234.0 Kentucky 2239800.0
453 Shelvin Mack Utah Jazz 8.0 PG 26.0 6-3 203.0 Butler 2433333.0
454 Raul Neto Utah Jazz 25.0 PG 24.0 6-1 179.0 NaN 900000.0
455 Tibor Pleiss Utah Jazz 21.0 C 26.0 7-3 256.0 NaN 2900000.0
456 Jeff Withey Utah Jazz 24.0 C 26.0 7-0 231.0 Kansas 947276.0
457 NaN NaN NaN NaN NaN NaN NaN NaN NaN
import pandas as pddf = pd.read_csv('nba.csv')print(df)
Name Team Number Position Age Height Weight \
0 Avery Bradley Boston Celtics 0.0 PG 25.0 6-2 180.0
1 Jae Crowder Boston Celtics 99.0 SF 25.0 6-6 235.0
2 John Holland Boston Celtics 30.0 SG 27.0 6-5 205.0
3 R.J. Hunter Boston Celtics 28.0 SG 22.0 6-5 185.0
4 Jonas Jerebko Boston Celtics 8.0 PF 29.0 6-10 231.0
.. ... ... ... ... ... ... ...
453 Shelvin Mack Utah Jazz 8.0 PG 26.0 6-3 203.0
454 Raul Neto Utah Jazz 25.0 PG 24.0 6-1 179.0
455 Tibor Pleiss Utah Jazz 21.0 C 26.0 7-3 256.0
456 Jeff Withey Utah Jazz 24.0 C 26.0 7-0 231.0
457 NaN NaN NaN NaN NaN NaN NaN College Salary
0 Texas 7730337.0
1 Marquette 6796117.0
2 Boston University NaN
3 Georgia State 1148640.0
4 NaN 5000000.0
.. ... ...
453 Butler 2433333.0
454 NaN 900000.0
455 NaN 2900000.0
456 Kansas 947276.0
457 NaN NaN [458 rows x 9 columns]
import pandas as pd# 三个字段 name, site, age
nme = ["Google", "Runoob", "Taobao", "Wiki"]
st = ["www.google.com", "www.runoob.com", "www.taobao.com", "www.wikipedia.org"]
ag = [90, 40, 80, 98]# 字典
dict = {'name': nme, 'site': st, 'age': ag}df = pd.DataFrame(dict)# 保存 dataframe
df.to_csv('site.csv')
import pandas as pddf = pd.read_csv('nba.csv')
# 读取前10行
print(df.head(10))
Name Team Number Position Age Height Weight \
0 Avery Bradley Boston Celtics 0.0 PG 25.0 6-2 180.0
1 Jae Crowder Boston Celtics 99.0 SF 25.0 6-6 235.0
2 John Holland Boston Celtics 30.0 SG 27.0 6-5 205.0
3 R.J. Hunter Boston Celtics 28.0 SG 22.0 6-5 185.0
4 Jonas Jerebko Boston Celtics 8.0 PF 29.0 6-10 231.0
5 Amir Johnson Boston Celtics 90.0 PF 29.0 6-9 240.0
6 Jordan Mickey Boston Celtics 55.0 PF 21.0 6-8 235.0
7 Kelly Olynyk Boston Celtics 41.0 C 25.0 7-0 238.0
8 Terry Rozier Boston Celtics 12.0 PG 22.0 6-2 190.0
9 Marcus Smart Boston Celtics 36.0 PG 22.0 6-4 220.0 College Salary
0 Texas 7730337.0
1 Marquette 6796117.0
2 Boston University NaN
3 Georgia State 1148640.0
4 NaN 5000000.0
5 NaN 12000000.0
6 LSU 1170960.0
7 Gonzaga 2165160.0
8 Louisville 1824360.0
9 Oklahoma State 3431040.0
import pandas as pddf = pd.read_csv('nba.csv')
# 读取末尾10行
print(df.tail(10))
Name Team Number Position Age Height Weight \
448 Gordon Hayward Utah Jazz 20.0 SF 26.0 6-8 226.0
449 Rodney Hood Utah Jazz 5.0 SG 23.0 6-8 206.0
450 Joe Ingles Utah Jazz 2.0 SF 28.0 6-8 226.0
451 Chris Johnson Utah Jazz 23.0 SF 26.0 6-6 206.0
452 Trey Lyles Utah Jazz 41.0 PF 20.0 6-10 234.0
453 Shelvin Mack Utah Jazz 8.0 PG 26.0 6-3 203.0
454 Raul Neto Utah Jazz 25.0 PG 24.0 6-1 179.0
455 Tibor Pleiss Utah Jazz 21.0 C 26.0 7-3 256.0
456 Jeff Withey Utah Jazz 24.0 C 26.0 7-0 231.0
