python - pandas: pandas.DataFrame.describe returns information on only one column -

for kaggle dataset (rules prohibit me sharing data here, readily accessible here),

import pandas df_train = pandas.read_csv(     "01 - data/act_train.csv.zip" ) df_train.describe() 

i get:

>>> df_train.describe()             outcome count  2.197291e+06 mean   4.439544e-01 std    4.968491e-01 min    0.000000e+00 25%    0.000000e+00 50%    0.000000e+00 75%    1.000000e+00 max    1.000000e+00 

whereas same dataset df_train.columns gives me:

>>> df_train.columns index(['people_id', 'activity_id', 'date', 'activity_category', 'char_1',        'char_2', 'char_3', 'char_4', 'char_5', 'char_6', 'char_7', 'char_8',        'char_9', 'char_10', 'outcome'],       dtype='object') 

and df_train.dtypes gives me:

>>> df_train.dtypes people_id            object activity_id          object date                 object activity_category    object char_1               object char_2               object char_3               object char_4               object char_5               object char_6               object char_7               object char_8               object char_9               object char_10              object outcome               int64 dtype: object 

am missing reason why pandas describes 1 column in dataset?

by default, describe works on numeric dtype columns. add keyword-argument include='all'. from documentation:

if include string ‘all’, output column-set match input one.

to clarify, default arguments describe include=none, exclude=none. behavior results is:

none both (default). result include numeric-typed columns or, if none are, categorical columns.

also, notes section:

the output dataframe index depends on requested dtypes:

for numeric dtypes, include: count, mean, std, min, max, , lower, 50, , upper percentiles.

for object dtypes (e.g. timestamps or strings), index include count, unique, common, , frequency of common. timestamps include first , last items.