df.loc[:, columns] 和 df.loc[:][columns] 的区别

Question

I want to normalize some columns of a pandas data frame using MinMaxScaler in this way:

scaler = MinMaxScaler()
numericals = ["TX_TIME_SECONDS",'TX_Amount']

while I do in this way:

df.loc[:][numericals] = scaler.fit_transform(df.loc[:][numericals])

it's not done inplace and df is not changed;

whereas, when I do in this way:

df.loc[:, numericals] = scaler.fit_transform(df.loc[:][numericals])

the numerical columns of df are changed in place,

So, What's the difference between df.loc[:, ~] and df.loc[:][~]

Answer 1

df.loc[:][numericals] selects all rows and then selects columns "TX_TIME_SECONDS" and 'TX_Amount' of the returning object , and assigns some value to it. The problem is, the returning object might be a copy so this may not change the actual DataFrame.

The correct way of making this assignment is using df.loc[:, numericals] , because with .loc you are guaranteed to modify the original DataFrame.

Answer 2

I suggest you read some documentation because this is pretty basic.

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html

https://www.geeksforgeeks.org/python-pandas-dataframe-loc/