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# Fixing "ValueError: Cannot mix tz-aware with tz-naive values" in Pandas
- URL: https://datascientyst.com/fixing-valueerror-cannot-mix-tz-aware-with-tz-naive-values-in-pandas/
- Published: 2025-03-25T16:49:04.000Z
- Updated: 2025-03-25T16:49:41.000Z
- Author: John D K
- Tags: Pandas Error

When using `pd.to_datetime()` in Pandas, you might encounter the error:

```
ValueError: Cannot mix tz-aware with tz-naive values

```

This happens when:

- **timezone-aware** (`tz-aware`) and
- **timezone-naive** (`tz-naive`) datetime values

exist in the same column. Pandas does not allow this combination for operations like comparison or merging.

## Understanding the Problem

- **Timezone-naive** timestamps do not have timezone information.
- **Timezone-aware** timestamps include a timezone (`UTC`, `America/New_York`, etc.).

Example of mixed values causing the error:

```python
import pandas as pd  

df = pd.DataFrame({"timestamp": ["2024-02-10 12:00:00", "2024-02-10 14:00:00+00:00"]})  
df["timestamp"] = pd.to_datetime(df["timestamp"]) 

```

which will result into:

```
ValueError: unconverted data remains when parsing with format "%Y-%m-%d %H:%M:%S": "+00:00", at position 1. You might want to try:
    - passing `format` if your strings have a consistent format;
    - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;
    - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this.

```

## How to Fix the Error

### 1\. Make All Datetimes Timezone-Naive

If you don’t need timezones, remove them using `.tz_localize(None)`:

```python
df['timestamp'].apply(lambda x: pd.to_datetime(x).tz_localize(None))

```

### 2\. Make All Datetimes Timezone-Aware

If you need timezones, localize all values explicitly:

```python
pd.to_datetime(df['timestamp'], format='mixed', utc=True)

```

or

```python
pd.to_datetime(df['timestamp'], format='ISO8601', utc=True)

```

Or specify a different timezone:

```python
df["timestamp"] = pd.to_datetime(df["timestamp"]).dt.tz_localize("America/New_York")

```

## Tips

- **Use `.dt.tz_localize(None)`** to remove timezones and make timestamps naive.
- **Use `pd.to_datetime(df["col"], utc=True)`** to ensure consistency in timezone-aware timestamps.
- **Convert timezones** before merging or comparing datetime values.

## Resources

- [Handling CSV with timezone-aware and timezone-naive datetime column](https://stackoverflow.com/questions/68182325/handling-csv-with-timezone-aware-and-timezone-naive-datetime-column?ref=datascientyst.com)
- [How to Convert Datetime to the Same Timezone in Pandas DataFrame](https://datascientyst.com/convert-datetime-the-same-timezone-pandas-dataframe/)
- [Timezone-aware and naive timestamp in Pandas](https://datascientyst.com/timezone-and-naive-timestamp-in-pandas/)