我想在我的 pandas 数据框中创建一列value_counts [英] I want to create a column of value_counts in my pandas dataframe

问题描述

我对R更熟悉，但我想看看是否有办法在熊猫中做到这一点.我想从我的一个数据框列中创建一个唯一值计数，然后将具有这些计数的新列添加到我的原始数据框中.我尝试了几种不同的方法.我创建了一个熊猫系列，然后使用value_counts方法计算了计数.我试图将这些值合并回我的原始数据帧，但是我要合并的键在Index(ix/loc)中.任何建议或解决方案将不胜感激

I am more familiar with R but I wanted to see if there was a way to do this in pandas. I want to create a count of unique values from one of my dataframe columns and then add a new column with those counts to my original data frame. I've tried a couple different things. I created a pandas series and then calculated counts with the value_counts method. I tried to merge these values back to my original dataframe, but I the keys that I want to merge on are in the Index(ix/loc). Any suggestions or solutions would be appreciated

Color Value
Red   100
Red   150
Blue  50

我想返回类似的内容

Color Value Counts
Red   100   2
Red   150   2 
Blue  50    1

推荐答案

df['Counts'] = df.groupby(['Color'])['Value'].transform('count')

例如，

In [102]: df = pd.DataFrame({'Color': 'Red Red Blue'.split(), 'Value': [100, 150, 50]})

In [103]: df
Out[103]: 
  Color  Value
0   Red    100
1   Red    150
2  Blue     50

In [104]: df['Counts'] = df.groupby(['Color'])['Value'].transform('count')

In [105]: df
Out[105]: 
  Color  Value  Counts
0   Red    100       2
1   Red    150       2
2  Blue     50       1

请注意，transform('count')会忽略NaN.如果要计算NaN，请使用transform(len).

Note that transform('count') ignores NaNs. If you want to count NaNs, use transform(len).

致匿名编辑器:如果您在使用transform('count')时遇到错误，则可能是由于您的Pandas版本过旧所致.以上适用于0.15或更高版本的熊猫.

To the anonymous editor: If you are getting an error while using transform('count') it may be due to your version of Pandas being too old. The above works with pandas version 0.15 or newer.

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