Python: Pandas Dataframe how to multiply entire column with a scalar
Question:
How do I multiply each element of a given column of my dataframe with a scalar?
(I have tried looking on SO, but cannot seem to find the right solution)
Doing something like:
df['quantity'] *= -1 # trying to multiply each row's quantity column with -1
gives me a warning:
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
Note: If possible, I do not want to be iterating over the dataframe and do something like this…as I think any standard math operation on an entire column should be possible w/o having to write a loop:
for idx, row in df.iterrows():
df.loc[idx, 'quantity'] *= -1
EDIT:
I am running 0.16.2 of Pandas
full trace:
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
Answers:
Try df['quantity'] = df['quantity'] * -1.
try using apply function.
df['quantity'] = df['quantity'].apply(lambda x: x*-1)
Here’s the answer after a bit of research:
df.loc[:,'quantity'] *= -1 #seems to prevent SettingWithCopyWarning
A bit old, but I was still getting the same SettingWithCopyWarning. Here was my solution:
df.loc[:, 'quantity'] = df['quantity'] * -1
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
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))
More recent pandas versions have the pd.DataFrame.multiply function.
df['quantity'] = df['quantity'].multiply(-1)
A little late to the game, but for future searchers, this also should work:
df.quantity = df.quantity * -1
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.
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()
Also it’s possible to use numerical indeces with .iloc.
df.iloc[:,0] *= -1
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 🙂
How do I multiply each element of a given column of my dataframe with a scalar?
(I have tried looking on SO, but cannot seem to find the right solution)
Doing something like:
df['quantity'] *= -1 # trying to multiply each row's quantity column with -1
gives me a warning:
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
Note: If possible, I do not want to be iterating over the dataframe and do something like this…as I think any standard math operation on an entire column should be possible w/o having to write a loop:
for idx, row in df.iterrows():
df.loc[idx, 'quantity'] *= -1
EDIT:
I am running 0.16.2 of Pandas
full trace:
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
Try df['quantity'] = df['quantity'] * -1.
try using apply function.
df['quantity'] = df['quantity'].apply(lambda x: x*-1)
Here’s the answer after a bit of research:
df.loc[:,'quantity'] *= -1 #seems to prevent SettingWithCopyWarning
A bit old, but I was still getting the same SettingWithCopyWarning. Here was my solution:
df.loc[:, 'quantity'] = df['quantity'] * -1
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
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))
More recent pandas versions have the pd.DataFrame.multiply function.
df['quantity'] = df['quantity'].multiply(-1)
A little late to the game, but for future searchers, this also should work:
df.quantity = df.quantity * -1
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.
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()
Also it’s possible to use numerical indeces with .iloc.
df.iloc[:,0] *= -1
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 🙂