Long and Wide Data Format Converter

Reshape, pivot, melt, and unpivot your data between long (tidy) and wide formats. Upload a CSV or Excel file, or paste your data directly. Equivalent to pandas melt()/pivot()/pivot_table(), tidyr pivot_longer()/pivot_wider(), and SQL PIVOT/UNPIVOT.

Created:December 17, 2024

Last Updated:September 4, 2025

Data Reshape Tool

1. Upload Your Data

Not sure about data formats? Check out the examples in the Learn More section below. Save your data when you are done because this tool does not save your data anywhere.

Try it out with sample data:

When to Use Long vs Wide Format

Use Long (Tidy) Format When

Plottingwith ggplot2, seaborn, Plotly, or Altair — these libraries expect tidy data
Statistical modeling— repeated measures ANOVA, mixed-effects models, and regression all require long format
Faceted visualizations— splitting plots by variable (e.g., one panel per measurement type)
Database storage— normalized relational schemas use long format for flexibility
Merging datasets— easier to join and append when data is tidy

Use Wide Format When

Summary tables— presenting results in reports, papers, or dashboards
Spreadsheet work— Excel and Google Sheets are naturally wide-format tools
Correlation matrices— computing pairwise correlations between variables
Machine learning— feature matrices for scikit-learn, XGBoost, etc. need one column per feature
Human readability— wide format is more compact and easier to scan

Learn More About Data Reshaping

Understanding Long and Wide Data Formats

Long Format (Tidy)

Each row is a single observation. Variables are stacked into key-value pairs. Also known as "melted" or "unpivoted" data.

ID,Year,Measurement,Value
1,2020,Temperature,18.5
1,2020,Humidity,30
1,2021,Temperature,19.0
1,2021,Humidity,32
2,2020,Temperature,17.0
2,2020,Humidity,40
2,2021,Temperature,16.5
2,2021,Humidity,42

Wide Format (Pivoted)

Each variable gets its own column. More compact and easier to read. Also known as "pivoted" or "spread" data.

ID,Year,Temperature,Humidity
1,2020,18.5,30
1,2021,19.0,32
2,2020,17.0,40
2,2021,16.5,42

Long to Wide Transformation (Pivot / Spread)

Original Data (Long Format)

ID	Year	Measurement	Value
1	2020	Temperature	18.5
1	2020	Humidity	30
1	2021	Temperature	19
1	2021	Humidity	32
2	2020	Temperature	17
2	2020	Humidity	40
2	2021	Temperature	16.5
2	2021	Humidity	42

Column Selection for Long to Wide

Select these columns:

ID Columns:

ID (identifies unique entries)
Year (identifies unique entries)

Value Columns:

Measurement (will become new column names)
Value (will fill the new columns)

Result (Wide Format)

ID	Year	Temperature	Humidity
1	2020	18.5	30
1	2021	19	32
2	2020	17	40
2	2021	16.5	42

Wide to Long Transformation (Melt / Unpivot)

Original Data (Wide Format)

ID	Year	Temperature	Humidity
1	2020	18.5	30
1	2021	19	32
2	2020	17	40
2	2021	16.5	42

Column Selection for Wide to Long

Select these columns:

ID Columns:

ID (stays as is)
Year (stays as is)

Value Columns:

Temperature (will be stacked)
Humidity (will be stacked)

This will create two new columns: "Measurement" (containing "Temperature" or "Humidity") and "Value" (containing the corresponding numbers)

Result (Long Format)

ID	Year	Measurement	Value
1	2020	Temperature	18.5
1	2020	Humidity	30
1	2021	Temperature	19
1	2021	Humidity	32
2	2020	Temperature	17
2	2020	Humidity	40
2	2021	Temperature	16.5
2	2021	Humidity	42

How to Reshape Data in Python, R, SQL, and Excel

Python: pandas melt() and pivot_table()

Reshape data using pandas — the most popular Python library for data manipulation

Wide to Long (melt) and Long to Wide (pivot)

