Tkinter CSV to JSON or XML Converter with Pandas

Q: How do I convert a CSV file to JSON with Pandas?

Use pd.read_csv() to create a DataFrame and then call DataFrame.to_json().

Q: How are missing values represented in JSON?

Pandas writes missing DataFrame values as JSON null where appropriate.

Tkinter CSV to JSON and XML converter using Pandas

This project uses Tkinter and Pandas to convert a CSV file into JSON or XML.

The user selects a CSV file through a Tkinter file browser. Pandas read_csv() creates a DataFrame. The same DataFrame can then be exported as JSON using to_json() or as XML using to_xml().

CSV file
    |
    v
pd.read_csv()
    |
    v
Pandas DataFrame
    |
    +--> to_json() --> JSON
    |
    +--> XML-safe columns
             |
             v
          to_xml()
             |
             v
            XML

Format difference: JSON object keys can contain spaces and punctuation. XML element names have stricter naming rules, so CSV headings may need to be normalized before XML export.

Show Table of Contents

Conversion Workflow
Load the CSV File
Convert DataFrame to JSON
JSON orient='records'
Keep Unicode Text Readable
Missing Values in JSON
Convert DataFrame to XML
Create Valid XML Column Names
Preview JSON or XML
Save the Converted File
Complete Converter
Video Tutorial
JSON vs XML Output
Continue the Conversion Projects
FAQs

CSV to JSON or XML Workflow Top ↑

Consider this CSV file:

id,name,mark
1,John Deo,75
2,Max Ruin,85
3,Arnold,55

Pandas creates a DataFrame:

   id      name  mark
0   1  John Deo    75
1   2  Max Ruin    85
2   3    Arnold    55

The DataFrame can then be converted without reading the CSV again.

Load the CSV File once Top ↑

Select the CSV file:

file_path=filedialog.askopenfilename(
    title='Select CSV file',
    filetypes=[
        ('CSV files','*.csv'),
        ('All files','*.*')
    ]
)

Create the DataFrame:

df=pd.read_csv(
    file_path
)

The application displays the first five records together with the number of rows and columns.

Convert Pandas DataFrame to JSON Top ↑

For this project, each DataFrame row becomes one JSON object.

df.to_json(
    save_path,
    orient='records',
    indent=2,
    force_ascii=False
)

For the sample data, the output is similar to:

[
  {
    "id":1,
    "name":"John Deo",
    "mark":75
  },
  {
    "id":2,
    "name":"Max Ruin",
    "mark":85
  }
]

Why Use orient='records'? Top ↑

Pandas supports several JSON orientations. For a CSV converter, records is easy to understand because every DataFrame row becomes one JSON object.

CSV row
   |
   v
JSON object

A DataFrame with three rows therefore becomes a JSON array containing three objects.

Keep Unicode Text Readable Top ↑

The option:

force_ascii=False

allows Unicode text to remain readable in the JSON file.

For example:

Delhi
Bhubaneswar
東京

can remain as readable text instead of converting every non-ASCII character into a Unicode escape sequence.

Missing Values in JSON Top ↑

Consider:

id,name,mark
1,John,75
2,Max,
3,Arnold,55

Pandas represents the missing value appropriately in JSON:

{
  "id":2,
  "name":"Max",
  "mark":null
}

If missing values need to be replaced before conversion, clean the DataFrame first. See Tkinter Pandas data cleaning.

Convert the Same DataFrame to XML Top ↑

Pandas to_xml() can create XML directly:

xml_df.to_xml(
    save_path,
    index=False,
    root_name='data',
    row_name='record',
    encoding='utf-8',
    xml_declaration=True,
    pretty_print=True,
    parser='etree'
)

The structure becomes:

<data>
  <record>
    <id>1</id>
    <name>John Deo</name>
    <mark>75</mark>
  </record>
</data>

Create Valid XML Column Names Top ↑

JSON can safely use a key such as:

"First Name"

but XML should not directly use arbitrary CSV headings as element names.

For example:

First Name
2nd Mark
Amount ($)

are normalized before XML conversion:

First_Name
field_2nd_Mark
Amount_

The normalization function is:

def xml_name(name,prefix='field'):
    name=re.sub(
        r'[^A-Za-z0-9_.-]+',
        '_',
        str(name).strip()
    )

    if not name:
        name=prefix

    if not re.match(
        r'[A-Za-z_]',
        name
    ):
        name=prefix+'_'+name

    return name

The original DataFrame column names remain unchanged for JSON output.

Preview the Selected Output Format Top ↑

The application allows the user to select:

JSON
XML

and preview the first five records before saving.

For JSON:

preview=df.head(
    5
).to_json(
    orient='records',
    indent=2,
    force_ascii=False
)

For XML:

preview=xml_df.head(
    5
).to_xml(
    index=False,
    root_name='data',
    row_name='record',
    parser='etree'
)

Save JSON or XML with Tkinter Top ↑

The chosen format determines the file extension:

extension='.json'
if format_name=='JSON'
else '.xml'

The user selects the output location with a Tkinter Save As dialog.

