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I want to append the rows of a dataframe as columns

What is the difference between Python's list methods append and extend?How to sort a dataframe by multiple column(s)Renaming columns in pandasFilter dataframe rows if value in column is in a set list of valuesAdding new column to existing DataFrame in Python pandasHow can I replace all the NaN values with Zero's in a column of a pandas dataframeDelete column from pandas DataFrameHow to iterate over rows in a DataFrame in Pandas?Select rows from a DataFrame based on values in a column in pandasGet list from pandas DataFrame column headers

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2

I have several small dataframes such as:

name x y z
A 1 2 3
A 1 23 4
A 3 5 6
B 0 2 3

And I want to append all the "A"s such that I get this dataframe

name x y z x2 y2 z2 x3 y3 z3
A 1 2 3 1 23 4 3 5 6
B 0 2 3 NaN-------------------> NaN

Any help would be appreciated, sorry if the above tables aren't spaced out properly

python pandas dataframe rows col

share|improve this question

edited Mar 25 at 2:50

U9-Forward

21.7k51847

asked Mar 25 at 2:46

Ashish MistryAshish Mistry

163

add a comment | 

2

I have several small dataframes such as:

name x y z
A 1 2 3
A 1 23 4
A 3 5 6
B 0 2 3

And I want to append all the "A"s such that I get this dataframe

name x y z x2 y2 z2 x3 y3 z3
A 1 2 3 1 23 4 3 5 6
B 0 2 3 NaN-------------------> NaN

Any help would be appreciated, sorry if the above tables aren't spaced out properly

python pandas dataframe rows col

share|improve this question

edited Mar 25 at 2:50

U9-Forward

21.7k51847

asked Mar 25 at 2:46

Ashish MistryAshish Mistry

163

add a comment | 

I have several small dataframes such as:

name x y z
A 1 2 3
A 1 23 4
A 3 5 6
B 0 2 3

And I want to append all the "A"s such that I get this dataframe

name x y z x2 y2 z2 x3 y3 z3
A 1 2 3 1 23 4 3 5 6
B 0 2 3 NaN-------------------> NaN

Any help would be appreciated, sorry if the above tables aren't spaced out properly

python pandas dataframe rows col

share|improve this question

edited Mar 25 at 2:50

U9-Forward

21.7k51847

asked Mar 25 at 2:46

Ashish MistryAshish Mistry

163

name x y z
A 1 2 3
A 1 23 4
A 3 5 6
B 0 2 3

name x y z x2 y2 z2 x3 y3 z3
A 1 2 3 1 23 4 3 5 6
B 0 2 3 NaN-------------------> NaN

share|improve this question

edited Mar 25 at 2:50

U9-Forward

21.7k51847

asked Mar 25 at 2:46

Ashish MistryAshish Mistry

163

share|improve this question

edited Mar 25 at 2:50

U9-Forward

21.7k51847

asked Mar 25 at 2:46

Ashish MistryAshish Mistry

163

edited Mar 25 at 2:50

U9-Forward

21.7k51847

edited Mar 25 at 2:50

U9-Forward

21.7k51847

asked Mar 25 at 2:46

Ashish MistryAshish Mistry

163

asked Mar 25 at 2:46

Ashish MistryAshish Mistry

163

2 Answers
2

active

oldest

votes

2

More like a pivot problem after create the key by cumcount--- I am using unstack here

df['Newkey']=df.groupby('name').cumcount()+1
yourdf=df.set_index(['name','Newkey']).unstack().sort_index(level=1,axis=1)
yourdf.columns=yourdf.columns.map('0[0]0[1]'.format)
yourdf
Out[20]: 
 x1 y1 z1 x2 y2 z2 x3 y3 z3
name 
A 1.0 2.0 3.0 1.0 23.0 4.0 3.0 5.0 6.0
B 0.0 2.0 3.0 NaN NaN NaN NaN NaN NaN

share|improve this answer

answered Mar 25 at 2:51

WeNYoBenWeNYoBen

140k84978

add a comment | 

2

Use pivot_table with some couple of other functions:

df['idx'] = df.groupby('name').cumcount()+1
df = df.pivot_table(index='name', columns='idx', values=['x', 'y', 'z'], aggfunc='first')
df = df.sort_index(axis=1, level=1)
df.columns = [f'x_y' for x,y in df.columns]
df = df.reset_index()

And now:

print(df)

Reproduces:

 name x_1 y_1 z_1 x_2 y_2 z_2 x_3 y_3 z_3
0 A 1.0 2.0 3.0 1.0 23.0 4.0 3.0 5.0 6.0
1 B 0.0 2.0 3.0 NaN NaN NaN NaN NaN NaN

share|improve this answer

answered Mar 25 at 2:59

U9-ForwardU9-Forward

21.7k51847

add a comment | 

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2

More like a pivot problem after create the key by cumcount--- I am using unstack here

df['Newkey']=df.groupby('name').cumcount()+1
yourdf=df.set_index(['name','Newkey']).unstack().sort_index(level=1,axis=1)
yourdf.columns=yourdf.columns.map('0[0]0[1]'.format)
yourdf
Out[20]: 
 x1 y1 z1 x2 y2 z2 x3 y3 z3
name 
A 1.0 2.0 3.0 1.0 23.0 4.0 3.0 5.0 6.0
B 0.0 2.0 3.0 NaN NaN NaN NaN NaN NaN

