问题：如何从一列中排序熊猫数据框
我有一个像这样的数据框：
print(df)
0 1 2
0 354.7 April 4.0
1 55.4 August 8.0
2 176.5 December 12.0
3 95.5 February 2.0
4 85.6 January 1.0
5 152 July 7.0
6 238.7 June 6.0
7 104.8 March 3.0
8 283.5 May 5.0
9 278.8 November 11.0
10 249.6 October 10.0
11 212.7 September 9.0
如您所见，月份不是按日历顺序排列的。因此，我创建了第二列以获取与每月（1-12）相对应的月份号。从那里，如何根据日历月的顺序对数据框进行排序？
I have a data frame like this:
print(df)
0 1 2
0 354.7 April 4.0
1 55.4 August 8.0
2 176.5 December 12.0
3 95.5 February 2.0
4 85.6 January 1.0
5 152 July 7.0
6 238.7 June 6.0
7 104.8 March 3.0
8 283.5 May 5.0
9 278.8 November 11.0
10 249.6 October 10.0
11 212.7 September 9.0
As you can see, months are not in calendar order. So I created a second column to get the month number corresponding to each month (1-12). From there, how can I sort this data frame according to calendar months’ order?
回答 0
用于sort_values按特定列的值对df进行排序：
In [18]:
df.sort_values('2')
Out[18]:
0 1 2
4 85.6 January 1.0
3 95.5 February 2.0
7 104.8 March 3.0
0 354.7 April 4.0
8 283.5 May 5.0
6 238.7 June 6.0
5 152.0 July 7.0
1 55.4 August 8.0
11 212.7 September 9.0
10 249.6 October 10.0
9 278.8 November 11.0
2 176.5 December 12.0
如果要按两列进行排序sort_values，请将列标签列表传递给，并根据排序优先级对列标签进行排序。如果使用df.sort_values(['2', '0'])，结果将按列2然后按列排序0。当然，对于这个示例，这实际上没有任何意义，因为其中的每个值df['2']都是唯一的。
Use sort_values to sort the df by a specific column’s values:
In [18]:
df.sort_values('2')
Out[18]:
0 1 2
4 85.6 January 1.0
3 95.5 February 2.0
7 104.8 March 3.0
0 354.7 April 4.0
8 283.5 May 5.0
6 238.7 June 6.0
5 152.0 July 7.0
1 55.4 August 8.0
11 212.7 September 9.0
10 249.6 October 10.0
9 278.8 November 11.0
2 176.5 December 12.0
If you want to sort by two columns, pass a list of column labels to sort_values with the column labels ordered according to sort priority. If you use df.sort_values(['2', '0']), the result would be sorted by column 2 then column 0. Granted, this does not really make sense for this example because each value in df['2'] is unique.
回答 1
我尝试了上述解决方案，但没有取得结果，因此我找到了一个对我有用的解决方案。该升=假是订购数据框在递减顺序，默认为真。我正在使用python 3.6.6和pandas 0.23.4版本。
final_df = df.sort_values(by=['2'], ascending=False)
您可以在此处查看pandas文档中的更多详细信息。
I tried the solutions above and I do not achieve results, so I found a different solution that works for me. The ascending=False is to order the dataframe in descending order, by default it is True. I am using python 3.6.6 and pandas 0.23.4 versions.
final_df = df.sort_values(by=['2'], ascending=False)
You can see more details in pandas documentation here.
回答 2
只是添加一些对数据的操作。假设我们有一个数据框df，我们可以执行几个操作以获得所需的输出
ID cost tax label
1 216590 1600 test
2 523213 1800 test
3 250 1500 experiment
(df['label'].value_counts().to_frame().reset_index()).sort_values('label', ascending=False)
将sorted标签输出作为dataframe
index label
0 test 2
1 experiment 1
Just adding some more operations on data. Suppose we have a dataframe df, we can do several operations to get desired outputs
ID cost tax label
1 216590 1600 test
2 523213 1800 test
3 250 1500 experiment
(df['label'].value_counts().to_frame().reset_index()).sort_values('label', ascending=False)
will give sorted output of labels as a dataframe
index label
0 test 2
1 experiment 1
回答 3
就像另一个解决方案：
您可以对字符串数据（月份名称）进行分类并按如下方式进行排序：
df.rename(columns={1:'month'},inplace=True)
df['month'] = pd.Categorical(df['month'],categories=['December','November','October','September','August','July','June','May','April','March','February','January'],ordered=True)
df = df.sort_values('month',ascending=False)
它将month name按照您在创建Categorical对象时指定的顺序为您提供排序的数据。
Just as another solution:
Instead of creating the second column, you can categorize your string data(month name) and sort by that like this:
df.rename(columns={1:'month'},inplace=True)
df['month'] = pd.Categorical(df['month'],categories=['December','November','October','September','August','July','June','May','April','March','February','January'],ordered=True)
df = df.sort_values('month',ascending=False)
It will give you the ordered data by month name as you specified while creating the Categorical object.