Complete Tkinter CSV to JSON/XML Converter Top ↑

import os
import re
import tkinter as tk
from tkinter import filedialog
from tkinter import ttk

import pandas as pd

df=None
xml_df=None

root=tk.Tk()
root.title('CSV to JSON / XML Converter - plus2net')
root.geometry('800x620')
root.minsize(640,500)

root.columnconfigure(0,weight=1)
root.rowconfigure(4,weight=1)

file_var=tk.StringVar(value='No CSV file selected')
format_var=tk.StringVar(value='JSON')
summary_var=tk.StringVar(value='Rows: 0 | Columns: 0')
status_var=tk.StringVar(value='Select a CSV file.')

def xml_name(name,prefix='field'):
    name=re.sub(
        r'[^A-Za-z0-9_.-]+',
        '_',
        str(name).strip()
    )

if not name:
        name=prefix

if not re.match(r'[A-Za-z_]',name):
        name=prefix+'_'+name

return name

def create_xml_dataframe(data):
    used=set()
    new_columns=[]

for index,column in enumerate(data.columns,1):
        base=xml_name(column,f'field_{index}')
        name=base
        counter=2

while name in used:
            name=f'{base}_{counter}'
            counter+=1

used.add(name)
        new_columns.append(name)

output=data.copy()
    output.columns=new_columns

return output

def show_text(text):
    output.delete('1.0',tk.END)
    output.insert(tk.END,text)

def load_csv():
    global df,xml_df

file_path=filedialog.askopenfilename(
        title='Select CSV file',
        filetypes=[
            ('CSV files','*.csv'),
            ('All files','*.*')
        ]
    )

if not file_path:
        status_var.set('No CSV file selected.')
        return

try:
        new_df=pd.read_csv(file_path)
    except (
        OSError,
        UnicodeDecodeError,
        pd.errors.ParserError,
        pd.errors.EmptyDataError
    ) as e:
        status_var.set(f'Unable to read CSV: {e}')
        return

df=new_df
    xml_df=create_xml_dataframe(df)

file_var.set(file_path)
    summary_var.set(
        f'Rows: {df.shape[0]} | Columns: {df.shape[1]}'
    )

preview=df.head(5).to_string(index=False)

show_text(
        'CSV PREVIEW\n\n'
        f'{preview}\n\n'
        'COLUMNS\n\n'
        +'\n'.join(map(str,df.columns))
    )

preview_button.config(state=tk.NORMAL)
    save_button.config(state=tk.NORMAL)

status_var.set(
        f'Loaded {os.path.basename(file_path)}.'
    )

def create_preview():
    if df is None:
        status_var.set('Load a CSV file first.')
        return

format_name=format_var.get()

try:
        if format_name=='JSON':
            preview=df.head(5).to_json(
                orient='records',
                indent=2,
                force_ascii=False
            )

else:
            preview=xml_df.head(5).to_xml(
                index=False,
                root_name='data',
                row_name='record',
                encoding='utf-8',
                xml_declaration=True,
                pretty_print=True,
                parser='etree'
            )

except (ValueError,TypeError) as e:
        status_var.set(f'Unable to create preview: {e}')
        return

show_text(
        f'{format_name} PREVIEW - FIRST 5 ROWS\n\n'
        +preview
    )

status_var.set(
        f'Showing {format_name} preview.'
    )

def save_file():
    if df is None:
        status_var.set('Load a CSV file first.')
        return

format_name=format_var.get()

if format_name=='JSON':
        extension='.json'
        filetypes=[('JSON files','*.json'),('All files','*.*')]
    else:
        extension='.xml'
        filetypes=[('XML files','*.xml'),('All files','*.*')]

save_path=filedialog.asksaveasfilename(
        title=f'Save {format_name} file',
        defaultextension=extension,
        filetypes=filetypes
    )

if not save_path:
        status_var.set('Save cancelled.')
        return

try:
        if format_name=='JSON':
            df.to_json(
                save_path,
                orient='records',
                indent=2,
                force_ascii=False
            )

else:
            xml_df.to_xml(
                save_path,
                index=False,
                root_name='data',
                row_name='record',
                encoding='utf-8',
                xml_declaration=True,
                pretty_print=True,
                parser='etree'
            )

except (OSError,ValueError,TypeError) as e:
        status_var.set(f'Unable to save {format_name}: {e}')
        return

status_var.set(
        f'Saved {len(df)} row(s) as {format_name} to '
        f'{os.path.basename(save_path)}.'
    )

top_frame=ttk.Frame(root,padding=10)
top_frame.grid(row=0,column=0,sticky='ew')
top_frame.columnconfigure(1,weight=1)

ttk.Button(
    top_frame,
    text='Load CSV',
    command=load_csv
).grid(row=0,column=0,padx=(0,10))

ttk.Label(
    top_frame,
    textvariable=file_var,
    wraplength=630
).grid(row=0,column=1,sticky='w')

