pandas describe by - additional parameters

Question

I see that the pandas library has a Describe by function which returns some useful statistics. However, is there a way to add additional rows to the output such as

Answer 1

The answer from piRSquared makes the most sense to me, but I get a deprecation warning about reindex_axis in Python 3.5. This works for me:

    stats = data.describe()
    stats.loc['IQR'] = stats.loc['75%'] - stats.loc['25%'] # appending interquartile range instead of recalculating it
    stats = stats.append(data.reindex(stats.columns, axis=1).agg(['skew', 'mad', 'kurt']))

Answer 2

Try this:

 df.describe()

      num1  num2
count   3.0   3.0
mean    2.0   5.0
std     1.0   1.0
min     1.0   4.0
25%     1.5   4.5
50%     2.0   5.0
75%     2.5   5.5
max     3.0   6.0

Build a second DataFrame.

 pd.DataFrame(df.mad() , columns = ["Mad"] ).T

         num1      num2
Mad  0.666667  0.666667

Join the two DataFrames.

 pd.concat([df.describe(),pd.DataFrame(df.mad() , columns = ["Mad"] ).T ])

          num1      num2
count  3.000000  3.000000
mean   2.000000  5.000000
std    1.000000  1.000000
min    1.000000  4.000000
25%    1.500000  4.500000
50%    2.000000  5.000000
75%    2.500000  5.500000
max    3.000000  6.000000
Mad    0.666667  0.666667

​

Answer 3

the default describe looks like this:

np.random.seed([3,1415])
df = pd.DataFrame(np.random.rand(100, 5), columns=list('ABCDE'))

df.describe()

                A           B           C           D           E
count  100.000000  100.000000  100.000000  100.000000  100.000000
mean     0.495871    0.472939    0.455570    0.503899    0.451341
std      0.303589    0.291968    0.294984    0.269936    0.284666
min      0.006453    0.001559    0.001068    0.015311    0.009526
25%      0.239379    0.219141    0.196251    0.294371    0.202956
50%      0.529596    0.456548    0.376558    0.532002    0.432936
75%      0.759452    0.739666    0.665563    0.730702    0.686793
max      0.999799    0.994510    0.997271    0.981551    0.979221

Updated for pandas 0.20
I'd make my own describe like below. It should be obvious how to add more.

def describe(df, stats):
    d = df.describe()
    return d.append(df.reindex_axis(d.columns, 1).agg(stats))

describe(df, ['skew', 'mad', 'kurt'])

                A           B           C           D           E
count  100.000000  100.000000  100.000000  100.000000  100.000000
mean     0.495871    0.472939    0.455570    0.503899    0.451341
std      0.303589    0.291968    0.294984    0.269936    0.284666
min      0.006453    0.001559    0.001068    0.015311    0.009526
25%      0.239379    0.219141    0.196251    0.294371    0.202956
50%      0.529596    0.456548    0.376558    0.532002    0.432936
75%      0.759452    0.739666    0.665563    0.730702    0.686793
max      0.999799    0.994510    0.997271    0.981551    0.979221
skew    -0.014942    0.048054    0.247244   -0.125151    0.066156
mad      0.267730    0.249968    0.254351    0.228558    0.242874
kurt    -1.323469   -1.223123   -1.095713   -1.083420   -1.148642

Old Answer

def describe(df):
    return pd.concat([df.describe().T,
                      df.mad().rename('mad'),
                      df.skew().rename('skew'),
                      df.kurt().rename('kurt'),
                     ], axis=1).T

describe(df)

                A           B           C           D           E
count  100.000000  100.000000  100.000000  100.000000  100.000000
mean     0.495871    0.472939    0.455570    0.503899    0.451341
std      0.303589    0.291968    0.294984    0.269936    0.284666
min      0.006453    0.001559    0.001068    0.015311    0.009526
25%      0.239379    0.219141    0.196251    0.294371    0.202956
50%      0.529596    0.456548    0.376558    0.532002    0.432936
75%      0.759452    0.739666    0.665563    0.730702    0.686793
max      0.999799    0.994510    0.997271    0.981551    0.979221
mad      0.267730    0.249968    0.254351    0.228558    0.242874
skew    -0.014942    0.048054    0.247244   -0.125151    0.066156
kurt    -1.323469   -1.223123   -1.095713   -1.083420   -1.148642