Sometimes, we want to detect and exclude outliers in Pandas data frame with Python.

In this article, we’ll look at how to detect and exclude outliers in Pandas data frame with Python.

### How to detect and exclude outliers in Pandas data frame with Python?

To detect and exclude outliers in Pandas data frame with Python, we can use NumPy to return a new DataFrame that has values within 3 standard deviations from the mean.

To do this, we can write:

```
import pandas as pd
import numpy as np
df = pd.DataFrame({'Data':np.random.normal(size=200)})
new_df = df[np.abs(df.Data-df.Data.mean()) <= (3*df.Data.std())]
print(new_df)
```

We create a Pandas DataFrame with a normal distribution with sample size 200 with `np.random.normal`

.

Then we pick the values that are within 3 standard deviations from the mean with `df[np.abs(df.Data-df.Data.mean()) <= (3*df.Data.std())]`

.

And we assign the returned DataFrame to `new_df`

.

Therefore, `new_df`

is something like:

```
Data
0 0.300805
1 -0.474140
2 -0.326278
3 0.566571
4 -1.391077
.. ...
195 0.500637
196 0.341858
197 -1.058419
198 -0.565920
199 -1.008344
[200 rows x 1 columns]
```

according to `print`

.

### Conclusion

To detect and exclude outliers in Pandas data frame with Python, we can use NumPy to return a new DataFrame that has values within 3 standard deviations from the mean.