Data Management  With Pandas Python 

การจัดการข้อมูลเบื้องต้นด้วย Pandas

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👉การเปิดข้อมูล (Read Data)

1. import pandas as pd

2. 
   

3. #Read CSV file

4. df = pd.read_csv(r"C:\Users\user\Desktop\data_pandas.csv")  

5. 
   

6. #Print results

7. print(df)

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👉การแสดงขนาดของข้อมูล

1. import pandas as pd

2. 
   

3. #Read CSV file

4. df = pd.read_csv(r"C:\Users\user\Desktop\data_pandas.csv")  

5. print(df.shape)

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👉การจำกัดการแสดงข้อมูล

1. import pandas as pd

2. 
   

3. #Read CSV file

4. df = pd.read_csv(r"C:\Users\user\Desktop\data_pandas.csv")  

5. 
   

6. df = df.head(2)

7. 
   

8. #Print results

9. print(df)

10. print(df.shape)

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👉การเลือกคอลัมน์แสดงข้อมูล

1. import pandas as pd

2. 
   

3. #Read CSV file

4. df = pd.read_csv(r"C:\Users\user\Desktop\data_pandas.csv")  

5. 
   

6. df = df.[['Value1','Value2']]

7. 
   

8. #Print results

9. print(df)

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👉การกำหนดช่องของการแสดงข้อมูล

1. import pandas as pd

2. 
   

3. #Read CSV file

4. df = pd.read_csv(r"C:\Users\user\Desktop\data_pandas.csv")  

5. 
   

6. df = df[['Value2','Value3']]

7. 
   

8. age_range = df.loc[(df['Value2'] >= 30) & (df['Value2'] <= 40), 'Value3']

9. print(age_range)

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👉การจัดกลุ่มข้อมูล (GroupBy Data)

1. import pandas as pd

2. 
   

3. # Read CSV file

4. df = pd.read_csv(r"C:\Users\user\Desktop\data_pandas.csv")  

5. 
   

6. # Group by 'Category' and sum the 'Value' column

7. grouped = df.groupby('Category')['Value'].sum()

8. 
   

9. # Print results

10. print(grouped)

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👉การจัดกลุ่มข้อมูล (GroupBy Data) Table

1. import pandas as pd

2. import tkinter as tk

3. from tkinter import Tk, Frame, ttk

4. import matplotlib.pyplot as plt

5. from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg

6. 
   

7. # Read CSV file

8. df = pd.read_csv(r"C:\Users\user\Desktop\data_GroupBy.csv")  # Ensure the file path is correct

9. 
   

10. # Group by 'Category' and sum the 'Value' column

11. grouped = df.groupby('Category', as_index=False)['Value'].sum()

12. 
    

13. # Create Tkinter Window

14. root = Tk()

15. root.title("Grouped Data Display with Bar Graph")

16. root.geometry("1000x600")

17. 
    

18. # Set Treeview style

19. style = ttk.Style()

20. style.configure("Treeview", font=("Courier", 15, "bold"), rowheight=25)

21. style.configure("Treeview.Heading", font=("Courier", 15, "bold"))

22. 
    

23. # Create frame for table and scrollbar

24. frame = Frame(root, width=400, height=200)  # Fixed frame size

25. frame.place(x=300, y=150)  # Adjust x to center horizontally

26. frame.pack_propagate(False)  # Prevent frame from shrinking

27. 
    

28. # Create Treeview

29. tree = ttk.Treeview(frame, columns=("Category", "Sum"), show="headings", height=5)

30. 
    

31. # Configure column headings

32. tree.heading("Category", text="Category", anchor="center")

33. tree.heading("Sum", text="Total Value", anchor="center")

34. 
    

35. # Configure column widths and alignment

36. tree.column("Category", anchor="center", width=200)

37. tree.column("Sum", anchor="center", width=200)

38. 
    

39. # Insert Data into Treeview

40. for index, row in grouped.iterrows():

41.     tree.insert("", tk.END, values=(row["Category"], row["Value"]))

42. 
    

43. tree.pack(side=tk.TOP, fill="both", expand=True)

44. 
    