share|improve this answer

answered Mar 25 at 2:51

WeNYoBenWeNYoBen

140k84978

add a comment | 

2

More like a pivot problem after create the key by cumcount--- I am using unstack here

df['Newkey']=df.groupby('name').cumcount()+1
yourdf=df.set_index(['name','Newkey']).unstack().sort_index(level=1,axis=1)
yourdf.columns=yourdf.columns.map('0[0]0[1]'.format)
yourdf
Out[20]: 
 x1 y1 z1 x2 y2 z2 x3 y3 z3
name 
A 1.0 2.0 3.0 1.0 23.0 4.0 3.0 5.0 6.0
B 0.0 2.0 3.0 NaN NaN NaN NaN NaN NaN

share|improve this answer

answered Mar 25 at 2:51

WeNYoBenWeNYoBen

140k84978

add a comment | 

More like a pivot problem after create the key by cumcount--- I am using unstack here

df['Newkey']=df.groupby('name').cumcount()+1
yourdf=df.set_index(['name','Newkey']).unstack().sort_index(level=1,axis=1)
yourdf.columns=yourdf.columns.map('0[0]0[1]'.format)
yourdf
Out[20]: 
 x1 y1 z1 x2 y2 z2 x3 y3 z3
name 
A 1.0 2.0 3.0 1.0 23.0 4.0 3.0 5.0 6.0
B 0.0 2.0 3.0 NaN NaN NaN NaN NaN NaN

share|improve this answer

answered Mar 25 at 2:51

WeNYoBenWeNYoBen

140k84978

df['Newkey']=df.groupby('name').cumcount()+1
yourdf=df.set_index(['name','Newkey']).unstack().sort_index(level=1,axis=1)
yourdf.columns=yourdf.columns.map('0[0]0[1]'.format)
yourdf
Out[20]: 
 x1 y1 z1 x2 y2 z2 x3 y3 z3
name 
A 1.0 2.0 3.0 1.0 23.0 4.0 3.0 5.0 6.0
B 0.0 2.0 3.0 NaN NaN NaN NaN NaN NaN

share|improve this answer

answered Mar 25 at 2:51

WeNYoBenWeNYoBen

140k84978

answered Mar 25 at 2:51

WeNYoBenWeNYoBen

140k84978

answered Mar 25 at 2:51

WeNYoBenWeNYoBen

140k84978

2

Use pivot_table with some couple of other functions:

df['idx'] = df.groupby('name').cumcount()+1
df = df.pivot_table(index='name', columns='idx', values=['x', 'y', 'z'], aggfunc='first')
df = df.sort_index(axis=1, level=1)
df.columns = [f'x_y' for x,y in df.columns]
df = df.reset_index()

And now:

print(df)

Reproduces:

 name x_1 y_1 z_1 x_2 y_2 z_2 x_3 y_3 z_3
0 A 1.0 2.0 3.0 1.0 23.0 4.0 3.0 5.0 6.0
1 B 0.0 2.0 3.0 NaN NaN NaN NaN NaN NaN

share|improve this answer

answered Mar 25 at 2:59

U9-ForwardU9-Forward

21.7k51847

add a comment | 

2

Use pivot_table with some couple of other functions:

df['idx'] = df.groupby('name').cumcount()+1
df = df.pivot_table(index='name', columns='idx', values=['x', 'y', 'z'], aggfunc='first')
df = df.sort_index(axis=1, level=1)
df.columns = [f'x_y' for x,y in df.columns]
df = df.reset_index()

And now:

print(df)

Reproduces:

 name x_1 y_1 z_1 x_2 y_2 z_2 x_3 y_3 z_3
0 A 1.0 2.0 3.0 1.0 23.0 4.0 3.0 5.0 6.0
1 B 0.0 2.0 3.0 NaN NaN NaN NaN NaN NaN

share|improve this answer

answered Mar 25 at 2:59

U9-ForwardU9-Forward

21.7k51847

add a comment | 

Use pivot_table with some couple of other functions:

df['idx'] = df.groupby('name').cumcount()+1
df = df.pivot_table(index='name', columns='idx', values=['x', 'y', 'z'], aggfunc='first')
df = df.sort_index(axis=1, level=1)
df.columns = [f'x_y' for x,y in df.columns]
df = df.reset_index()

And now:

print(df)

Reproduces:

 name x_1 y_1 z_1 x_2 y_2 z_2 x_3 y_3 z_3
0 A 1.0 2.0 3.0 1.0 23.0 4.0 3.0 5.0 6.0
1 B 0.0 2.0 3.0 NaN NaN NaN NaN NaN NaN

share|improve this answer

answered Mar 25 at 2:59

U9-ForwardU9-Forward

21.7k51847

df['idx'] = df.groupby('name').cumcount()+1
df = df.pivot_table(index='name', columns='idx', values=['x', 'y', 'z'], aggfunc='first')
df = df.sort_index(axis=1, level=1)
df.columns = [f'x_y' for x,y in df.columns]
df = df.reset_index()

print(df)

 name x_1 y_1 z_1 x_2 y_2 z_2 x_3 y_3 z_3
0 A 1.0 2.0 3.0 1.0 23.0 4.0 3.0 5.0 6.0
1 B 0.0 2.0 3.0 NaN NaN NaN NaN NaN NaN

share|improve this answer

answered Mar 25 at 2:59

U9-ForwardU9-Forward

21.7k51847

answered Mar 25 at 2:59

U9-ForwardU9-Forward

21.7k51847

answered Mar 25 at 2:59

U9-ForwardU9-Forward

21.7k51847