On selection of row of Treeview details can be shown and row can be deleted from DataFrame

Selecting and deleting row from DataFrame and showing result in Treeview Part V

To generate DataFrame df2 from the source DataFrame df we have used the function my_search().
In part 1 of DataFrame search, we crated the filtered output and displayed the same in Treeiew trv.

Read Part 1 on how to filter datafrme based on user inputs
Once a row in Treeview is selected, we can trigger the function data_collect()

trv.bind("<<TreeviewSelect>>", data_collect)

Inside the function data_collect() we will read the user selected row values. By using config() we are updating the text option of the Label l3 to show the values of the selected row.

def data_collect(*args):
    global p_id
    p_id = trv.selection()[0] # collect selected row id
    #print(trv.item(p_id)) # Dictionary of all options
    #print(trv.item(p_id)['values']) # List of values
    l3.config(text=trv.item(p_id)['values']) # Update the Label with values

We will use global variables as we will be changing the values inside different functions.

df2: Filtered or the result output DataFrame after applying query to the source DataFrame df.
trv : Treeview created to display the DataFrame
l1 : List of Column headers taken from source DataFrame df

global df2,trv,l1

Here is a list of functions we will be using.
my_search() : Searching souce DataFrame and creating output DataFrame as df2.
trv_refresh() : Creating Treeview by using DataFrame df2 and header by l1
data_collect() : Collect the Treeview selected row details.
my_save() : Save the resultant DataFrame df2 in different file formats.
my_copy() : Copy the resultant DataFrame df2 to Clipboard.
my_delete() : Delete the selected row of the Treeview trv

my_search()

This function already explained in Part I, this creates the resultant DataFrame df2 based on the user inputs. From Part I the Treeview creation part is taken into separate function trv_refresh() as Treeview is to be updated to reflect the changes done by the user while selecting and deleting row. The last line of the function below calls trv_refresh() to show the Treeview.

def my_search(*args): # Searching source dataframe and create filtered one
    global df2,trv,l1
    df2 = df.iloc[:0] # create dataframe of same structure or columns 
    l1=list(df) # List of column names as header 
    query=e1.get().strip() 
    if query.isdigit():
        df2=df[df['id']==int(query)]
    else:
        words=query.split(' ') # List of words by breaking a string. 
        words_stop=['to','and','or'] # List of words to be removed from searching
        words=list(set(words)-set(words_stop)) # final list of words to search
        for w in words:
            #df2=df2.append(df[df.name.str.contains(w,case=False)],ignore_index=True)
            df2=pd.concat([df2,df[df.name.str.contains(w,case=False)]])
    df2=df2.drop_duplicates()  # remove duplicate rows
    l2.config(text='No of records: '+str(len(df2)))
    trv_refresh() # Show the DataFrame in Treeview

trv_refresh()

Rows of data from DataFrame df2 is used to display the Treeview. Here the header list l1 is taken from the source DataFrame df. The last line of the code below binds the row selection to trigger the funtion data_collect().
Calling this function will refresh the Treeview trv to reflect the changes in DataFrame df2.

def trv_refresh(): # Refresh the Treeview to reflect changes
    global df2,trv,l1 
    r_set=df2.to_numpy().tolist() # create list of list using rows
    trv=ttk.Treeview(my_w,selectmode='browse',height=10,
        show='headings',columns=l1)
    trv.grid(row=3,column=1,columnspan=3,padx=10,pady=20)
    
    for i in l1:
        trv.column(i,width=90,anchor='c')
        trv.heading(i,text=str(i))
    for dt in r_set:
        v=[r for r in dt]
        trv.insert("",'end',iid=v[0],values=v)
    trv.bind("<<TreeviewSelect>>", data_collect)# Triggers on selection of row

data_collect()

Once any row of the Treeview is selected by user, the function data_collect() is triggered. Inside this function the value of the iid of the selected row is collected ( In this case this is same as id column value) . This is used to collect all values of row and same is displayed on the Label l3 by using config() method.

def data_collect(*args): # collect values of selected row
    global p_id
    p_id = trv.selection()[0] # collect selected row id
    l3.config(text=trv.item(p_id)['values']) # display values

my_save()

We can save the filtered output by using asksaveasfile dialog. User can browse the local directory system and enter the file name to save at desired location.

