Pandas Convert Float to Integer in DataFrame

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  • Post last modified:November 22, 2024
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Use pandas DataFrame.astype(int) and DataFrame.apply() methods to cast float column to integer(int/int64) type. I believe you would know float is bigger than int type, so you can easily downcase but the catch is you would lose any value after the decimal. Note that while casting it doesn’t do any rounding and flooring and it just truncates the fraction values (anything after .). In this article, I will explain different ways to convert columns with float values to integer values.

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Key Points –

  • Use astype() to convert float columns to integers by specifying 'int' or 'int64' as the target type.
  • Converting from float to integer may result in data loss due to truncation of decimal places.
  • You can convert multiple columns at once by selecting them and applying astype().
  • Using .astype() ensures the DataFrame retains its structure but may cause issues with overflows in large numbers.
  • Converting float to integer may impact performance, especially with large datasets, due to the change in memory usage.
  • NaN values in float columns must be handled before conversion, as integers do not support NaN.

Quick Examples of Pandas Convert Float to Integer

If you are in a hurry, below are some of the quick examples of how to convert float to integer type in DataFrame.


# Quick examples of pandas convert float to integer

# Converting "Fee" from float to int 
# Using DataFrame.astype()
df["Fee"]=df["Fee"].astype(int)
print(df.dtypes)

# Converting "Fee" and "Discount" from float to int 
# Using DataFrame.astype()
df = df.astype({"Fee":"int","Discount":"int"})
print(df.dtypes)

# Convert "Fee" from float to int 
# Using DataFrame.apply(np.int64)
df["Fee"] = df["Fee"].apply(np.int64)
print(df.dtypes)

# Converting "Fee" and "Discount" from float to int 
# Using DataFrame.apply(np.int64)
df["Fee"] = df["Fee"].apply(np.int64)
df["Discount"] = df["Discount"].apply(np.int64)

# Convert "Fee" from float to int and replace NaN values
df['Fee'] = df['Fee'].fillna(0).astype(int)
print(df)
print(df.dtypes)

Now, let’s create a DataFrame with a few rows and columns and execute some examples and validate the results. Our DataFrame contains column names Courses, Fee, Duration and Discount. 


# Create DataFrame
import pandas as pd
import numpy as np
technologies= {
    'Courses':["Spark","PySpark","Hadoop","Python","Pandas"],
    'Fee' :[22000.30,25000.40,23000.20,24000.50,26000.10],
    'Duration':['30days','50days','35days', '40days','55days'],
    'Discount':[1000.10,2300.15,1000.5,1200.22,2500.20]
          }
df = pd.DataFrame(technologies)
print(df)
print(df.dttypes)

Yields below output. 


# Output:
   Courses      Fee Duration  Discount
0    Spark  22000.3   30days   1000.10
1  PySpark  25000.4   50days   2300.15
2   Hadoop  23000.2   35days   1000.50
3   Python  24000.5   40days   1200.22
4   Pandas  26000.1   55days   2500.20

Courses      object
Fee         float64
Duration     object
Discount    float64
dtype: object

Pandas Convert Float to int (Integer)

Use pandas DataFrame.astype() function to convert float to int (integer), you can apply this on a specific column. Below example converts Fee column to int32 from float64. You can also use numpy.dtype as a param to this method.


# Convert "Fee" from float to int
df["Fee"]=df["Fee"].astype(int)
print(df)
print(df.dtypes)

Yields below output. 


# Output:
   Courses    Fee Duration  Discount
0    Spark  22000   30days   1000.10
1  PySpark  25000   50days   2300.15
2   Hadoop  23000   35days   1000.50
3   Python  24000   40days   1200.22
4   Pandas  26000   55days   2500.20

Courses      object
Fee           int32
Duration     object
Discount    float64
dtype: object

Casting Multiple Columns From Float to Integer

Similarly, you can also convert multiple columns from float to integer by sending dict of column name -> data type to astype() method. The below example converts both columns Fee and Discount to int types. 


# Converting "Fee" and "Discount" from float to int
df = df.astype({"Fee":"int","Discount":"int"})
print(df.dtypes)

Yields below output.


# Output:
Courses     object
Fee          int32
Duration    object
Discount     int32
dtype: object

Using apply(np.int64) to Cast From Float to Integer

You can also use DataFrame.apply() method to convert Fee column from float to integer in pandas. As you see in this example we are using numpy.dtype (np.int64).


import numpy as np
# Convert "Fee" from float to int 
# Using DataFrame.apply(np.int64)
df["Fee"] = df["Fee"].apply(np.int64)
print(df.dtypes)

Yields below output. 


# Output:
Courses      object
Fee           int64
Duration     object
Discount    float64
dtype: object

Convert Column Containing NaNs to astype(int)

In order to demonstrate some NaN/Null values, let’s create a DataFrame using NaN Values. To convert a column that includes a mixture of float and NaN values to int, first replace NaN values with zero on pandas DataFrame and then use astype() to convert.


import pandas as pd
import numpy as np
technologies= {
    'Fee' :[22000.30,25000.40,np.nan,24000.50,26000.10,np.nan]
          }
df = pd.DataFrame(technologies)
print(df)
print(df.dtypes)

Use .fillna() to replace the NaN values with integer value zero. For Example df['Fee']=df['Fee'].fillna(0).astype(int) method.


# Convert "Fee" from float to int and replace NaN values
df['Fee'] = df['Fee'].fillna(0).astype(int)
print(df)
print(df.dtypes)

Yields below output. 


# Output:
     Fee
0  22000
1  25000
2      0
3  24000
4  26000
5      0
Fee    int32
dtype: object

FAQ on Pandas Convert Float to Integer in DataFrame

How do I convert a single column from float to integer in a Pandas DataFrame?

To convert a single column from float to integer in a Pandas DataFrame, you can use the astype() method.

Can I convert multiple columns from float to integer at once?

You can convert multiple columns from float to integer at once using the astype() method.

How can I round the float values before converting them to integers?

To round the float values before converting them to integers in a Pandas DataFrame, you can use the round() method before applying the astype(int) conversion. This ensures the values are rounded to the nearest integer rather than truncated.

Is there a way to convert all float columns in the DataFrame to integers at once?

You can convert all float columns in a DataFrame to integers at once. To do this, you can use select_dtypes() to target only the float columns, and then apply the conversion using astype(int).

What if I want to convert float to integer but keep the DataFrame unchanged?

If you want to convert a float column to an integer without modifying the original DataFrame, you can use the astype() method without reassigning it back to the DataFrame. This way, the conversion is temporary, and the original DataFrame remains unchanged.

Conclusion

In this article, you have learned how to convert float column to integer in DataFrame using DataFrame.astype(int) and DataFrame.apply() method. Also, you have learned how to convert float to integers when you have Nan/null values in a column.

Happy Learning !!

References

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