Python

import pandas as pd

# create wide format data
wide_df = pd.DataFrame({
    'ID': [1, 1, 2, 2],
    'Year': [2020, 2021, 2020, 2021],
    'Temperature': [18.5, 19.0, 17.0, 16.5],
    'Humidity': [30, 32, 40, 42]
})

print(wide_df)

# Convert wide to long format (melt / unpivot)
long_df = pd.melt(
    wide_df,
    id_vars=['ID', 'Year'],
    value_vars=['Temperature', 'Humidity'],
    var_name='Measurement',
    value_name='Value'
)

print(long_df)

# Convert back to wide format (pivot)
wide_df = long_df.pivot_table(
    index=['ID', 'Year'],
    columns='Measurement',
    values='Value'
).reset_index()

print(wide_df)

R: pivot_longer() and pivot_wider()

Reshape data using tidyr from the tidyverse — the modern replacement for reshape2

Wide to Long (pivot_longer) and Long to Wide (pivot_wider)

library(tidyverse)

# wide format data
wide_data <- data.frame(
  ID = c(1, 1, 2, 2),
  Year = c(2020, 2021, 2020, 2021),
  Temperature = c(18.5, 19.0, 17.0, 16.5),
  Humidity = c(30, 32, 40, 42)
)
print(wide_data)

# Convert wide to long format (pivot_longer / melt)
long_data <- wide_data |>
  pivot_longer(
    cols = c(Temperature, Humidity),
    names_to = "Measurement",
    values_to = "Value"
  )

print(long_data)

# Convert long back to wide format (pivot_wider / spread)
wide_data <- long_data |>
  pivot_wider(
    id_cols = c(ID, Year),
    names_from = Measurement,
    values_from = Value
  )

print(wide_data)

SQL: PIVOT and UNPIVOT

Reshape data directly in your database using SQL PIVOT/UNPIVOT or conditional aggregation

Long to Wide using PIVOT (SQL Server)

SQL

-- Long to Wide: PIVOT (SQL Server / Oracle)
SELECT ID, Year, Temperature, Humidity
FROM measurements
PIVOT (
    MAX(Value)
    FOR Measurement IN (Temperature, Humidity)
) AS pivoted;

Long to Wide using conditional aggregation (PostgreSQL / MySQL)

SQL

-- Long to Wide: conditional aggregation (works in all databases)
SELECT
    ID,
    Year,
    MAX(CASE WHEN Measurement = 'Temperature' THEN Value END) AS Temperature,
    MAX(CASE WHEN Measurement = 'Humidity' THEN Value END) AS Humidity
FROM measurements
GROUP BY ID, Year
ORDER BY ID, Year;

Wide to Long using UNPIVOT (SQL Server)

SQL

-- Wide to Long: UNPIVOT (SQL Server / Oracle)
SELECT ID, Year, Measurement, Value
FROM weather_data
UNPIVOT (
    Value FOR Measurement IN (Temperature, Humidity)
) AS unpivoted;

Wide to Long using UNION ALL (PostgreSQL / MySQL)

SQL

-- Wide to Long: UNION ALL (works in all databases)
SELECT ID, Year, 'Temperature' AS Measurement, Temperature AS Value
FROM weather_data
UNION ALL
SELECT ID, Year, 'Humidity' AS Measurement, Humidity AS Value
FROM weather_data
ORDER BY ID, Year, Measurement;

Excel and Google Sheets

Reshape data using built-in spreadsheet features without any code

Long to Wide: PivotTable

Select your long-format data range (including headers)
Go to Insert → PivotTable
Drag ID and Year to Rows
Drag Measurement to Columns
Drag Value to Values (summarize by Max or Average)

Wide to Long: Power Query (Excel) or Apps Script (Google Sheets)

Excel Power Query:

Select your data and go to Data → From Table/Range
In Power Query Editor, select the columns to unpivot (Temperature, Humidity)
Right-click and choose Unpivot Columns
Rename the "Attribute" and "Value" columns as needed
Click Close & Load

