# 0411 https://www.learncodewithmike.com/2020/11/python-pandas-dataframe-tutorial.html ### 上課筆記 ``` 容器:串列 字典 panda ``` ![](https://i.imgur.com/6XorLpY.png) ``` # 手工掛載雲端硬碟 # 寫入CSV #'/路徑///資料夾/檔名.副檔名(txt)', 'w(寫入)' with open('/content/drive/MyDrive/___DataSet/001_Hello.txt', 'w') as f:#以打開檔案為基礎 並把檔案命名為f f.write('Hello Google Drive 溫瑩瑄') #把()寫入f檔案 ``` ``` # 讀取CSV with open('/content/drive/MyDrive/___DataSet/001_Hello.txt', 'r') as f: ss=f.read() #r(讀取) print(ss) Hello Google Drive 溫瑩瑄 ``` ``` # 刪除檔案 import os if os.path.exists("/content/drive/MyDrive/___DataSet//001_Hello.txt"): #檢查路徑是否存在 os.remove("/content/drive/MyDrive/___DataSet//001_Hello.txt") #移除檔案 print("檔案已經刪除") #如果刪除就印出字串('檔案已經刪除') else: print("檔案不存在") #如果上面的if不成立就跑else如果上面的if不成立就跑else 檔案已經刪除 ``` ``` #讀取雲端硬碟中的excel檔 import pandas as pd #pd命名 df = pd.read_excel('/content/drive/My Drive/___DataSet/001_Income_F.xlsx') df ``` ![](https://i.imgur.com/c0G5vtR.jpg) ``` #讀取雲端硬碟中的excel檔 import pandas as pd data = pd.read_excel("/content/drive/My Drive/___DataSet/110 學年度全國大專校院及校長名錄(含學校本部地址).xlsx") data 159 rows × 14 columns ``` ![](https://i.imgur.com/sCtd6RD.jpg) ``` import pandas as pd data = pd.read_excel("/content/drive/My Drive/___DataSet/程設0411.xlsx") data ``` ![](https://i.imgur.com/5kp2EaK.jpg) ``` import pandas as pd grades = { "name": ["Mike", "Sherry", "Cindy", "John"], "math": [80, 75, 93, 86], "chinese": [63, 90, 85, 70] } df = pd.DataFrame(grades) print("使用字典來建立df:") print(df) df ``` ![](https://i.imgur.com/mJbCGYm.jpg) ``` grades = [ ["Mike", 80, 63], ["Sherry", 75, 90], ["Cindy", 93, 85], ["John", 86, 70] ] new_df = pd.DataFrame(grades) print("使用陣列來建立df:") print(new_df) ``` ![](https://i.imgur.com/PVcNOKF.jpg) ``` import pandas as pd grades = { "name": ["Mike", "Sherry", "Cindy", "John"], "math": [80, 75, 93, 86], "chinese": [63, 90, 85, 70] } df = pd.DataFrame(grades) df.index = ["s1", "s2", "s3", "s4"] #自訂索引值 df.columns = ["student_name", "math_score", "chinese_score"] #自訂欄位名稱 print(df) df ``` ![](https://i.imgur.com/iBeAvgG.jpg) ``` head():取得最前面的n筆資料,並且會回傳一個新的Pandas ``` ``` import pandas as pd grades = { "name": ["Mike", "Sherry", "Cindy", "John"], "math": [80, 75, 93, 86], "chinese": [63, 90, 85, 70] } df = pd.DataFrame(grades) print("原來的df") print(df) print("=================================") new_df = df.head(2) print("取得最前面的兩筆資料") print(new_df) print("=================================") print("取得單一欄位資料(型別為DataFrame)") print(df[["name"]]) print("=================================") print("取得多欄位資料(型別為DataFrame)") print(df[["name", "chinese"]]) #[[串列]] print("=================================") print("取得索引值0~2的資料") print(df[0:3]) print("=================================") print("利用at()方法取得索引值為1的math欄位資料") print(df.at[1, "math"]) print("=================================") print("利用iat()方法取得索引值為1的第一個欄位資料") print(df.iat[1, 2]) #先切列在切欄 print("=================================") print("取得資料索引值為1和3的name及chinese欄位資料集") print(df.loc[[1, 3], ["name", "chinese"]]) print("=================================") print("取得資料索引值為1和3的第一個及第三個欄位資料集") print(df.iloc[[1, 3], [0, 2]]) #1.sherry2.john列,0name 2chhinese欄 ``` ![](https://i.imgur.com/HbPEmy3.jpg) ![](https://i.imgur.com/Muec3j4.jpg) ![](https://i.imgur.com/tw2bcmP.jpg) ``` import pandas as pd grades = { "編號": ["A001", "A002", "A003", "A004","A005"], "體重": [60, 50, 80, 75, 72], "身高": [165, 157, 182, 175, 170] } df = pd.DataFrame(grades) df ``` ![](https://i.imgur.com/nzHBRUB.jpg) ``` for i in range(0,5): for j in range(0,5): print(f'(i),(j)') print("=============") print(df.at[i, "編號"]) (i),(j) ============= A001 (i),(j) ============= A001 (i),(j) ============= A001 (i),(j) ============= A001 (i),(j) ============= A001 (i),(j) ============= A002 (i),(j) ============= A002 (i),(j) ============= A002 (i),(j) ============= A002 (i),(j) ============= A002 (i),(j) ============= A003 (i),(j) ============= A003 (i),(j) ============= A003 (i),(j) ============= A003 (i),(j) ============= A003 (i),(j) ============= A004 (i),(j) ============= A004 (i),(j) ============= A004 (i),(j) ============= A004 (i),(j) ============= A004 (i),(j) ============= A005 (i),(j) ============= A005 (i),(j) ============= A005 (i),(j) ============= A005 (i),(j) ============= A005 ```
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