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# Replacing values with NaNs in Pandas DataFrame

schedule Aug 10, 2023
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PythonPandas
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To replace values with `NaN`, use the DataFrame's `replace(~)` method.

# Replacing value with NaN

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[3,"NONE"]})df A0 31 NONE ```

To replace `"NONE"` values with `NaN`:

``` import numpy as npdf.replace("NONE", np.nan) A0 3.01 NaN ```

Note that the replacement is not done in-place, that is, a new DataFrame is returned and the original `df` is kept intact. To perform the replacement in-place, set `inplace=True`.

# Replacing multiple values with NaN

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[3,5],"B":[4,6]}, index=["a","b"])df A Ba 3 4b 5 6 ```

To replace values `3` and `4` with `np.nan`:

``` df.replace([3,4], np.nan) A Ba NaN NaNb 5.0 6.0 ```

# Conditionally replacing values with NaN

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[3,5],"B":[4,6]}, index=["a","b"])df A Ba 3 4b 5 6 ```

To replace values larger than `4` with `NaN`:

``` df[df > 4] = np.nandf A Ba 3.0 4.0b NaN NaN ```
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