Google Sheets FLATTEN formula:

Plaintext

=ARRAYFORMULA({
  {"ID","Year","Measurement","Value";
   FLATTEN(IF({1,1},A2:A5)),
   FLATTEN(IF({1,1},B2:B5)),
   FLATTEN(IF(ROW(C1:D1),C1:D1)),
   FLATTEN(C2:D5)}
})

Frequently Asked Questions

Long and Wide Data Format Converter

Created:December 17, 2024

Last Updated:September 4, 2025

Data Reshape Tool

1. Upload Your Data

Not sure about data formats? Check out the examples in the Learn More section below. Save your data when you are done because this tool does not save your data anywhere.

Try it out with sample data:

When to Use Long vs Wide Format

Use Long (Tidy) Format When

Plottingwith ggplot2, seaborn, Plotly, or Altair — these libraries expect tidy data
Statistical modeling— repeated measures ANOVA, mixed-effects models, and regression all require long format
Faceted visualizations— splitting plots by variable (e.g., one panel per measurement type)
Database storage— normalized relational schemas use long format for flexibility
Merging datasets— easier to join and append when data is tidy

Use Wide Format When

Summary tables— presenting results in reports, papers, or dashboards
Spreadsheet work— Excel and Google Sheets are naturally wide-format tools
Correlation matrices— computing pairwise correlations between variables
Machine learning— feature matrices for scikit-learn, XGBoost, etc. need one column per feature
Human readability— wide format is more compact and easier to scan

Learn More About Data Reshaping

Understanding Long and Wide Data Formats

Long Format (Tidy)

Each row is a single observation. Variables are stacked into key-value pairs. Also known as "melted" or "unpivoted" data.

ID,Year,Measurement,Value
1,2020,Temperature,18.5
1,2020,Humidity,30
1,2021,Temperature,19.0
1,2021,Humidity,32
2,2020,Temperature,17.0
2,2020,Humidity,40
2,2021,Temperature,16.5
2,2021,Humidity,42

Wide Format (Pivoted)

Each variable gets its own column. More compact and easier to read. Also known as "pivoted" or "spread" data.

ID,Year,Temperature,Humidity
1,2020,18.5,30
1,2021,19.0,32
2,2020,17.0,40
2,2021,16.5,42

Long to Wide Transformation (Pivot / Spread)

Original Data (Long Format)

ID	Year	Measurement	Value
1	2020	Temperature	18.5
1	2020	Humidity	30
1	2021	Temperature	19
1	2021	Humidity	32
2	2020	Temperature	17
2	2020	Humidity	40
2	2021	Temperature	16.5
2	2021	Humidity	42

Column Selection for Long to Wide

Select these columns:

ID Columns:

ID (identifies unique entries)
Year (identifies unique entries)

Value Columns:

Measurement (will become new column names)
Value (will fill the new columns)

Result (Wide Format)

ID	Year	Temperature	Humidity
1	2020	18.5	30
1	2021	19	32
2	2020	17	40
2	2021	16.5	42

Wide to Long Transformation (Melt / Unpivot)

Original Data (Wide Format)

ID	Year	Temperature	Humidity
1	2020	18.5	30
1	2021	19	32
2	2020	17	40
2	2021	16.5	42

Column Selection for Wide to Long

Select these columns:

ID Columns:

ID (stays as is)
Year (stays as is)

Value Columns:

Temperature (will be stacked)
Humidity (will be stacked)

This will create two new columns: "Measurement" (containing "Temperature" or "Humidity") and "Value" (containing the corresponding numbers)

Result (Long Format)

ID	Year	Measurement	Value
1	2020	Temperature	18.5
1	2020	Humidity	30
1	2021	Temperature	19
1	2021	Humidity	32
2	2020	Temperature	17
2	2020	Humidity	40
2	2021	Temperature	16.5
2	2021	Humidity	42

How to Reshape Data in Python, R, SQL, and Excel

Python: pandas melt() and pivot_table()

