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Lets say i Have 3 Pandas DF
DF1
Words Score
The Man 2
The Girl 4
Df2
Words2 Score2
The Boy 6
The Mother 7
Df3
Words3 Score3
The Son 3
The Daughter 4
Right now, I have them concatenated together so that it becomes 6 columns in one DF. That's all well and good but I was wondering, is there a pandas function to stack them vertically into TWO columns and change the headers?
So to make something like this?
Family Members Score
The Man 2
The Girl 4
The Boy 6
The Mother 7
The Son 3
The Daughter 4
everything I'm reading here http://pandas.pydata.org/pandas-docs/stable/merging.html seems to only have "horizontal" methods of joining DF!
763
Concat() function simply adds DataFrames on top of each other or adds them side-by-side. It is more like appending DataFrames. Merge() combines DataFrames based on values in shared columns.
When we concatenate DataFrames, we need to specify the axis. axis=0 tells pandas to stack the second DataFrame UNDER the first one. It will automatically detect whether the column names are the same and will stack accordingly. axis=1 will stack the columns in the second DataFrame to the RIGHT of the first DataFrame.
We can use either pandas. merge() or DataFrame. merge() to merge multiple Dataframes. Merging multiple Dataframes is similar to SQL join and supports different types of join inner , left , right , outer , cross .
As long as you rename the columns so that they're the same in each dataframe, pd.concat() should work fine:
# I read in your data as df1, df2 and df3 using:
# df1 = pd.read_clipboard(sep='\s\s+')
# Example dataframe:
Out[8]:
Words Score
0 The Man 2
1 The Girl 4
all_dfs = [df1, df2, df3]
# Give all df's common column names
for df in all_dfs:
df.columns = ['Family_Members', 'Score']
pd.concat(all_dfs).reset_index(drop=True)
Out[16]:
Family_Members Score
0 The Man 2
1 The Girl 4
2 The Boy 6
3 The Mother 7
4 The Son 3
5 The Daughter 4
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