45. root.mainloop()

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👉การจัดกลุ่มข้อมูล (GroupBy Data) Table + Bar Graph

1. import pandas as pd

2. import tkinter as tk

3. from tkinter import Tk, Frame, ttk

4. import matplotlib.pyplot as plt

5. from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg

6. 
   

7. # Read CSV file

8. df = pd.read_csv(r"C:\Users\user\Desktop\data_GroupBy.csv")  # Ensure the file path is correct

9. 
   

10. # Group by 'Category' and sum the 'Value' column

11. grouped = df.groupby('Category', as_index=False)['Value'].sum()

12. 
    

13. # Create Tkinter Window

14. root = Tk()

15. root.title("Grouped Data Display with Bar Graph")

16. root.geometry("1000x800")

17. 
    

18. # Set Treeview style

19. style = ttk.Style()

20. style.configure("Treeview", font=("Courier", 15, "bold"), rowheight=25)

21. style.configure("Treeview.Heading", font=("Courier", 15, "bold"))

22. 
    

23. # Create frame for table

24. frame = Frame(root, width=400, height=200)

25. frame.place(x=300, y=50)

26. frame.pack_propagate(False)

27. 
    

28. # Create Treeview

29. tree = ttk.Treeview(frame, columns=("Category", "Sum"), show="headings", height=5)

30. 
    

31. # Configure column headings

32. tree.heading("Category", text="Category", anchor="center")

33. tree.heading("Sum", text="Total Value", anchor="center")

34. 
    

35. # Configure column widths and alignment

36. tree.column("Category", anchor="center", width=200)

37. tree.column("Sum", anchor="center", width=200)

38. 
    

39. # Insert Data into Treeview

40. for index, row in grouped.iterrows():

41.     tree.insert("", tk.END, values=(row["Category"], row["Value"]))

42. 
    

43. tree.pack(side=tk.TOP, fill="both", expand=True)

44. 
    

45. # -------------------- ADDING BAR GRAPH --------------------

46. # Create a Matplotlib figure

47. fig, ax = plt.subplots(figsize=(5, 4))

48. 
    

49. # Plot bar graph

50. ax.bar(grouped["Category"], grouped["Value"], color=["blue", "green", "red"])

51. 
    

52. # Set labels and title

53. ax.set_xlabel("Category")

54. ax.set_ylabel("Total Value")

55. ax.set_title("Category Wise Total Value")

56. 
    

57. # Embed Matplotlib figure in Tkinter

58. canvas = FigureCanvasTkAgg(fig, root)

59. canvas.get_tk_widget().place(x=240, y=300)  # Adjust position below the table

60. canvas.draw()

61. 
    

62. root.mainloop()

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👉การจัดกลุ่มข้อมูล (GroupBy Data) Table + PIE CHART

1. import pandas as pd

2. import tkinter as tk

3. from tkinter import Tk, Frame, ttk

4. import matplotlib.pyplot as plt

5. from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg

6. 
   

7. # Read CSV file

8. df = pd.read_csv(r"C:\Users\user\Desktop\data_GroupBy.csv")  

9. 
   

10. # Group by 'Category' and sum the 'Value' column

11. grouped = df.groupby('Category', as_index=False)['Value'].sum()

12. 
    

13. # Create Tkinter Window

14. root = Tk()

15. root.title("Grouped Data Display with Pie Chart")

16. root.geometry("1000x800")

17. 
    

18. # Set Treeview style

19. style = ttk.Style()

20. style.configure("Treeview", font=("Courier", 15, "bold"), rowheight=25)

21. style.configure("Treeview.Heading", font=("Courier", 15, "bold"))

22. 
    

23. # Create frame for table

24. frame = Frame(root, width=400, height=200)

25. frame.place(x=300, y=50)

26. frame.pack_propagate(False)

27. 
    

28. # Create Treeview

29. tree = ttk.Treeview(frame, columns=("Category", "Sum"), show="headings", height=5)

30. 
    

31. # Configure column headings

32. tree.heading("Category", text="Category", anchor="center")

33. tree.heading("Sum", text="Total Value", anchor="center")

34. 
    

35. # Configure column widths and alignment

36. tree.column("Category", anchor="center", width=200)

37. tree.column("Sum", anchor="center", width=200)

38. 
    

