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

Answer 1

Here I'm providing code with output:

Code:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emily'],
    'Age': [25, 30, 22, 35, 28],
    'Salary': [5000, 6000, 4000, 7000, 5500]
}

df = pd.DataFrame(data)

i_want_to_multiply = 2
df['Salary'] = df['Salary'] * i_want_to_multiply

print("Updated DataFrame:")
print(df)

Output:

Updated DataFrame:
Name  Age  Salary
0    Alice   25   10000
1      Bob   30   12000
2  Charlie   22    8000
3    David   35   14000
4    Emily   28   11000

Answer 2

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 :)

Answer 3

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

df.iloc[:,0]  *= -1

Answer 4

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()

Answer 5

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.

Answer 6

A little late to the game, but for future searchers, this also should work:

df.quantity = df.quantity  * -1

Answer 7

More recent pandas versions have the pd.DataFrame.multiply function.

df['quantity'] = df['quantity'].multiply(-1)

Answer 8

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

Answer 9

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))

Answer 10

A bit old, but I was still getting the same SettingWithCopyWarning. Here was my solution:

df.loc[:, 'quantity'] = df['quantity'] * -1

Answer 11

Here's the answer after a bit of research:

df.loc[:,'quantity'] *= -1 #seems to prevent SettingWithCopyWarning 

Answer 12

try using apply function.

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

Answer 13

Try df['quantity'] = df['quantity'] * -1.