options_frame=ttk.Frame(root,padding=(10,5))
options_frame.grid(row=1,column=0,sticky='w')

ttk.Label(
    options_frame,
    text='Output format:'
).grid(row=0,column=0,padx=(0,5))

format_combo=ttk.Combobox(
    options_frame,
    textvariable=format_var,
    values=['JSON','XML'],
    state='readonly',
    width=10
)
format_combo.grid(row=0,column=1,padx=5)

preview_button=ttk.Button(
    options_frame,
    text='Preview',
    command=create_preview,
    state=tk.DISABLED
)
preview_button.grid(row=0,column=2,padx=5)

save_button=ttk.Button(
    options_frame,
    text='Convert and Save',
    command=save_file,
    state=tk.DISABLED
)
save_button.grid(row=0,column=3,padx=5)

ttk.Label(
    root,
    textvariable=summary_var,
    font=('Arial',10,'bold')
).grid(row=2,column=0,sticky='w',padx=10,pady=(5,0))

ttk.Separator(
    root,
    orient='horizontal'
).grid(row=3,column=0,sticky='ew',padx=10,pady=5)

output_frame=ttk.Frame(root,padding=(10,5))
output_frame.grid(row=4,column=0,sticky='nsew')
output_frame.columnconfigure(0,weight=1)
output_frame.rowconfigure(0,weight=1)

output=tk.Text(output_frame,wrap='none')
output.grid(row=0,column=0,sticky='nsew')

y_scroll=ttk.Scrollbar(
    output_frame,
    orient='vertical',
    command=output.yview
)
y_scroll.grid(row=0,column=1,sticky='ns')

x_scroll=ttk.Scrollbar(
    output_frame,
    orient='horizontal',
    command=output.xview
)
x_scroll.grid(row=1,column=0,sticky='ew')

output.configure(
    yscrollcommand=y_scroll.set,
    xscrollcommand=x_scroll.set
)

ttk.Label(
    root,
    textvariable=status_var,
    wraplength=750
).grid(row=5,column=0,sticky='w',padx=10,pady=(5,10))

root.mainloop()

Video: Convert CSV to JSON or XML Top ↑

Convert CSV to JSON or XML with Tkinter and Pandas

JSON and XML Output from the Same DataFrame Top ↑

For this DataFrame:

id | name | mark
1  | John | 75
2  | Max  | 85

JSON Output Top ↑

[
  {
    "id":1,
    "name":"John",
    "mark":75
  },
  {
    "id":2,
    "name":"Max",
    "mark":85
  }
]

XML Output Top ↑

<data>
  <record>
    <id>1</id>
    <name>John</name>
    <mark>75</mark>
  </record>
  <record>
    <id>2</id>
    <name>Max</name>
    <mark>85</mark>
  </record>
</data>

JSON represents each row as an object. XML represents each row as an element containing child elements.

Why XML Gets a Separate DataFrame Top ↑

The application keeps:

df
xml_df

df keeps the original CSV headings for JSON:

"First Name"

xml_df uses XML-safe column names:

<First_Name>

This prevents XML requirements from unnecessarily changing the JSON output.

JSON Conversion without Tkinter Top ↑

If a GUI is not required:

import pandas as pd

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

df.to_json(
    'data.json',
    orient='records',
    indent=2,
    force_ascii=False
)

Continue the Tkinter Pandas Conversion Projects Top ↑

For a dedicated CSV to XML converter:

CSV to XML

Convert Excel data to XML:

Excel to XML

Export SQLite table data to CSV:

SQLite to CSV

Frequently Asked Questions Top ↑

Q1: How do I convert a CSV file to JSON with Pandas?

Use pd.read_csv() to create a DataFrame and then call DataFrame.to_json().

Q2: What does orient='records' do?

It converts every DataFrame row into one JSON object and returns the records as a JSON array.

Q3: Why use force_ascii=False?

It keeps Unicode characters readable in the JSON output instead of forcing them into escaped ASCII representations.

Q4: How are missing values represented in JSON?

Pandas writes missing DataFrame values as JSON null where appropriate.

Q5: Why are CSV headings normalized only for XML?

JSON keys can contain spaces and punctuation, but XML element names follow stricter naming rules.

Q6: Can the same DataFrame be exported as both JSON and XML?

Yes. The loaded DataFrame is reused. A separate copy with XML-safe column names is created only for XML output.

Q7: Does the converter modify the original CSV file?

No. The source CSV remains unchanged. The application creates a new JSON or XML output file.

CSV to XML Excel to XML

Tkinter Projects Tkinter Pandas Projects

Python Tkinter Pandas JSON XML to_xml() Python Tutorials

Subhendu Mohapatra

Author & Instructor at plus2net

I write and maintain practical tutorials on Python, PHP, SQL, JavaScript, HTML, jQuery, and web development at plus2net. The tutorials focus on clear explanations, working examples, and code that readers can test and adapt while learning.

About the author Watch on YouTube

Connect: