I have a pandas DataFrame with 24 columns (questions) and 207 rows (people responding) from a survey. The column values (answers) are within a fixed range: completely agree, rather agree, no idea, rather not agree, completely not agree.

I’ve tried this to know the frequency of values (answers) for a specific column (question):

```
df['Q1'].value_counts()

```

I’ve tried a lot of things to know the frequency of values (answers) for a selection of columns (questions) but it doesn’t work.

**Desired outcome:**

* Is there a way to see the frequency of values (answers) for multiple columns (questions) in one overview?
* I want to know the 3 questions that have most support (ie sum of frequencies of ‘completely agree’ and ‘rather agree’), in order of most support.

**Here's the code to create a sample dataset:**

```
import pandas as pd
df = pd.read_csv('sampledata.csv')
df = pd.DataFrame({
'Q1':['Completely disagree','Completely disagree','Rather not agree','Rather agree'],
'Q2':['Completely disagree','Rather not agree','Rather agree','Rather agree'],
'Q3':['No idea / no opinion','Rather not agree','Rather agree','Rather agree'],
'Q4':['Completely disagree','Rather not agree','Completely disagree','Completely agree']
})
df.head()

```

**Here's the sample dataset:**

```
0
Q1
Completely disagree
Completely disagree
No idea / no opinion
Completely disagree
1
Q2
Completely disagree
Rather not agree
Rather not agree
Rather not agree
2
Q3
Rather not agree
Rather agree
Rather agree
Completely disagree
3
Q4
Rather agree
Rather agree
Rather agree
Completely agree

```