Python pandas 入门 04 CSV 文件

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

import pandas as pd

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

import pandas as pd

df = pd.read_csv('nba.csv')

print(df) 

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
..             ...             ...     ...      ...   ...    ...     ...                ...        ...
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

import pandas as pd
   
# 三个字段 name, site, age
nme = ["Google", "Run", "Taobao", "Wiki"]
st = ["www.google.com", "www.Run.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。

 

---

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

import pandas as pd

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

import pandas as pd

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

import pandas as pd

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

import pandas as pd

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

import pandas as pd

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。

 

 

REF

https://www.runoob.com/pandas/pandas-csv-file.html