CODEWITHSUNDEEP
pythonpandascsvdataframeprofiling
Tyler Wood
Tyler Wood

Reputation: 1967

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

Upvotes: 60

Views: 153210

Answers (3)

akilat90
akilat90

Reputation: 5706

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.

Upvotes: 29

Ken Wallace
Ken Wallace

Reputation: 2421

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.

Upvotes: 6

EdChum
EdChum

Reputation: 394319

describe may give you everything you want otherwise you can perform aggregations using groupby and pass a list of agg functions: http://pandas.pydata.org/pandas-docs/stable/groupby.html#applying-multiple-functions-at-once

In [43]:

df.describe()

Out[43]:

       shopper_num is_martian  number_of_items  count_pineapples
count      14.0000         14        14.000000                14
mean        7.5000          0         3.357143                 0
std         4.1833          0         6.452276                 0
min         1.0000      False         0.000000                 0
25%         4.2500          0         0.000000                 0
50%         7.5000          0         0.000000                 0
75%        10.7500          0         3.500000                 0
max        14.0000      False        22.000000                 0

[8 rows x 4 columns]

Note that some columns cannot be summarised as there is no logical way to summarise them, for instance columns containing string data

As you prefer you can transpose the result if you prefer:

In [47]:

df.describe().transpose()

Out[47]:

                 count      mean       std    min   25%  50%    75%    max
shopper_num         14       7.5    4.1833      1  4.25  7.5  10.75     14
is_martian          14         0         0  False     0    0      0  False
number_of_items     14  3.357143  6.452276      0     0    0    3.5     22
count_pineapples    14         0         0      0     0    0      0      0

[4 rows x 8 columns]

Upvotes: 107

Related Questions