Convert Floats to Integers in Pandas DataFrame

April 27, 2024

You can convert floats to integers in Pandas DataFrame using:

(1) astype(int):

df["DataFrame Column"] = df["DataFrame Column"].astype(int)

(2) apply(int):

df["DataFrame Column"] = df["DataFrame Column"].apply(int)

In this guide, you’ll see 4 scenarios of converting floats to integers for:

  1. Specific DataFrame column using astype(int) or apply(int)
  2. Entire DataFrame where the data type of all columns is float
  3. Mixed DataFrame where the data type of some columns is float
  4. DataFrame that contains NaN values

4 Scenarios of Converting Floats to Integers in Pandas DataFrame

(1) Convert floats to integers for a specific DataFrame column

To start with a simple example, let’s create a DataFrame with two columns, where:

  • The first column (called ‘numeric_values‘) will contain only floats
  • The second column (called ‘string_values‘) will contain only strings

The goal is to convert all the floats to integers under the first DataFrame column.

Here is the code to create the DataFrame:

import pandas as pd

data = {
    "numeric_values": [3.0, 5.0, 7.0, 15.995, 225.12],
    "string_values": ["AA", "BB", "CCC", "DD", "EEEE"],
}
df = pd.DataFrame(data)

print(df)
print(df.dtypes)

As you can see, the data type for the ‘numeric_values‘ column is float:

   numeric_values  string_values
0           3.000             AA
1           5.000             BB
2           7.000            CCC
3          15.995             DD
4         225.120           EEEE
numeric_values    float64
string_values      object
dtype: object

You can then use astype(int) in order to convert the floats to integers:

df["DataFrame Column"] = df["DataFrame Column"].astype(int)

So the complete code to perform the conversion is as follows:

import pandas as pd

data = {
    "numeric_values": [3.0, 5.0, 7.0, 15.995, 225.12],
    "string_values": ["AA", "BB", "CCC", "DD", "EEEE"],
}
df = pd.DataFrame(data)

df["numeric_values"] = df["numeric_values"].astype(int)

print(df)
print(df.dtypes)

You’ll now notice that the data type for the ‘numeric_values‘ column is integer:

   numeric_values  string_values
0               3             AA
1               5             BB
2               7            CCC
3              15             DD
4             225           EEEE
numeric_values     int32
string_values     object
dtype: object

Alternatively, you can use apply(int) to convert the floats to integers:

df["DataFrame Column"] = df["DataFrame Column"].apply(int)

For our example:

import pandas as pd

data = {
    "numeric_values": [3.0, 5.0, 7.0, 15.995, 225.12],
    "string_values": ["AA", "BB", "CCC", "DD", "EEEE"],
}
df = pd.DataFrame(data)

df["numeric_values"] = df["numeric_values"].apply(int)

print(df)
print(df.dtypes)

You’ll get the data type of integer:

   numeric_values  string_values
0               3             AA
1               5             BB
2               7            CCC
3              15             DD
4             225           EEEE
numeric_values     int64
string_values     object
dtype: object

(2) Convert an entire DataFrame where the data type of all columns is float

What if you have a DataFrame where the data type of all the columns is float?

Rather than specifying the conversion to integers column-by-column, you can do it instead on a DataFrame level using:

df = df.astype(int)

For example, let’s create a new DataFrame with two columns that contain only floats:

import pandas as pd

data = {
    "numeric_values_1": [3.2, 5.9, 7.0, 15.995, 225.12],
    "numeric_values_2": [7.7, 23.0, 522.0, 4275.5, 22.3],
}
df = pd.DataFrame(data)

print(df)
print(df.dtypes)

You’ll now get this DataFrame with the two float columns:

   numeric_values_1  numeric_values_2
0             3.200               7.7
1             5.900              23.0
2             7.000             522.0
3            15.995            4275.5
4           225.120              22.3
numeric_values_1    float64
numeric_values_2    float64
dtype: object

