计算数据框中列的汇总统计信息 [英] Calculate summary statistics of columns in dataframe

问题描述

我有以下形式的数据框(例如)

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,

如何使用Pandas计算每列的汇总统计信息(列数据类型是可变的，有些列没有信息

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

然后返回表单的一个数据框:

And then return the a dataframe of the form:

columnname, max, min, median,

is_martian, NA, NA, FALSE

依此类推

推荐答案

describe 可能会给你你想要的一切，否则你可以使用 groupby 执行聚合并传递 agg 函数列表:http://pandas.pydata.org/pandas-docs/stable/groupby.html#applying-multiple-functions-一次

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]

请注意，某些列无法汇总，因为没有逻辑方法可以汇总它们，例如包含字符串数据的列

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

如果你愿意，你可以转置结果:

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]

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