Pandas dataframe: how to apply describe() to each group and add to new columns?

Question

df:

name score
A      1
A      2
A      3
A      4
A      5
B      2
B      4
B      6 
B      8

Want to get the following new dataframe in

Answer 1

there is even a shorter one :)

print df.groupby('name').describe().unstack(1)

Nothing beats one-liner:

In [145]:

print df.groupby('name').describe().reset_index().pivot(index='name', values='score', columns='level_1')

Answer 2

Nothing beats one-liner:

In [145]:

print df.groupby('name').describe().reset_index().pivot(index='name', values='score', columns='level_1')

level_1  25%  50%  75%  count  max  mean  min       std
name                                                   
A        2.0    3  4.0      5    5     3    1  1.581139
B        3.5    5  6.5      4    8     5    2  2.581989

Answer 3

Define some data

In[1]:
import pandas as pd
import io

data = """
name score
A      1
A      2
A      3
A      4
A      5
B      2
B      4
B      6
B      8
    """

df = pd.read_csv(io.StringIO(data), delimiter='\s+')
print(df)

.

Out[1]:
  name  score
0    A      1
1    A      2
2    A      3
3    A      4
4    A      5
5    B      2
6    B      4
7    B      6
8    B      8

Solution

A nice approach to this problem uses a generator expression (see footnote) to allow pd.DataFrame() to iterate over the results of groupby, and construct the summary stats dataframe on the fly:

In[2]:
df2 = pd.DataFrame(group.describe().rename(columns={'score':name}).squeeze()
                         for name, group in df.groupby('name'))

print(df2)

.

Out[2]:
   count  mean       std  min  25%  50%  75%  max
A      5     3  1.581139    1  2.0    3  4.0    5
B      4     5  2.581989    2  3.5    5  6.5    8

Here the squeeze function is squeezing out a dimension, to convert the one-column group summary stats Dataframe into a Series.

Footnote: A generator expression has the form my_function(a) for a in iterator, or if iterator gives us back two-element tuples, as in the case of groupby: my_function(a,b) for a,b in iterator

Answer 4

Well I managed to get what you wanted but it doesn't scale very well.

import pandas as pd

name = ['a','a','a','a','a','b','b','b','b','b']
score = [1,2,3,4,5,2,4,6,8]

d = pd.DataFrame(zip(name,score), columns=['Name','Score'])
d = d.groupby('Name').describe()
d = d.reset_index()
df2 = pd.DataFrame(zip(d.level_1[8:], list(d.Score)[:8], list(d.Score)[8:]), columns = ['Name','A','B']).T

print df2

          0     1         2    3    4    5    6    7
Name  count  mean       std  min  25%  50%  75%  max
A         5     3  1.581139    1    2    3    4    5
B         4     5  2.581989    2  3.5    5  6.5    8

Answer 5

Table is stored in dataframe named df

df= pd.read_csv(io.StringIO(data),delimiter='\s+')

Just specify column name and describe give you required output. In this way you calculate w.r.t any column

df.groupby('name')['score'].describe()

Answer 6

import pandas as pd
import io
import numpy as np

data = """
name score
A      1
A      2
A      3
A      4
A      5
B      2
B      4
B      6
B      8
    """

df = pd.read_csv(io.StringIO(data), delimiter='\s+')

df2 = df.groupby('name').describe().reset_index().T.drop('name')
arr = np.array(df2).reshape((4,8))

df2 = pd.DataFrame(arr[1:], index=['name','A','B'])

print(df2)

That will give you df2 as:

              0     1        2    3    4    5    6    7
    name  count  mean      std  min  25%  50%  75%  max
    A         5     3  1.58114    1    2    3    4    5
    B         4     5  2.58199    2  3.5    5  6.5    8