Pandas CSV 文件
CSV(Comma-Separated Values,逗号分隔值,有时也称为字符分隔值,因为分隔字符也可以不是逗号),其文件以纯文本形式存储表格数据(数字和文本)。
CSV 是一种通用的、相对简单的文件格式,被用户、商业和科学广泛应用。
Pandas 可以很方便的处理 CSV 文件,本文以 nba.csv 为例,你可以下载 nba.csv 或打开 nba.csv 查看。
实例
df = 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。
实例
df = pd. read_csv ( 'nba.csv' )
print ( df )
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 .. ... ... ... ... ... ... ... ... ... 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
我们也可以使用 to_csv() 方法将 DataFrame 存储为 csv 文件:
实例
# 三个字段 name, site, age
nme = [ "Google" , "yssmx" , "Taobao" , "Wiki" ]
st = [ "www.google.com" , "www.yssmx.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' )
执行成功后,我们打开 site.csv 文件,显示结果如下:
数据处理
head()
head( n ) 方法用于读取前面的 n 行,如果不填参数 n ,默认返回 5 行。
实例 - 读取前面 5 行
df = pd. read_csv ( 'nba.csv' )
print ( df. head ( ) )
输出结果为:
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
实例 - 读取前面 10 行
df = pd. read_csv ( 'nba.csv' )
print ( df. head ( 10 ) )
输出结果为:
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
tail()
tail( n ) 方法用于读取尾部的 n 行,如果不填参数 n ,默认返回 5 行,空行各个字段的值返回 NaN 。
实例 - 读取末尾 5 行
df = pd. read_csv ( 'nba.csv' )
print ( df. tail ( ) )
输出结果为:
Name Team Number Position Age Height Weight College Salary 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
实例 - 读取末尾 10 行
df = pd. read_csv ( 'nba.csv' )
print ( df. tail ( 10 ) )
输出结果为:
Name Team Number Position Age Height Weight College Salary 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
info()
info() 方法返回表格的一些基本信息:
实例
df = pd. read_csv ( 'nba.csv' )
print ( df. info ( ) )
输出结果为:
<class 'pandas.core.frame.DataFrame'> RangeIndex: 458 entries, 0 to 457 # 行数,458 行,第一行编号为 0 Data columns (total 9 columns): # 列数,9列 # Column Non-Null Count Dtype # 各列的数据类型 --- ------ -------------- ----- 0 Name 457 non-null object 1 Team 457 non-null object 2 Number 457 non-null float64 3 Position 457 non-null object 4 Age 457 non-null float64 5 Height 457 non-null object 6 Weight 457 non-null float64 7 College 373 non-null object # non-null,意思为非空的数据 8 Salary 446 non-null float64 dtypes: float64(4), object(5) # 类型
non-null 为非空数据,我们可以看到上面的信息中,总共 458 行,College 字段的空值最多。