Calculate average between columns in dataset
Question:
I have a dataset like this:
State
%internet2016
%internet2018
%white 2016
%white 2018
QLD
85
90
85
82
NSW
83
92
80
72
WA
82
88
82
78
SA
86
86
88
84
TAS
78
80
92
88
NT
48
65
76
75
My goal is to create a dataset like this
State
%internet2016-2018
%white2016-2018
QLD
87.5
83.5
NSW
87.5
76
WA
85
80
SA
86
86
TAS
79
90
NT
56.5
75.5
I would like to have the mean of %internet and %white.
I tried this but it was not what I want:
df = df["%internet2016","%internet2018"].mean()
Answers:
You can easily get the mean using this
df['%internet2016-2018']=(df['%internet2016']+df['%internet2018'])/2
df['%white2016-2018']= (df['%white 2016']+df['%white 2018'])/2
df=df.drop(['%internet2016', '%internet2018', '%white 2016', '%white 2018'],axis=1)
Result
Concatenating means of %internet and %white groups of columns:
pd.concat([df['State'],
df.filter(like='%internet').pipe(lambda df: df.mean(axis=1)
.rename('%internet'+'-'.join(c[-4:] for c in df.columns))),
df.filter(like='%white').pipe(lambda df: df.mean(axis=1)
.rename('%white'+'-'.join(c[-4:] for c in df.columns)))], axis=1)
State %internet2016-2018 %white2016-2018
0 QLD 87.5 83.5
1 NSW 87.5 76.0
2 WA 85.0 80.0
3 SA 86.0 86.0
4 TAS 79.0 90.0
5 NT 56.5 75.5
I have a dataset like this:
| State | %internet2016 | %internet2018 | %white 2016 | %white 2018 |
|---|---|---|---|---|
| QLD | 85 | 90 | 85 | 82 |
| NSW | 83 | 92 | 80 | 72 |
| WA | 82 | 88 | 82 | 78 |
| SA | 86 | 86 | 88 | 84 |
| TAS | 78 | 80 | 92 | 88 |
| NT | 48 | 65 | 76 | 75 |
My goal is to create a dataset like this
| State | %internet2016-2018 | %white2016-2018 |
|---|---|---|
| QLD | 87.5 | 83.5 |
| NSW | 87.5 | 76 |
| WA | 85 | 80 |
| SA | 86 | 86 |
| TAS | 79 | 90 |
| NT | 56.5 | 75.5 |
I would like to have the mean of %internet and %white.
I tried this but it was not what I want:
df = df["%internet2016","%internet2018"].mean()
You can easily get the mean using this
df['%internet2016-2018']=(df['%internet2016']+df['%internet2018'])/2
df['%white2016-2018']= (df['%white 2016']+df['%white 2018'])/2
df=df.drop(['%internet2016', '%internet2018', '%white 2016', '%white 2018'],axis=1)
Result
Concatenating means of %internet and %white groups of columns:
pd.concat([df['State'],
df.filter(like='%internet').pipe(lambda df: df.mean(axis=1)
.rename('%internet'+'-'.join(c[-4:] for c in df.columns))),
df.filter(like='%white').pipe(lambda df: df.mean(axis=1)
.rename('%white'+'-'.join(c[-4:] for c in df.columns)))], axis=1)
State %internet2016-2018 %white2016-2018
0 QLD 87.5 83.5
1 NSW 87.5 76.0
2 WA 85.0 80.0
3 SA 86.0 86.0
4 TAS 79.0 90.0
5 NT 56.5 75.5