In this short guide, you’ll see two approaches to convert integers to floats in Pandas DataFrame:

**(1) The astype(float) approach:**

df['DataFrame Column'] = df['DataFrame Column'].astype(float)

**(2) The to_numeric approach:**

df['DataFrame Column'] = pd.to_numeric(df['DataFrame Column'], downcast='float')

In the next section, you’ll see an example with the steps to apply the above two approaches in practice.

## Steps to Convert Integers to Floats in Pandas DataFrame

### Step 1: Create a DataFrame

To start, create a DataFrame that contains integers.

For example, let’s create a simple DataFrame based on the following data (where the *Price* column contains the integers):

Product |
Price |

AAA | 300 |

BBB | 500 |

CCC | 700 |

DDD | 900 |

Here is the complete code to create the DataFrame in Python:

import pandas as pd data = {'Product': ['AAA','BBB','CCC','DDD'], 'Price': [300,500,700,900] } df = pd.DataFrame(data) print (df) print (df.dtypes)

Run the code, and you’ll see that the *Price* column indeed contains the integers:

```
Product Price
0 AAA 300
1 BBB 500
2 CCC 700
3 DDD 900
Product object
Price int64
dtype: object
```

Note that **print (df.dtypes)** was added at the bottom of the code to check the data type of each column in the DataFrame.

### Step 2: Convert the Integers to Floats in Pandas DataFrame

You can apply the first approach of astype(float) in order to convert the integers to floats in Pandas DataFrame:

df['DataFrame Column'] = df['DataFrame Column'].astype(float)

Since in our example the ‘DataFrame Column’ is the **Price** column (which contains the integers), you’ll then need to add the following syntax:

df['Price'] = df['Price'].astype(float)

So this the complete code to perform the conversion for our example:

import pandas as pd data = {'Product': ['AAA','BBB','CCC','DDD'], 'Price': [300,500,700,900] } df = pd.DataFrame(data) df['Price'] = df['Price'].astype(float) print (df) print (df.dtypes)

As you can see below, the values under the Price column are now floats:

```
Product Price
0 AAA 300.0
1 BBB 500.0
2 CCC 700.0
3 DDD 900.0
Product object
Price float64
dtype: object
```

### Step 3 (optional): Convert the Integers to Floats using to_numeric

For this optional step, you may use the to_numeric approach to convert the integers to floats:

df['DataFrame Column'] = pd.to_numeric(df['DataFrame Column'], downcast='float')

This is the syntax that you’ll need to add in the context of our example:

df['Price'] = pd.to_numeric(df['Price'], downcast='float')

Here is the full Python code to perform the conversion from integers to floats:

import pandas as pd data = {'Product': ['AAA','BBB','CCC','DDD'], 'Price': [300,500,700,900] } df = pd.DataFrame(data) df['Price'] = pd.to_numeric(df['Price'], downcast='float') print (df) print (df.dtypes)

Run the code in Python, and you’ll get the float values:

```
Product Price
0 AAA 300.0
1 BBB 500.0
2 CCC 700.0
3 DDD 900.0
Product object
Price float32
dtype: object
```

At times, you may need to convert strings to floats. If that’s the case, you may want to check the following guide that explains the steps to convert strings to floats in Pandas DataFrame.