Python 减去数据框中的两列
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Subtract two columns in dataframe
提问by Peter
My df looks as follows:
我的 df 如下所示：
Index Country Val1 Val2 ... Val10
1 Australia 1 3 ... 5
2 Bambua 12 33 ... 56
3 Tambua 14 34 ... 58
I'd like to substract Val10 from Val1 for each country, so output looks like:
我想为每个国家/地区从 Val1 中减去 Val10，因此输出如下所示：
Country Val10-Val1
Australia 4
Bambua 23
Tambua 24
So far I've got:
到目前为止，我有：
def myDelta(row):
data = row[['Val10', 'Val1']]
return pd.Series({'Delta': np.subtract(data)})
def runDeltas():
myDF = getDF() \
.apply(myDelta, axis=1) \
.sort_values(by=['Delta'], ascending=False)
return myDF
runDeltas results in this error:
runDeltas 导致此错误：
ValueError: ('invalid number of arguments', u'occurred at index 9')
What's the proper way to fix this?
解决这个问题的正确方法是什么？
采纳答案by Alberto Chiusole
Given the following dataframe:
给定以下数据框：
df = pd.DataFrame([["Australia", 1, 3, 5],
["Bambua", 12, 33, 56],
["Tambua", 14, 34, 58]
], columns=["Country", "Val1", "Val2", "Val10"]
)
It comes down to a simple broadcasting operation:
归结为一个简单的广播操作：
>>> val1_minus_val10 = df["Val1"] - df["Val10"]
>>> print(val1_minus_val10)
0 -4
1 -44
2 -44
dtype: int64
回答by Henry Owens
Using this as the df:
使用它作为 df：
df = pd.DataFrame([["Australia", 1, 3, 5],
["Bambua", 12, 33, 56],
["Tambua", 14, 34, 58]
], columns=["Country", "Val1", "Val2", "Val10"]
)
You can also do the subtraction and put it into a new column as follows.
您也可以进行减法并将其放入新列中，如下所示。
>>>df['Val_Diff'] = df['Val10'] - df['Val1']
Country Val1 Val2 Val10 Val_Diff
0 Australia 1 3 5 4
1 Bambua 12 33 56 44
2 Tambua 14 34 58 44
回答by Rishi Bansal
You can do this by using lambda function and assign to new column.
您可以通过使用 lambda 函数并分配给新列来执行此操作。
df['Val10-Val1'] = df.apply(lambda x: x['Val10'] - x['Val1'], axis=1)
print df
回答by Prayson W. Daniel
You can also use pandas.DataFrame.assignfunction: e,g
您还可以使用pandas.DataFrame.assign函数：e,g
import numpy as np
import pandas as pd
df = pd.DataFrame([["Australia", 1, 3, 5],
["Bambua", 12, 33, 56],
["Tambua", 14, 34, 58]
], columns=["Country", "Val1", "Val2", "Val10"]
)
df = df.assign(Val10_minus_Val1 = df['Val10'] - df['Val1'])
The best part of assign is you can add as many assignments as you wish. e.g. getting both the difference and then the log of it
分配的最佳部分是您可以根据需要添加任意数量的作业。例如，获得差异，然后获得它的日志
df = df.assign(Val10_minus_Val1 = df['Val10'] - df['Val1'], log_result = lambda x: np.log(x.Val10_minus_Val1) )
Results:
结果：
回答by Navid
What I have faced today, makes me ambitious to share it with you. As people mentioned above you can used easily:
我今天所面临的，让我雄心勃勃地与你们分享。如上所述，您可以轻松使用：
df['Val10-Val1'] = df['Val10']-df['Val1']
but sometimes you might need to use apply function, so you might use the following line:
但有时您可能需要使用 apply 函数，因此您可以使用以下行：
df['Val10-Val1'] = df.apply(lambda row: row['Val10']-row['Val1'])
