如何将Pandas DataFrame 转换为相关矩阵的形状

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英文:

How to convert Pandas Dataframe to the shape of a correlation matrix

问题

  1. 我有一个pandas数据框，大致看起来像这样：
  3. ```plaintext
  4.      xvar            yvar                   meanRsquared
  5. 0    filled_water    precip                 0.119730
  6. 1    filled_water    snow                   0.113214
  7. 2    filled_water    filled_wetland         0.119529
  8. 3    filled_wetland  precip                 0.104826
  9. 4    filled_wetland  snow                   0.121540
  10. 5    filled_wetland  filled_water           0.121540
  11. [676 rows x 3 columns]

我想将其形状转换为更传统的相关矩阵，其中列和索引是变量，而值是meanRsquared。

有没有简单的方法可以做到这一点？我已经试了一个小时了，但无法弄清楚如何做到这一点。

免责声明：是的，我知道pandas有一个用于创建相关矩阵的内置函数。但是，我的当前数据框是许多流域上数百个相关矩阵的平均值，所以我不能使用它。

这是我的最佳尝试，但显然逻辑在最后失败了。

  1. listOfdicts = []
  2. for xvar in df['xvar'].unique():
  3.     for yvar in df['yvar'].unique():
  4.         adict = {}
  5.         adict['index'] = xvar 
  6.         adict[yvar] = yvar
  7.         adict['r'] = df['insert r value here']
  8.         listOfdicts.append(adict)
  9. answer = pd.DataFrame.from_dict(listOfdicts)

我不指望这会起作用，但这是我最好的尝试。

  2. <details>
  3. <summary>英文:</summary>
  5. I have a pandas dataframe which looks vaguely like this:

Out[130]:
xvar yvar meanRsquared
0 filled_water precip 0.119730
1 filled_water snow 0.113214
2 filled_water filled_wetland 0.119529
3 filled_wetland precip 0.104826
4 filled_wetland snow 0.121540
5 filled_wetland filled_water 0.121540
[676 rows x 3 columns]

  1. I would like to transform it&#39;s shape into a more traditional correlation matrix, where the columns and the index are the variables, and the values are the meanRsquared.
  3. Is there any easy way to do this? I&#39;ve been playing around for an hour and can&#39;t figure out how I could do this.
  5. DISCLAIMER: Yes, I know pandas has a built in function for creating a correlation matrix. However my current df is the average of hundreds of correlation matrices over many watersheds, so I cannot use that.
  7. This is my best attempt, but obviously the logic failed towards the end.

listOfdicts = []
for xvar in df['xvar'].unique():
for yvar in df['yvar'].unique():
adict = {}
adict['index'] = xvar
adict[yvar] = yvar
adict['r'] = df['insert r value here']
listOfdicts.append(adict)
answer = pd.Dataframe.from_dict(listOfdicts)

  1. I don&#39;t expect this to work, but this was my best shot.
  3. </details>
  6. # 答案1
  7. **得分**: 1
  9. 请查看透视方法（https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.pivot.html）。
  11. ```python
  12. import pandas as pd
  13. df = pd.DataFrame(
  14.     data={
  15.         'xvar': ['filled_water', 'filled_water', 'filled_water', 'filled_wetland', 'filled_wetland', 'filled_wetland'],
  16.         'yvar': ['precip', 'snow', 'filled_wetland', 'precip', 'snow', 'filled_water'],
  17.         'meanRsquared': [1, 2, 3, 4, 5, 6]
  18.     }, index=range(6)
  19. )
  21. df.pivot(index='xvar', columns='yvar', values='meanRsquared')

输出：

  1. yvar            filled_water  filled_wetland  precip  snow
  2. xvar                                                      
  3. filled_water             NaN             3.0     1.0   2.0
  4. filled_wetland           6.0             NaN     4.0   5.0
英文:

You need to look at pivot method (https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.pivot.html).

  1. import pandas as pd
  2. df =pd.DataFrame(
  3.     data={
  4.         &#39;xvar&#39;: [&#39;filled_water&#39;, &#39;filled_water&#39;, &#39;filled_water&#39;,
  5.                  &#39;filled_wetland&#39;, &#39;filled_wetland&#39;, &#39;filled_wetland&#39;],   
  7.         &#39;yvar&#39;:[&#39;precip&#39;,&#39;snow&#39;,&#39;filled_wetland&#39;,                  
  8.                 &#39;precip&#39;,&#39;snow&#39;,&#39;filled_water&#39; ], 
  9.         &#39;meanRsquared&#39;:[1,2,3,4,5,6] 
  10.     }, index=range(6)
  11. )
  13. df.pivot(index=&#39;xvar&#39;, columns=&#39;yvar&#39;, values=&#39;meanRsquared&#39;)

Output:

  1.     yvar            filled_water  filled_wetland  precip  snow
  2. xvar                                                      
  3. filled_water             NaN             3.0     1.0   2.0
  4. filled_wetland           6.0             NaN     4.0   5.0

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  • 本文由 发表于 2023年7月7日 06:56:43
  • 转载请务必保留本文链接：https://go.coder-hub.com/76632984.html
  • correlation
  • data-science
  • dataframe
  • pandas
  • python
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