英文:
How to use groupby for multiple columns in pandas for the below shown image?
问题
- 这是在pandas中的输入表格：
- [![在这里输入图片描述][1]][1]
- [1]: https://i.stack.imgur.com/Du7js.png
- 这是如下所示的输出表格：
- [![在这里输入图片描述][2]][2]
- [2]: https://i.stack.imgur.com/3GZuK.png
- dtype: int64
- 亲爱的朋友们，
- 我是pandas的新手，如何使用pandas获得第二张图片中显示的结果。
- 我使用以下方法得到如下输出：
- "df.groupby(['Months', 'Status']).size()"
- Months Status
- Apr-20 IW 2
- OW 1
- Jun-20 IW 4
- OW 4
- May-20 IW 3
- OW 2
- dtype: int64
- 但如何将这个输出转换为第二张图片所示的样式呢？
- 如果有人能帮助我，将会更有帮助。提前感谢。
英文:
<pre><br> This is input table in pandas:
[![enter image description here][1]][1]
[1]: https://i.stack.imgur.com/Du7js.png
<br>
this is an output table as shown below:
[![enter image description here][2]][2]
[2]: https://i.stack.imgur.com/3GZuK.png
dtype: int64
<br>
<br>
Dear Friends,
<br>
I am new to pandas, how to get the result is shown in the second image using pandas.
I am getting output as shown below using this approach <br>
"df.groupby(['Months', 'Status']).size()"
<br>
Months Status
<br>
Apr-20 IW 2
<br>
OW 1
<br>
Jun-20 IW 4
<br>
OW 4
<br>
May-20 IW 3
<br>
OW 2
<br>
dtype: int64
<br>
But how to convert this output as shown in the second image?
It will be more helpful if someone is able to help me. Thanks in advance.
<br>
答案1
得分: 2
使用crosstab函数，带有margins=True参数，然后如果需要，移除最后的Total列，通过DataFrame.reindex按照原始列的顺序重新排列列，最后通过DataFrame.reset_index将索引转换为列，并通过DataFrame.rename_axis移除列名：
df = (pd.crosstab(df['Status'], df['Months'], margins_name='Total', margins=True).iloc[:, :-1].reindex(df['Months'].unique(), axis=1).reset_index().rename_axis(None, axis=1))print (df)Status Apr_20 May_20 Jun_200 IW 4 2 41 OW 1 2 42 Total 5 4 8
英文:
Use crosstab with margins=True parameter, then if necessary remove last Total column, change order of columns by DataFrame.reindex with ordering of original column and last convert index to column by DataFrame.reset_index and remove columns names by DataFrame.rename_axis:
df = (pd.crosstab(df['Status'], df['Months'], margins_name='Total', margins=True).iloc[:, :-1].reindex(df['Months'].unique(), axis=1).reset_index().rename_axis(None, axis=1))print (df)Status Apr_20 May_20 Jun_200 IW 4 2 41 OW 1 2 42 Total 5 4 8
答案2
得分: 1
Unstack，然后转置：
df = df.groupby(['Months', 'Status']).size().unstack().T
获得一个 total 行：
df.sum().rename('Total').to_frame().T.append(df)
英文:
Unstack, and then transpose:
df = df.groupby(['Months', 'Status']).size().unstack().T
To get a total row:
df.sum().rename('Total').to_frame().T.append(df)
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