str.contains 使用 na=Nan 标志

Question

I still can't wrap my head around what the na flag does exactly in the df.str.contains(string na=True/False) .

The documentation says: Fill value for missing values .

But what does it replace those missing values with?

Also what happens if you set it to True and and what happens if you set it to False.

Can someone please provide me with some examples of both scenarios.

Answer 1

Your df.str.contains() returns :

A Series or Index of boolean values indicating whether the given pattern is contained within the string of each element of the Series or Index.

So you will get a series of boolean values (True/False) for each element in your df series based on whether or not the substring is present in the element.

Here is an example :

sr = pd.Series(['Mouse', 'dog', 'house and parrot', '23', np.NaN])
sr.str.contains('og', na=False)


0    False
1     True
2    False
3    False
4    False
dtype: bool

So here I was checking if substring og is present in the elements of my series. It returned a series of boolean values for each element in input series.

Also, notice I had a Nan value in my original series.

Now what should happen incase the element is Nan ? What should we consider as the output of .str.contain() in this case ?

Ans. - Here is where the flag na comes into play. We can specify what to consider as the boolean outcome for elements having Nan value.

In the above example I set the flag na=False which will return False incase element in the series is Nan .

Hope this helps:

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.contains.html

Answer 2

First str.contains function only works on pandas series. Whatever value you give for na, it will fill that value in the output.

import numpy as np
import pandas as pd
df = pd.DataFrame({'v1':['dog','cat','cog',np.nan],'v2':['23','zip',np.nan,'4']})

df['v1'].str.contains('g',na=False)
0     True
1    False
2     True
3    False
Name: v1, dtype: bool

df['v1'].str.contains('g',na=True)
0     True
1    False
2     True
3     True
Name: v1, dtype: bool

df['v1'].str.contains('g',na=2)
0     True
1    False
2     True
3        2
Name: v1, dtype: object

As you can see, it fills the last na value with the given value.