# How to Format Thousand Separator for Integers in a Pandas DataFrame

In this article, we will show you how to format thousand separators for integers in a pandas DataFrame.

## What is a Thousand Separator?

A thousand separator is a symbol used to separate groups of digits in large numbers to make them more readable. In many countries, a comma (,) is used as a thousand separator, while others use a period (.) or a space. For example, the number 1000000 can be written as 1,000,000 (comma-separated), 1.000.000 (period-separated), or 1 000 000 (space-separated).

## Formatting Thousand Separators in a Pandas DataFrame

Pandas is a popular data manipulation library in Python, commonly used by data scientists and software engineers. In pandas, we can format numerical data using the `map()`

method. This method applies a function to each element of a DataFrame.

```
Note: applymap() has been deprecated. We are using map() instead
```

To format thousand separators for integers in a pandas DataFrame, we can define a function that takes a number as input and returns a string representation of the number with thousand separators.

```
def format_int_with_commas(x):
"""
Formats an integer with commas as thousand separators.
"""
return f"{x:,}"
```

In this function, we use Python’s f-string formatting syntax to format the number with commas as thousand separators (`{x:,}`

). The comma in the curly braces tells Python to use the comma as a thousand separator.

We can then apply this function to each element of the DataFrame using the `map()`

method.

```
import pandas as pd
# Create a sample DataFrame
df = pd.DataFrame({
'A': [1000, 2000000, 300000000],
'B': [4000, 5000000, 600000000],
'C': [7000, 8000000, 900000000]
})
# Apply the format_int_with_commas function to each element of the DataFrame
df = df.map(format_int_with_commas)
print(df)
```

Output:

```
A B C
0 1,000 4,000 7,000
1 2,000,000 5,000,000 8,000,000
2 300,000,000 600,000,000 9,000,000,000
```

In this example, we create a sample DataFrame with three columns (`A`

, `B`

, and `C`

) containing large integers. We then apply the `format_int_with_commas`

function to each element of the DataFrame using the `map()`

method. The resulting DataFrame contains the same values, but with thousand separators added for readability.

Note that the `map()`

method applies the function to each element of the DataFrame, so it works for both integer and float data types. If you want to apply the function to a specific column or subset of columns, you can use the `apply()`

method instead.

## Conclusion

Formatting numerical data with thousand separators is a common task in data analysis and visualization. In this article, we showed you how to format thousand separators for integers in a pandas DataFrame using the `map()`

method and a custom function. By using this technique, you can improve the readability of your data and make it easier to understand.

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