I’m trying to plot a ROC curve using seaborn (python).

With matplotlib I simply use the function `plot`

:

```
plt.plot(one_minus_specificity, sensitivity, 'bs--')
```

where `one_minus_specificity`

and `sensitivity`

are two lists of paired values.

Is there a simple counterparts of the plot function in seaborn? I had a look at the gallery but I didn’t find any straightforward method.

Since seaborn also uses matplotlib to do its plotting you can easily combine the two. If you only want to adopt the styling of seaborn the `set_style`

function should get you started:

```
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set_style("darkgrid")
plt.plot(np.cumsum(np.random.randn(1000,1)))
plt.show()
```

Result:

### Answer：

It’s possible to get this done using ** seaborn.lineplot()** but it involves some additional work of converting numpy arrays to pandas dataframe. Here’s a complete example:

```
# imports
import seaborn as sns
import numpy as np
import pandas as pd
# inputs
In [41]: num = np.array([1, 2, 3, 4, 5])
In [42]: sqr = np.array([1, 4, 9, 16, 25])
# convert to pandas dataframe
In [43]: d = {'num': num, 'sqr': sqr}
In [44]: pdnumsqr = pd.DataFrame(d)
# plot using lineplot
In [45]: sns.set(style='darkgrid')
In [46]: sns.lineplot(x='num', y='sqr', data=pdnumsqr)
Out[46]: <matplotlib.axes._subplots.AxesSubplot at 0x7f583c05d0b8>
```

And we get the following plot:

### Answer：

Yes, you can do the same in Seaborn directly. This is done with tsplot() which allows either a single array as input, or two arrays where the other is ‘time’ i.e. x-axis.

```
import seaborn as sns
data = [1,5,3,2,6] * 20
time = range(100)
sns.tsplot(data, time)
```