Python: PandasDataframe 如何用标量乘以整个列

如何将数据框架中给定列的每个元素与标量相乘？ (我已经尝试了 SO，但似乎找不到正确的解决方案)

比如:

df['quantity'] *= -1 # trying to multiply each row's quantity column with -1

给我一个警告:

A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

注意: 如果可能的话，我不希望在数据框架上迭代并做类似的事情... ... 因为我认为对整个列进行任何标准的数学运算都可能需要写一个循环:

for idx, row in df.iterrows():
df.loc[idx, 'quantity'] *= -1

编辑:

我正在运行熊猫的 0.16.2

完全追踪:

 SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead


See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
self.obj[item] = s
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Try df['quantity'] = df['quantity'] * -1.

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try using apply function. 

df['quantity'] = df['quantity'].apply(lambda x: x*-1)
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最佳答案

Here's the answer after a bit of research:

df.loc[:,'quantity'] *= -1 #seems to prevent SettingWithCopyWarning
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A bit old, but I was still getting the same SettingWithCopyWarning. Here was my solution:

df.loc[:, 'quantity'] = df['quantity'] * -1
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Note: for those using pandas 0.20.3 and above, and are looking for an answer, all these options will work:

df = pd.DataFrame(np.ones((5,6)),columns=['one','two','three',
'four','five','six'])
df.one *=5
df.two = df.two*5
df.three = df.three.multiply(5)
df['four'] = df['four']*5
df.loc[:, 'five'] *=5
df.iloc[:, 5] = df.iloc[:, 5]*5

which results in

   one  two  three  four  five  six
0  5.0  5.0    5.0   5.0   5.0  5.0
1  5.0  5.0    5.0   5.0   5.0  5.0
2  5.0  5.0    5.0   5.0   5.0  5.0
3  5.0  5.0    5.0   5.0   5.0  5.0
4  5.0  5.0    5.0   5.0   5.0  5.0
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I got this warning using Pandas 0.22. You can avoid this by being very explicit using the assign method:

df = df.assign(quantity = df.quantity.mul(-1))
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More recent pandas versions have the pd.DataFrame.multiply function.

df['quantity'] = df['quantity'].multiply(-1)
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A little late to the game, but for future searchers, this also should work:

df.quantity = df.quantity  * -1
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You can use the index of the column you want to apply the multiplication for 

df.loc[:,6] *= -1

This will multiply the column with index 6 with -1.

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The real problem of why you are getting the error is not that there is anything wrong with your code: you can use either iloc, loc, or apply, or *=, another of them could have worked.

The real problem that you have is due to how you created the df DataFrame. Most likely you created your df as a slice of another DataFrame without using .copy(). The correct way to create your df as a slice of another DataFrame is df = original_df.loc[some slicing].copy().

The problem is already stated in the error message you got " SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead"
You will get the same message in the most current version of pandas too.

Whenever you receive this kind of error message, you should always check how you created your DataFrame. Chances are you forgot the .copy()

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Also it's possible to use numerical indeces with .iloc.

df.iloc[:,0]  *= -1
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Update 2022-08-10

Python: 3.10.5 - pandas: 1.4.3

As Mentioned in Previous comments, one the applicable approaches is using lambda. But, Be Careful with data types when using lambda approach.

Suppose you have a pandas Data Frame like this:

# Create List of lists
products = [[1010, 'Nokia', '200', 1800], [2020, 'Apple', '150', 3000], [3030, 'Samsung', '180', 2000]]


# Create the pandas DataFrame
df = pd.DataFrame(products, columns=['ProductId', 'ProductName', 'Quantity', 'Price'])


# print DataFrame
print(df)


ProductId ProductName Quantity  Price
0       1010       Nokia      200   1800
1       2020       Apple      150   3000
2       3030     Samsung      180   2000

So, if you want to triple the value of Quantity for all rows in Products and use the following Statement:

# This statement considers the values of Quantity as string and updates the DataFrame
df['Quantity'] = df['Quantity'].apply(lambda x:x*3)


# print DataFrame
print(df)

The Result will be:

   ProductId ProductName   Quantity  Price
0       1010       Nokia  200200200   1800
1       2020       Apple  150150150   3000
2       3030     Samsung  180180180   2000

The above statement considers the values of Quantity as string.

So, in order to do the multiplication in the right way, the following statement with a convert could generate correct output:

# This statement considers the values of Quantity as integer and updates the DataFrame
df['Quantity'] = df['Quantity'].apply(lambda x:int(x)*3)


# print DataFrame
print(df)

Therefore the output will be like this:

   ProductId ProductName  Quantity  Price
0       1010       Nokia       600   1800
1       2020       Apple       450   3000
2       3030     Samsung       540   2000

I Hope this could help :)


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