# Python Change Column Dtype in Pandas With astype()

> Convert DataFrame column types using astype() for specific type casting, to_numeric() for automatic number conversion, or convert_dtypes() for best-fit types

**URL:** https://sentry.io/answers/change-a-column-type-in-a-dataframe-in-python-pandas/

---

## The Problem

How can I change the data type of a column in a Pandas DataFrame?

## The Solution

There are a few different ways to do this in Pandas. Which one to use will depend on the data types we're converting from and to.

1. If we want to convert a column from any data type to one specific data type (e.g. integer, float, string), we should use the `astype` method.
2. If we want to convert a column to a sensible numeric data type (integer or float), we should use the `to_numeric` function.
3. If we want Pandas to decide which data types to use for each column, we should use the `convert_dtypes` method.

Each of these methods is detailed in the subsections below.

### 1. Conversion with `astype`

The first and most versatile method to use is the [`astype`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.astype.html) method. When called on a Pandas DataFrame or Series, this method will attempt to cast the values within to the specified type. We can use this method to change the type of one or more columns at a time, as shown in the example below:

```python
import pandas as pd

# Create and print DataFrame
df = pd.DataFrame({
   'A': ['1', '2', '3'],
   'B': ['4', '5', '6'],
   'C': ['7', '8', '9']
})
print(df)

# Print data types of each column in DataFrame
print("\n")
print(df.dtypes)

# Change column A's values to floats
df['A'] = df['A'].astype(float)

# Change column B and C's values to integers
df = df.astype({'B': int, 'C': int})

print("\nConverted:\n")

# Print altered DataFrame
print(df)
# Print data types of each column in DataFrame
print("\n")
print(df.dtypes)
```

This script produces the following output:

```
   A  B  C
0  1  4  7
1  2  5  8
2  3  6  9

A    object
B    object
C    object
dtype: object

Converted:

     A  B  C
0  1.0  4  7
1  2.0  5  8
2  3.0  6  9

A    float64
B      int64
C      int64
dtype: object
```

### 2. Conversion with `to_numeric`

If we want to convert a column to a numeric type, we can use the [`to_numeric`](https://pandas.pydata.org/docs/reference/api/pandas.to_numeric.html) function. Depending on the data in our columns, they will be converted into either integers or floats.

```python
import pandas as pd

# Create and print DataFrame
df = pd.DataFrame({
   'A': ['1', '2', '3'],
   'B': ['4.0', '5.1', '6.2'],
   'C': ['7', '8', '9']
})
print(df)

# Print data types of each column in DataFrame
print("\n")
print(df.dtypes)

# Change column A's values to a numeric type
df['A'] = pd.to_numeric(df['A'])
# Change column B and C's values to a numeric type
df[['B', 'C']] = df[['B', 'C']].apply(pd.to_numeric)

print("\nConverted:\n")

# Print altered DataFrame
print(df)
# Print data types of each column in DataFrame
print("\n")
print(df.dtypes)
```

This script produces the following output:

```
   A    B  C
0  1  4.0  7
1  2  5.1  8
2  3  6.2  9

A    object
B    object
C    object
dtype: object

Converted:

   A    B  C
0  1  4.0  7
1  2  5.1  8
2  3  6.2  9

A      int64
B    float64
C      int64
dtype: object
```

Unlike with `astype`, we must use the [`apply`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html) method if we want to convert multiple columns at once.

### 3. Conversion with `convert_dtypes`

The [`convert_dtypes`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.convert_dtypes.html) DataFrame method will convert all columns to the best possible types that support `pd.NA`, the Pandas missing value. Note that this method will not convert numeric strings to integers or floats.

```python
import pandas as pd

# Create and print DataFrame
df = pd.DataFrame({
   'A': [1.0, 2.0, 5.3],
   'B': ['z', 'x', 'c'],
   'C': [7, 8, 4],
   'D': ['1', '2', '3']
})
print(df)

# Print data types of each column in DataFrame
print("\n")
print(df.dtypes)

# Change all columns to the appropriate types
df = df.convert_dtypes()

print("\nConverted:\n")

# Print altered DataFrame
print(df)
# Print data types of each column in DataFrame
print("\n")
print(df.dtypes)
```

This script produces the following output:

```
     A  B  C  D
0  1.0  z  7  1
1  2.0  x  8  2
2  5.3  c  4  3

A    float64
B     object
C      int64
D     object
dtype: object

Converted:

     A  B  C  D
0  1.0  z  7  1
1  2.0  x  8  2
2  5.3  c  4  3

A    Float64
B     string
C      Int64
D     string
dtype: object
```

---

*Source: [sentry.io/answers/change-a-column-type-in-a-dataframe-in-python-pandas/](https://sentry.io/answers/change-a-column-type-in-a-dataframe-in-python-pandas/)*