Building individual classes, count and show in separate DF
Question:
I use a DataFrame looking like this:
Index Type Value
0 A 4
1 A 9
2 C 51
3 B 40
4 C 32
5 C 14
I want to classify the items from the DataFrame in the classes/ranges 0-1, 11-50, 51-100 and create a separate DataFrame which shows the amount of Type for each of those classes.
Class A B C
0-10 2 0 0
11-50 0 1 2
51-100 0 0 1
Can anybody help?
Answers:
You can accomplish this in two lines:
df['class'] = pd.cut(df['Value'], [0, 10, 50, 100])
df.groupby('class')['Type'].value_counts().rename("count").reset_index().pivot('class', 'Type', 'count').fillna(0).astype(int)
The first line creates the classes that you’re after, and the second gives the counts. Your final result here is a pivot table (hence the usage of pivot). The result is:
Type A B C
class
(0, 10] 2 0 0
(10, 50] 0 1 2
(50, 100] 0 0 1
Feel free to map class to the exact format you request, but the general idea is to use pd.cut, which assigns a value based on the interval a value belongs to.
I use a DataFrame looking like this:
Index Type Value
0 A 4
1 A 9
2 C 51
3 B 40
4 C 32
5 C 14
I want to classify the items from the DataFrame in the classes/ranges 0-1, 11-50, 51-100 and create a separate DataFrame which shows the amount of Type for each of those classes.
Class A B C
0-10 2 0 0
11-50 0 1 2
51-100 0 0 1
Can anybody help?
You can accomplish this in two lines:
df['class'] = pd.cut(df['Value'], [0, 10, 50, 100])
df.groupby('class')['Type'].value_counts().rename("count").reset_index().pivot('class', 'Type', 'count').fillna(0).astype(int)
The first line creates the classes that you’re after, and the second gives the counts. Your final result here is a pivot table (hence the usage of pivot). The result is:
Type A B C
class
(0, 10] 2 0 0
(10, 50] 0 1 2
(50, 100] 0 0 1
Feel free to map class to the exact format you request, but the general idea is to use pd.cut, which assigns a value based on the interval a value belongs to.