Reshape data using pandas — the most popular Python library for data manipulation

Wide to Long (melt) and Long to Wide (pivot)

Python

import pandas as pd

# create wide format data
wide_df = pd.DataFrame({
    'ID': [1, 1, 2, 2],
    'Year': [2020, 2021, 2020, 2021],
    'Temperature': [18.5, 19.0, 17.0, 16.5],
    'Humidity': [30, 32, 40, 42]
})

print(wide_df)

# Convert wide to long format (melt / unpivot)
long_df = pd.melt(
    wide_df,
    id_vars=['ID', 'Year'],
    value_vars=['Temperature', 'Humidity'],
    var_name='Measurement',
    value_name='Value'
)

print(long_df)

# Convert back to wide format (pivot)
wide_df = long_df.pivot_table(
    index=['ID', 'Year'],
    columns='Measurement',
    values='Value'
).reset_index()

print(wide_df)

R: pivot_longer() and pivot_wider()

Reshape data using tidyr from the tidyverse — the modern replacement for reshape2

Wide to Long (pivot_longer) and Long to Wide (pivot_wider)

library(tidyverse)

# wide format data
wide_data <- data.frame(
  ID = c(1, 1, 2, 2),
  Year = c(2020, 2021, 2020, 2021),
  Temperature = c(18.5, 19.0, 17.0, 16.5),
  Humidity = c(30, 32, 40, 42)
)
print(wide_data)

# Convert wide to long format (pivot_longer / melt)
long_data <- wide_data |>
  pivot_longer(
    cols = c(Temperature, Humidity),
    names_to = "Measurement",
    values_to = "Value"
  )

print(long_data)

# Convert long back to wide format (pivot_wider / spread)
wide_data <- long_data |>
  pivot_wider(
    id_cols = c(ID, Year),
    names_from = Measurement,
    values_from = Value
  )

print(wide_data)

SQL: PIVOT and UNPIVOT

Reshape data directly in your database using SQL PIVOT/UNPIVOT or conditional aggregation

Long to Wide using PIVOT (SQL Server)

SQL

-- Long to Wide: PIVOT (SQL Server / Oracle)
SELECT ID, Year, Temperature, Humidity
FROM measurements
PIVOT (
    MAX(Value)
    FOR Measurement IN (Temperature, Humidity)
) AS pivoted;

Long to Wide using conditional aggregation (PostgreSQL / MySQL)

SQL

-- Long to Wide: conditional aggregation (works in all databases)
SELECT
    ID,
    Year,
    MAX(CASE WHEN Measurement = 'Temperature' THEN Value END) AS Temperature,
    MAX(CASE WHEN Measurement = 'Humidity' THEN Value END) AS Humidity
FROM measurements
GROUP BY ID, Year
ORDER BY ID, Year;

Wide to Long using UNPIVOT (SQL Server)

SQL

-- Wide to Long: UNPIVOT (SQL Server / Oracle)
SELECT ID, Year, Measurement, Value
FROM weather_data
UNPIVOT (
    Value FOR Measurement IN (Temperature, Humidity)
) AS unpivoted;

Wide to Long using UNION ALL (PostgreSQL / MySQL)

SQL

-- Wide to Long: UNION ALL (works in all databases)
SELECT ID, Year, 'Temperature' AS Measurement, Temperature AS Value
FROM weather_data
UNION ALL
SELECT ID, Year, 'Humidity' AS Measurement, Humidity AS Value
FROM weather_data
ORDER BY ID, Year, Measurement;

Excel and Google Sheets

Reshape data using built-in spreadsheet features without any code

Long to Wide: PivotTable

Select your long-format data range (including headers)
Go to Insert → PivotTable
Drag ID and Year to Rows
Drag Measurement to Columns
Drag Value to Values (summarize by Max or Average)

Wide to Long: Power Query (Excel) or Apps Script (Google Sheets)

Excel Power Query:

