[python] Calculate summary statistics of columns in dataframe

I have a dataframe of the following form (for example)

shopper_num,is_martian,number_of_items,count_pineapples,birth_country,tranpsortation_method
1,FALSE,0,0,MX,
2,FALSE,1,0,MX,
3,FALSE,0,0,MX,
4,FALSE,22,0,MX,
5,FALSE,0,0,MX,
6,FALSE,0,0,MX,
7,FALSE,5,0,MX,
8,FALSE,0,0,MX,
9,FALSE,4,0,MX,
10,FALSE,2,0,MX,
11,FALSE,0,0,MX,
12,FALSE,13,0,MX,
13,FALSE,0,0,CA,
14,FALSE,0,0,US,

How can I use Pandas to calculate summary statistics of each column (column data types are variable, some columns have no information

And then return the a dataframe of the form:

columnname, max, min, median,

is_martian, NA, NA, FALSE

So on and so on

Now there is the pandas_profiling package, which is a more complete alternative to df.describe().

If your pandas dataframe is df, the below will return a complete analysis including some warnings about missing values, skewness, etc. It presents histograms and correlation plots as well.

import pandas_profiling
pandas_profiling.ProfileReport(df)

See the example notebook detailing the usage.

To clarify one point in @EdChum's answer, per the documentation, you can include the object columns by using df.describe(include='all'). It won't provide many statistics, but will provide a few pieces of info, including count, number of unique values, top value. This may be a new feature, I don't know as I am a relatively new user.