4

I have a pandas data frame known as "df":

  x y
0 1 2
1 2 4
2 3 8

I am splitting it up into two frames, and then trying to merge back together:

df_1 = df[df['x']==1]  
df_2 = df[df['x']!=1]

My goal is to get it back in the same order, but when I concat, I am getting the following:

frames = [df_1, df_2]
solution = pd.concat(frames)
solution.sort_values(by='x', inplace=False)

  x y
1 2 4
2 3 8
0 1 2

The problem is I need the 'x' values to go back into the new dataframe in the same order that I extracted. Is there a solution?

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4 Answers 4

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use .loc to specify the order you want. Choose the original index.

solution.loc[df.index]

Or, if you trust the index values in each component, then

solution.sort_index()

enter image description here

setup 

df = pd.DataFrame([[1, 2], [2, 4], [3, 8]], columns=['x', 'y'])

df_1 = df[df['x']==1]  
df_2 = df[df['x']!=1] 

frames = [df_1, df_2]
solution = pd.concat(frames)
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0

Try this:

In [14]: pd.concat([df_1, df_2.sort_values('y')])
Out[14]:
   x  y
0  1  2
1  2  4
2  3  8
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0

When you are sorting the solution using solution.sort_values(by='x', inplace=False) you need to specify inplace = True. That would take care of it.

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0

Based on these assumptions on df:

  1. Columns x and y are note necessarily ordered.
  2. The index is ordered.

Just order your result by index:

df = pd.DataFrame({'x': [1, 2, 3], 'y': [2, 4, 8]})
df_1 = df[df['x']==1]  
df_2 = df[df['x']!=1] 
frames = [df_2, df_1]
solution = pd.concat(frames).sort_index()

Now, solution looks like this:

   x  y
0  1  2
1  2  4
2  3  8
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