Select your data and go to Data → From Table/Range
In Power Query Editor, select the columns to unpivot (Temperature, Humidity)
Right-click and choose Unpivot Columns
Rename the "Attribute" and "Value" columns as needed
Click Close & Load

Google Sheets FLATTEN formula:

Plaintext

=ARRAYFORMULA({
  {"ID","Year","Measurement","Value";
   FLATTEN(IF({1,1},A2:A5)),
   FLATTEN(IF({1,1},B2:B5)),
   FLATTEN(IF(ROW(C1:D1),C1:D1)),
   FLATTEN(C2:D5)}
})

Frequently Asked Questions

Long and Wide Data Format Converter

Created:December 17, 2024

Last Updated:September 4, 2025

Data Reshape Tool

1. Upload Your Data

Not sure about data formats? Check out the examples in the Learn More section below. Save your data when you are done because this tool does not save your data anywhere.

Try it out with sample data:

When to Use Long vs Wide Format

Use Long (Tidy) Format When

Plottingwith ggplot2, seaborn, Plotly, or Altair — these libraries expect tidy data
Statistical modeling— repeated measures ANOVA, mixed-effects models, and regression all require long format
Faceted visualizations— splitting plots by variable (e.g., one panel per measurement type)
Database storage— normalized relational schemas use long format for flexibility
Merging datasets— easier to join and append when data is tidy

Use Wide Format When

Summary tables— presenting results in reports, papers, or dashboards
Spreadsheet work— Excel and Google Sheets are naturally wide-format tools
Correlation matrices— computing pairwise correlations between variables
Machine learning— feature matrices for scikit-learn, XGBoost, etc. need one column per feature
Human readability— wide format is more compact and easier to scan

Learn More About Data Reshaping

Understanding Long and Wide Data Formats

Long Format (Tidy)

Each row is a single observation. Variables are stacked into key-value pairs. Also known as "melted" or "unpivoted" data.

ID,Year,Measurement,Value
1,2020,Temperature,18.5
1,2020,Humidity,30
1,2021,Temperature,19.0
1,2021,Humidity,32
2,2020,Temperature,17.0
2,2020,Humidity,40
2,2021,Temperature,16.5
2,2021,Humidity,42

Wide Format (Pivoted)

Each variable gets its own column. More compact and easier to read. Also known as "pivoted" or "spread" data.

ID,Year,Temperature,Humidity
1,2020,18.5,30
1,2021,19.0,32
2,2020,17.0,40
2,2021,16.5,42

Long to Wide Transformation (Pivot / Spread)

Original Data (Long Format)

ID	Year	Measurement	Value
1	2020	Temperature	18.5
1	2020	Humidity	30
1	2021	Temperature	19
1	2021	Humidity	32
2	2020	Temperature	17
2	2020	Humidity	40
2	2021	Temperature	16.5
2	2021	Humidity	42

Column Selection for Long to Wide

Select these columns:

ID Columns:

ID (identifies unique entries)
Year (identifies unique entries)

Value Columns:

Measurement (will become new column names)
Value (will fill the new columns)

Result (Wide Format)

ID	Year	Temperature	Humidity
1	2020	18.5	30
1	2021	19	32
2	2020	17	40
2	2021	16.5	42

Wide to Long Transformation (Melt / Unpivot)

Original Data (Wide Format)

ID	Year	Temperature	Humidity
1	2020	18.5	30
1	2021	19	32
2	2020	17	40
2	2021	16.5	42

Column Selection for Wide to Long

Select these columns:

ID Columns:

ID (stays as is)
Year (stays as is)

Value Columns:

Temperature (will be stacked)
Humidity (will be stacked)

This will create two new columns: "Measurement" (containing "Temperature" or "Humidity") and "Value" (containing the corresponding numbers)

Result (Long Format)

ID	Year	Measurement	Value
1	2020	Temperature	18.5
1	2020	Humidity	30
1	2021	Temperature	19
1	2021	Humidity	32
2	2020	Temperature	17
2	2020	Humidity	40
2	2021	Temperature	16.5
2	2021	Humidity	42

