pandas处理CSV文件

一、准备CSV文件

数据文件下载地址：nba.csv

CSV（Comma-Separated Values，逗号分割值）是一种文件格式，以纯文本形式存储表格数据。

注：CSV有时也称为符号分隔值，因为分隔字符也可不是逗号

二、读CSV文件

2.1 读取CSV文件

用pandas.read_csv()读取csv文件

程序

import pandas as pddf=pd.read_csv('./Data/nba.csv') # 读取csv文件
print(df)

运行结果

              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]

2.2 读取CSV文件前几行

df.head(a)

读取文件前a行（默认是前5行）
空行返回NaN

程序

import pandas as pddf=pd.read_csv('./Data/nba.csv') # 读取csv文件
print(df.head(10)) # 只输出前10行的数据

运行结果

            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

2.3 读取CSV文件后几行

df.tail(a)

读取文件后a行（默认是后5行）
空行返回NaN

程序

import pandas as pddf=pd.read_csv('./Data/nba.csv') # 读取csv文件
print(df.tail(10)) # 只输出后10行的数据

运行结果

               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

2.4 查看CSV文件相关信息

df.info()

返回文件的基本信息

程序

import pandas as pddf=pd.read_csv('./Data/nba.csv') # 读取csv文件
print(df.info()) # 输出文件基本信息

运行结果

<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
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