agf1997 - 1 year ago 459

Python Question

I have 2 similar data frames structured like this :

`ind = pd.MultiIndex.from_product([['Day 1','Day 2'],['D1','D2'],['Mean','StDev','StErr']], names = ['interval','device','stats'])`

df = pd.DataFrame({'col1':[1,2,3,4,5,6,7,8,9,10,11,12]}, index = ind)

print(df)

col1

interval device stats

Day 1 D1 Mean 1

StDev 2

StErr 3

D2 Mean 4

StDev 5

StErr 6

Day 2 D1 Mean 7

StDev 8

StErr 9

D2 Mean 10

StDev 11

StErr 12

ind2 = pd.MultiIndex.from_product([['Day 1','Day 2'],['D1','D2'],['Ratio']], names = ['interval','device','stats'])

df2 = pd.DataFrame({'col1':[100,200,300,400]}, index = ind2)

print(df2)

col1

interval device stats

Day 1 D1 Ratio 100

D2 Ratio 200

Day 2 D1 Ratio 300

D2 Ratio 400

I'm trying to merge them to get this :

`col1`

interval device stats

Day 1 D1 Mean 1

StDev 2

StErr 3

Ratio 100

D2 Mean 4

StDev 5

StErr 6

Ratio 200

Day 2 D1 Mean 7

StDev 8

StErr 9

Ratio 300

D2 Mean 10

StDev 11

StErr 12

Ratio 400

I tried a bunch of different things using

`join`

`concat`

`merge`

`df3 = pd.concat([df, df2], axis=1)`

`col1 col1`

interval device stats

Day 1 D1 Mean 1 NaN

Ratio NaN 100

StDev 2 NaN

StErr 3 NaN

D2 Mean 4 NaN

Ratio NaN 200

StDev 5 NaN

StErr 6 NaN

Day 2 D1 Mean 7 NaN

Ratio NaN 300

StDev 8 NaN

StErr 9 NaN

D2 Mean 10 NaN

Ratio NaN 400

StDev 11 NaN

StErr 12 NaN

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Answer Source

Don't use `axis=1`

when using `concat`

, as it means appending column-wise, not row-wise. You want `axis=0`

for row-wise, which happens to be the default, so you don't need to specify it:

```
df3 = pd.concat([df, df2]).sort_index()
```

The resulting output:

```
col1
interval device stats
Day 1 D1 Mean 1
Ratio 100
StDev 2
StErr 3
D2 Mean 4
Ratio 200
StDev 5
StErr 6
Day 2 D1 Mean 7
Ratio 300
StDev 8
StErr 9
D2 Mean 10
Ratio 400
StDev 11
StErr 12
```

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