How to Reshape Data in Python, R, SQL, and Excel

Python: pandas melt() and pivot_table()

Reshape data using pandas — the most popular Python library for data manipulation

Wide to Long (melt) and Long to Wide (pivot)

Python

import pandas as pd

# create wide format data
wide_df = pd.DataFrame({
    'ID': [1, 1, 2, 2],
    'Year': [2020, 2021, 2020, 2021],
    'Temperature': [18.5, 19.0, 17.0, 16.5],
    'Humidity': [30, 32, 40, 42]
})

print(wide_df)

# Convert wide to long format (melt / unpivot)
long_df = pd.melt(
    wide_df,
    id_vars=['ID', 'Year'],
    value_vars=['Temperature', 'Humidity'],
    var_name='Measurement',
    value_name='Value'
)

print(long_df)

# Convert back to wide format (pivot)
wide_df = long_df.pivot_table(
    index=['ID', 'Year'],
    columns='Measurement',
    values='Value'
).reset_index()

print(wide_df)

R: pivot_longer() and pivot_wider()

Reshape data using tidyr from the tidyverse — the modern replacement for reshape2

Wide to Long (pivot_longer) and Long to Wide (pivot_wider)

library(tidyverse)

# wide format data
wide_data <- data.frame(
  ID = c(1, 1, 2, 2),
  Year = c(2020, 2021, 2020, 2021),
  Temperature = c(18.5, 19.0, 17.0, 16.5),
  Humidity = c(30, 32, 40, 42)
)
print(wide_data)

# Convert wide to long format (pivot_longer / melt)
long_data <- wide_data |>
  pivot_longer(
    cols = c(Temperature, Humidity),
    names_to = "Measurement",
    values_to = "Value"
  )

print(long_data)

# Convert long back to wide format (pivot_wider / spread)
wide_data <- long_data |>
  pivot_wider(
    id_cols = c(ID, Year),
    names_from = Measurement,
    values_from = Value
  )

print(wide_data)

SQL: PIVOT and UNPIVOT

Reshape data directly in your database using SQL PIVOT/UNPIVOT or conditional aggregation

Long to Wide using PIVOT (SQL Server)

SQL

-- Long to Wide: PIVOT (SQL Server / Oracle)
SELECT ID, Year, Temperature, Humidity
FROM measurements
PIVOT (
    MAX(Value)
    FOR Measurement IN (Temperature, Humidity)
) AS pivoted;

Long to Wide using conditional aggregation (PostgreSQL / MySQL)

SQL

-- Long to Wide: conditional aggregation (works in all databases)
SELECT
    ID,
    Year,
    MAX(CASE WHEN Measurement = 'Temperature' THEN Value END) AS Temperature,
    MAX(CASE WHEN Measurement = 'Humidity' THEN Value END) AS Humidity
FROM measurements
GROUP BY ID, Year
ORDER BY ID, Year;

Wide to Long using UNPIVOT (SQL Server)

SQL

-- Wide to Long: UNPIVOT (SQL Server / Oracle)
SELECT ID, Year, Measurement, Value
FROM weather_data
UNPIVOT (
    Value FOR Measurement IN (Temperature, Humidity)
) AS unpivoted;

Wide to Long using UNION ALL (PostgreSQL / MySQL)

SQL

-- Wide to Long: UNION ALL (works in all databases)
SELECT ID, Year, 'Temperature' AS Measurement, Temperature AS Value
FROM weather_data
UNION ALL
SELECT ID, Year, 'Humidity' AS Measurement, Humidity AS Value
FROM weather_data
ORDER BY ID, Year, Measurement;

Excel and Google Sheets

Reshape data using built-in spreadsheet features without any code

Long to Wide: PivotTable

Select your long-format data range (including headers)
Go to Insert → PivotTable
Drag ID and Year to Rows
Drag Measurement to Columns
Drag Value to Values (summarize by Max or Average)