457 NaN NaN NaN NaN NaN NaN NaN College Salary
448 Butler 15409570.0
449 Duke 1348440.0
450 NaN 2050000.0
451 Dayton 981348.0
452 Kentucky 2239800.0
453 Butler 2433333.0
454 NaN 900000.0
455 NaN 2900000.0
456 Kansas 947276.0
457 NaN NaN
import pandas as pddf = pd.read_csv('nba.csv')print(df.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 458 entries, 0 to 457
Data columns (total 9 columns):# Column Non-Null Count Dtype
--- ------ -------------- ----- 0 Name 457 non-null object 1 Team 457 non-null object 2 Number 457 non-null float643 Position 457 non-null object 4 Age 457 non-null float645 Height 457 non-null object 6 Weight 457 non-null float647 College 373 non-null object 8 Salary 446 non-null float64
dtypes: float64(4), object(5)
memory usage: 32.3+ KB
None
JSON的读取与操作
JSON（JavaScript Object Notation，JavaScript 对象表示法），是存储和交换文本信息的语法，类似 XML。
JSON 比 XML 更小、更快，更易解析，更多 JSON 内容可以参考 JSON 教程。
Pandas 可以很方便的处理 JSON 数据，本文以 sites.json 为例，内容如下：
import pandas as pddata =[{"id": "A001","name": "菜鸟教程","url": "www.runoob.com","likes": 61},{"id": "A002","name": "Google","url": "www.google.com","likes": 124},{"id": "A003","name": "淘宝","url": "www.taobao.com","likes": 45}
]
df = pd.DataFrame(data)print(df)
id name url likes
0 A001 菜鸟教程 www.runoob.com 61
1 A002 Google www.google.com 124
2 A003 淘宝 www.taobao.com 45
JSON 对象与 Python 字典具有相同的格式，所以我们可以直接将 Python 字典转化为 DataFrame 数据：
实例
import pandas as pd# 字典格式的 JSON
s = {"col1":{"row1":1,"row2":2,"row3":3},"col2":{"row1":"x","row2":"y","row3":"z"}
}# 读取 JSON 转为 DataFrame
df = pd.DataFrame(s)
print(df)
col1 col2
row1 1 x
row2 2 y
row3 3 z
import pandas as pdURL = 'https://static.runoob.com/download/sites.json'
df = pd.read_json(URL)
print(df)
id name url likes
0 A001 菜鸟教程 www.runoob.com 61
1 A002 Google www.google.com 124
2 A003 淘宝 www.taobao.com 45
数据清洗
数据清洗是对一些没有用的数据进行处理的过程。
很多数据集存在数据缺失、数据格式错误、错误数据或重复数据的情况，如果要对使数据分析更加准确，就需要对这些没有用的数据进行处理。
在这个教程中，我们将利用 Pandas包来进行数据清洗。
f = pd.read_csv('property-data.csv')
print(f.to_string())
PID ST_NUM ST_NAME OWN_OCCUPIED NUM_BEDROOMS NUM_BATH SQ_FT
0 100001000.0 104.0 PUTNAM Y 3 1 1000
1 100002000.0 197.0 LEXINGTON N 3 1.5 --
2 100003000.0 NaN LEXINGTON N NaN 1 850
3 100004000.0 201.0 BERKELEY 12 1 NaN 700
4 NaN 203.0 BERKELEY Y 3 2 1600
5 100006000.0 207.0 BERKELEY Y NaN 1 800
6 100007000.0 NaN WASHINGTON NaN 2 HURLEY 950
7 100008000.0 213.0 TREMONT Y 1 1 NaN
8 100009000.0 215.0 TREMONT Y na 2 1800
上表包含来四种空数据：
n/a
NA
—
na
如果我们要删除包含空字段的行，可以使用 dropna() 方法，语法格式如下：
DataFrame.dropna(axis=0, how=‘any’, thresh=None, subset=None, inplace=False)
import pandas as pddf = pd.read_csv('property-data.csv')print (df['NUM_BEDROOMS'])
print (df['NUM_BEDROOMS'].isnull())
0 3
1 3
2 NaN
3 1
4 3
5 NaN
6 2
7 1
8 na
Name: NUM_BEDROOMS, dtype: object
0 False
1 False
2 True
3 False
4 False
5 True
6 False
7 False
8 False
Name: NUM_BEDROOMS, dtype: bool
import pandas as pdmissing_values = ["n/a", "na", "--"] # 空值包含
