# Randomly insert NA's values in a pandas dataframe

## Question:

How can I randomly insert `np.nan`

‘s in a DataFrame ?

Let’s say I want 10% null values inside my DataFrame.

My data looks like this :

```
df = pd.DataFrame(np.random.randn(5, 3),
index=['a', 'b', 'c', 'd', 'e'],
columns=['one', 'two', 'three'])
one two three
a 0.695132 1.044791 -1.059536
b -1.075105 0.825776 1.899795
c -0.678980 0.051959 -0.691405
d -0.182928 1.455268 -1.032353
e 0.205094 0.714192 -0.938242
```

Is there an easy way to insert the null values?

## Answers:

Here’s a way to clear exactly 10% of cells (or rather, as close to 10% as can be achieved with the existing data frame’s size).

```
import random
ix = [(row, col) for row in range(df.shape[0]) for col in range(df.shape[1])]
for row, col in random.sample(ix, int(round(.1*len(ix)))):
df.iat[row, col] = np.nan
```

Here’s a way to clear cells independently with a per-cell probability of 10%.

```
df = df.mask(np.random.random(df.shape) < .1)
```

You can easily iterate over data frame columns and assign `NaN`

value to every cell produced by `pandas.DataFrame.sample()`

method.

The code is following.

```
for col in df.columns:
df.loc[df.sample(frac=0.1).index, col] = pd.np.nan
```

To add to and modify @Jaroslav Bezděk’s code a bit, here is my view. Here, I am assuming that you want to apply the NaNs to numeric variables.

```
# select only numeric columns to apply the missingness to
cols_list = df.select_dtypes('number').columns.tolist()
# randomly remove cases from the dataframe
for col in df[cols_list]:
df.loc[df.sample(frac=0.05).index, col] = np.nan
```

**Note**: if you use `pd.np.nan`

you get `ipython-input-5-e9827aa92133>:9: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead.`