Python Pandas – 變更與資料新增/刪除

測試環境為 CentOS 8 (虛擬機)

參考資料 – https://ithelp.ithome.com.tw/articles/10251664

安裝所需模組

[root@localhost ~]# pip install pandas

匯入模組

[root@localhost ~]# python3
Python 3.6.8 (default, Sep 10 2021, 09:13:53)
[GCC 8.5.0 20210514 (Red Hat 8.5.0-3)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd

從網路抓的資料大多是 JSON 格式,下面就以此為範例.

>>> data = pd.DataFrame({
    "Chinese":
    {
        "Ben": 68,
        "Alex": 86,
        "Jeff": 57
    },
    "English":
    {
        "Ben": 63,
        "Alex": 92,
        "Jeff": 83
    },
    "Math":
    {
        "Ben": 65,
        "Alex": 89,
        "Jeff": 77
    }
})

>>> data
      Chinese  English  Math
Ben        68       63    65
Alex       86       92    89
Jeff       57       83    77

使用索引名稱變更值

DataFrame.loc[“索引名稱”, “欄位名稱”]=值

>>> data.loc["Ben","Chinese"]=99

>>> data
      Chinese  English  Math
Ben        99       63    65
Alex       86       92    89
Jeff       57       83    77

刪除資料

DataFrame.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors=’raise’)

• labels – 索引名稱或欄位名稱可為單一 Label 或是 List
• axis – 是用來決定刪除的是行還是列, 預設為 0 表示為列(前面需指定索引名稱), 1 表示為欄 (前面需指定欄位名稱)
• index – Column index to drop
• columns – single label or list-like.
• level – int or level name, optional, use for Multiindex.
• inplace – Default False and returns a copy of DataFrame. When used True, it drop’s column inplace and returns None.
• errors – {‘ignore’, ‘raise’}, default ‘raise’

下面來看一下要怎麼刪除 行 或是 列 的資料.

• 刪除 Ben 這一行的資料

  >>> data1=data.drop("Ben")
  

  >>> data1
        Chinese  English  Math
  Alex       86       92    89
  Jeff       57       83    77
  

  >>> data
        Chinese  English  Math
  Ben        99       63    65
  Alex       86       92    89
  Jeff       57       83    77
  

• 刪除 Chinese 欄位資料

  >>> data.drop("Chinese",axis=1)
        English  Math
  Ben        63    65
  Alex       92    89
  Jeff       83    77
  

新增行資料

DataFrame.append(other, ignore_index=False, verify_integrity=False, sort=False)

• other – 須為 DataFrame 或是 Series/dict-like object, 以及 list of these.
• ignore_index – bool, default False. When set to True, It creates axis with incremental numeric number.
• verify_integrity – bool, default False. When set to True, raises error for duplicate index.
• sort – bool, default False.

這邊新增資料使用 DataFrame 格式.

data1 = pd.DataFrame({
    "Chinese":
    {
        "Ken": 68
    },
    "English":
    {
        "Ken": 63,
    },
    "Math":
    {
        "Ken": 65,
    }
})

>>> data.append(data1)
      Chinese  English  Math
Ben        99       63    65
Alex       86       92    89
Jeff       57       83    77
Ken        68       63    65

新增列資料

有以下幾種方式來新增列的資料.

data["SUM"]=data.sum(axis=1)
>>> data
      Chinese  English  Math  SUM
Ben        99       63    65  227
Alex       86       92    89  267
Jeff       57       83    77  217

>>> data["PE Class"] = [80, 80, 80]
>>> data
      Chinese  English  Math  SUM  PE Class
Ben        99       63    65  227        80
Alex       86       92    89  267        80
Jeff       57       83    77  217        80

>>> data["From"]=pd.Series(data=["Taipei", "Taichung", "Tainan"] , index=["Ben" , "Alex" , "Jeff"])
>>> data
      Chinese  English  Math  SUM  PE Class      From
Ben        99       63    65  227        80    Taipei
Alex       86       92    89  267        80  Taichung
Jeff       57       83    77  217        80    Tainan