To convert the floats to integers throughout the entire DataFrame, you’ll need to add df = df.astype(int) to the code:

import pandas as pd

data = {
    "numeric_values_1": [3.2, 5.9, 7.0, 15.995, 225.12],
    "numeric_values_2": [7.7, 23.0, 522.0, 4275.5, 22.3],
}
df = pd.DataFrame(data)

df = df.astype(int)

print(df)
print(df.dtypes)

As you can see, all the columns in the DataFrame are now converted to integers:

   numeric_values_1  numeric_values_2
0                 3                 7
1                 5                23
2                 7               522
3                15              4275
4               225                22
numeric_values_1    int32
numeric_values_2    int32
dtype: object

Note that the above approach would only work if all the columns in the DataFrame have the data type of float.

What if you have a mixed DataFrame where the data type of some (but not all) columns is float?

The section below deals with this scenario.

(3) Convert a mixed DataFrame where the data type of some columns is float

Let’s now create a new DataFrame with 3 columns, where the first 2 columns will contain float values, while the third column will include only strings:

import pandas as pd

data = {
    "numeric_values_1": [3.2, 5.9, 7.0, 15.995, 225.12],
    "numeric_values_2": [7.7, 23.0, 522.0, 4275.5, 22.3],
    "string_values": ["AA", "BB", "CCC", "DD", "EEEE"],
}
df = pd.DataFrame(data)

print(df)
print(df.dtypes)

Here is the DataFrame with the 3 columns that you’ll get:

   numeric_values_1  numeric_values_2  string_values
0             3.200               7.7             AA
1             5.900              23.0             BB
2             7.000             522.0            CCC
3            15.995            4275.5             DD
4           225.120              22.3           EEEE
numeric_values_1    float64
numeric_values_2    float64
string_values        object
dtype: object

You can then specify multiple columns (in this example, the first two columns) that you’d like to convert to integers:

import pandas as pd

data = {
    "numeric_values_1": [3.2, 5.9, 7.0, 15.995, 225.12],
    "numeric_values_2": [7.7, 23.0, 522.0, 4275.5, 22.3],
    "string_values": ["AA", "BB", "CCC", "DD", "EEEE"],
}
df = pd.DataFrame(data)

df[["numeric_values_1", "numeric_values_2"]] = df[
    ["numeric_values_1", "numeric_values_2"]
].astype(int)

print(df)
print(df.dtypes)

As you may observe, the first 2 columns are now converted to integers:

   numeric_values_1  numeric_values_2  string_values
0                 3                 7             AA
1                 5                23             BB
2                 7               522            CCC
3                15              4275             DD
4               225                22           EEEE
numeric_values_1     int32
numeric_values_2     int32
string_values       object
dtype: object

(4) Convert a DataFrame that contains NaN values

In the final scenario, you’ll see how to convert a column that includes a mixture of floats and NaN values.

The goal is to convert the float values to integers, as well as replace the NaN values with zeros.

Here is the code to create the DataFrame:

import pandas as pd
import numpy as np

data = {"numeric_values": [3.0, 5.0, np.nan, 15.0, np.nan]}
df = pd.DataFrame(data)

print(df)
print(df.dtypes)

You’ll get this DataFrame that contains both floats and NaNs:

   numeric_values
0             3.0
1             5.0
2             NaN
3            15.0
4             NaN
numeric_values    float64
dtype: object

You can then replace the NaN values with zeros by adding fillna(0), and then perform the conversion to integers using astype(int):

import pandas as pd
import numpy as np

data = {"numeric_values": [3.0, 5.0, np.nan, 15.0, np.nan]}
df = pd.DataFrame(data)

df["numeric_values"] = df["numeric_values"].fillna(0).astype(int)

print(df)
print(df.dtypes)

Here is the newly converted DataFrame:

   numeric_values
0               3
1               5
2               0
3              15
4               0
numeric_values    int32
dtype: object

Additional Resources

You can check the Pandas Documentation to read more about astype.

Alternatively, you may review the following guides for other types of conversions:

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