5 Pro Tips: Stop Pandas .dt Returning Floats (2025)

The Root of the Problem: Why Pandas .dt Returns Floats

You've meticulously imported your dataset, converted a column to datetime objects, and you're ready to extract features. You run a simple command like df['transaction_date'].dt.year, expecting clean integers. Instead, you're greeted with a Series of floats: 2023.0, 2024.0, NaN, 2025.0. Why does this happen? It’s one of the most common "gotchas" for new and intermediate Pandas users.

The issue boils down to how Pandas handles missing data. Here’s the chain of events:

1. Missing Datetimes: When a date can't be parsed or is missing, Pandas represents it with NaT (Not a Time). This is the datetime equivalent of None or np.nan.
2. Component Extraction: When you use the .dt accessor to get a component like the year, month, or day, the NaT value has no corresponding integer. It must be converted to a missing value marker.
3. NumPy's Limitation: By default, Pandas uses NumPy arrays for its underlying data structures. Standard NumPy integer arrays (e.g., `int64`) cannot store a missing value indicator. There is no integer version of `NaN`.
4. The Upcast: To accommodate the missing value, Pandas performs an "upcast." It converts the entire Series to a data type that can hold missing values: `float64`. The integer years become floats (e.g., 2024 becomes 2024.0), and the `NaT` becomes NaN (Not a Number).

This automatic type conversion can cause silent bugs, break type-checking in your code, and lead to incorrect calculations if not handled properly. Fortunately, modern versions of Pandas provide elegant solutions.

5 Pro Tips to Keep Your Integers Intact (2025)

Let's move from understanding the problem to solving it. Here are five professional methods, from the modern standard to high-performance options, to ensure your datetime components remain integers.

Tip 1: The Modern Standard: Use Nullable Integer Types ('Int64')

Since Pandas 1.0, the library has included its own extension types that natively handle missing values. The nullable integer type, Int64Dtype, is the perfect solution for this problem.

Unlike NumPy's int64, Pandas' Int64 (note the capital 'I') can hold both integers and a special missing value marker, pd.NA. This prevents the upcast to float entirely.

Simply chain .astype('Int64') after your .dt accessor call. This is the recommended, most Pythonic way to solve the problem in 2025.

import pandas as pd
import numpy as np

s = pd.Series(["2023-01-15", "2024-05-20", np.nan, "2025-11-30"])
dates = pd.to_datetime(s)

# The problematic float output
print(dates.dt.year)
# 0    2023.0
# 1    2024.0
# 2       NaN
# 3    2025.0
# dtype: float64

# The solution: Cast to nullable integer
years_int = dates.dt.year.astype('Int64')
print(years_int)
# 0    2023
# 1    2024
# 2    <NA>
# 3    2025
# dtype: Int64
  

This approach is clean, explicit, and preserves the missing data information without corrupting the data type. It should be your default choice.

Tip 2: Proactive Handling with `pd.to_datetime`

An ounce of prevention is worth a pound of cure. The NaT values are often introduced when you first create the datetime column, typically using pd.to_datetime(..., errors='coerce'). This powerful option turns any unparseable strings into NaT instead of raising an error.

A proactive strategy is to deal with these NaTs immediately after creation, before you even attempt to extract components.

Inspect for and decide the fate of your missing datetimes. You could fill them, or drop the rows entirely if they are not useful.

df = pd.DataFrame({'date_str': ["2023-01-15", "not a date", "2025-11-30"]})
df['date'] = pd.to_datetime(df['date_str'], errors='coerce')

# Check for NaTs created by 'coerce'
print(f"Found {df['date'].isnull().sum()} missing dates.")

# Strategy 1: Drop rows with missing dates
df_clean = df.dropna(subset=['date'])
years = df_clean['date'].dt.year # Now returns a clean int64 Series

# Strategy 2: Fill with a placeholder (if appropriate)
df_filled = df.fillna({'date': pd.Timestamp('1970-01-01')})
years = df_filled['date'].dt.year # Also returns a clean int64 Series
  

This method forces you to be deliberate about your data cleaning process and prevents the float issue from ever surfacing.

Tip 3: The Fill-and-Replace Workaround

Sometimes, you need to preserve the NaN in the final output but can't use nullable types (perhaps due to library compatibility). This workaround is less elegant but effective.

The idea is to temporarily fill NaT with a valid date, extract the integer component, and then replace the component from the placeholder date with NaN.

# Starting with our 'dates' Series containing NaT
placeholder_date = pd.Timestamp.min

# Fill NaT, get year, then replace the placeholder's year with NaN
years_workaround = dates.fillna(placeholder_date).dt.year.replace(placeholder_date.year, np.nan)

# The result is a float Series, but we avoided errors
print(years_workaround)
# 0    2023.0
# 1    2024.0
# 2       NaN
# 3    2025.0
# dtype: float64
  

This is a multi-step process that is more verbose and less readable than using 'Int64'. It's a fallback option when modern types aren't available.

Tip 4: Direct Casting with a Sentinel Value

If your application logic can accommodate a "sentinel value"—a special value like -1 or 0 to represent missingness—you can force a conversion to a standard integer type.

Warning: This approach should be used with extreme caution. If -1 could be a legitimate value in your data (e.g., when working with BCE dates), this method will corrupt your data. It's only safe when the sentinel value is guaranteed to be outside the range of valid data.

First, use .fillna() on the float Series, then cast to a standard integer type like int.

# Starting with the problematic float Series
years_float = dates.dt.year

# Fill NaN with -1 and cast to a standard integer type
years_sentinel = years_float.fillna(-1).astype(int)

print(years_sentinel)
# 0    2023
# 1    2024
# 2      -1
# 3    2025
# dtype: int64
  

This is a quick and computationally cheap method, but it changes the meaning of your data. You are replacing "unknown" with a concrete, but artificial, number.

Tip 5: The High-Performance Option: PyArrow Backend

For data professionals working with large datasets where performance and memory efficiency are critical, Pandas offers a pluggable backend system. Using the PyArrow backend for your dataframes can solve this problem at its root.

Arrow's data types have native support for missing values and do not require upcasting to float. When you use an Arrow-backed datetime column, extracting a component will yield an Arrow-backed integer column, complete with missing values.

First, ensure you have PyArrow installed (pip install pyarrow). Then, specify the dtype when creating the Series or column.

# Ensure pyarrow is installed

# Convert the original Series to a pyarrow-backed timestamp type
dates_arrow = dates.astype(pd.ArrowDtype(pd.TimestampType('ns')))

# Extracting the year now works as expected
years_arrow = dates_arrow.dt.year

print(years_arrow)
# 0    2023
# 1    2024
# 2    <NA>
# 3    2025
# dtype: int64[pyarrow]
  

This is an advanced technique that offers significant performance benefits on large datasets, in addition to solving the float-casting issue elegantly.

Comparison of Methods

To help you choose the right approach, here’s a summary table comparing the five tips.

Conclusion: Embrace Modern Pandas

The unexpected conversion of datetime components to floats is a classic Pandas behavior rooted in its NumPy foundation. While it can be confusing, understanding that it's all about handling missing values (NaT) is the key.

For any work in 2025 and beyond, you should make the nullable integer type (.astype('Int64')) your go-to solution. It is the cleanest, safest, and most explicit way to get the integer columns you expect while properly representing missing data. For those pushing the boundaries of performance with large datasets, exploring the PyArrow backend is a worthy investment. By adopting these modern features, you can write more robust, predictable, and bug-free data analysis code.