Wide to Long: Power Query (Excel) or Apps Script (Google Sheets)

Excel Power Query:

Select your data and go to Data → From Table/Range
In Power Query Editor, select the columns to unpivot (Temperature, Humidity)
Right-click and choose Unpivot Columns
Rename the "Attribute" and "Value" columns as needed
Click Close & Load

Google Sheets FLATTEN formula:

Plaintext

=ARRAYFORMULA({
  {"ID","Year","Measurement","Value";
   FLATTEN(IF({1,1},A2:A5)),
   FLATTEN(IF({1,1},B2:B5)),
   FLATTEN(IF(ROW(C1:D1),C1:D1)),
   FLATTEN(C2:D5)}
})

Long and Wide Data Format Converter

Data Reshape Tool

1. Upload Your Data

When to Use Long vs Wide Format

Use Long (Tidy) Format When

Use Wide Format When

Learn More About Data Reshaping

Understanding Long and Wide Data Formats

Long Format (Tidy)

Wide Format (Pivoted)

Long to Wide Transformation (Pivot / Spread)

Original Data (Long Format)

Column Selection for Long to Wide

Result (Wide Format)

Wide to Long Transformation (Melt / Unpivot)

Original Data (Wide Format)

Column Selection for Wide to Long

Result (Long Format)

How to Reshape Data in Python, R, SQL, and Excel

Python: pandas melt() and pivot_table()

Wide to Long (melt) and Long to Wide (pivot)

R: pivot_longer() and pivot_wider()

Wide to Long (pivot_longer) and Long to Wide (pivot_wider)

SQL: PIVOT and UNPIVOT

Long to Wide using PIVOT (SQL Server)

Long to Wide using conditional aggregation (PostgreSQL / MySQL)

Wide to Long using UNPIVOT (SQL Server)

Wide to Long using UNION ALL (PostgreSQL / MySQL)

Excel and Google Sheets

Long to Wide: PivotTable

Wide to Long: Power Query (Excel) or Apps Script (Google Sheets)

Excel Power Query:

Google Sheets FLATTEN formula:

Frequently Asked Questions

What is the difference between long and wide data formats?

How do I reshape data from wide to long format in Python?

How do I pivot data from long to wide format in R?

What is the SQL equivalent of reshaping data between long and wide formats?

When should I use long format vs wide format?

Can I reshape data with multiple value columns?

What does "tidy data" mean?

Long and Wide Data Format Converter

Data Reshape Tool

1. Upload Your Data

When to Use Long vs Wide Format

Use Long (Tidy) Format When

Use Wide Format When

Learn More About Data Reshaping

Understanding Long and Wide Data Formats

Long Format (Tidy)

Wide Format (Pivoted)

Long to Wide Transformation (Pivot / Spread)

Original Data (Long Format)

Column Selection for Long to Wide

Result (Wide Format)

Wide to Long Transformation (Melt / Unpivot)

Original Data (Wide Format)

Column Selection for Wide to Long

Result (Long Format)

How to Reshape Data in Python, R, SQL, and Excel

Python: pandas melt() and pivot_table()

Wide to Long (melt) and Long to Wide (pivot)

R: pivot_longer() and pivot_wider()

Wide to Long (pivot_longer) and Long to Wide (pivot_wider)

SQL: PIVOT and UNPIVOT

Long to Wide using PIVOT (SQL Server)

Long to Wide using conditional aggregation (PostgreSQL / MySQL)

Wide to Long using UNPIVOT (SQL Server)

Wide to Long using UNION ALL (PostgreSQL / MySQL)

Excel and Google Sheets

Long to Wide: PivotTable

Wide to Long: Power Query (Excel) or Apps Script (Google Sheets)

Excel Power Query:

Google Sheets FLATTEN formula:

Frequently Asked Questions

What is the difference between long and wide data formats?

How do I reshape data from wide to long format in Python?

How do I pivot data from long to wide format in R?

What is the SQL equivalent of reshaping data between long and wide formats?

When should I use long format vs wide format?