You have four main options for converting types in pandas:

`to_numeric()`

- provides functionality to safely convert non-numeric types (e.g. strings) to a suitable numeric type. (See also `to_datetime()`

and `to_timedelta()`

.)

`astype()`

- convert (almost) any type to (almost) any other type (even if it's not necessarily sensible to do so). Also allows you to convert to categorial types (very useful).

`infer_objects()`

- a utility method to convert object columns holding Python objects to a pandas type if possible.

`convert_dtypes()`

- convert DataFrame columns to the "best possible" dtype that supports `pd.NA`

(pandas' object to indicate a missing value).

Read on for more detailed explanations and usage of each of these methods.

# 1. `to_numeric()`

The best way to convert one or more columns of a DataFrame to numeric values is to use `pandas.to_numeric()`

.

This function will try to change non-numeric objects (such as strings) into integers or floating-point numbers as appropriate.

## Basic usage

The input to `to_numeric()`

is a Series or a single column of a DataFrame.

```
>>> s = pd.Series(["8", 6, "7.5", 3, "0.9"]) # mixed string and numeric values
>>> s
0 8
1 6
2 7.5
3 3
4 0.9
dtype: object
```

```
>>> pd.to_numeric(s) # convert everything to float values
0 8.0
1 6.0
2 7.5
3 3.0
4 0.9
dtype: float64
```

As you can see, a new Series is returned. Remember to assign this output to a variable or column name to continue using it:

```
# convert Series
my_series = pd.to_numeric(my_series)
```# convert column “a” of a DataFrame

```
df[“a”] = pd.to_numeric(df[“a”])
```

You can also use it to convert multiple columns of a DataFrame via the `apply()`

method:

```
# convert all columns of DataFrame
df = df.apply(pd.to_numeric) # convert all columns of DataFrame
```# convert just columns “a” and “b”

```
df[[“a”, “b”]] = df[[“a”, “b”]].apply(pd.to_numeric)
```

As long as your values can all be converted, that's probably all you need.

## Error handling

But what if some values can't be converted to a numeric type?

`to_numeric()`

also takes an `errors`

keyword argument that allows you to force non-numeric values to be `NaN`

, or simply ignore columns containing these values.

Here's an example using a Series of strings `s`

which has the object dtype:

```
>>> s = pd.Series(['1', '2', '4.7', 'pandas', '10'])
>>> s
0 1
1 2
2 4.7
3 pandas
4 10
dtype: object
```

The default behaviour is to raise if it can't convert a value. In this case, it can't cope with the string 'pandas':

```
>>> pd.to_numeric(s) # or pd.to_numeric(s, errors='raise')
ValueError: Unable to parse string
```

Rather than fail, we might want 'pandas' to be considered a missing/bad numeric value. We can coerce invalid values to `NaN`

as follows using the `errors`

keyword argument:

```
>>> pd.to_numeric(s, errors='coerce')
0 1.0
1 2.0
2 4.7
3 NaN
4 10.0
dtype: float64
```

The third option for `errors`

is just to ignore the operation if an invalid value is encountered:

```
>>> pd.to_numeric(s, errors='ignore')
# the original Series is returned untouched
```

This last option is particularly useful for converting your entire DataFrame, but don't know which of our columns can be converted reliably to a numeric type. In that case, just write:

```
df.apply(pd.to_numeric, errors='ignore')
```

The function will be applied to each column of the DataFrame. Columns that can be converted to a numeric type will be converted, while columns that cannot (e.g. they contain non-digit strings or dates) will be left alone.

## Downcasting

By default, conversion with `to_numeric()`

will give you either an `int64`

or `float64`

dtype (or whatever integer width is native to your platform).

That's usually what you want, but what if you wanted to save some memory and use a more compact dtype, like `float32`

, or `int8`

?