df = pd.read_csv('property-data.csv', na_values = missing_values)print (df['NUM_BEDROOMS'])
print (df['NUM_BEDROOMS'].isnull())
0 3.0
1 3.0
2 NaN
3 1.0
4 3.0
5 NaN
6 2.0
7 1.0
8 NaN
Name: NUM_BEDROOMS, dtype: float64
0 False
1 False
2 True
3 False
4 False
5 True
6 False
7 False
8 True
Name: NUM_BEDROOMS, dtype: bool
import pandas as pddf = pd.read_csv('property-data.csv')new_df = df.dropna()
# 删掉空值后的数据
print(new_df.to_string())
PID ST_NUM ST_NAME OWN_OCCUPIED NUM_BEDROOMS NUM_BATH SQ_FT
0 100001000.0 104.0 PUTNAM Y 3 1 1000
1 100002000.0 197.0 LEXINGTON N 3 1.5 --
8 100009000.0 215.0 TREMONT Y na 2 1800
import pandas as pddf = pd.read_csv('property-data.csv')df.dropna(inplace = True) # 可以修改原数据的Dataframeprint(df.to_string())
PID ST_NUM ST_NAME OWN_OCCUPIED NUM_BEDROOMS NUM_BATH SQ_FT
0 100001000.0 104.0 PUTNAM Y 3 1 1000
1 100002000.0 197.0 LEXINGTON N 3 1.5 --
8 100009000.0 215.0 TREMONT Y na 2 1800
import pandas as pddf = pd.read_csv('property-data.csv')df.dropna(subset=['ST_NUM'], inplace = True) # 移除ST_NUM列中为空的数据print(df.to_string())
PID ST_NUM ST_NAME OWN_OCCUPIED NUM_BEDROOMS NUM_BATH SQ_FT
0 100001000.0 104.0 PUTNAM Y 3 1 1000
1 100002000.0 197.0 LEXINGTON N 3 1.5 --
3 100004000.0 201.0 BERKELEY 12 1 NaN 700
4 NaN 203.0 BERKELEY Y 3 2 1600
5 100006000.0 207.0 BERKELEY Y NaN 1 800
7 100008000.0 213.0 TREMONT Y 1 1 NaN
8 100009000.0 215.0 TREMONT Y na 2 1800
import pandas as pddf = pd.read_csv('property-data.csv')df.fillna(12345, inplace = True) # 填充NA值print(df.to_string())
PID ST_NUM ST_NAME OWN_OCCUPIED NUM_BEDROOMS NUM_BATH SQ_FT
0 100001000.0 104.0 PUTNAM Y 3 1 1000
1 100002000.0 197.0 LEXINGTON N 3 1.5 --
2 100003000.0 12345.0 LEXINGTON N 12345 1 850
3 100004000.0 201.0 BERKELEY 12 1 12345 700
4 12345.0 203.0 BERKELEY Y 3 2 1600
5 100006000.0 207.0 BERKELEY Y 12345 1 800
6 100007000.0 12345.0 WASHINGTON 12345 2 HURLEY 950
7 100008000.0 213.0 TREMONT Y 1 1 12345
8 100009000.0 215.0 TREMONT Y na 2 1800
import pandas as pddf = pd.read_csv('property-data.csv')df['PID'].fillna(12345, inplace = True) # 替换PID列的空值为12345print(df.to_string())
PID ST_NUM ST_NAME OWN_OCCUPIED NUM_BEDROOMS NUM_BATH SQ_FT
0 100001000.0 104.0 PUTNAM Y 3 1 1000
1 100002000.0 197.0 LEXINGTON N 3 1.5 --
2 100003000.0 NaN LEXINGTON N NaN 1 850
3 100004000.0 201.0 BERKELEY 12 1 NaN 700
4 12345.0 203.0 BERKELEY Y 3 2 1600
5 100006000.0 207.0 BERKELEY Y NaN 1 800
6 100007000.0 NaN WASHINGTON NaN 2 HURLEY 950
7 100008000.0 213.0 TREMONT Y 1 1 NaN
8 100009000.0 215.0 TREMONT Y na 2 1800
import pandas as pddf = pd.read_csv('property-data.csv')
# 使用 mean() 方法计算列的均值并替换空单元格
x = df["ST_NUM"].mean()df["ST_NUM"].fillna(x, inplace = True)print(df.to_string())
PID ST_NUM ST_NAME OWN_OCCUPIED NUM_BEDROOMS NUM_BATH SQ_FT
0 100001000.0 104.000000 PUTNAM Y 3 1 1000
1 100002000.0 197.000000 LEXINGTON N 3 1.5 --
2 100003000.0 191.428571 LEXINGTON N NaN 1 850