Here our DataFrame df2 is the filtered output so we will use to_excel() to save the DataFrame in Excel format as per the selected path.
Note that we have used df2 as global variable as it is used inside different functions.

def my_save(): # save DataFrame as file
    global df2
    file_path = filedialog.asksaveasfilename(
        filetypes=[("Excel file", ".xlsx"),("CSV file", ".csv")],
    defaultextension=".xlsx")
    if(file_path): # user has not Cancelled the browse window 
        if file_path.endswith('.csv'):
            df2.to_csv(file_path,index=False) # Save in csv format 
        else:    
            df2.to_excel(file_path,index=False) # Save in Excel file

my_copy()

On click of the Copy button, this function will use the pandas method to_clipboard() to copy the DataFrame df2 to system clipboard.
The message is displayed for 3 seconds to indicate the user that the DataFrame df2 is copied.

def my_copy(): # Copy the dataframe to clipboard
    df2.to_clipboard() # copy dataframe to clipboard 
    l3.config(text='copied') # Show message to user
    l3.after(3000,lambda:l3.config(text='')) # delete after 3 seconds

my_delete()

This function reads resultant DataFrame df2 and the p_id which is the id column value of the user selected row. It uses drop() to delete the row from the DataFrame df2.
After deletion of the row trv_refresh() is called to reflect the changes by creating the Treeview with updated DataFrame df2.

def my_delete(): # Delete the selected row 
    global df2,p_id
    if p_id: # check if not None 
        df2.drop(df2[df2['id'] == int(p_id)].index, inplace = True)
        #print(df2)
    trv_refresh() # refresh the Treeview to reflect changes

import pandas as pd
from tkinter import ttk
from tkinter import filedialog # to show file dialog box for saving
df=pd.read_excel("E:\\data\\student.xlsx") # create dataframe
import tkinter as tk

my_w = tk.Tk()
my_w.geometry("620x350")  # width x height
my_w.title("plus2net.com")  # Adding a title
font1=['Times',28,'normal']
l1=tk.Label(my_w,text="Search", width=5,font=18)
l1.grid(row=1,column=1,padx=3,pady=10)
e1=tk.Entry(my_w,width=15,bg='yellow',font=font1)
e1.grid(row=1,column=2,padx=1)
b1 = tk.Button(my_w, text="Save Result",bg='lightgreen',
     font=18, command=lambda: my_save())
b1.grid(row=1, column=3, padx=2)
l2=tk.Label(my_w,text='',bg='lightgreen',anchor='w')
l2.grid(row=2,column=1,sticky='W',padx=3,pady=3)
l3=tk.Label(my_w,text='',bg='lightyellow',anchor='w')
l3.grid(row=2,column=2,columnspan=2,sticky='W',padx=3,pady=3)
b2 = tk.Button(my_w, text="Copy",bg='lightblue',
     font=18, command=lambda: my_copy())
b2.grid(row=2, column=3, padx=2)
b3 = tk.Button(my_w, text="Delete",bg='lightyellow',
     font=18, command=lambda: my_delete())
b3.grid(row=2, column=4, padx=2)
df2 = df.iloc[:0] # create dataframe of same structure or columns 
l1=[]  # List to hold column headers 
p_id=None
def my_search(*args): # Searching source dataframe and create filtered one
    global df2,trv,l1
    df2 = df.iloc[:0] # create dataframe of same structure or columns 
    l1=list(df) # List of column names as header 
    query=e1.get().strip() 
    if query.isdigit():
        df2=df[df['id']==int(query)]
    else:
        words=query.split(' ') # List of words by breaking a string. 
        words_stop=['to','and','or'] # List of words to be removed from searching
        words=list(set(words)-set(words_stop)) # final list of words to search
        for w in words:
            #df2=df2.append(df[df.name.str.contains(w,case=False)],ignore_index=True)
            df2=pd.concat([df2,df[df.name.str.contains(w,case=False)]])
    df2=df2.drop_duplicates()  # remove duplicate rows
    l2.config(text='No of records: '+str(len(df2)))
    trv_refresh() # Show the DataFrame in Treeview 
def trv_refresh(): # Refresh the Treeview to reflect changes
    global df2,trv,l1 
    r_set=df2.to_numpy().tolist() # create list of list using rows
    trv=ttk.Treeview(my_w,selectmode='browse',height=10,
        show='headings',columns=l1)
    trv.grid(row=3,column=1,columnspan=3,padx=10,pady=20)
    
