Pandas 的数据透视表或分组依据？
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Pivot Tables or Group By for Pandas?
提问by SteelyDanish
I have a hopefully straightforward question that has been giving me a lot of difficulty for the last 3 hours. It should be easy.
我有一个希望直截了当的问题，在过去的 3 个小时里一直给我带来很多困难。这应该很容易。
Here's the challenge.
这就是挑战。
I have a pandas dataframe:
我有一个Pandas数据框：
+--------------------------+
| Col 'X' Col 'Y' |
+--------------------------+
| class 1 cat 1 |
| class 2 cat 1 |
| class 3 cat 2 |
| class 2 cat 3 |
+--------------------------+
What I am looking to transform the dataframe into:
我希望将数据框转换为：
+------------------------------------------+
| cat 1 cat 2 cat 3 |
+------------------------------------------+
| class 1 1 0 0 |
| class 2 1 0 1 |
| class 3 0 1 0 |
+------------------------------------------+
Where the values are value counts. Anybody have any insight? Thanks!
其中值是值计数。有人有任何见解吗？谢谢！
回答by Zero
Here are couple of ways to reshape your data df
以下是重塑数据的几种方法 df
In [27]: df
Out[27]:
Col X Col Y
0 class 1 cat 1
1 class 2 cat 1
2 class 3 cat 2
3 class 2 cat 3
1)Using pd.crosstab()
1)使用pd.crosstab()
In [28]: pd.crosstab(df['Col X'], df['Col Y'])
Out[28]:
Col Y cat 1 cat 2 cat 3
Col X
class 1 1 0 0
class 2 1 0 1
class 3 0 1 0
2)Or, use groupbyon 'Col X','Col Y'with unstackover Col Y, then fill NaNswith zeros.
2)或者，使用groupbyon'Col X','Col Y'和unstackover Col Y，然后NaNs用零填充。
In [29]: df.groupby(['Col X','Col Y']).size().unstack('Col Y', fill_value=0)
Out[29]:
Col Y cat 1 cat 2 cat 3
Col X
class 1 1 0 0
class 2 1 0 1
class 3 0 1 0
3)Or, use pd.pivot_table()with index=Col X, columns=Col Y
3)或者，pd.pivot_table()与index=Col X, 一起使用columns=Col Y
In [30]: pd.pivot_table(df, index=['Col X'], columns=['Col Y'], aggfunc=len, fill_value=0)
Out[30]:
Col Y cat 1 cat 2 cat 3
Col X
class 1 1 0 0
class 2 1 0 1
class 3 0 1 0
4)Or, use set_indexwith unstack
4）或者，set_index与unstack
In [492]: df.assign(v=1).set_index(['Col X', 'Col Y'])['v'].unstack(fill_value=0)
Out[492]:
Col Y cat 1 cat 2 cat 3
Col X
class 1 1 0 0
class 2 1 0 1
class 3 0 1 0
