How to stack two columns of a pandas dataframe in python

Question

I want to stack two columns on top of each other

So I have Left and Right values in one column each, and want to combine them into a single one. How do I do this in Python? I'm working with Pandas Dataframes.

Basically from this

To this:

It doesn't matter how they are combined as I will plot it anyway, and the new column name also doesn't matter because I can rename it.

Josip Juros · Accepted Answer · 2022-03-09 09:13:42Z

6

You can create a list of the cols, and call squeeze to anonymise the data so it doesn't try to align on columns, and then call concat on this list, passing ignore_index=True creates a new index, otherwise you'll get the names as index values repeated:

cols = [df[col].squeeze() for col in df]
pd.concat(cols, ignore_index=True)

answered Mar 9, 2022 at 9:13

Josip Juros

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timgeb · Accepted Answer · 2022-03-09 09:14:06Z

3

Many options, stack, melt, concat, ...

Here's one:

>>> df.melt(value_name='New Name').drop('variable', 1)
   New Name
0        20
1        15
2        10
3         0
4        25
5        18
6        35
7         5

answered Mar 9, 2022 at 9:14

timgeb

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Comments

Corralien · Accepted Answer · 2022-03-09 09:42:41Z

1

You can also use np.ravel:

import numpy as np

out = pd.DataFrame(np.ravel(df.values.T), columns=['New name'])
print(out)

# Output
   New name
0        20
1        15
2        10
3         0
4        25
5        18
6        35
7         5

Update

If you have only 2 cols:

out = pd.concat([df['Left'], df['Right']], ignore_index=True).to_frame('New name')
print(out)

# Output
   New name
0        20
1        15
2        10
3         0
4        25
5        18
6        35
7         5

edited Mar 9, 2022 at 9:42

answered Mar 9, 2022 at 9:36

Corralien

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Comments

Klaus78 · Accepted Answer · 2022-03-09 09:51:52Z

1

Solution with unstack

df2 = df.unstack()
# recreate index
df2.index = np.arange(len(df2))

answered Mar 9, 2022 at 9:51

Klaus78

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Comments

JAdel · Accepted Answer · 2022-03-09 10:16:24Z

0

A solution with masking.

# Your data
import numpy as np
import pandas as pd
df = pd.DataFrame({"Left":[20,15,10,0], "Right":[25,18,35,5]})

# Masking columns to ravel
df2 = pd.DataFrame({"New Name":np.ravel(df[["Left","Right"]])})

df2

   New Name
0        20
1        25
2        15
3        18
4        10
5        35
6         0
7         5

answered Mar 9, 2022 at 10:16

JAdel

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Comments

thisisforwork · Accepted Answer · 2022-03-09 09:59:14Z

-2

I ended up using this solution, seems to work fine

df1 = dfTest[['Left']].copy()
df2 = dfTest[['Right']].copy()
df2.columns=['Left']
df3 = pd.concat([df1, df2],ignore_index=True)

answered Mar 9, 2022 at 9:59

thisisforwork

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3 Comments

Josip Juros Over a year ago

Seems very memory intensive, you are using 3 dataframes, which if they are big is very inefficient. You might wanna try and understand other solutions here that edit an existing dataframe.

thisisforwork Over a year ago

fair enough, I will definitely check out the other answers

Josip Juros Over a year ago

Check the two most upvoted, best bet, they both work, just take some time open up documentation to understand them.

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