计算dataframe中列的摘要统计信息 [英] Calculate summary statistics of columns in dataframe

问题描述

我有一个以下表单的数据框架（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,

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

然后返回以下形式的数据框：

And then return the a dataframe of the form:

columnname, max, min, median,

is_martian, NA, NA, FALSE

推荐答案

describe 可以给你你想要的一切，否则你可以使用groupby执行聚合和传递agg函数列表： http：//pandas.pydata .org / pandas-docs / stable / groupby.html＃applied-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]

请注意，有些列不能被概括为没有逻辑方法来总结它们，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

如果你喜欢，你可以转换结果：

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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