ramesh - 2 years ago 307

Python Question

my df:

`dframe = pd.DataFrame({"A":list("aaaabbbbccc"), "C":range(1,12)}, index=range(1,12))`

Out[9]:

A C

1 a 1

2 a 2

3 a 3

4 a 4

5 b 5

6 b 6

7 b 7

8 b 8

9 c 9

10 c 10

11 c 11

to subset based on column value:

`In[11]: first = dframe.loc[dframe["A"] == 'a']`

In[12]: first

Out[12]:

A C

1 a 1

2 a 2

3 a 3

4 a 4

To drop based on column value:

`In[16]: dframe = dframe[dframe["A"] != 'a']`

In[17]: dframe

Out[16]:

A C

5 b 5

6 b 6

7 b 7

8 b 8

9 c 9

10 c 10

11 c 11

Is there any way to do both in one shot? Like subsetting rows based on a column value and deleting same rows in the original df.

Recommended for you: Get network issues from **WhatsUp Gold**. **Not end users.**

Answer Source

It's not really in one shot, but typically the way to do this is reuse a boolean mask, like this:

```
In [28]: mask = dframe['A'] == 'a'
In [29]: first, dframe = dframe[mask], dframe[~mask]
In [30]: first
Out[30]:
A C
1 a 1
2 a 2
3 a 3
4 a 4
In [31]: dframe
Out[31]:
A C
5 b 5
6 b 6
7 b 7
8 b 8
9 c 9
10 c 10
11 c 11
```

Recommended from our users: **Dynamic Network Monitoring from WhatsUp Gold from IPSwitch**. ** Free Download**