`to_numeric()`

gives you the option to downcast to either `'integer'`

, `'signed'`

, `'unsigned'`

, `'float'`

. Here's an example for a simple series `s`

of integer type:

```
>>> s = pd.Series([1, 2, -7])
>>> s
0 1
1 2
2 -7
dtype: int64
```

Downcasting to `'integer'`

uses the smallest possible integer that can hold the values:

```
>>> pd.to_numeric(s, downcast='integer')
0 1
1 2
2 -7
dtype: int8
```

Downcasting to `'float'`

similarly picks a smaller than normal floating type:

```
>>> pd.to_numeric(s, downcast='float')
0 1.0
1 2.0
2 -7.0
dtype: float32
```

# 2. `astype()`

The `astype()`

method enables you to be explicit about the dtype you want your DataFrame or Series to have. It's very versatile in that you can try and go from one type to any other.

## Basic usage

Just pick a type: you can use a NumPy dtype (e.g. `np.int16`

), some Python types (e.g. bool), or pandas-specific types (like the categorical dtype).

Call the method on the object you want to convert and `astype()`

will try and convert it for you:

```
# convert all DataFrame columns to the int64 dtype
df = df.astype(int)
```# convert column “a” to int64 dtype and “b” to complex type

df = df.astype({“a”: int, “b”: complex})

# convert Series to float16 type

s = s.astype(np.float16)

# convert Series to Python strings

s = s.astype(str)

# convert Series to categorical type - see docs for more details

```
s = s.astype(‘category’)
```

Notice I said "try" - if `astype()`

does not know how to convert a value in the Series or DataFrame, it will raise an error. For example, if you have a `NaN`

or `inf`

value you'll get an error trying to convert it to an integer.

As of pandas 0.20.0, this error can be suppressed by passing `errors='ignore'`

. Your original object will be returned untouched.

## Be careful

`astype()`

is powerful, but it will sometimes convert values "incorrectly". For example:

```
>>> s = pd.Series([1, 2, -7])
>>> s
0 1
1 2
2 -7
dtype: int64
```

These are small integers, so how about converting to an unsigned 8-bit type to save memory?

```
>>> s.astype(np.uint8)
0 1
1 2
2 249
dtype: uint8
```

The conversion worked, but the -7 was wrapped round to become 249 (i.e. 2^{8} - 7)!

Trying to downcast using `pd.to_numeric(s, downcast='unsigned')`

instead could help prevent this error.

# 3. `infer_objects()`

Version 0.21.0 of pandas introduced the method `infer_objects()`

for converting columns of a DataFrame that have an object datatype to a more specific type (soft conversions).

For example, here's a DataFrame with two columns of object type. One holds actual integers and the other holds strings representing integers:

```
>>> df = pd.DataFrame({'a': [7, 1, 5], 'b': ['3','2','1']}, dtype='object')
>>> df.dtypes
a object
b object
dtype: object
```

Using `infer_objects()`

, you can change the type of column 'a' to int64:

```
>>> df = df.infer_objects()
>>> df.dtypes
a int64
b object
dtype: object
```

Column 'b' has been left alone since its values were strings, not integers. If you wanted to force both columns to an integer type, you could use `df.astype(int)`

instead.

# 4. `convert_dtypes()`

Version 1.0 and above includes a method `convert_dtypes()`

to convert Series and DataFrame columns to the best possible dtype that supports the `pd.NA`

missing value.

Here "best possible" means the type most suited to hold the values. For example, this a pandas integer type, if all of the values are integers (or missing values): an object column of Python integer objects are converted to `Int64`

, a column of NumPy `int32`

values, will become the pandas dtype `Int32`

.

With our `object`

DataFrame `df`

, we get the following result:

```
>>> df.convert_dtypes().dtypes
a Int64
b string
dtype: object
```

Since column 'a' held integer values, it was converted to the `Int64`

type (which is capable of holding missing values, unlike `int64`

).

Column 'b' contained string objects, so was changed to pandas' `string`

dtype.

By default, this method will infer the type from object values in each column. We can change this by passing `infer_objects=False`

:

```
>>> df.convert_dtypes(infer_objects=False).dtypes
a object
b string
dtype: object
```

Now column 'a' remained an object column: pandas knows it can be described as an 'integer' column (internally it ran `infer_dtype`

) but didn't infer exactly what dtype of integer it should have so did not convert it. Column 'b' was again converted to 'string' dtype as it was recognised as holding 'string' values.