39. # Insert Data into Treeview

40. for index, row in grouped.iterrows():

41.     tree.insert("", tk.END, values=(row["Category"], row["Value"]))

42. 
    

43. tree.pack(side=tk.TOP, fill="both", expand=True)

44. 
    

45. # -------------------- ADDING PIE CHART --------------------

46. 
    

47. # Create a Matplotlib figure

48. fig, ax = plt.subplots(figsize=(5, 5))

49. random_colors = ['red','green','blue']

50. # Pie chart with percentage labels

51. ax.pie(

52.     grouped["Value"], 

53.     labels=grouped["Category"],

54.     autopct="%1.1f%%",

55.     colors=random_colors,

56.     startangle=140

57. )

58. # Ensure the pie chart is circular

59. ax.axis("equal")

60. 
    

61. # Embed Matplotlib figure in Tkinter

62. canvas = FigureCanvasTkAgg(fig, root)

63. canvas.get_tk_widget().place(x=240, y=300)  # Adjust position below the table

64. canvas.draw()

65. 
    

66. root.mainloop()

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👉การแสดงข้อมูลจากไฟล์ DataLogger ในอุตสาหกรรม

1. import pandas as pd

2. import matplotlib.pyplot as plt

3. 
   

4. # Step 1: Read the CSV file into a DataFrame

5. df = pd.read_csv(r"C:\Users\user\Desktop\datalogger.csv")

6. 
   

7. # Step 2: Combine 'date' and 'time' columns into a single datetime column

8. # Adjust the year in the 'date' column

9. df['date'] = df['date'].apply(lambda x: x.replace('/2568', '/2025'))

10. 
    

11. # Step 3: Proceed with the rest of the code

12. df['datetime'] = pd.to_datetime(df['date'] + ' ' + df['time'], format='%d/%m/%Y %H:%M:%S')

13. df.set_index('datetime', inplace=True)

14. 
    

15. # Step 4: Plot the data

16. plt.figure(figsize=(10, 6))

17. plt.plot(df.index, df['value1'], label='Value1')

18. plt.title('Value1 Over Time')

19. plt.xlabel('Date and Time')

20. plt.ylabel('Value1')

21. plt.legend()

22. plt.grid(True)

23. plt.xticks(rotation=45)

24. plt.tight_layout()

25. plt.show()

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👉การแสดงข้อมูลจากไฟล์ DataLogger ในอุตสาหกรรม

1. import pandas as pd

2. import matplotlib.pyplot as plt

3. 
   

4. # Step 1: Read the CSV file into a DataFrame

5. df = pd.read_csv(r"C:\Users\user\Desktop\datalogger.csv")

6. 
   

7. # Adjust the year in the 'date' column

8. df['date'] = df['date'].apply(lambda x: x.replace('/2568', '/2025'))

9. 
   

10. # Step 2: Combine 'date' and 'time' columns into a single datetime column

11. df['datetime'] = pd.to_datetime(df['date'] + ' ' + df['time'], format='%d/%m/%Y %H:%M:%S')

12. 
    

13. # Step 3: Set the combined datetime column as the index

14. df.set_index('datetime', inplace=True)

15. 
    

16. # Step 4: Define the time range

17. start_time = pd.to_datetime('15:09:31', format='%H:%M:%S').time()

18. end_time = pd.to_datetime('15:09:45', format='%H:%M:%S').time()

19. 
    

20. # Step 5: Filter the data for the specified time range

21. filtered_df = df.between_time(start_time, end_time)

22. 
    

23. # Step 6: Plot the filtered data as a line chart

24. plt.figure(figsize=(10, 6))

25. plt.plot(filtered_df.index, filtered_df['value1'], label='Value1', marker='o')

26. 
    

27. # Customize the plot

28. plt.title('Value1 Over Time (15:09:30 to 15:09:40)')

29. plt.xlabel('Time')

30. plt.ylabel('Value1')

31. plt.legend()

32. plt.grid(True)

33. plt.xticks(rotation=45)  # Rotate x-axis labels for better readability

34. 
    

35. # Show the plot

36. plt.tight_layout()

37. plt.show()

38. plt.xticks(rotation=45)

39. plt.tight_layout()

40. plt.show()

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