Python 使用 Pandas 查找列的最大值并返回相应的行值
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Find maximum value of a column and return the corresponding row values using Pandas
提问by richie
Using Python Pandas I am trying to find the Country& Placewith the maximum value.
使用 Python Pandas 我试图找到具有最大值的Country& Place。
This returns the maximum value:
这将返回最大值：
data.groupby(['Country','Place'])['Value'].max()
But how do I get the corresponding Countryand Placename?
但我怎么得到相应Country和Place的名字吗？
采纳答案by unutbu
Assuming dfhas a unique index, this gives the row with the maximum value:
假设df有一个唯一索引，这给出了具有最大值的行：
In [34]: df.loc[df['Value'].idxmax()]
Out[34]:
Country US
Place Kansas
Value 894
Name: 7
Note that idxmaxreturns index labels. So if the DataFrame has duplicates in the index, the label may not uniquely identify the row, so df.locmay return more than one row.
请注意，idxmax返回索引标签。所以如果DataFrame在索引中有重复，标签可能不会唯一标识该行，因此df.loc可能返回多于一行。
Therefore, if dfdoes not have a unique index, you must make the index unique before proceeding as above. Depending on the DataFrame, sometimes you can use stackor set_indexto make the index unique. Or, you can simply reset the index (so the rows become renumbered, starting at 0):
因此，如果df没有唯一索引，则必须在进行上述操作之前使索引唯一。根据 DataFrame，有时您可以使用stack或set_index使索引唯一。或者，您可以简单地重置索引（因此行重新编号，从 0 开始）：
df = df.reset_index()
回答by waitingkuo
Use the indexattribute of DataFrame. Note that I don't type all the rows in the example.
使用 的index属性DataFrame。请注意，我没有键入示例中的所有行。
In [14]: df = data.groupby(['Country','Place'])['Value'].max()
In [15]: df.index
Out[15]:
MultiIndex
[Spain Manchester, UK London , US Mchigan , NewYork ]
In [16]: df.index[0]
Out[16]: ('Spain', 'Manchester')
In [17]: df.index[1]
Out[17]: ('UK', 'London')
You can also get the value by that index:
您还可以通过该索引获取值：
In [21]: for index in df.index:
print index, df[index]
....:
('Spain', 'Manchester') 512
('UK', 'London') 778
('US', 'Mchigan') 854
('US', 'NewYork') 562
Edit
编辑
Sorry for misunderstanding what you want, try followings:
抱歉误解了您想要的内容，请尝试以下操作：
In [52]: s=data.max()
In [53]: print '%s, %s, %s' % (s['Country'], s['Place'], s['Value'])
US, NewYork, 854
回答by HYRY
The country and place is the index of the series, if you don't need the index, you can set as_index=False:
country 和 place 是系列的索引，如果不需要索引，可以设置as_index=False：
df.groupby(['country','place'], as_index=False)['value'].max()
Edit:
编辑：
It seems that you want the place with max value for every country, following code will do what you want:
似乎您想要每个国家/地区都具有最大值的地方，以下代码将执行您想要的操作：
df.groupby("country").apply(lambda df:df.irow(df.value.argmax()))
回答by Arpit Sharma
In order to print the Country and Place with maximum value, use the following line of code.
为了打印具有最大值的国家和地区，请使用以下代码行。
print(df[['Country', 'Place']][df.Value == df.Value.max()])
回答by Gaurav
df[df['Value']==df['Value'].max()]
This will return the entire row with max value
这将返回具有最大值的整行
回答by sharad kakran
I think the easiest way to return a row with the maximum value is by getting its index. argmax()can be used to return the index of the row with the largest value.
我认为返回具有最大值的行的最简单方法是获取其索引。argmax()可用于返回具有最大值的行的索引。
index = df.Value.argmax()
Now the index could be used to get the features for that particular row:
现在可以使用索引来获取该特定行的特征：
df.iloc[df.Value.argmax(), 0:2]
回答by Marcin Lentner
My solution for finding maximum values in columns:
我在列中查找最大值的解决方案：
df.ix[df.idxmax()]
, also minimum:
, 也是最小值：
df.ix[df.idxmin()]
回答by saran3h
I'd recommend using nlargestfor better performance and shorter code. import pandas
我建议使用nlargest以获得更好的性能和更短的代码。进口pandas
df[col_name].value_counts().nlargest(n=1)
回答by Jefferson Sankara
I encountered a similar error while trying to import data using pandas, The first column on my dataset had spaces before the start of the words. I removed the spaces and it worked like a charm!!
我在尝试使用 Pandas 导入数据时遇到了类似的错误，数据集的第一列在单词开始前有空格。我删除了空格，它就像一个魅力！
回答by kelvinkahuro
You can use:
您可以使用：
print(df[df['Value']==df['Value'].max()])
打印(df[df['Value']==df['Value'].max()])
