The **`to_numeric` function** in pandas is used to convert the given argument to a numeric type. The argument is converted to `float64` or `int64` by default, based on what is provided in the arguments. We can use the `downcast` parameter if we want to convert data to a particular type.

The illustration below shows how the `to_numeric` function works:

# Syntax

The syntax of the `to_numeric` function is as follows:

```python
pd.to_numeric(arg, errors, downcast)
```
# Parameters

The `to_numeric` function has the following parameters:

| Parameter  | Description  |
| - | - |
| `arg`  |  Argument to be converted. Can be scalar, list, tuple, 1-d array, or Series.  |
| `errors`  | Determines how to handle errors during conversions. Can be `ignore`, `raise` or `coerce`. It is `raise` by default.  |
| `downcast`  | Specifies conversion to a particular datatype. Can be `int`, `signed`, `unsigned` or `float`. By default, it is `None`. If it is not `None`, pandas will downcast the data to the smallest data type possible. |

> Only the `arg` parameter is compulsory. The rest are optional.

# Return value

Data in the converted format is returns based on what is passed in the argument.

# Handling errors

Different types of error handling are possible within the `to_numeric` function:

* If the error argument is passed as `raise`, then invalid parsing will raise an exception.

* If the error argument is passed as `coerce`, then invalid parsing will be set as `NaN`.

* If the error argument is passed as `ignore`, then invalid parsing will return the input.

# Example

The code snippet below shows how we can use the `to_numeric` function in pandas:

import pandas as pd
import numpy as np

s = pd.Series(['1.0', '2', -3])
print("Series in string")
print(s)
print("Default Numeric Form")
print(pd.to_numeric(s))
print('\n')

print("Numeric Form using downcast = signed")
print(pd.to_numeric(s, downcast='signed'))
print('\n')

print("Numeric Form using downcast = int")
print(pd.to_numeric(s, downcast='integer'))
print('\n')

print("Ignoring errors")
s2 = pd.Series(['apple', '1.0', '2', -3])
print(pd.to_numeric(s2, errors='ignore'))
print('\n')

print("Corerce errors")
s2 = pd.Series(['apple', '1.0', '2', -3])
print(pd.to_numeric(s2, errors='coerce'))
print('\n')


What is the to_numeric function in pandas?

The to_numeric function in pandas is used to convert the given argument to a numeric type. The argument is converted to float64 or int64 by default, based on what is provided in the arguments. We can use the downcast parameter if we want to convert data to a particular type. The illustration below shows how the to_numeric function works: 

Parameter	Description
`arg`	Argument to be converted. Can be scalar, list, tuple, 1-d array, or Series.
`errors`	Determines how to handle errors during conversions. Can be `ignore`, `raise` or `coerce`. It is `raise` by default.
`downcast`	Specifies conversion to a particular datatype. Can be `int`, `signed`, `unsigned` or `float`. By default, it is `None`. If it is not `None`, pandas will downcast the data to the smallest data type possible.