    for i in l1:
        trv.column(i,width=90,anchor='c')
        trv.heading(i,text=str(i))
    for dt in r_set:
        v=[r for r in dt]
        trv.insert("",'end',iid=v[0],values=v)
    trv.bind("<<TreeviewSelect>>", data_collect) # Triggers on selection of row
e1.bind("<KeyRelease>",my_search)  # Update search 
def data_collect(*args): # collect values of selected row
    global p_id
    p_id = trv.selection()[0] # collect selected row id
    l3.config(text=trv.item(p_id)['values']) # display values

def my_save(): # save DataFrame as file
    global df2
    file_path = filedialog.asksaveasfilename(
        filetypes=[("Excel file", ".xlsx"),("CSV file", ".csv")],
    defaultextension=".xlsx")
    if(file_path): # user has not Cancelled the browse window 
        if file_path.endswith('.csv'):
            df2.to_csv(file_path,index=False) # Save in csv format 
        else:    
            df2.to_excel(file_path,index=False) # Save in Excel file
def my_copy(): # Copy the dataframe to clipboard
    global df2,p_id
    df2.to_clipboard() # copied dataframe to clipboard
    l3.config(text='copied') # Show message to user
    l3.after(3000,lambda:l3.config(text='')) # delete after 3 seconds
def my_delete(): # Delete the selected row 
    global df2,p_id
    if p_id: # check if not None 
        df2.drop(df2[df2['id'] == int(p_id)].index, inplace = True)
        #print(df2)
        trv_refresh() # refresh the Treeview to reflect changes 
	l3.config(text='') # Update the label by removing the text
my_w.mainloop()

Integrating Scrollbar

In above code we have option height=10 is set for the Treeview. When we have more than 10 results to show, we have to use down arrow key or mouse to scroll down by selecting any row. Here we can integrate one vertical scroll bar to the Treeview so we can easily navigate through large number of records.
Note that our Treeview has used 3 columnspan, so our vertical scroll bar should be placed on column=4.
We are adding code to trv_refresh() function

def trv_refresh(): # Refresh the Treeview to reflect changes
    global df2,trv,l1 
    r_set=df2.to_numpy().tolist() # create list of list using rows
    trv=ttk.Treeview(my_w,selectmode='browse',height=10,
        show='headings',columns=l1)
    trv.grid(row=3,column=1,columnspan=3,padx=10,pady=20)
    
    for i in l1:
        trv.column(i,width=90,anchor='c')
        trv.heading(i,text=str(i))
    for dt in r_set:
        v=[r for r in dt]
        trv.insert("",'end',iid=v[0],values=v)
    trv.bind("<<TreeviewSelect>>", data_collect) # Triggers on selection of row	
    vs = ttk.Scrollbar(my_w,orient="vertical", command=trv.yview)#V Scrollbar
    trv.configure(yscrollcommand=vs.set)  # connect to Treeview
    vs.grid(row=3,column=4,sticky='ns') # Place on grid

Part I : Searching DataFrame and displaying result in Treeview Part III : Selection Columns of DataFrame using CheckButtons
Adding row to Treeview after inserting to MySQL table
Treeview Records of MySQL Pagination of Records of MySQL Delete Records
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Select row in Treeview to Delete from DataFrame