3 100004000.0 201.000000 BERKELEY 12 1 NaN 700
4 NaN 203.000000 BERKELEY Y 3 2 1600
5 100006000.0 207.000000 BERKELEY Y NaN 1 800
6 100007000.0 191.428571 WASHINGTON NaN 2 HURLEY 950
7 100008000.0 213.000000 TREMONT Y 1 1 NaN
8 100009000.0 215.000000 TREMONT Y na 2 1800
import pandas as pddf = pd.read_csv('property-data.csv')
# 使用 median() 方法计算列的中位数并替换空单元格
x = df["ST_NUM"].median()df["ST_NUM"].fillna(x, inplace = True)print(df.to_string())
PID ST_NUM ST_NAME OWN_OCCUPIED NUM_BEDROOMS NUM_BATH SQ_FT
0 100001000.0 104.0 PUTNAM Y 3 1 1000
1 100002000.0 197.0 LEXINGTON N 3 1.5 --
2 100003000.0 203.0 LEXINGTON N NaN 1 850
3 100004000.0 201.0 BERKELEY 12 1 NaN 700
4 NaN 203.0 BERKELEY Y 3 2 1600
5 100006000.0 207.0 BERKELEY Y NaN 1 800
6 100007000.0 203.0 WASHINGTON NaN 2 HURLEY 950
7 100008000.0 213.0 TREMONT Y 1 1 NaN
8 100009000.0 215.0 TREMONT Y na 2 1800
import pandas as pddf = pd.read_csv('property-data.csv')
# 使用 mode() 方法计算列的众数并替换空单元格
x = df["ST_NUM"].mode()df["ST_NUM"].fillna(x, inplace = True)print(df.to_string())
PID ST_NUM ST_NAME OWN_OCCUPIED NUM_BEDROOMS NUM_BATH SQ_FT
0 100001000.0 104.0 PUTNAM Y 3 1 1000
1 100002000.0 197.0 LEXINGTON N 3 1.5 --
2 100003000.0 201.0 LEXINGTON N NaN 1 850
3 100004000.0 201.0 BERKELEY 12 1 NaN 700
4 NaN 203.0 BERKELEY Y 3 2 1600
5 100006000.0 207.0 BERKELEY Y NaN 1 800
6 100007000.0 215.0 WASHINGTON NaN 2 HURLEY 950
7 100008000.0 213.0 TREMONT Y 1 1 NaN
8 100009000.0 215.0 TREMONT Y na 2 1800
清洗格式错误数据
数据格式错误的单元格会使数据分析变得困难，甚至不可能。
我们可以通过包含空单元格的行，或者将列中的所有单元格转换为相同格式的数据。
import pandas as pd# 第三个日期格式错误
data = {"Date": ['2020/12/01', '2020/12/02' , '20201226'],"duration": [50, 40, 45]
}df = pd.DataFrame(data, index = ["day1", "day2", "day3"])df['Date'] = pd.to_datetime(df['Date'])print(df.to_string())
Date duration
day1 2020-12-01 50
day2 2020-12-02 40
day3 2020-12-26 45
数据错误也是很常见的情况，我们可以对错误的数据进行替换或移除。
import pandas as pdperson = {"name": ['Google', 'Runoob' , 'Taobao'],"age": [50, 40, 12345] # 12345 年龄数据是错误的
}df = pd.DataFrame(person)df.loc[2, 'age'] = 30 # 修改数据print(df.to_string())
name age
0 Google 50
1 Runoob 40
2 Taobao 30
import pandas as pd
# 条件判断
person = {"name": ['Google', 'Runoob' , 'Taobao'],"age": [50, 200, 12345]
}df = pd.DataFrame(person)for x in df.index:if df.loc[x, "age"] > 120:df.loc[x, "age"] = 120print(df.to_string())
name age
0 Google 50
1 Runoob 120
2 Taobao 120
import pandas as pdperson = {"name": ['Google', 'Runoob' , 'Taobao'],"age": [50, 40, 12345] # 12345 年龄数据是错误的
}df = pd.DataFrame(person)
# 将错误的数据删除
for x in df.index:if df.loc[x, "age"] > 120:df.drop(x, inplace = True)print(df.to_string())
name age
0 Google 50
1 Runoob 40
import pandas as pdpersons = {"name": ['Google', 'Runoob', 'Runoob', 'Taobao'],"age": [50, 40, 40, 23]
}df = pd.DataFrame(persons)
# 删除重复数据
df.drop_duplicates(inplace = True)
print(df)
name age
0 Google 50
1 Runoob 40
3 Taobao 23
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